NIH needs to invest in both the infrastructure and funding to undertake rigorous nutrition clinical trials, so that we can rapidly improve food and make progress on obesity and nutrition-related chronic disease.

The notion that ‘what we eat impacts our health’ has risen to national prominence with the rise of the “Make America Healthy Again” movement, placing nutrition at the center of American politics. This high degree of interest and enthusiasm is starkly contrasted by the limited high quality data to inform many key nutrition questions, a result of the limited and waning investment in controlled, experimental research on diet’s impact on health and disease. With heightened public interest and increasing societal costs due to diet-related diseases (>$250 billion), it is imperative to re-envision a nutrition research ecosystem that is capable of rapidly producing robust evidence relevant to policymakers, regulators and the food industry. This begins with establishing a network of clinical research centers capable of undertaking controlled human nutrition intervention studies at scale. Such a network, combined with enhanced commitment to nutrition research funding, will revolutionize nutrition science and our understanding of how food impacts obesity and metabolic health.

The proposed clinical trial network would be endowed with high capacity metabolic wards and kitchens, with the ability to oversee the long-term stay of large numbers of participants. The network would be capable of deeply phenotyping body composition, metabolic status, clinical risk factors, and the molecular mediators of diet. Such a clinical trial network would publish numerous rigorous clinical trials per year, testing the leading hypotheses that exist in the literature that lack meaningful clinical trials. Such a network would produce evidence of direct relevance to policy makers and the food industry, to inform how to best make long-overdue progress on reforming the food system and reducing the burden of diet-related diseases like obesity and Type 2 diabetes.

Challenge and Opportunity

While commonly viewed in the modern era as a medical science, nutrition research historically began as an agricultural science. Early researchers sought to define food composition of common agricultural products and ensure the food system supplied adequate levels of nutrients at affordable prices to meet nutrient requirements. This history firmly established the field of nutrition in universities embedded in the agricultural extension network and funded in large part by the United States Department of Agriculture (USDA) and relevant food industries. It took decades, until the late 1980s and early 1990s, for nutrition’s impact on chronic diseases like obesity and cardiometabolic to be taken seriously and viewed through a more medicalized lens. Despite this updated view of nutrition as ostensibly a serious medical science, the study of food has arguably never received the level of attention and resources commensurate with both its importance and the challenges in its rigorous study.

Our understanding of obesity and related chronic diseases has increased dramatically over the past 30 years. Despite improved understanding, many nutrition questions remain. For example, what is the impact of food processing and additives on health? What is the role of factors such as genetics and the microbiome (“Precision Nutrition”) in determining the effect of diet? These and more have emerged as key questions facing the field, policymakers and industry. Unfortunately during this time, the capacity to undertake controlled nutrition interventions, the strongest form of evidence generating causal relationships, has atrophied substantially.

Early research examining nutrition and its relationship to chronic diseases (e.g. type of fat and blood cholesterol responses). benefited from the availability of the general clinical research center (GCRCs). GCRCs were largely extramurally funded clinical research infrastructure that provided the medical and laboratory services, as well as metabolic kitchens and staff funding, to conduct controlled dietary interventions. This model produced evidence that continues to serve as the backbone of existing nutritional recommendations. In the mid-2000s, the GCRC infrastructure was largely defunded and replaced with the Clinical Translational Science Awards (CTSAs). CTSAs’ funding model is significantly less generous and provides limited if any funds for key infrastructure such as metabolic kitchens, nursing and laboratory services, and registered dietitian staff, all essential for undertaking controlled nutrition research. The model outsources the burden of cost from the NIH to the funder, a price tag that the pharmaceutical and medical device industries can bear but is simply not met by the food and supplement industry and is beyond the limited research budgets of academic or government research. Without public investment, there is simply no way for nutrition science to keep up with other fields of biomedicine, exacerbating a perception of the American medical system ignoring preventive measures like nutrition and ensuring that nutrition research is rated as ‘low quality’ in systematic reviews of the evidence.

The results attributed  to this funding model are strikingly evident, and were readily predicted in two high profile commentaries mourning the loss of the GCRC model. When the field systematically reviews the data, the evidence from controlled feeding trials and chronic disease risk factors are largely published between the 1980s-2000s. More modern data is overwhelmingly observational in nature or relies on dietary interventions that educate individuals to consume a specific diet, rather than providing food – both types of evidence significantly reduce the confidence in results and introduce various biases that downgrade the certainty of evidence. The reality of the limited ability to generate high quality controlled feeding trial data was most evident in the last edition of the Dietary Guidelines Advisory Committee’s report, which conducted a systematic review of ultraprocessed foods (UFPs) and obesity. This review identified only a single, small experimental study, a two-week clinical trial in 20 adults, with the rest of the literature being observational in nature and graded as too limited to draw firm conclusions about UPFs and obesity risk. This state of the literature is the expected reality for all forthcoming questions in the field of nutrition until the field receives a massive infusion of resources. Without funding for infrastructure and research, the situation will worsen, as both the facilities and investigators trained in this work continue to wither, and academic tenure track lines are filled instead by areas currently prioritized by funders (e.g., basic science, global health). How can we expect ‘high certainty’ conclusions in the evidence to inform dietary guidelines when we simply don’t fund research with the capability of producing such evidence? While the GCRCs were far from perfect, the impact of their defunding on nutrition science over the past two decades is apparent from the quality of evidence on emerging topics and an even cursory look at the faculty at legacy academic nutrition departments. Legislators and policymakers should be alarmed at what the trajectory of the field over the last two decades means for public health.

As we deal with crisis levels of obesity and nutrition-related chronic diseases, we must face the realities of our failures to fund nutrition science seriously over the last two  decades, and the data drought a lack of funding  has caused. It is a critical failure of the biomedical research infrastructure in the United States that controlled nutrition interventions have fallen by the wayside while rates of diet-related chronic diseases have only worsened. It is essential for the health of our nation and our economy to reinvest in nutrition research to a degree never-before-seen in American history, and produce a state-of-the-art network of clinical trial centers capable of elucidating how food impacts health. 

Several key challenges exist to produce a coordinated clinical research center network capable of producing evidence that transforms our understanding of diet’s impact on health: 

Challenge 1. Few Existing Research Centers Have the Existing Interdisciplinary Expertise Needed to Tackle Pressing Food and Nutrition Challenges

Both food and health are wildly interdisciplinary in nature, requiring the right mix of expertises across plant and animal agriculture, food science, human nutrition, and various fields of medicine to adequately tackle the pressing nutrition-related challenges facing society. However, the current ‘nutrition’ research landscape of the United States reflects its natural, uncoordinated evolution across diverse agricultural colleges and medical centers.

Any proposed clinical research network needs to harmonize the divides and bring together the broad expertises needed to conduct rigorous experimental human nutrition studies into a coordinated network. Conquering this divide will require funding to intentionally build out research centers with the appropriate mix of researchers, staff, infrastructure and equipment necessary to tackle key questions on large cohorts of study participants consuming controlled diets for extended time periods.

Challenge 2. The Study of Food and Nutrition is Intrinsically Challenging

Despite less investment relative to pharmaceuticals and medical devices, the conduct of rigorous nutrition science is often more cost burdensome due to its unique methodological burdens. Typical gold-standard pharmaceutical designs of placebo-controlled randomized double blind trials are impossible for most research questions. Many interventions cannot be blinded. Placebos do not exist for foods, necessitating comparisons between active interventions, of which there are many viable options. Foods are complex interventions, serving as vehicles for many bioactive compounds, making isolating causal factors challenging in the setting of a single study. Researchers must often make zero-sum decisions that balance internal versus external validity, often trading off between rigorous inference and ‘real-world’ application.

Challenge 3. The Study of Food and Nutrition Is Practically Challenging

Historically, controlled trials, including those conducted in GCRC facilities, have been restricted to shorter term interventions (e.g. 1-4 weeks in duration). These short-term trials are the subject of relevant critique, for both failing to capture long-term adaptations to diet as well as relying on surrogate endpoints, of which there are few with significant prognostic capacity. Observing differences in actual disease endpoints in response to food interventions is ideal but investment in such studies has historically been limited. Attempts at a definitive ‘National Diet Heart Study’ trial to address diet-cardiovascular disease hypotheses in the 1960s were ultimately not funded beyond pilot trials. These challenges have long been used to justify underinvestment in experimental nutrition research and exacerbated the field’s reliance on observational data. While presenting real challenges, investment and innovation are needed to tackle these challenges rather than continue avoiding.

These challenges presented by investing in a nutrition clinical research center network pale in comparison to the benefits of its successful implementation. We need only look at the current state of the scientific literature on how to modify diets and food composition to prevent obesity to understand the harms of not investing. The opportunities from doing so are many:

Benefit 1. Build Back Trust in the Scientific Process Surrounding Dietary Recommendation

The deep distrust of the scientific process and in the dietary recommendations from the federal government should be impetus alone for investing heavily in rigorous nutrition research. It is essential for the public to see that the government is taking nutrition seriously. Data alone will not fix public trust but investing in nutrition research, engaging citizens in the process, and ensuring transparency in the conduct of studies and their results will begin to make a dent in the deep distrust that underlies the MAHA movement and that of many food activists over the past several decades.

Benefit 2. Produce Policy- and Formulator-relevant Data

The atrophying of the clinical research network, limited funding and historical separation of expertises in nutrition have led to a situation where we know little about how food influences disease risk, beyond oft-cited links between sodium and blood pressure and saturated fats and LDL-cholesterol. It should be evident from these two long-standing recommendations that have survived many politicized criticisms that controlled human intervention research is the critical foundation of rigorous policy.

In two decades, we need to look back and be able to say the same things about the discoveries ultimately made from studying the next generation of topics around food and food processing. Such findings will be critical for not only policy makers but also from the food industry, who have shown a willingness to reformulate products but often lack the policy guidance and level of evidence needed to do so in an informed manner, leaving their actions to chase trends over science.

Benefit 3. Enhanced Discovery in Emerging Health Research Topics, such as the Microbiome

The potential to rigorously control and manipulate diets to understand their impact on health and disease holds great promise to improve not only public policy and dietary guidance but also shape our fundamental understanding of human physiology, the gut microbiome, diet-x-gene interactions, and impact of environmental chemicals. The previous GCRC network expired prior to numerous technical revolutions in nucleotide (DNA, RNA) sequencing, mass spectrometry, and cell biology that have left nutrition decades behind other advances in medicine.

Benefit 4. Improved Public Health and Reduced Societal Costs

Ultimately, the funding of a clinical research center network that supports the production of rigorous data on links between diet and disease will address the substantial degree of morbidity and mortality caused by obesity and related chronic conditions. This research can be applied to reduce health risks, improve patient outcomes, and lessen the costly burden of an unhealthy nation.

Plan of Action

Congress must pass legislation that mandates the revival and revolution of experimental human nutrition research through the appropriation of funds to establish a network of Centers of Excellence in Human Nutrition (CEHN) research across the country.

Recommendation 1. Congressional Mandate to Review Historical Human Nutrition Research Funding in America and the GCRCs, and:

• Examine of the current landscape of nutrition research happening across the United States;
• Investigate of the deficiencies in the current funding models and clinical research infrastructure;
• Identify key existing infrastructure and infrastructure needs;
• Establish a 10 year timeline to coordinate and fund high priority nutrition research, appropriating at least $5,000,000,000 per year to the CEHN (representing 200% of the current NIH investment in nutrition research, spanning basic to clinical and epidemiological studies).

Congress should seek NIH, USDA, university, industry and public input to inform the establishment of the CEHN and the initial rounds of projects it ultimately funds. Within six months, Congress should have a clear roadmap for the CEHN, including participating centers and researchers, feasibility analyses and cost estimates, and three  initial approved proposals. At least (1) proposal should advance existing intramurally funded work on processed foods that has identified several characteristics, including energy density and palatability, as potential drivers of energy intake.

Recommendation 2. Congress Should Mandate that CEHN establish an Operating Charter that Details a Governing Council for the Network Composed of Multi-sector Stakeholders.

This charter will oversee the network’s management and coordination. Specifically, it will:

• Identify key nutrition science stakeholders, including those with perceived contrasting viewpoints, to guide trial design and prioritization efforts;
• Engage non-nutrition science trialists and form partnerships with contract research organizations (CROs) with experience and demonstrated success managing large pharmaceutical trials to ensure innovative and rigorous trial designs are undertaken and CEHN operations are rigorous;
• Identify methods for public involvement in the proposal and funding of proposals, and; issue quarterly mandates to inform the public on updated progress on studies operating within the CEHN,providing new results from its studies and ways for the public to actively participate in this research.

CEHN should be initiated by Congress. It should also explore novel funding mechanisms that pools resources from specific NIH institutes that have historically supported nutrition research (such as the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Heart, Lung, and Blood Institute (NHLBI), NIA, National Institute of Child Health and Human Development (NICHD), the USDA, the Department of Defense, agricultural commodity boards and the food industry to ensure cost-sharing across relevant sectors and a robust, sustainable CEHN. CEHN will ultimately study topics that may produce results that are perceived as adversarial to the food industry and its independence should be protected. However, it is clear that America is not going back to a world where the majority of food is produced in the home in resource scarce settings as occurred when rates of overweight and obesity were low. Thus, engagement with industry actors across the food system will be critical, including food product formulators and manufacturers, restaurants and food delivery companies. Funds should be appropriated to facilitate collaboration between the CEHN and these industries to study effective reformulations and modifications that impact the health of the population.

Conclusion

The current health, social and economic burden of obesity and nutrition-related diseases are indefensible, and necessitate a multi-sector, coordinated approach to reenvisioning our food environment. Such a reenvisioning needs to be based on rigorous science that describes causal links between food and health, and serves innovative solutions to address food and nutrition-related problems. Investment in an intentional, coordinated and well-funded research network capable of conducting rigorous and long-term nutrition intervention trials is long overdue and holds substantial capacity to revolutionize nutritional guidance, food formulation and policy. It is imperative that visionary political leaders overhaul our nutrition research landscape and invest in a network of Centers of Excellence in Human Nutrition that can meet the demands for rigorous nutrition evidence, build back trust in public health, and dramatically mitigate the impacts of nutrition- and obesity-related chronic disease.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

Editor’s note: This policy memo was written by Jake Seliger and his wife Bess Stillman. Jake passed away before meeting his child, Athena, born on October 31, 2024. Except where indicated, the first-person voice is that of Jake. This memo advocates for user-centric technology modernization and data interoperability. More crucially, he urges expanded patient rights to opt-in to experimental drug trials and FDA rule revisions to enable terminal patients to take more risks on behalf of themselves, for the benefit of others.

The FDA is supposed to ensure that treatments are safe and effective, but terminal cancer patients like me are already effectively dead. If I don’t receive advanced treatment quickly, I will die. My hope, in the time I have remaining, is to promote policies that will speed up access to treatments and clinical trials for cancer patients throughout the U.S. 

There are about two million cancer diagnoses and 600,000 deaths annually in the United States. Cancer treatments are improved over time via the clinical trial system: thousands of clinical trials are conducted each year (many funded by the government via the National Institutes of Health, or NIH, and many funded by pharmaceutical companies hoping to get FDA approval for their products).

But the clinical trial system is needlessly slow, and as discussed below, is nearly impossible for any layperson to access without skilled consulting. As a result, clinical trials are far less useful than they could be.

The FDA is currently “protecting” me from being harmed or killed by novel, promising, advanced cancer treatments that could save or extend my life, so that I can die from cancer instead. Like most patients, I would prefer a much faster system in which the FDA conditionally approves promising, early-phase advanced cancer treatments, even if those treatments haven’t yet been proven fully effective. Drugmakers will be better incentivized to invest in novel cancer treatments if they can begin receiving payment for those treatments sooner. The true risks to terminal patients like me are low—I’m already dying—and the benefits to both existing terminal patients and future patients of all kinds are substantial.

I would also prefer a clinical trial system that was easy for patients to navigate, rather than next-to-impossible. Easier access for patients could radically lower the cost and time for clinical trials by making recruitment far cheaper and more widespread, rather than including only about 6% of patients. In turn, speeding up the clinical-trial process means that future cancer patients will be helped by more quickly approving novel treatments. About half of pharmaceutical R&D spending goes not to basic research, but to the clinical trial process. If we can cut the costs of clinical trials by streamlining the process to improve access to terminal patients, more treatments will make it to patients, and will be faster in doing so.

Cancer treatment is a non-partisan issue. To my knowledge, both left and right agree that prematurely dying from cancer is bad. Excess safety-ism and excessive caution from the FDA costs lives, including in the near future, my own. Three concrete actions would improve clinical research, particular for terminal cancer patients like me, but for many other patients as well:

Clinical trials should be easier and cheaper. The chief obstacles to this are recruitment and retention.

Congress and NIH should modernize the website ClinicalTrials.gov and vastly expand what drug companies and research sites are required to report there, and the time in which they must report it. Requiring timely updates that include comprehensive eligibility criteria, availability for new participants,  and accurate site contact information,would mean that patients and doctors will have much more complete information about what trials are available and for whom. 

The process of determining patient eligibility and enrolling in a trial should be easier. Due to narrow eligibility criteria, studies struggle to enroll an adequate number of local patients, which severely delays trial progression. A patient who wishes to participate in a trial must “establish care” with the hospital system hosting the trial, before they are even initially screened for eligibility or told if a slot is available. Due to telemedicine practice restrictions across state lines, this means that patients who aren’t already cared for at that site— patients who are ill and for whom unnecessary travel is a huge burden— must use their limited energy to travel to a site just to find out if they can proceed to requesting a trial slot and starting further eligibility testing. Then, if approved for the study, they must be able to uproot their lives to move to, or spend extensive periods of time at, the study location 

Improved access to remote care for clinical trials would solve both these problems. First, by allowing the practice of telemedicine across state lines for visits directly related to screening and enrollment into clinical trials. Second, by incentivizing decentralization—meaning a participant in the study can receive the experimental drug and most monitoring, labs and imaging at a local hospital or infusion clinic—by accepting data from sites that can follow a standardized study protocol. 

We should require the FDA to allow companies with prospective treatments for fatal diseases to bring those treatments to market after initial safety studies, with minimal delays and with a lessened burden for demonstrating benefit.

Background

[At the time of writing this] I’m a 40-year-old man whose wife is five months pregnant, and the treatment that may keep me alive long enough to meet my child is being kept from me because of current FDA policies. That drug may be in a clinical trial I cannot access. Or, it may be blocked from coming to market by requirements for additional testing to further prove efficacy that has already been demonstrated. 

Instead of giving me a chance to take calculated risks on a new therapy that might allow me to live and to be with my family, current FDA regulations are choosing for me: deciding that my certain death from cancer is somehow less harmful to me than taking a calculated, informed risk that might save or prolong my life. Who is asking the patients being protected what they would rather be protected from? The FDA errs too much on the side of extreme caution around drug safety and efficacy, and that choice leads to preventable deaths.

One group of scholars attempted to model how many lives are lost versus gained from a more or less conservative FDA. They find that “from the patient’s perspective, the approval criteria in [FDA program accelerators] may still seem far too conservative.” Their analysis is consistent with the FDA being too stringent and slow in approving use of drugs for fatal diseases like cancer: “Our findings suggest that conventional standards of statistical significance for approving drugs may be overly conservative for the most deadly diseases.”

Drugmakers also find it difficult to navigate what exactly the FDA wants: “their deliberations are largely opaque—even to industry insiders—and the exact role and weight of patient preferences are unclear.” This exacerbates the difficulty drugmakers face in seeking to get treatments to patients faster. Inaction in the form of delaying patient access to drugs is killing people. Inaction is choosing death for cancer patients.

I’m an example of this; I was diagnosed with squamous cell carcinoma (SCC) of the tongue in Oct. 2022. I had no risk factors, like smoking or alcoholism, that put me at risk for SCC. The original tumor was removed in October 2022, and I then had radiation that was supposed to cure me. It didn’t, and the cancer reappeared in April 2023. At that point, I would’ve been a great candidate for a drug like MCLA-158, which has been stuck in clinical trials, despite “breakthrough therapy designation” by the FDA and impressive data, for more than five years. This, despite the fact that MCLA-158 is much easier to tolerate than chemotherapy and arrests cancer in about 70% of patients. Current standard of care chemotherapy and immunotherapy has a positive response rate of only 20-30%. 

Had MCLA-158 been approved, I might have received it in April 2023, and still have my tongue. Instead, in May 2023, my entire tongue was surgically removed in an attempt to save my life, forever altering my ability to speak and eat and live a normal life. That surgery removed the cancer, but two months later it recurred again in July 2023. While clinical-trial drugs are keeping me alive right now, I’m dying in part because promising treatments like MCLA-158 are stuck in clinical trials, and I couldn’t get them in a timely fashion, despite early data showing their efficacy. Merus, the maker of MCLA-158, is planning a phase 3 trial for MCLA-158, despite its initial successes. This is crazy: head and neck cancer patients need MCLA-158 now.

Memo authors Bess Stillman and Jake Seliger

I’m only writing this brief because I was one of the few patients who was indeed, at long last, able to access MCLA-158 in a clinical trial, which incredibly halted my rapidly expanding, aggressive tumors. Without it, I’d have been dead nine months ago. Without it, many other patients already are.  Imagine that you, or your spouse, or parent, or child, finds him or herself in a situation like mine. Do you want the FDA to keep testing a drug that’s already been shown to be effective and allow patients who could benefit to suffer and die? Or do you want your loved one to get the drug, and avoid surgical mutilation and death? I know what I’d choose. 

Multiply this situation across hundreds of thousands of people per year and you’ll understand the frustration of the dying cancer patients like me.

As noted above, about 600,000 people die annually from cancer—and yet cancer drugs routinely take a decade or more to move from lab to approval. The process is slow: “By the time a drug gets into phase III, the work required to bring it to that point may have consumed half a decade, or longer, and tens if not hundreds of millions of dollars.” If you have a fatal disease today, a treatment that is five to ten years away won’t help. Too few people participate in clinical trials partly because participation is so difficult; one study finds that “across the entire U.S. system, the estimated participation rate to cancer treatment trials was 6.3%.” Given how many people die, the prospect of life is attractive.

There is another option in between waiting decades for a promising drug to come to market and opening the market to untested drugs: Allowing terminal patients to consent to the risk of novel, earlier-phase treatments, instead of defaulting to near-certain death, would potentially benefit those patients as well as generate larger volumes of important data regarding drug safety and efficacy, thus improving the speed of drug approval for future patients. Requiring basic safety data is reasonable, but requiring complete phase 2 and 3 data for terminal cancer patients is unreasonably slow, and results in deaths that could be prevented through faster approval.

Again, imagine you, your spouse, parent, or child, is in a situation like mine: they’ve exhausted current standard-of-care cancer treatments and are consequently facing certain death. New treatments that could extend or even save their life may exist, or be on the verge of existing, but are held up  by the FDA’s requirements that treatments be redundantly proven to be safe and highly effective. Do you want your family member to risk unproven but potentially effective treatments, or do you want your family member to die?

I’d make the same choice. The FDA stands in the way.

Equally important, we need to shorten the clinical trial process: As Alex Telford notes, “Clinical trials are expensive because they are complex, bureaucratic, and reliant on highly skilled labour. Trials now cost as much as $100,000 per patient to run, and sometimes up to $300,000 or even $500,000 per patient.” And as noted above, about half of pharmaceutical R&D spending goes not to basic research, but to the clinical trial process.

Cut the costs of clinical trials, and more treatments will make it to patients, faster. And while it’s not reasonable to treat humans like animal models, a lot of us who have fatal diagnoses have very little to lose and consequently want to try drugs that may help us, and people in the future with similar diseases. Most importantly, we understand the risks of potentially dying from a drug that might help us and generate important data, versus waiting to certainly die from cancer in a way that will not benefit anybody. We are capable of, and willing to give, informed consent. We can do better and move faster than we are now. In the grand scheme of things, “When it comes to clinical trials, we should aim to make them both cheaper and faster. There is as of yet no substitute for human subjects, especially for the complex diseases that are the biggest killers of our time. The best model of a human is (still) a human.” Inaction will lead to the continued deaths of hundreds of thousands of people annually. 

Trials need patients, but the process of searching for a trial in which to enroll is archaic. We found ClinicalTrials.gov nearly impossible to navigate. Despite the stakes, from the patient’s perspective, the clinical trial process is impressively broken, obtuse and confusing, and one that we gather no one likes: patients don’t, their families don’t, hospitals and oncologists who run the clinical trials don’t, drug companies must not, and the people who die while waiting to get into a trial probably don’t.

I [Bess] knew that a clinical trial was Jake’s only chance. But how would we find one? I’d initially  hoped that a head and neck oncologist would recommend a specific trial, preferably one that they could refer us to. But most doctors didn’t know of trial options outside their institution, or, frequently, within it, unless they were directly involved. ,Most recommended large research institutions that had a good reputation for hard cases, assuming they’d have more studies and one might be a match. 

How were they, or we, supposed to find out what trials actually existed?

The only large-scale search option is ClinicalTrials.gov. But many oncologists I spoke with don’t engage with ClinicalTrials.gov, because the information is out-of-date, difficult to navigate, and inaccurate. It can’t be relied on. For example,  I shared a summary of Jake’s relevant medical information (with his permission) in a group of physicians who had offered to help us with the clinical trial search. Ten physicians shared their top-five search results. Ominously, none listed the same trials. 

How is it that ten doctors can put in the same basic, relevant clinical data into an engine meant to list and search for all existing clinical trials, only for no two to surface the same study?   The problem is simple: There’s a lack of keyword standardization. 

Instead of a drop-down menu or click-boxes with diagnoses to choose from, the first search “filter” on ClinicalTrials.gov is a text box that says “Condition\Disease.” If I search: “Head and Neck Cancer” I get ___________ results. If I search “Tongue Cancer,” I get _________ results. Although Tongue Cancer is a subset of Head and Neck Cancer, I don’t see the studies listed as “Head and Neck Cancer”, unless I type in both, or the person who created the ClinicalTrials.gov post for the study chose to type out multiple variations on a diagnosis. Nothing says they have to. If I search for both, I will still miss studies filed as:  “HNSCC” or “All Solid Tumors” or “Squamous Cell Carcinoma of the Tongue.” 

The good news is that online retailers solved this problem for us years ago. It’s easier to find a dress to my exact specifications out of thousands on H&M,com than it is to find a clinical trial. I can open a search bar, click “dress,” select my material from another click box (which allows me to select from the same options the people listing the garments chose from), then click on the boxes for my desired color, dry clean or machine wash, fabric, finish, closure, and any other number of pre-selected categories before adding additional search keywords if I choose. I find a handful of options all relevant to my desires within a matter of minutes. H&M provides a list of standardized keywords describing what they are offering, and I can filter from there. This way, H&M and I are speaking the same language. And a dress isn’t life or death. For much more on my difficulties with ClinicalTrials.gov, see here.

Further slowing a patient’s ability to find a relevant clinical trial is a lack of comprehensive, searchable, eligibility criteria. Every study has eligibility criteria, and eligibility criteria—like keywords—aren’t standardized on ClinicalTrials.gov. Nor is it required that an exhaustive explanation of eligibility criteria be provided, which may lead to a patient wasting precious weeks attempting to establish care and enroll in a trial, only to discover there was unpublished eligibility criteria they don’t meet. Instead, the information page for each study outlines inclusion and exclusion criteria using whatever language whoever is typing feels like using. Many have overlapping inclusion and exclusion criteria, but there can be long lists of additional criteria for each arm of a study, and it’s up to the patient or their doctor to read through them line by line—if they’re even listed— to see if prior medications, current medications, certain genomic sequencing findings, numbers of lines of therapy, etc. makes the trial relevant. 

In the end, we hired a consultant (Eileen), who leveraged her full-time work helping pharmaceutical companies determine which novel compounds might be worth pouring their R&D efforts into assisting patients find potential clinical trials.  She helped us narrow it down to the top 5 candidate trials from the thousands that turned up in initial queries. 

• NCT04815720: Pepinemab in Combination With Pembrolizumab in Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck (KEYNOTE-B84)
• NCT03526835: A Study of Bispecific Antibody MCLA-158 in Patients With Advanced Solid Tumors.
• NCT05743270: Study of RP3 in Combination With Nivolumab and Other Therapy in Patients With Locoregionally Advanced or Recurrent SCCHN.”
• NCT03485209: Efficacy and Safety Study of Tisotumab Vedotin for Patients With Solid Tumors (innovaTV 207)
• NCT05094336: AMG 193, Methylthioadenosine (MTA) Cooperative Protein Arginine Methyltransferase 5 (PRMT5) Inhibitor, Alone and in Combination With Docetaxel in Advanced Methylthioadenosine Phosphorylase (MTAP)-Null Solid Tumors (MTAP).

Based on the names alone, you can see why it would be difficult if not impossible for someone without some expertise in cancer oncology to evaluate trials. Even with Eileen’s expertise, two of the trials were stricken from our list when we discovered unlisted eligibility criteria, which excluded Jake. When exceptionally motivated patients, the oncologists who care for them, and even consultants selling highly specialized assistance can’t reliably navigate a system that claims to be desperate to enroll patients into trials, there is a fundamental problem with the system. But this is a mismatch we can solve, to everyone’s benefit. 

Plan of Action

We propose three major actions. Congress should:

Recommendation 1. Direct the National Library of Medicine (NLM) at the National Institutes of Health (NIH) to modernize ClinicalTrials.gov so that patients and doctors have complete and relevant information about available trials, as well as requiring more regular updates from companies as to all the details of available trials, and 

Recommendation 2. Allow the practice of telemedicine across state lines for visits related to clinical trials.

Recommendation 3. Require the FDA to allow companies with prospective treatments for fatal diseases to bring those treatments to market after initial safety studies.

Modernizing ClinicalTrials.gov will empower patients, oncologists, and others to better understand what trials are available, where they are available, and their up-to-date eligibility criteria, using standardized search categories to make them more easily discoverable. Allowing telemedicine across state lines for clinical trial care will significantly improve enrollment and retention. Bringing treatments to market after initial safety studies will speed the clinical trial process, and get more patients treatments, sooner. In cancer, delays cause death. 

To get more specific:

The FDA already has a number of accelerated approval options. Instead of the usual “right to try” legislation, we propose creating a second, provisional market for terminal patients that allows partial approval of a drug while it’s still undergoing trials, making it available to trial-ineligible (or those unable to easily access a trial) patients for whom standard of care doesn’t provide a meaningful chance at remission. This partial approval would ideally allow physicians to prescribe the drug to this subset of patients as they see fit: be that monotherapy or a variety of personalized combination therapies, tailored to a patient’s needs, side-effect profile and goals. This wouldn’t just expand access to patients who are otherwise out of luck, but can give important data about real-world reaction and response. 

As an incentive, the FDA could require pharmaceutical companies to provide drug access to terminal patients as a condition of continuing forward in the trial process on the road to a New Drug Application. While this would be federally forced compassion, it would differ from “compassionate use.” To currently access a study drug via compassionate use, a physician has to petition both the drug company and the FDA on the patient’s behalf, which comes with onerous rules and requirements. Most drug companies with compassionate use programs won’t offer a drug until there’s already a large amount of compelling phase 2 data demonstrating efficacy, a patient must have “failed” standard of care and other available treatments, and the drug must (usually) be given as a monotherapy. Even if the drugmaker says yes, the FDA can still say no. Compassionate use is an option available to very small numbers, in limited instances, and with bureaucratic barriers to overcome. Not terribly compassionate, in my opinion.

The benefits of this “terminal patient” market can and should go both ways, much as lovers should benefit from each other instead of trying to create win-lose situations. Providing the drug to patients would come with reciprocal benefits to the pharmaceutical companies.  Any physician prescribing the drug to patients should be required to report data regarding how the drug was used, in what combination, and to what effect. This would create a large pool of observational, real-world data gathered from the patients who aren’t ideal candidates for trials, but better represent the large subset of patients who’ve exhausted multiple lines of therapies yet aren’t ready for the end. Promising combinations and unexpected effects might be identified from these observational data sets, and possibly used to design future trials.

Dying patients would get drugs faster, and live longer, healthier lives, if we can get better access to both information about clinical trials, and treatments for diseases. The current system errs too much on proving effectiveness and too little on the importance of speed itself, and of the number of people who die while waiting for new treatments. Patients like me, who have fatal diagnoses and have already failed “standard of care” therapies, routinely die while waiting for new or improved treatments. Moreover, patients like me have little to lose: because cancer is going to kill us anyway, many of us would prefer to roll the dice on an unproven treatment, or a treatment that has early, incomplete data showing its potential to help, than wait to be killed by cancer. Right now, however, the FDA does not consider how many patients will die while waiting for new treatments. Instead, the FDA requires that drugmakers prove both the safety and efficacy of treatments prior to allowing any approval whatsoever.

As for improving ClinicalTrials.gov, we have several ideas: 

First, the NIH should hire programmers with UX experience, and should be empowered to pay market rates to software developers with experience at designing websites for, say, Amazon or Shein. Clinicaltrials.gov was designed to be a study registry, but needs an overhaul to be more useful to actual patients and doctors.  

Second, UX is far from the only problem. Data itself can be a problem. For example, the NIH could require a patient-friendly summary of each trial; it could standardize the names of drugs and conditions so that patients and doctors could see consistent and complete search results; and it could use a consistent and machine-readable format for inclusion and exclusion criteria. 

We are aware of one patient group that, in trying to build an interface to ClinicalTrials.gov, found a remarkable degree of inconsistency and chaos: “We have analyzed the inclusion and exclusion criteria for all the cancer-related trials from clinicaltrials.gov that are recruiting for interventional studies (approximately 8,500 trials). Among these trials, there are over 1,000 ways to indicate the patient must not be pregnant during a trial, and another 1,000 ways to indicate that the patient must use adequate birth control.” ClinicalTrials.gov should use a Domain Specific Language that standardizes all of these terms and conditions, so that patients and doctors can find relevant trials with 100x less effort. 

Third, the long-run goal should be a real-time, searchable database that matches patients to studies using EMR data and allows doctors to see what spots are available and where. Pharmaceutical and biotech companies would need to be required to contribute up-to-date information on all extant clinical trials on a regular basis (e.g., monthly). 

Conclusion

Indeed, we need a national clinical trial database that EMRs can connect to, in the style of Epic’s Care Everywhere. A patient signs a release to share their information, like Care Everywhere allows hospital A to download a patient’s information from hospital B if the patient signs a release. Trials with open slots could mark themselves as “recruiting” and update in real time. A doctor could press a button and identify potential open studies. A patient available for a trial could flag their EMR profile as actively searching, allowing clinical trial sites to browse patients in their region who are looking for a study. Access to that patient’s EMR would allow them to scan for eligibility. 

This would be easy to do. OKCupid can do it. The tech exists. Finding a clinical trial already feels a bit like online dating, except if you get the wrong match, you die.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

There is an urgent need to manage the escalating costs of federal R&D infrastructure and the increasing risk that failing facilities pose to the scientific missions of the federal research enterprise.  Many of the laboratories and research support facilities operating under the federal research umbrella are near or beyond their life expectancy, creating significant safety hazards for federal workers and local communities.  Unfortunately, the nature of the federal budget process forces agencies into a position where the actual cost of operations are not transparent in agency budget requests to OMB before becoming further obscured to appropriators, leading to potential appropriations disasters (including an approximately 60% cut to National Institute of Standards and Technology (NIST) facilities in 2024 after the agency’s challenges became newsworthy).  Providing both Congress and OMB with a complete accounting of the actual costs of agency facilities may break the gamification of budget requests and help the government prioritize infrastructure investments.

Challenge and Opportunity 

Recent reports by the National Research Council and the National Science and Technology Council, including the congressionally-mandated Quadrennial Science and Technology Review have highlighted the dire state of federal facilities.  Maintenance backlogs have ballooned in recent years, forcing some agencies to shut down research activities in strategic R&D domains including Antarctic research and standards development.  At NIST, facilities outages due to failing steam pipes, electricity, and black mold have led to outages reducing research productivity from 10-40 percent.  NASA and NIST have both reported their maintenance backlogs have increased to exceed 3 billion dollars. The Department of Defense forecasts that bringing their buildings up to modern standards would cost approximately 7 billion “putting the military at risk of losing its technological superiority.”  The shutdown of many Antarctic science operations and collapse of the Arecibo Observatory have been placed in stark contrast with the People’s Republic of China opening rival and more capable facilities in both research domains.  In the late 2010s, Senate staffers were often forced to call national laboratories, directly, to ask them what it would actually cost for the country to fully fund a particular large science activity.

This memo does not suggest that the government should continue to fund old or outdated facilities; merely that there is a significant opportunity for appropriators to understand the actual cost of our legacy research and development ecosystem, initially ramped up during the Cold War.  Agencies should be able to provide a straight answer to Congress about what it would cost to operate their inventory of facilities.  Likewise, Congress should be able to decide which facilities should be kept open, where placing a facility on life support is acceptable, and which facilities should be shut down.  The cost of maintaining facilities should also be transparent to the Office of Management and Budget so examiners can help the President make prudent decisions about the direction of the federal budget.

The National Science and Technology Council’s mandated research and development infrastructure report to Congress is a poor delivery vehicle.  As coauthors of the 2024 research infrastructure report, we can attest to the pressure that exists within the White House to provide a positive narrative about the current state of play as well as OMB’s reluctance to suggest additional funding is needed to maintain our inventory of facilities outside the budget process.  It would be much easier for agencies who already have a sense of what it costs to maintain their operations to provide that information directly to appropriators (as opposed to a sanitized White House report to an authorizing committee that may or may not have jurisdiction over all the agencies covered in the report)–assuming that there is even an Assistant Director for Research Infrastructure serving in OSTP to complete the America COMPETES mandate.  Current government employees suggest that the Trump Administration intends to discontinue the Research and Development Infrastructure Subcommittee.

Agencies may be concerned that providing such cost transparency to Congress could result in greater micromanagement over which facilities receive which investments.  Given the relevance of these facilities to their localities (including both economic benefits and environmental and safety concerns) and the role that legacy facilities can play in training new generations of scientists, this is a matter that deserves public debate.  In our experience, the wider range of factors considered by appropriation staff are relevant to investment decisions.  Further, accountability for macro-level budget decisions should ultimately fall on decisionmakers who choose whether or not to prioritize investments in both our scientific leadership and the health and safety of the federal workforce and nearby communities.  Facilities managers who are forced to make agonizing choices in extremely resource-constrained environments currently bear most of that burden.

Plan of Action 

Recommendation 1:  Appropriations committees should require from agencies annual reports on the actual cost of completed facilities modernization, operations, and maintenance, including utility distribution systems.

Transparency is the only way that Congress and OMB can get a grip on the actual cost of running our legacy research infrastructure.  This should be done by annual reporting to the relevant appropriators the actual cost of facilities operations and maintenance.  Other costs that should be accounted for include obligations to international facilities (such as ITER) and facilities and collections that are paid for by grants (such as scientific collections which support the bioeconomy). Transparent accounting of facilities costs against what an administration chooses to prioritize in the annual President’s Budget Request may help foster meaningful dialogue between agencies, examiners, and appropriations staff.

The reports from agencies should describe the work done in each building and impact of disruption.  Using the NIST as an example, the Radiation Physics Building (still without the funding to complete its renovation) is crucial to national security and the medical community. If it were to go down (or away), every medical device in the United States that uses radiation would be decertified within 6 months, creating a significant single point of failure that cannot be quickly mitigated. The identification of such functions may also enable identification of duplicate efforts across agencies.

The costs of utility systems should be included because of the broad impacts that supporting infrastructure failures can have on facility operations. At NIST’s headquarters campus in Maryland, the entire underground utility distribution system is beyond its designed lifespan and suffering nonstop issues. The Central Utility Plant (CUP), which creates steam and chilled water for the campus, is in a similar state. The CUP’s steam distribution system will be at the complete end of life (per forensic testing of failed pipes and components) in less than a decade and potentially as soon as 2030. If work doesn’t start within the next year (by early 2026), it is likely the system could go down.  This would result in a complete loss of heat and temperature control on the campus; particularly concerning given the sensitivity of modern experiments and calibrations to changes in heat and humidity.  Less than a decade ago, NASA was forced to delay the launch of a satellite after NIST’s steam system was down for a few weeks and calibrations required for the satellite couldn’t be completed.

Given the varying business models for infrastructure around the Federal government, standardization of accounting and costs may be too great a lift–particularly for agencies that own and operate their own facilities (government owned, government operated, or GOGOs) compared with federally funded research and development centers (FFRDCs) operated by companies and universities (government owned, contractor operated, or GOCOs).

These reports should privilege modernization efforts, which according to former federal facilities managers should help account for 80-90 percent of facility revitalization, while also delivering new capabilities that help our national labs maintain (and often re-establish) their world-leading status.  It would also serve as a potential facilities inventory, allowing appropriators the ability to de-conflict investments as necessary.

It would be far easier for agencies to simply provide an itemized list of each of their facilities, current maintenance backlog, and projected costs for the next fiscal year to both Congress and OMB at the time of annual budget submission to OMB.  This should include the total cost of operating facilities, projected maintenance costs, any costs needed to bring a federal facility up to relevant safety and environmental codes (many are not).  In order to foster public trust, these reports should include an assessment of systems that are particularly at risk of failure, the risk to the agency’s operations, and their impact on surrounding communities, federal workers, and organizations that use those laboratories.  Fatalities and incidents that affect local communities, particularly in laboratories intended to improve public safety, are not an acceptable cost of doing business.  These reports should be made public (except for those details necessary to preserve classified activities).

Recommendation 2: Congress should revisit the idea of a special building fund from the General Services Administration (GSA) from which agencies can draw loans for revitalization.

During the first Trump Administration, Congress considered the establishment of a special building fund from the GSA from which agencies could draw loans at very low interest (covering the staff time of GSA officials managing the program).  This could allow agencies the ability to address urgent or emergency needs that happen out of the regular appropriations cycle.  This approach has already been validated by the Government Accountability Office for certain facilities, who found that “Access to full, upfront funding for large federal capital projects—whether acquisition, construction, or renovation—could save time and money.”  Major international scientific organizations that operate large facilities, including CERN (the European Organization for Nuclear Research), have similar ability to take loans to pay for repairs, maintenance, or budget shortfalls that helps them maintain financial stability and reduce the risk of escalating costs as a result of deferred maintenance.

Up-front funding for major projects enabled by access to GSA loans can also reduce expenditures in the long run.  In the current budget environment, it is not uncommon for the cost of major investments to double due to inflation and doing the projects piecemeal.  In 2010, NIST proposed a renovation of its facilities in Boulder with an expected cost of $76 million.  The project, which is still not completed today, is now estimated to cost more than $450 million due to a phased approach unsupported by appropriations.  Productivity losses as a result of delayed construction (or a need to wait for appropriations) may have compounding effects on industry that may depend on access to certain capabilities and harm American competitiveness, as described in the previous recommendation.

Conclusion

As the 2024 RDI Report points out “Being a science superpower carries the burden of supporting and maintaining the advanced underlying infrastructure that supports the research and development enterprise.” Without a transparent accounting of costs it is impossible for Congress to make prudent decisions about the future of that enterprise. Requiring agencies to provide complete information to both Congress and OMB at the beginning of each year’s budget process likely provides the best chance of allowing us to address this challenge.

As the impacts of climate change worsen and become salient to more communities across the country, state and local planners need access to robust and replicable predictive models in order to effectively plan for emergencies like extreme flooding. However, planning agencies often lack the resources to build these models themselves. And models developed by federal agencies are often built on outdated data and are limited in their interoperability. Many planners have therefore begun turning to private-sector providers of models they say offer higher quality and more up-to-date information. But access to these models can be prohibitively expensive, and many remain “black boxes” as these providers rarely open up their methods and underlying data.

The federal government can support more proactive, efficient, and cost-effective resiliency planning by certifying predictive models to validate and publicly indicate their quality. Additionally, Congress and the new Presidential Administration should protect funding at agencies like FEMA, NOAA, and the National Institute of Standards and Technology (NIST) who have faced budget shortfalls in recent years or are currently facing staffing reductions and proposed budget cuts, to support the collection and sharing of high quality and up-to-date information. A certification system and clearinghouse would enable state and local governments to more easily discern the quality and robustness of a growing number of available climate models. Ultimately, such measures could increase cost-efficiencies and empower local communities by supporting more proactive planning and the mitigation of environmental disasters that are becoming more frequent and intense.

Challenge and Opportunity

The United States experienced an unprecedented hurricane season in 2024. Even as hurricanes continued to affect states like Texas, Louisiana, and Florida, the effects of hurricanes and other climate-fueled storms also expanded to new geographies—including inland and northern regions like Asheville, North Carolina and Burlington, Vermont. Our nation’s emergency response systems can no longer keep up—the Federal Emergency Management Agency (FEMA) spent nearly half the agency’s disaster relief fund within the first two weeks of the 2025 fiscal year. More must be done to support proactive planning and resilience measures at state and local levels. Robust climate and flooding models are critical to planners’ abilities to predict the possible impacts of storms, hurricanes, and flooding, and to inform infrastructure updates, funding prioritization, and communication strategies.

Developing useful climate models requires large volumes of data and considerable computational resources, as well as time and data science expertise, making it difficult for already-strapped state and local planning agencies to build their own. Many global climate models have proven to be highly accurate, but planners must often integrate more granular data for these to be useful at local levels. And while federal agencies like FEMA, the National Oceanic and Atmospheric Administration (NOAA), and the Army Corps of Engineers make their flooding and sea level rise models publicly available, these models have limited predictive capacity, and the datasets they are built on are often outdated or contain large gaps. For example, priority datasets, such as FEMA’s Flood Insurance Rate Maps (FIRMs) and floodplain maps, are notoriously out of date or do not integrate accurate information on local drainage systems, preventing meaningful and broad public use. A lack of coordination across government agencies at various levels, low data interoperability, and variations in data formats and standards also further prevent the productive integration of climate and flooding data into planning agencies’ models, even when data are available. Furthermore, recent White House directives to downsize agencies, freeze funding, and in some cases directly remove information from federal websites, have made some public climate datasets, including FEMA’s, inaccessible and put many more at risk.

A growing private-sector market has begun to produce highly granular flooding models, but these are often cost-prohibitive for state and local entities to access. In addition, these models tend to be black boxes; their underlying methods are rarely publicly available, and thus are difficult or impossible to rigorously evaluate or reproduce. A 2023 article in the Arizona State Law Journal found widely varying levels of uncertainty involved in these models’ predictions and their application of different climate scenarios. And a report from the President’s Council of Advisors on Science and Technology also questioned the quality of these private industry models, and called on NOAA and FEMA to develop guidelines for measuring their accuracy.

To address these issues, public resources should be invested in enabling broader access to robust and replicable climate and flooding models, through establishment of a certification system and clearinghouse for models not developed by government agencies. Several realities make implementing this idea urgent. First, research predicts that even with aggressive and coordinated action, the impacts of hurricanes and other storms are likely to worsen (especially for already disadvantaged communities), as will the costs associated with their clean up. A 2024 U.S. Chamber of Commerce report estimates that “every $1 spent on climate resilience and preparedness saves communities $13 in damages, cleanup costs, and economic impact,” potentially adding up to billions of dollars in savings across the country. Second, flooding data and models may need to be updated to accommodate not only new scientific information, but also updates to built infrastructure as states and municipalities continue to invest in infrastructure upgrades. Finally, government agencies at all levels, as well as private sector entities, are already responding to more frequent or intensified flooding events. These agencies, as well as researchers and community organizations, already hold a wealth of data and knowledge that, if effectively integrated into robust and accessible models, could help vulnerable communities plan for and mitigate the worst impacts of flooding.

Plan of Action

Congress should direct the National Institute of Standards and Technology (NIST) to establish a certification system or stamp of approval for predictive climate and weather models, starting with flood models. Additionally, Congress should support the maintenance of these and agencies’ capacities to build and maintain such a system, as well as that of other agencies whose data are regularly integrated into climate models, including FEMA, NOAA, the Environmental Protection Agency (EPA), National Aeronautics and Space Administration (NASA), U.S. Geological Survey (USGS), and Army Corps of Engineers. Congressional representatives can do this through imposing moratoria on Reductions in Force and opposing budget cuts imposed by the Department of Government Efficiency and the budget reconciliation process. 

Following the publication of the Office of Science and Technology Policy’s Memorandum on “Ensuring Free, Immediate, and Equitable Access to Federally Funded Research,” agencies that fund or conduct research are now required to update their open access policies by the end of 2025 to make all federally funded publications and data publicly accessible. While this may help open up agency models, it cannot compel private organizations to make their models open or less expensive to access. However, federal agencies can develop guidance, standards, and a certification system to make it easier for state and local agencies and organizations to navigate what’s been called the “Wild West of climate modeling.”

A robust certification system would require both an understanding of the technical capabilities of climate models, as well as the modeling and data needs of resilience planners and floodplain managers. Within NIST, the Special Programs Office or Information Technology Laboratory could work with non-governmental organizations that already convene these stakeholders to gather input on what a certification system should consider and communicate. For example, the Association of State Floodplain Managers, American Flood Coalition, American Society of Adaptation Professionals, and American Geophysical Union are all well-positioned to reach researchers and planners across a range of geographies and capacities. Additionally, NIST could publish requests for information to source input more widely. Alternatively, NOAA’s National Weather Service or Office of Oceanic and Atmospheric Research could perform similar functions. However, this would require concerted effort on Congress’s part to protect and finance the agency and its relevant offices. In the face of impending budget cuts, it would benefit NIST to consult with relevant NOAA offices and programs on the design, scope, and rollout of such a system.

Gathered input could be translated into a set of minimum requirements and nice-to-have features of models, indicating, for example, proven accuracy or robustness, levels of transparency in the underlying data or source code, how up-to-date underlying data are, ease of use, or interoperability. The implementing agency could also look to other certification models such as the Leadership in Energy and Environmental Design (LEED) rating system, which communicates a range of performance indicators for building design. Alternatively, because some of the aforementioned features would be challenging to assess in the short term, a stamp of approval system would communicate that a model has met some minimum standard.

Importantly, the design and maintenance of this system would be best led by a federal agency like NIST, rather than third-party actors, because NIST would be better positioned to coordinate efficiently with other agencies that collect and supply climate and other relevant data such as FEMA, USGS, EPA, and the Army Corps of Engineers. Moreover, there are likely to be cost efficiencies associated with integrating such a system into an existing agency program rather than establishing a new third-party organization whose long-term sustainability is not guaranteed. The fact that this system’s purpose would be to mediate trustworthy information and support the prevention of damage and harm to communities represented by the federal government also necessitates a higher level of accountability and oversight than a third-party organization could offer. 

NIST could additionally build and host a clearinghouse or database of replicable models and results, as well as relevant contact information to make it easy for users to find reliable models and communicate with their developers. Ideally information would be presented for technical experts and professionals, as well as non-specialists. Several federal agencies currently host clearinghouses for models, evidence, and interventions, including the Environmental Protection Agency, Department of Labor, and Department of Health and Human Services, among many others. NIST could look to these to inform the goals, design, and structure of a climate model clearinghouse.

Conclusion

Establishing an objective and widely recognized certification standard for climate and weather models would support actors both within and outside of government to use a growing wealth of flooding and climate data for a variety of purposes. For example, state and local agencies could more accurately predict and plan for extreme flooding events more quickly and efficiently, and prioritize infrastructure projects and spending. And if successful, this idea could be adapted for other climate-related emergencies such as wildfire and extreme drought. Ultimately, public resources and data would be put to use to foster safer and more resilient communities across the country, and potentially save billions of dollars in damages, clean up efforts, and other economic impacts.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

PLEASE NOTE (February 2025): Since publication several government websites have been taken offline. We apologize for any broken links to once accessible public data.

The policy discourse about high-risk, high-reward research has been too narrow. When that term is used, people are usually talking about DARPA-style moonshot initiatives with extremely ambitious goals. Given the overly conservative nature of most scientific funding, there’s a fair appetite (and deservedly so) for creating new agencies like ARPA-H, and other governmental and private analogues.

The “moonshot” definition, however, omits other types of high-risk, high-reward research that are just as important for the government to fund—perhaps even more so, because they are harder for anyone else to support or even to recognize in the first place.

Far too many scientific breakthroughs and even Nobel-winning discoveries had trouble getting funded at the outset. The main reason at the time was that the researcher’s idea seemed irrelevant or fanciful. For example, CRISPR was originally thought to be nothing more than a curiosity about bacterial defense mechanisms.

Perhaps ironically, the highest rewards in science often come from the unlikeliest places. Some of our “high reward” funding should therefore be focused on projects, fields, ideas, theories, etc. that are thought to be irrelevant, including ideas that have gotten turned down elsewhere because they are unlikely to “work.” The “risk” here isn’t necessarily technical risk, but the risk of being ignored.

Traditional funders are unlikely to create funding lines specifically for research that they themselves thought was irrelevant. Thus, we need a new agency that specializes in uncovering funding opportunities that were overlooked elsewhere. Judging from the history of scientific breakthroughs, the benefits could be quite substantial. 

Challenge and Opportunity

There are far too many cases where brilliant scientists had trouble getting their ideas funded or even faced significant opposition at the time. For just a few examples (there are many others): 

• The team that discovered how to manufacture human insulin applied for an NIH grant for an early stage of their work. The rejection notice said that the project looked “extremely complex and time-consuming,” and “appears as an academic exercise.” 
• Katalin Karikó’s early work on mRNA was a key contributor to multiple Covid vaccines, and ultimately won the Nobel Prize. But she repeatedly got demoted at the University of Pennsylvania because she couldn’t get NIH funding.
• Carol Grieder’s work on telomerase was rejected by an NIH panel on literally the same day that she won the Nobel Prize, on the grounds that she didn’t have enough preliminary data about telomerase.
• Francisco Mojica (who identified CRISPR while studying archaebacteria in the 1990s) has said, “When we didn’t have any idea about the role these systems played, we applied for financial support from the Spanish government for our research. The government received complaints about the application and, subsequently, I was unable to get any financial support for many years.”
• Peyton Rous’s early 20th century studies on transplanting tumors between purebred chickens was ridiculed at the time, but his work won the Nobel Prize over 50 years later, and provided the basis for other breakthroughs involving reverse transcription, retroviruses, oncogenes, and more.

One could fill an entire book with nothing but these kinds of stories. 

Why do so many brilliant scientists struggle to get funding and support for their groundbreaking ideas? In many cases, it’s not because of any reason that a typical “high risk, high reward” research program would address. Instead, it’s because their research can be seen as irrelevant, too far removed from any practical application, or too contrary to whatever is currently trendy.

To make matters worse, the temptation for government funders is to opt for large-scale initiatives with a lofty goal like “curing cancer” or some goal that is equally ambitious but also equally unlikely to be accomplished by a top-down mandate. For example, the U.S. government announced a National Plan to Address Alzheimer’s Disease in 2012, and the original webpage promised to “prevent and effectively treat Alzheimer’s by 2025.” Billions have been spent over the past decade on this objective, but U.S. scientists are nowhere near preventing or treating Alzheimer’s yet. (Around October 2024, the webpage was updated and now aims to “address Alzheimer’s and related dementias through 2035.”)

The challenge is whether quirky, creative, seemingly irrelevant, contrarian science—which is where some of the most significant scientific breakthroughs originated—can survive in a world that is increasingly managed by large bureaucracies whose procedures don’t really have a place for that type of science, and by politicians eager to proclaim that they have launched an ambitious goal-driven initiative.

The answer that I propose: Create an agency whose sole raison d’etre is to fund scientific research that other agencies won’t fund—not for reasons of basic competence, of course, but because the research wasn’t fashionable or relevant.

The benefits of such an approach wouldn’t be seen immediately. The whole point is to allocate money to a broad portfolio of scientific projects, some of which would fail miserably but some of which would have the potential to create the kind of breakthroughs that, by definition, are unpredictable in advance. This plan would therefore require a modicum of patience on the part of policymakers. But over the longer term, it would likely lead to a number of unforeseeable breakthroughs that would make the rest of the program worth it.

Plan of Action

The federal government needs to establish a new National Institute for High-Reward Research (NIHRR) as a stand-alone agency, not tied to the National Institutes of Health or the National Science Foundation. The NIHRR would be empowered to fund the potentially high-reward research that goes overlooked elsewhere. More specifically, the aim would be to cast a wide net for: 

• Researchers (akin to Katalin Karikó or Francisco Mojica) who are perfectly well-qualified but have trouble getting funding elsewhere; 
• Research projects or larger initiatives that are seen as contrary to whatever is trendy in a given field;
• Research projects or larger initiatives that are seen as irrelevant (e.g., bacterial or animal research that is seen as unrelated to human health).

NIHRR should be funded at, say, $100m per year as a starting point ($1 billion would be better). This is an admittedly ambitious proposal. It would mean increasing the scientific and R&D expenditure by that amount, or else reassigning existing funding (which would be politically unpopular).  But it is a worthy objective, and indeed, should be seen as a starting point. 

Significant stakeholders with an interest in a new NIHRR would obviously include universities and scholars who currently struggle for scientific funding. In a way, that stacks the deck against the idea, because the most politically powerful institutions and individuals might oppose anything that tampers with the status quo of how research funding is allocated. Nonetheless, there may be a number of high-status individuals (e.g., current Nobel winners) who would be willing to support this idea as something that would have aided their earlier work. 

A new fund like this would also provide fertile ground for metascience experiments and other types of studies. Consider the striking fact that as yet, there is virtually no rigorous empirical evidence as to the relative strengths and weaknesses of top-down, strategically-driven scientific funding versus funding that is more open to seemingly irrelevant, curiosity-driven research. With a new program for the latter, we could start to derive comparisons between the results of that funding as compared to equally situated researchers funded through the regular pathways. 

Moreover, a common metascience proposal in recent years is to use a limited lottery to distribute funding, on the grounds that some funding is fairly random anyway and we might as well make it official. One possibility would be for part of the new program to be disbursed by lottery amongst researchers who met a minimum bar of quality and respectability, and who had got a high enough score on “scientific novelty.” One could imagine developing an algorithm to make an initial assessment as well. Then we could compare the results of lottery-based funding versus decisions made by program officers versus algorithmic recommendations. 

Conclusion

A new line of funding like the National Institute for High-Reward Research (NIHRR) could drive innovation and exploration by funding the potentially high-reward research that goes overlooked elsewhere. This would elevate worthy projects with unknown outcomes so that unfashionable or unpopular ideas can be explored. Funding these projects would have the added benefit of offering many opportunities to build in metascience studies from the outset, which is easier than retrofitting projects later. 

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

The U.S. economy is being held back by outdated, linear supply chains that waste valuable materials, expose businesses to counterfeits, and limit consumer choice. American companies lose billions each year to fraudulent goods—everything from fake pharmaceuticals to faulty electronics—while consumers are left in the dark about what they’re buying. At the same time, global disruptions like the COVID-19 pandemic revealed just how fragile and opaque our supply chains really are, especially in critical industries. Without greater transparency and accountability, the U.S. economy will remain vulnerable to these risks, stifling growth and innovation while perpetuating inequities and environmental harm. 

A shift toward more circular, transparent systems would not only reduce waste and increase efficiency, but also unlock new business models, strengthen supply chain resilience, and give consumers better, more reliable information about the products they choose. Digital Product Passports (DPP) – standardized digital records that contain key information about a product’s origin, materials, lifecycle, and authenticity – are a key tool that will help the United States achieve these goals.

The administration should establish a comprehensive Digital Product Passport Initiative that creates the legal, technical, and organizational frameworks for businesses to implement decentralized digital passports for their products while ensuring consumer ownership rights, supply chain integrity, and international interoperability. This plan should consider which entities provide up-front investment until the benefits of a digital product passport (DPP) are manifested.

Challenge and Opportunity 

The United States faces an urgent sustainability challenge driven by its linear economic model, which prioritizes resource extraction, production, and disposal over reuse and recycling. This approach has led to severe environmental degradation, excessive waste generation, and unsustainable resource consumption, with marginalized communities—often communities of color and low-income areas—bearing the brunt of the damage. From toxic pollution to hazardous waste dumps, these populations are disproportionately affected, exacerbating environmental injustice. If this trajectory continues, the U.S. will not only fall short of its climate commitments but also deepen existing economic inequities. To achieve a sustainable future, the nation must transition to a more circular economy, where resources are responsibly managed, reused, and kept in circulation, rather than being discarded after a single use. 

At the same time, the U.S. is contending with widespread counterfeiting and fragile supply chains that threaten both economic security and public health. Counterfeit goods, from unsafe pharmaceuticals to faulty electronics, flood the market, endangering lives and undermining consumer confidence, while costing the economy billions in lost revenue. Furthermore, the COVID-19 pandemic exposed deep weaknesses in global supply chains, particularly in critical sectors like healthcare and technology, leading to shortages that disproportionately affected vulnerable populations. These opaque and fragmented supply chains allow counterfeit goods to flourish and make it difficult to track and verify the authenticity of products, leaving businesses and consumers at risk. 

Achieving true sustainability in the United States requires a shift to item circularity, where products and materials are kept in use for as long as possible through repair, reuse, and recycling. This model not only minimizes waste but also reduces the demand for virgin resources, alleviating the environmental pressures created by the current linear economy. Item circularity helps to close the loop, ensuring that products at the end of their life cycles re-enter the economy rather than ending up in landfills. It also promotes responsible production and consumption by making it easier to track and manage the flow of materials, extending the lifespan of products, and minimizing environmental harm. By embracing circularity, industries can cut down on resource extraction, reduce greenhouse gas emissions, and mitigate the disproportionate impact of pollution on marginalized communities.

One of the most powerful tools to facilitate this transition is the digital product passport (DPP). A DPP is a digital record that provides detailed information about a product’s entire life cycle, including its origin, materials, production process, and end-of-life options like recycling or refurbishment. With this information easily accessible, consumers, businesses, and regulators can make informed decisions about the use, maintenance, and eventual disposal of products. DPPs enable seamless tracking of products through supply chains, making it easier to repair, refurbish, or recycle items. This ensures that valuable materials are recovered and reused, contributing to a circular economy. Additionally, DPPs empower consumers by offering transparency into the sustainability and authenticity of products, encouraging responsible purchasing, and fostering trust in both the products and the companies behind them.

In addition to promoting circularity, digital product passports (DPPs) are a powerful solution for combating counterfeits and ensuring supply chain integrity. In 2016, counterfeits and pirated products represented $509B and 3.3% of world trade. By assigning each product a unique digital identifier, a DPP enables transparent and verifiable tracking of goods at every stage of the supply chain, from raw materials to final sale. This transparency makes it nearly impossible for counterfeit products to infiltrate the market, as every legitimate product can be traced back to its original manufacturer with a clear, tamper-proof digital record. In industries where counterfeiting poses serious safety and financial risks—such as pharmaceuticals, electronics, and luxury goods—DPPs provide a critical layer of protection, ensuring consumers receive authentic products and helping companies safeguard their brands from fraud.

Moreover, DPPs offer real-time insights into supply chain operations, identifying vulnerabilities or disruptions more quickly. This allows businesses to respond to issues such as production delays, supplier failures, or the introduction of fraudulent goods before they cause widespread damage. With greater visibility into where products are sourced, produced, and transported, companies can better manage their supply chains, ensuring that products meet regulatory standards and maintaining the integrity of goods as they move through the system. This level of traceability strengthens trust between businesses, consumers, and regulators, ultimately creating more resilient and secure supply chains.

Beyond sustainability and counterfeiting, digital product passports (DPPs) offer transformative potential in four additional key areas: 

• First, they enhance compliance and regulatory oversight by providing clear, accessible records of a product’s materials, production methods, and supply chain journey, helping industries meet environmental, labor, and safety standards. 
• Second, DPPs strengthen supply chain risk mitigation and resilience by improving real-time visibility and accountability, allowing businesses to detect and address disruptions or vulnerabilities faster. 
• Third, they empower informed consumer choices and consumer protection by offering transparency into a product’s origin, sustainability, and authenticity, enabling people to make ethical, safe purchasing decisions. 
• Finally, DPPs fuel data-driven innovation and new business models by generating insights that can inform better product design, maintenance strategies, and circular economy opportunities, such as take-back programs or leasing services. In these ways, DPPs act as a versatile tool that not only addresses immediate challenges but also positions industries for long-term, sustainable growth.

Plan of Action

The administration should establish a comprehensive Digital Product Passport Initiative that creates the legal, technical, and organizational frameworks for businesses to implement decentralized digital passports for their products while ensuring consumer ownership rights, supply chain integrity, and international interoperability. This plan should consider which entities provide up-front investment until the benefits of DPP are realized.

Recommendation 1. Legal Framework Development (Lead: White House Office of Science and Technology Policy)

The foundation of any successful federal initiative must be a clear legal framework that establishes authority, defines roles, and ensures enforceability. The Office of Science and Technology Policy is uniquely positioned to lead this effort given its cross-cutting mandate to coordinate science and technology policy across federal agencies and its direct line to the Executive Office of the President. 

• Draft executive order establishing federal DPP program authority
• Coordinate with Department of Commerce (DOC) and Environmental Protection Agency (EPA) to identify rulemaking authority, engaging Congress as needed
• Define enforcement mechanisms and penalties
• Coordinate with DOC to define legal requirements for DPP data portability
• Establish liability framework for DPP data accuracy
• Create legal framework for consumer DPP ownership rights, engaging Congress as needed
• Identify the role of Congress, if needed; for example, in defining rulemaking authorities, DPP data probabilities, and consumer DPP ownership rights
• Timeline: First 9 months

Recommendation 2. Product Category Definition & Standards Development (Lead: DOC/NIST)

The success of the DPP initiative depends on clear, technically sound standards that define which products require passports and what information they must contain. This effort must consider the industries and products that will benefit from DPPs, as goods of varying value will find different returns on the investment of DPPs. NIST, as the nation’s lead standards body with deep expertise in digital systems and measurement science, is the natural choice to lead this critical definitional work. 

• Establish an interagency working group led by NIST to define priority product categories
• Develop technical standards for DPP data structure and interoperability
• Timeline: First 6 months

Recommendation 3. Consumer Rights & Privacy Framework (Lead: FTC Bureau of Consumer Protection)

A decentralized DPP system must protect consumer privacy while ensuring consumers maintain control over the digital passports of products they own. The FTC’s Bureau of Consumer Protection, with its statutory authority to protect consumer interests and experience in digital privacy issues, is best equipped to develop and enforce these critical consumer protections.

• Define consumer DPP ownership rights and transfer mechanisms
• Establish privacy standards for DPP data
• Develop consumer access and control protocols
• Create standards for consumer authorization of third-party DPP access
• Define requirements for consumer notification of DPP changes
• Timeline: 12-18 months

Recommendation 4. DPP Architecture & Verification Framework (Lead: GSA Technology Transformation Services)

A decentralized DPP system requires robust technical architecture that enables secure data storage, seamless transfers, and reliable verification across multiple private databases. GSA’s Technology Transformation Services, with its proven capability in building and maintaining federal digital infrastructure and its experience in implementing emerging technologies across government, is well-equipped to design and oversee this complex technical ecosystem.

• Define methodology for storing and verifying DPPs
• Develop API standards for industry integration
• Ensure cybersecurity protocols meet NIST standards
• Implement blockchain or distributed ledger technology for traceability
• Develop standards for DPP transfer between product clouds
• Create protocols for consumer DPP ownership transfer
• Establish verification registry for authorized product clouds
• Define minimum security requirements for private DPP databases
• Timeline: 12-18 months

Recommendation 5. Industry Engagement & Compliance Program (Lead: DOC Office of Business Liaison)

Successful implementation of DPPs requires active participation and buy-in from the private sector, as businesses will be responsible for creating and maintaining their product clouds. The DOC Office of Business Liaison, with its established relationships across industries and experience in facilitating public-private partnerships, is ideally suited to lead this engagement and ensure that implementation guidelines meet both government requirements and business needs.

• Create industry advisory board with representatives from key sectors
• Develop compliance guidelines and technical assistance programs
• Establish pilot programs with volunteer companies
• Partner with trade associations for outreach and education
• Develop guidelines for product cloud certification
• Create standards for DPP ownership transfer during resale
• Establish protocols for managing orphaned DPPs
• Timeline: Ongoing from months 3-24

Recommendation 6. Supply Chain Verification System (Lead: Customs and Border Protection)

Digital Product Passports must integrate seamlessly with existing import/export processes to effectively combat counterfeiting and ensure supply chain integrity. Customs and Border Protection, with its existing authority over imports and expertise in supply chain security, is uniquely positioned to incorporate DPP verification into its existing systems and risk assessment frameworks.

• Integrate DPP verification into existing Customs and Border Protection systems
• Develop automated scanning and verification protocols
• Create risk assessment frameworks for import screening
• Timeline: 18-24 months

Recommendation 7. Sustainability Metrics Integration (Lead: EPA Office of Pollution Prevention)

For DPPs to meaningfully advance sustainability goals, they must capture standardized, verifiable environmental impact data throughout product lifecycles. The EPA’s Office of Pollution Prevention brings decades of expertise in environmental assessment and verification protocols, making it the ideal leader for developing and overseeing these critical sustainability metrics.

• Define required environmental impact data points
• Develop lifecycle assessment standards
• Create verification protocols for environmental claims
• Timeline: 12-18 months

Recommendation 8. International Coordination (Lead: State Department Bureau of Economic Affairs)

The global nature of supply chains requires that U.S. DPPs be compatible with similar initiatives worldwide, particularly the EU’s DPP system. The State Department’s Bureau of Economic Affairs, with its diplomatic expertise and experience in international trade negotiations, is best positioned to ensure U.S. DPP standards align with global frameworks while protecting U.S. interests.

• Survey and coordinate with similar efforts around the world (e.g., European Commission’s Digital Product Passport initiative)
• Engage with WTO to ensure compliance with trade rules
• Develop framework for international data sharing
• Develop protocols for cross-border DPP transfers
• Establish international product cloud interoperability standards
• Timeline: Ongoing from months 6-24

Recommendation 9. Small Business Support Program (Lead: Small Business Administration)

The technical and financial demands of implementing DPPs could disproportionately burden small businesses, potentially creating market barriers. The Small Business Administration, with its mandate to support small business success and experience in providing technical assistance and grants, is the natural choice to lead efforts ensuring small businesses can effectively participate in the DPP system.

• Create technical assistance programs
• Provide implementation grants
• Develop simplified compliance pathways
• Timeline: Launch by month 18

Conclusion

Digital Product Passports represent a transformative opportunity to address two critical challenges facing the United States: the unsustainable waste of our linear economy and the vulnerability of our supply chains to counterfeiting and disruption. Through a comprehensive nine-step implementation plan led by key federal agencies, the administration can establish the frameworks necessary for businesses to create and maintain digital passports for their products while ensuring consumer rights and international compatibility. This initiative will not only advance environmental justice and sustainability goals by enabling product circularity, but will also strengthen supply chain integrity and security, positioning the United States as a leader in the digital transformation of global commerce.

Studies estimate that only one in 10 recommendations made by major professional societies are supported by high-quality evidence. Medical care that is not evidence-based can result in unnecessary care that burdens public finances, harms patients, and damages trust in the medical profession. Clearly, we must do a better job of figuring out the right treatments, for the right patients, at the right time. To meet this challenge, it is essential to improve our ability to capture reusable data at the point of care that can be used to improve care, discover new treatments, and make healthcare more efficient. To achieve this vision, we will need to shift financial incentives to reward data generation, change how we deliver care using AI, and continue improving the technological standards powering healthcare.

The Challenge and Opportunity of health data

Many have hailed health data collected during everyday healthcare interactions as the solution to some of these challenges. Congress directed the U.S. Food and Drug Administration (FDA) to increase the use of real-world data (RWD) for making decisions about medical products. However, FDA’s own records show that in the most recent year for which data are available, only two out of over one hundred new drugs and biologics approved by FDA were approved based primarily on real-world data.

A major problem is that our current model in healthcare doesn’t allow us to generate reusable data at the point of care. This is even more frustrating because providers face a high burden of documentation, and patients report repetitive questions from providers and questionnaires. 

To expand a bit: while large amounts of data are generated at the point of care, these data lack the quality, standardization, and interoperability to enable downstream functions such as clinical trials, quality improvement, and other ways of generating more knowledge about how to improve outcomes. 

By better harnessing the power of data, including results of care,  we could finally build a learning healthcare system where outcomes drive continuous improvement and where healthcare value leads the way.  There are, however, countless barriers to such a transition. To achieve this vision,  we need to develop new strategies for the capture of high-quality data in clinical environments, while reducing the burden of data entry on patients and providers. 

Efforts to achieve this vision follow a few basic principles:

1. Data should be entered only once– by the person or entity most qualified to do so – and be used many times.
2. Data capture should be efficient, so as to minimize the burden on those entering the data, allowing them to focus their time on doing what actually matters, like providing patient care.
3. Data generated at the point of care needs to be accessible for appropriate secondary uses (quality improvement, trials, registries), while respecting patient autonomy and obtaining informed consent where required. Data should not be stuck in any one system but should flow freely between systems, enabling linkages across different data sources.
4. Data need to be used to provide real value to patients and physicians. This is​ achieved by developing data visualizations, automated data summaries, and decision support (e.g. care recommendations, trial matching) that allow data users to spend less time searching for data and more time on analysis, problem solving, and patient care– and help them see the value in entering data in the first place.

Barriers to capturing high-quality data at the point of care:

• Incentives: Providers and health systems are paid for performing procedures or logging diagnoses. As a result, documentation is optimized for maximizing reimbursement, but not for maximizing the quality, completeness, and accuracy of data generated at the point of care.
• Workflows: Influenced by the prevailing incentives, clinical workflows are not currently optimized to enable data capture at the point of care. Patients are often asked the same questions at multiple stages, and providers document the care provided as part of free-text notes, which are frequently required for billing but can make it challenging to find information.
• Technology: Shaped by incentives and workflows, technology has evolved to capture information in formats that frequently lack standardization and interoperability.
  Plan of Action 

Plan of Action

Recommendation 1. Incentivize generation of reusable data at the point of care

Financial incentives are needed to drive the development of workflows and technology to capture high-quality data at the point of care. There are several payment programs already in existence that could provide a template for how these incentives could be structured.

For example, the Centers for Medicare and Medicaid Services (CMS) recently announced the Enhancing Oncology Model (EOM), a voluntary model for oncology providers caring for patients with common cancer types. As part of the EOM, providers are required to report certain data fields to CMS, including staging information and hormone receptor status for certain cancer types. These data fields are essential for clinical care, research, quality improvement, and ongoing care observation  involving cancer patients. Yet,  at present, these data are rarely recorded in a way that makes it easy to exchange and reuse this information. To reduce the burden of reporting this data, CMS has collaborated with the HHS Assistant Secretary for Technology Policy (ASTP) to develop and implement technological tools that can facilitate automated reporting of these data fields.

CMS also has a long-standing program that requires participation in evidence generation as a prerequisite for coverage, known as coverage with evidence development (CED). For example, hospitals that would like to provide Transcatheter Aortic Valve Replacement (TAVR) are required to participate in a registry that records data on these procedures.

To incentivize evidence generation as part of routine care, CMS should refine these programs and expand their use. This would involve strengthening collaborations across the federal government to develop technological tools for data capture, and increasing the number of payment models that require generation of data at the point of care. Ideally, these models should evolve to reward 1) high-quality chart preparation (assembly of structured data) 2) establishing diagnoses and development of a care plan, and 3) tracking outcomes.  These payment policies are powerful tools because they incentivize the generation of reusable infrastructure that can be deployed for many purposes.

Recommendation 2. Improve workflows to capture evidence at the point of care

With the right payment models, providers can be incentivized to capture reusable data at the point of care. However, providers are already reporting being crushed by the burden of documentation and patients are frequently filling out multiple questionnaires with the same information. To usher in the era of the learning health system (a system that includes continuous data collection to improve service delivery), without increasing the burden on providers and patients, we need to redesign how care is provided. Specifically, we must focus on approaches that integrate generation of reusable data into the provision of routine clinical care. 

While the advent of AI is an opportunity to do just that, current uses of AI have mainly focused on drafting documentation in free-text formats, essentially replacing human scribes. Instead, we need to figure out how we can use AI to improve the usability of the resulting data. While it is not feasible to capture all data in a structured format on all patients, a core set of data are needed to provide high-quality and safe care. At a minimum, those should be structured and part of a basic core data set across disease types and health maintenance scenarios.

In order to accomplish this, NIH and the Advanced Research Projects Agency for Health (ARPA-H) should fund learning laboratories that develop, pilot, and implement new approaches for data capture at the point of care. These centers would leverage advances in human-centered design and artificial intelligence (AI) to revolutionize care delivery models for different types of care settings, ranging from outpatient to acute care and intensive care settings. Ideally, these centers would be linked to existing federally funded research sites that could implement the new care and discovery processes in ongoing clinical investigations.

The federal government already spends billions of dollars on grants for clinical research- why not use some of that funding to make clinical research more efficient, and improve the experience of patients and physicians in the process?

Recommendation 3. Enable technology systems to improve data standardization and interoperability

Capturing high-quality data at the point of care is of limited utility if the data remains stuck within individual electronic health record (EHR) installations. Closed systems hinder innovation and prevent us from making the most of the amazing trove of health data. 

We must create a vibrant ecosystem where health data can travel seamlessly between different systems, while maintaining patient safety and privacy. This will enable an ecosystem of health data applications to flourish. HHS has recently made progress by agreeing to a unified approach to health data exchange, but several gaps remain. To address these we must

• Increase standardization of data elements: The federal government requires certain data elements to be standardized for electronic export from the EHR. However, this list of data elements, called the United States Core Data for Interoperability (USCDI) currently does not include enough data elements for many uses of health data. HHS could rapidly expand the USCDI by working with federal partners and professional societies to determine which data elements are critical for national priorities, like vaccine safety and use, or protection from emerging pathogens.
• Enable writeback into the EHR: While current efforts focused on interoperability have focused on the ability to export EHR data, developing a vibrant ecosystem of health data applications that are available to patients, physicians, and other data users, requires the capability to write data back into the EHR. This would enable the development of a competitive ecosystem of applications that use health data generated in the EHR, much like the app store on our phones.
• Create  widespread interoperability of data for multiple purposes: HHS has made great progress towards allowing health data to be exchanged between any two entities in our healthcare system, thanks to the Trusted Exchange Framework and Common Agreement (TEFCA). TEFCA could allow any two healthcare sites to exchange data, but unfortunately, participation remains spotty and TEFCA currently does not allow data exchange solely for research. HHS should work to close these gaps by allowing TEFCA to be used for research, and incentivizing participation in TEFCA, for example by making joining TEFCA a condition of participation in Medicare.

Conclusion

The treasure trove of health data generated during routine care has given us a huge opportunity to generate knowledge and improve health outcomes. These data should serve as a shared resource for clinical trials, registries, decision support, and outcome tracking to improve the quality of care. This is necessary for society to advance towards personalized medicine, where treatments are tailored to biology and patient preference. However, to make the most of these data, we must improve how we capture and exchange these data at the point of care.

Essential to this goal is evolving our current payment systems from rewarding documentation of complexity or time spent, to generation of data that supports learning and improvement. HHS should use its payment authorities to encourage data generation at the point of care and promote the tools that enable health data to flow seamlessly between systems, building on the success stories of existing programs like coverage with evidence development. To allow capture of this data without making the lives of providers and patients even more difficult, federal funding bodies need to invest in developing technologies and workflows that leverage AI to create usable data at the point of care. Finally, HHS must continue improving the standards that allow health data to travel seamlessly between systems. This is essential for creating a vibrant ecosystem of applications that leverage the benefits of AI to improve care.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

There exists a low-effort, low-cost way to reduce administrative burden for our scientists, and make it easier for everyone – scientists, funders, legislators, and the public – to document the incredible productivity of federal science agencies. If adopted throughout government research these tools would maximize interoperability across reporting systems, reduce the administrative burden and costs, and increase the accountability of our scientific community. The solution: persistent digital identifiers (Digital Object Identifiers, or DOIs) and Open Researcher and Contributor IDs (ORCIDs) for key personnel. ORCIDs are already used by most federal science agencies. We propose that federal science agencies also adopt digital object identifiers for research awards, an industry-wide standard. A practical and detailed implementation guide for this already exists. 

The Opportunity

Tracking the impact and outputs of federal research awards is labor-intensive and expensive. Federally funded scientists spend over 900,000 hours a year writing interim progress reports alone. Despite that tremendous effort, our ability to analyze the productivity of federal research awards is limited. These reports only capture research products created while the award is active, but many exciting papers and data sets are not published until after the award is over, making it hard for the funder to associate them with a particular award or agency initiative. Further, these data are often not structured in ways that support easy analysis or collaboration. When it comes time for the funding agency to examine the impact of an award, a call for applications, or even an entire division, staff rely on a highly manual process that is time-intensive and expensive. Thus, such evaluations are often not done. Deep analysis of federal spending is next to impossible, and simple questions regarding which type of award is better suited for one scientific problem over another, or whether one administrative funding unit is more impactful than a peer organization with the same spending level, are rarely investigated by federal research agencies. These questions are difficult to answer without a simple way to tie award spending to specific research outputs such as papers, patents, and datasets.

To simplify tracking of research outputs, the Office of Science and Technology Policy (OSTP) directed federal research agencies to “assign unique digital persistent identifiers to all scientific research and development awards and intramural research protocols […] through their digital persistent identifiers.” This directive builds on work from the Trump White House in 2018 to reduce the burden on researchers and the National Security Strategy guidance. It is a great step forward, but it has yet to be fully implemented, and allows implementation to take different paths. Agencies are now taking a fragmented, agency-specific approach, which will undermine the full potential of the directive by making it difficult to track impact using the same metrics across federal agencies.

Without a unified federal standard, science publishers, awards management systems, and other disseminators of federal research output will continue to treat award identifiers as unstructured text buried within a long document, or URLs tucked into acknowledgement sections or other random fields of a research product. These ad hoc methods make it difficult to link research outputs to their federal funding. It leaves scientists and universities looking to meet requirements for multiple funding agencies, relying on complex software translations of different agency nomenclatures and award persistent identifiers, or, more realistically, continue to track and report productivity by hand. It remains too confusing and expensive to provide the level of oversight our federal research enterprise deserves.

There is an existing industry standard for associating digital persistent identifiers with awards that has been adopted by the Department of Energy and other funders such as the ALS Association, the American Heart Association, and the Wellcome Trust. It is a low-effort, low-cost way to reduce administrative burden for our scientists and make it easier for everyone – scientists, federal agencies, legislators, and the public – to document the incredible productivity of federal science expenditures.

Adopting this standard means funders can automate the reporting of most award products (e.g., scientific papers, datasets), reducing administrative burden, and allowing research products to be reliably tracked even after the award ends. Funders could maintain their taxonomy linking award DOIs to specific calls for proposals, study sections, divisions, and other internal structures, allowing them to analyze research products in much easier ways. Further, funders would be able to answer the fundamental questions about their programs that are usually too labor-intensive to even ask, such as: did a particular call for applications result in papers that answered the underlying question laid out in that call? How long should awards for a specific type of research problem last to result in the greatest scientific productivity? In the light of rapid advances in artificial intelligence (AI) and other analytic tools, making the linkages between research funding and products standardized and easy to analyze opens possibilities for an even more productive and accountable federal research enterprise going forward. In short, assigning DOIs to awards fulfills the requirements of the 2022 directive to maximize interoperability with other funder reporting systems, the promise of the 2018 NSTC report to reduce burden, and new possibilities for a more accountable and effective federal research enterprise.

Plan of Action

The overall goal is to increase accountability and transparency for federal research funding agencies and dramatically reduce the administrative burden on scientists and staff. Adopting a uniform approach allows for rapid evaluation and improvements across the research enterprise. It also enables and for the creation of comparable data on agency performance. We propose that federal science agencies adopt the same industry-wide standard – the DOI – for awards. A practical and detailed implementation guide already exists.

These steps support the existing directive and National Security Strategy guidance issued by OSTP and build on 2018 work from the NSTC:.

Recommendation 1. An interagency committee led by OSTP should coordinate and harmonize implementation to:

• Develop implementation timelines and budgets for each agency that are consistent with existing industry standards;
• Consult with other stakeholders such as scientific publishers and awardee institutions, but consider that guidance and industry standards already exist, so there is no need for lengthy consultation.

Recommendation 2. Agencies should fully adopt the industry standard persistent identifier infrastructure for research funding—DOIs—for awards. Specifically, funders should:

• Ensure they are listed in the Research Organization Registry at the administrative level (e.g., agency, division) that suits their reporting and analytic needs.
• Require and collect ORCIDs, a digital identifier for researchers widely used by academia and scientific publishers, for the key personnel of an award.
• Issue DOIs for individual awards, and link those awards to the appropriate organizational units and research funding initiatives in the metadata to facilitate evaluation.

Recommendation 3. Agencies should require the Principal Investigator (PI) to cite the award DOI in research products (e.g., scientific papers, datasets). This requirement could be included in the terms and conditions of each award. Using DOIs to automate much of progress reporting, as described below, provides a natural incentive for investigators to comply. 

Recommendation 4. Agencies should use award persistent identifiers from ORCID and award DOI systems to identify research products associated with an award to reduce PI burden. Awardees would still be required to certify that the product arose directly from their federal research award. After the award and reporting obligation ends, the agency can continue to use these systems to link products to awards based on information provided by the product creators to the product distributors (e.g., authors citing an award DOI when publishing a paper), but without the direct certification of the awardee. This compromise provides the public and the funder with better information about an award’s output, but does not automatically hold the awardee liable if the product conflicts with a federal policy.

Recommendation 5. Agencies should adopt or incorporate award DOIs into their efforts to describe agency productivity and create more efficient and consistent practices for reporting research progress across all federal research funding agencies. Products attributable to the award should be searchable by individual awards, and by larger collections of awards, such as administrative Centers or calls for applications. As an example of this transparency, PubMed, with its publicly available indexing of the biomedical literature, supports the efforts of the National Institutes of Health (NIH)’s RePORTER), and could serve as a model for other fields as persistent identifiers for awards and research products become more available.

Recommendation 6. Congress should issue appropriations reporting language to ensure that implementation costs are covered for each agency and that the agencies are adopting a universal standard. Given that the DOI for awards infrastructure works even for small non-profit funders, the greatest costs will be in adapting legacy federal systems, not in utilizing the industry standard itself.

Challenges 

We envision the main opposition to come from the agencies themselves, as they have multiple demands on their time and might have shortcuts to implementation that meet the letter of the requirement but do not offer the full benefits of an industry standard. This short-sighted position denies both the public transparency needed on research award performance and the massive time and cost savings for the agencies and researchers.

A partial implementation of this burden-reducing workflow already exists. Data feeds from ORCID and PubMed populate federal tools such as My Bibliography, and in turn support the biosketch generator in SciENcv or an agency’s Research Performance Progress Report. These systems are feasible because they build on PubMed’s excellent metadata and curation. But PubMed does not index all scientific fields.

Adopting DOIs for awards means that persistent identifiers will provide a higher level of service across all federal research areas. DOIs work for scientific areas not supported by PubMed. And even for the sophisticated existing systems drawing from PubMed, user effort could be reduced and accuracy increased if awards were assigned DOIs. Systems such as NIH RePORTER and PubMed currently have to pull data from citation of award numbers in the acknowledgment sections of research papers, which is more difficult to do.

Conclusion

OSTP and the science agencies have put forth a sound directive to make American science funding even more accountable and impactful, and they are on the cusp of implementation. It is part of a long-standing effort  to reduce burden and make the federal research enterprise more accountable and effective. Federal research funding agencies are susceptible to falling into bureaucratic fragmentation and inertia by adopting competing approaches that meet the minimum requirements set forth by OSTP, but offer minimal benefit. If these agencies instead adopt the industry standard that is being used by many other funders around the world, there will be a marked reduction in the burden on awardees and federal agencies, and it will facilitate greater transparency, accountability, and innovation in science funding. Adopting the standard is the obvious choice and well within America’s grasp, but avoiding bureaucratic fragmentation is not simple. It takes leadership from each agency, the White House, and Congress.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

President Trump challenged the Director of the Office of Science and Technology Policy (OSTP), Michael Kratsios, to “ensure that scientific progress and technological innovation fuel economic growth and better the lives of all Americans”. Much of this progress and innovation arises from federal research grants. Federal research grant applications include detailed plans for cutting-edge scientific research. They describe the hypothesis, data collection, experiments, and methods that will ultimately produce discoveries, inventions, knowledge, data, patents, and advances. They collectively represent a blueprint for future innovations.

AI now makes it possible to use these resources to create extraordinary tools for refining how we award research dollars. Further, AI can provide unprecedented insight into future discoveries and needs, shaping both public and private investment into new research and speeding the application of federal research results. 

We recommend that the Office of Science and Technology Policy (OSTP) oversee a multiagency development effort to fully subject grant applications to AI analysis to predict the future of science, enhance peer review, and encourage better research investment decisions by both the public and the private sector. The federal agencies involved should include all the member agencies of the National Science and Technology Council (NSTC). 

Challenge and Opportunity

The federal government funds approximately 100,000 research awards each year across all areas of science. The sheer human effort required to analyze this volume of records remains a barrier, and thus, agencies have not mined applications for deep future insight. If agencies spent just 10 minutes of employee time on each funded award, it would take 16,667 hours in total—or more than eight years of full-time work—to simply review the projects funded in one year. For each funded award, there are usually 4–12 additional applications that were reviewed and rejected. Analyzing all these applications for trends is untenable. Fortunately, emerging AI can analyze these documents at scale. Furthermore, AI systems can work with confidential data and provide summaries that conform to standards that protect confidentiality and trade secrets. In the course of developing these public-facing data summaries, the same AI tools could be used to support a research funder’s review process.

There is a long precedent for this approach. In 2009, the National Institutes of Health (NIH) debuted its Research, Condition, and Disease Categorization (RCDC) system, a program that automatically and reproducibly assigns NIH-funded projects to their appropriate spending categories. The automated RCDC system replaced a manual data call, which resulted in savings of approximately $30 million per year in staff time, and has been evolving ever since. To create the RCDC system, the NIH pioneered digital fingerprints of every scientific grant application using sophisticated text-mining software that assembled a list of terms and their frequencies found in the title, abstract, and specific aims of an application. Applications for which the fingerprints match the list of scientific terms used to describe a category are included in that category; once an application is funded, it is assigned to categorical spending reports.

NIH staff soon found it easy to construct new digital fingerprints for other things, such as research products or even scientists, by scanning the title and abstract of a public document (such as a research paper) or by all terms found in the existing grant application fingerprints associated with a person.

NIH review staff can now match the digital fingerprints of peer reviewers to the fingerprints of the applications to be reviewed and ensure there is sufficient reviewer expertise. For NIH applicants, the RePORTER webpage provides the Matchmaker tool to create digital fingerprints of title, abstract, and specific aims sections, and match them to funded grant applications and the study sections in which they were reviewed. We advocate that all agencies work together to take the next logical step and use all the data at their disposal for deeper and broader analyses.

We offer five recommendations for specific use cases below:

Use Case 1: Funder support. Federal staff could use AI analytics to identify areas of opportunity and support administrative pushes for funding.

When making a funding decision, agencies need to consider not only the absolute merit of an application but also how it complements the existing funded awards and agency goals. There are some common challenges in managing portfolios. One is that an underlying scientific question can be common to multiple problems that are addressed in different portfolios. For example, one protein may have a role in multiple organ systems. Staff are rarely aware of all the studies and methods related to that protein if their research portfolio is restricted to a single organ system or disease. Another challenge is to ensure proper distribution of investments across a research pipeline, so that science progresses efficiently. Tools that can rapidly and consistently contextualize applications across a variety of measures, including topic, methodology, agency priorities, etc., can identify underserved areas and support agencies in making final funding decisions. They can also help funders deliberately replicate some studies while reducing the risk of unintentional duplication.

Use Case 2: Reviewer support. Application reviewers could use AI analytics to understand how an application is similar to or different from currently funded federal research projects, providing reviewers with contextualization for the applications they are rating.

Reviewers are selected in part for their knowledge of the field, but when they compare applications with existing projects, they do so based on their subjective memory. AI tools can provide more objective, accurate, and consistent contextualization to ensure that the most promising ideas receive funding.

Use Case 3: Grant applicant support: Research funding applicants could be offered contextualization of their ideas among funded projects and failed applications in ways that protect the confidentiality of federal data.

NIH has already made admirable progress in this direction with their Matchmaker tool—one can enter many lines of text describing a proposal (such as an abstract), and the tool will provide lists of similar funded projects, with links to their abstracts. New AI tools can build on this model in two important ways. First, they can help provide summary text and visualization to guide the user to the most useful information. Second, they can broaden the contextual data being viewed. Currently, the results are only based on funded applications, making it impossible to tell if an idea is excluded from a funded portfolio because it is novel or because the agency consistently rejects it. Private sector attempts to analyze award information (e.g., Dimensions) are similarly limited by their inability to access full applications, including those that are not funded. AI tools could provide high-level summaries of failed or ‘in process’ grant applications that protect confidentiality but provide context about the likelihood of funding for an applicant’s project.

Use Case 4: Trend mapping. AI analyses could help everyone—scientists, biotech, pharma, investors— understand emerging funding trends in their innovation space in ways that protect the confidentiality of federal data.

The federal science agencies have made remarkable progress in making their funding decisions transparent, even to the point of offering lay summaries of funded awards. However, the sheer volume of individual awards makes summarizing these funding decisions a daunting task that will always be out of date by the time it is completed. Thoughtful application of AI could make practical, easy-to-digest summaries of U.S. federal grants in close to real time, and could help to identify areas of overlap, redundancy, and opportunity. By including projects that were unfunded, the public would get a sense of the direction in which federal funders are moving and where the government might be underinvested. This could herald a new era of transparency and effectiveness in science investment.

Use Case 5: Results prediction tools. Analytical AI tools could help everyone—scientists, biotech, pharma, investors—predict the topics and timing of future research results and neglected areas of science in ways that protect the confidentiality of federal data.

It is standard practice in pharmaceutical development to predict the timing of clinical trial results based on public information. This approach can work in other research areas, but it is labor-intensive. AI analytics could be applied at scale to specific scientific areas, such as predictions about the timing of results for materials being tested for solar cells or of new technologies in disease diagnosis. AI approaches are especially well suited to technologies that cross disciplines, such as applications of one health technology to multiple organ systems, or one material applied to multiple engineering applications. These models would be even richer if the negative cases—the unfunded research applications—were included in analyses in ways that protect the confidentiality of the failed application. Failed applications may signal where the science is struggling and where definitive results are less likely to appear, or where there are underinvested opportunities.

Plan of Action

Leadership

We recommend that OSTP oversee a multiagency development effort to achieve the overarching goal of fully subjecting grant applications to AI analysis to predict the future of science, enhance peer review, and encourage better research investment decisions by both the public and the private sector. The federal agencies involved should include all the member agencies of the NSTC. A broad array of stakeholders should be engaged because much of the AI expertise exists in the private sector, the data are owned and protected by the government, and the beneficiaries of the tools would be both public and private. We anticipate four stages to this effort.

Recommendation 1. Agency Development

Pilot: Each agency should develop pilots of one or more use cases to test and optimize training sets and output tools for each user group. We recommend this initial approach because each funding agency has different baseline capabilities to make application data available to AI tools and may also have different scientific considerations. Despite these differences, all federal science funding agencies have large archives of applications in digital formats, along with records of the publications and research data attributed to those awards.

These use cases are relatively new applications for AI and should be empirically tested before broad implementation. Trend mapping and predictive models can be built with a subset of historical data and validated with the remaining data. Decision support tools for funders, applicants, and reviewers need to be tested not only for their accuracy but also for their impact on users. Therefore, these decision support tools should be considered as a part of larger empirical efforts to improve the peer review process.

Solidify source data: Agencies may need to enhance their data systems to support the new functions for full implementation. OSTP would need to coordinate the development of data standards to ensure all agencies can combine data sets for related fields of research. Agencies may need to make changes to the structure and processing of applications, such as ensuring that sections to be used by the AI are machine-readable.

Recommendation 2. Prizes and Public–Private Partnerships

OSTP should coordinate the convening of private sector organizations to develop a clear vision for the profound implications of opening funded and failed research award applications to AI, including predicting the topics and timing of future research outputs. How will this technology support innovation and more effective investments?

Research agencies should collaborate with private sector partners to sponsor prizes for developing the most useful and accurate tools and user interfaces for each use case refined through agency development work. Prize submissions could use test data drawn from existing full-text applications and the research outputs arising from those applications. Top candidates would be subject to standard selection criteria.

Conclusion

Research applications are an untapped and tremendously valuable resource. They describe work plans and are clearly linked to specific research products, many of which, like research articles, are already rigorously indexed and machine-readable. These applications are data that can be used for optimizing research funding decisions and for developing insight into future innovations. With these data and emerging AI technologies, we will be able to understand the trajectory of our science with unprecedented breadth and insight, perhaps to even the same level of accuracy that human experts can foresee changes within a narrow area of study. However, maximizing the benefit of this information is not inevitable because the source data is currently closed to AI innovation. It will take vision and resources to build effectively from these closed systems—our federal science agencies have both, and with some leadership, they can realize the full potential of these applications.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

In the last decade, American data center energy use has tripled. By 2028, the Department of Energy predicts it will either double or triple again. To meet growing tech industry energy demands without imposing a staggering toll on individual energy consumers, and to best position the United States to benefit from the advancements of artificial intelligence (AI), Congress should invest in innovative approaches to powering data centers. Namely, Congress should create a pathway for data centers to be viably integrated into Thermal Energy Networks (TENs) in order to curb costs, increase efficiency, and support grid resilience and reliability for all customers. 

Congress should invest in American energy security and maximize benefits from data center use by: 

1. Authorizing a program for a new TEN pilot program that ties grants to performance metrics such as reducing the cost of installing underground infrastructure, 
2. Including requirements for data centers related to Power Usage Effectiveness (PUE) in the National Defense Authorization Act for Fiscal Year 2026, and 
3. Updating the 2018 Commercial Buildings Energy Consumption Survey (CBECS) Data Center Pilot to increase data center participation. 

These actions will position the federal government to deploy innovative approaches to energy infrastructure while unlocking technological advancement and economic growth from AI.

Challenge and Opportunity

By 2028, American data center energy demands are expected to account for up to 12% of the country’s electricity consumption from 4.4% in 2023. The development of artificial intelligence (AI) technologies is  driving this increase because they consume more compute resources than other technologies. As a result of their significant energy demand, data centers face two hurdles to development: (1) interconnection delays due to infrastructure development requirements and (2) the resulting costs borne by consumers in those markets, which heighten resident resistance to siting centers nearby.

Interconnection rates across the country are lengthy. In 2023, the interconnection request to commercial operations period was five years for typical power plant projects. In states like Virginia, widely-known as the “Data Center Capital of the World,” waits can stretch to seven years for data centers specifically. These interconnection timelines have grown over time, and are expected to continue growing based on queue lengths.

Interconnection is also costly. The primary cost drivers are various upgrade requirements to the broader transmission system. Unlike upgrades for energy generators, which are typically paid for by the energy generators, the cost of interconnection for new energy consumers such as data centers affects everyone around them as well. Experts believe that by socializing the costs of new data center infrastructure, utilities are passing these costs to ratepayers.

Efforts are underway to minimize data center energy costs while improving operational efficiency. One way to do that is to reclaim the energy that data centers consume by repurposing waste heat through thermal energy networks (TENs). TENs are shared networks of pipes that move heat between locations; they may incorporate any number of heat sources, including data centers. Data centers can not only generate heat for these systems, but also benefit from cooling—a major source of current data center energy consumption—provided by integrated systems.

Like other energy infrastructure projects, TENs require significant upfront financial investment to reap long-term rewards. However, they can potentially offset some of those upfront costs by shortening interconnection timelines based on demonstrated lower energy demand and reduced grid load. Avoiding larger traditional grid infrastructure upgrades would also avert the skyrocketing consumer costs described above.

At a community or utility level, TENs also offer other benefits. They improve grid resiliency and reliability: The network loops that compose a TEN increase redundancy, reducing the likelihood that a single point of failure will yield systemic failure, especially in light of increasing energy demands brought about by weather events such as extreme heat. Further, TENs allow utilities to decrease and transfer electrical demand, offering a way to balance peak loads. TENs offer building tradespeople such as pipefitters ”plentiful and high-paying jobs” as they become more prevalent, especially in rural areas. They also provide employment paths for employees of utilities and natural gas companies with expertise in underground infrastructure. By creating jobs, reducing water stress and grid strain, and decreasing the risk of quickly rising utility costs, investing in TENs to bolster data center development would reduce the current trend of community resistance to development. Many of these benefits extend to non-data center TEN participants, like nearby homes and businesses, as well. 

Federal coordination is essential to accelerating the creation of TENs in data-center heavy areas. Some states, like New York and Colorado, have passed legislation to promote TEN development. However, the states with the densest data center markets, many of which also rank poorly on grid reliability, are not all putting forth efforts to develop TENs. Because the U.S. grid is divided into multiple regions and managed by the Federal Energy Regulatory Commission, the federal government is uniquely well positioned to invest in improvements in grid resiliency through TENs and to make the U.S. a world leader in this technology.

Plan of Action

The Trump Administration and Congress can promote data center development while improving grid resiliency and reliability and reducing consumers’ financial burden through a three-part strategy:

Recommendation 1. Create a new competitive grant program to help states launch TEN pilots.

Congress should create a new TEN pilot competitive grant program administered by the Department of Energy. The federal TEN program should allow states to apply for funding to run their own TEN programs administered by states’ energy offices and organizations. This program could build on two strong precedents:

1. The Department of Energy’s 2022 funding opportunity for Community Geothermal Heating and Cooling Design and Deployment. This opportunity supported geothermal heating and cooling networks, which are a type of TEN that relies on the earth’s constant temperature and heat pumps to heat or cool buildings. Though this program generated significant interest, an opportunity remains for the federal government to invest in non-geothermal TEN projects. These would be projects that rely on exchanging heat with other sources, such as bodies of water, waste systems, or even energy-intensive buildings like data centers. The economic advantages are promising: one funded project reported expecting “savings of as much as 70% on utility bills” for beneficiaries of the proposed design.

2. The New York State’s Large-Scale Thermal program, run by its Energy Research and Development Authority (NYSERDA), has offered multiple funding opportunities that specifically include the development of TENs. In 2021, it launched a Community Heat Pump Systems (PON 4614) program that has since awarded multiple projects that include data centers. One project reported its design would save $2.4 million or roughly 77% annually in operations costs. 

Congress should authorize a new pilot program with $30 million to be distributed to state TEN programs, which states could disperse via grants and performance contracts. Such a program would support the Trump administration’s goal of fast-tracking AI data center development.

To ensure that the funding benefits both grant recipients and their host communities, requirements should be attached to these grants that incentivize consumer benefits such as reduced electricity or heating bills, improved air quality and decreased pollution. The grant awards should be prioritized according to performance metrics such as projected cost reductions related to drilling or to installing underground infrastructure and greater operational efficiency. 

Recommendation 2. Include power usage effectiveness in the amendments to the National Defense Authorization Act for Fiscal Year 2026 (2026 NDAA).

In the National Defense Authorization Act of 2024, Sec. 5302 (“Federal Data Center Consolidation Initiative amendments”) amended Section 834 of the Carl Levin and Howard P. “Buck” McKeon National Defense Authorization Act for Fiscal Year 2015 by specifying minimum requirements for new data centers.  Sec. 5302(b)(2)(b)(2)(A)(ii) currently reads:

 […The minimum requirements established under paragraph (1) shall include requirements relating to—…] “the use of new data centers, including costs related to the facility, energy consumption, and related infrastructure;.” 

To couple data center development with improved grid resilience and stability, the 2026 NDAA should amend Sec. 5302(b)(2)(b)(2)(A)(ii) as follows:

 […The minimum requirements established under paragraph (1) shall include requirements relating to—…] “the use of new data centers, including power usage effectiveness, costs related to the facility, energy consumption, and related infrastructure.” 

Power usage effectiveness (PUE) is a common metric to measure the efficiency of data center power use. It is the ratio of total power used by the facility over the amount of that power dedicated to IT services. The PUE metric has limitations, such as its inability to provide an apples-to-apples comparison of data center energy efficiency based on variability in underlying technology and its lack of precision, especially given the growth of AI data centers. However, introducing the PUE metric as part of the regulatory framework for data centers would provide a specific target for new builds to use, making it easier for both developers and policymakers to identify progress. Requirements related to PUE would also encourage developers to invest in technologies that increase energy efficiency without unduly hurting their bottom lines. In the future, legislators should continue to amend this section of the NDAA as new, more accurate, and useful efficiency metrics develop.

Recommendation 3. The U.S. Energy Information Administration (EIA) should update the 2018 Commercial Buildings Energy Consumption Survey (CBECS) Data Center Pilot. 

To facilitate community acceptance and realize benefits like better financing terms based on lower default risk, data center developers should seek to benchmark their facilities’ energy consumptions. Energy consumption benchmarking, the process of analyzing consumption data and comparing to both past performance and the performance of similar facilities, results in operational cost savings. These savings amplify the economic benefits of vehicles like TENs for cost-sensitive developers and lower the potential increase of community utility costs.

Data center developers should create industry-standard benchmarking tools, much as other industries do. However, it’s challenging for them to embark on this work without accurate and current information that facilitates the development of useful models and targets, especially in such a fast-changing field. Yet data sources such as those used to create benchmarks for other industries are unavailable. One popular source is the CBECS, which does not include data centers as a separate building type. This issue is longstanding; in 2018, the EIA released a report detailing the results of their data center pilot, which they undertook to address this gap. The pilot cited three main hurdles to accurately account for data centers’ energy consumption: the lack of a comprehensive frame or list of data centers, low cooperation rates, and a high rate of nonresponse to important survey questions. 

With the proliferation of data centers since the pilot, it has become only more pressing to differentiate this building type and enable data centers to seek accurate representation and develop industry benchmarks. To address the framing issue, CBECS should use a commercial data source like Data Center Map. At the time the EIA considered this source “unvalidated,” but it has been used as a data source by the U.S. Department of Commerce and the International Energy Agency. Additionally, the EIA should also perform the “cognitive research and pretests” recommended in the pilot to find ways to encourage complete responses in order to recreate its pilot and seek an improved outcome.

Conclusion

Data center energy demand has exploded in recent years and continues to climb, due in part to the advent of widespread AI development. Data centers need access to reliable energy without creating grid instability or dramatically increasing utility costs for individual consumers. This creates a unique opportunity for the federal government to develop and implement innovative technology such as TENs in areas working to support changing energy demands. The government should also seize this moment to define and update standards for site developers to ensure they are building cost-effective and operationally efficient facilities. By progressing systems and tools that benefit other area energy consumers down to the individual ratepayer, the federal government can transform data centers from infrastructural burdens to good neighbors.

The American research enterprise, long the global leader, faces intensifying competition and mounting criticism regarding its productivity and relevance to societal challenges. At the same time, a vital component of a healthy research enterprise has been lost: corporate research labs, epitomized by the iconic Bell Labs of the 20th century. Such labs uniquely excelled at reverse translational research, where real-world utility and problem-rich environments served as powerful inspirations for fundamental learning and discovery. Rebuilding such labs in a 21^st century “Bell Labs X” form would restore a powerful and uniquely American approach to technoscientific discovery—harnessing the private sector to discover and invent in ways that fundamentally improve U.S. national and economic competitiveness. Moreover, new metaresearch insights into “how to innovate how we innovate” provide principles that can guide their rebuilding. The White House Office of Science and Technology Policy (OSTP) can help turn these insights into reality by convening a working group of stakeholders (philanthropy, business, and science agency leaders), alongside policy and metascience scholars, to make practical recommendations for implementation.

Challenge and Opportunity

The American research enterprise faces intensifying competition and mounting criticism regarding its productivity and relevance to societal challenges. While a number of reasons have been proposed for why, among the most important is that corporate research labs, a vital piece of a healthy research enterprise, are missing. Exemplified by the Bell Labs, these labs dominated the research enterprise of the first half of the 20^th century but became defunct in the second half. The reason: formalization of profits as the prime goal of corporations, which is incompatible with research, particularly the basic research that produces public-goods science and technology. Instead, academic research is now dominant. The reason: the rise of federal agencies like the National Science Foundation (NSF) with a near-total focus on academia. This dynamic, however, is not fundamental: federal agencies could easily fund research at corporations and not just in academia.

Moreover, there is a compelling reason to do so. Utility and learning are cyclical and build on each other. In one direction, learning serves as a starting point for utility. Academia excels at such translational research. In the other direction, utility serves as a starting point for learning. Corporations in principle excel at such reverse translational research. Corporations are where utility lives and breathes and where real-world problem-rich environments and inspiration for learning thrives. This reverse translational half of the utility-learning cycle, however, is currently nearly absent, and is a critical void that could be filled by corporate research.

For example, at Bell Labs circa WWII, Claude Shannon’s exposure to real-world problems in cryptography and noisy communications inspired his surprising idea to treat information as a quantifiable and manipulable entity independent of its physical medium, revolutionizing information science and technology. Similarly, Mervyn Kelly’s exposure to the real-world benefit of compact and reliable solid-state amplifiers inspired him to create a research activity at Bell Labs that invented the transistor and discovered the transistor effect. These advances, inspired by real-world utility, laid the foundations for our modern information age.

Importantly, these advances were given freely to the nation because Bell Labs’ host corporation, the AT&T of the 20^th century, was a monopoly and could be altruistic with respect to its research. Now, in the 21^st century, corporations, even when they have dominant market power, are subject to intense competitive pressures on their bottom-line profit which make it difficult for them to engage in research that is given freely to the nation. But to throw away corporate research along with the monopolies that could afford to do such research is to throw away the baby with the bathwater. Instead, the challenge is to rebuild corporate research in a 21st century: “Bell Labs X” form without relying on monopolies, using public-private partnerships instead.

Moreover, new insights into the nature and nurture of research provide principles that can guide the creation of such public-private partnerships for the purpose of public-goods research.

1. Inspire, but Don’t Constrain, Research by Particular Use. Reverse-translational research should start with real-world challenges but not be constrained by them as it seeks the greatest advances in learning—advances that surprise and contradict prevailing wisdom. This principle combines Donald Stokes’ “use-inspired research” with Ken Stanley and Joel Lehman’s “why greatness cannot be planned” with Gold Standard Science’s informed contrariness and dissent.
   
2. Fund and Execute Research at the Institution, not Individual Researcher, Level. This would be very different from the dominant mode of research funding in the U.S.: matrix-funding to principal investigators (PIs) in academia. Here, instead, research funding would be to research institutes that employ researchers rather than contract with researchers employed by other institutions. Leadership would be empowered to nurture and orchestrate the people, culture, and organizational structure of the institute for the singular purpose of empowering researchers to achieve groundbreaking discoveries.
   
3. Evolve Research Institutions by Retrospective, Competitive Reselection. There should be many research institutes and none should have guaranteed perpetual funding. Instead, they should be subject to periodic evaluation “with teeth” where research institutions only continue to receive support if they are significantly changing the way we think and/or do. This creates a dynamic market-like ecosystem within which the population of research institutes evolves in response to a competitive re-selection pressure towards ever-increasing research productivity.

Plan of Action

The White House Office of Science and Technology Policy (OSTP) should convene a working group of stakeholders, alongside policy and metaresearch scholars, to make practical recommendations for public-private partnerships that enable corporate research akin to the Bell Labs of the 20^th century, but in a 21^st century “Bell Labs X” form.

Among the stakeholders would be government agencies, corporations and philanthropies—perhaps along the lines of the Government-University-Industry-Philanthropy Research Roundtable (GUIPRR) of the National Academies of Sciences, Engineering and Medicine (NASEM).

Importantly, the working group does not need to start from scratch. A high-level, funding and organizational model was recently articulated.

Its starting point is the initial selection of ten or so Bell Labs Xs based on their potential for major advances in public-goods science and technology. Each Bell Labs X would be hosted and cost-shared by a corporation that brings with it its problem-rich use environment and state-of-the-art technological contexts, but majority block-funded by a research funder (federal agencies and/or philanthropies) with broad societal benefit in mind. To establish a sense of scale, we might imagine each Bell Labs X having a $120M/year operating budget and a 20% cost share—so $20M/year coming from the corporate host and $100M/year coming from the research funder. 

This plan also envisions a market-like competitive renewal structure of these corporate research labs. At the end of a period of time (say, ten years) appropriate for long-term basic research, all ten or so Bell Labs Xs would be evaluated for their contributions to public-goods science and technology independent of their contributions to commercial applications of the host corporation. Only the most productive seven or eight of the ten would be renewed. In between selection, re-selection and subsequent re-re-selections, leadership of each Bell Labs X would be free to nurture its people, culture and organizational structure as it believes will maximize research productivity. Each Bell Labs X would thus be an experiment in research institution design. And each Bell Labs X would make its own bet on the knowledge domain it believes is ripe for the greatest disruptive advances. Government’s role would be largely confined to retrospectively rewarding or disrewarding those Bell Labs Xs that made better or worse bets, without itself making bets.

Conclusion

Imagine a private institution whose researchers routinely disrupted knowledge and changed the world. That’s the story of Bell Labs—a legendary research institute that gave us scientific and technological breakthroughs we now take for granted. In its heyday in the mid-20th century, Bell Labs was a crucible of innovation where brilliant minds were exposed to and inspired by real-world problems, then given the freedom to explore those problems in deep and fundamental ways, often pivoting to and solving unanticipated new problems of even greater importance.

Recreating that innovative environment is possible and its impact on American research productivity would be profound. By innovating how we innovate, we would leap-frog other nations who are investing heavily in their own research productivity but are largely copying the structure of the current U.S. research enterprise. The resulting network of Bell Labs Xs would flip the relationship between corporations and the nation’s public-goods science and technology from asking not what the nation’s public-goods science and technology can do for corporations, but what corporations can do for the nation’s public-goods science and technology. Disruptive and useful ideas are not getting harder to find; our current research enterprise is just not well optimized to find them.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS

Fraud in scientific research is more common than we’d like to think. Such research can mislead entire scientific fields for years, driving futile and wasteful followup studies, and slowing down real scientific discoveries. To truly push the boundaries of knowledge, researchers should be able to base their theories and decisions on a more trustworthy scientific record.

Currently there are insufficient incentives to identify fraud and correct the record. Meanwhile, fraudsters can continue to operate with little chance of being caught. That should change: Scientific funders should establish one or more bounty programs aimed at rewarding people who identify significant problems with federally-funded research, and should particularly reward fraud whistleblowers whose careers are on the line. 

Challenge and Opportunity

In 2023 it was revealed that 20 papers from Hoau-Yan Wang, an influential Alzheimer’s researcher, were marred by doctored images and other scientific misconduct. Shockingly, his research led to the development of a drug that was tested on 2,000 patients. A colleague described the situation as “embarrassing beyond words”.

There is a common belief that science is self-correcting. But what’s interesting about this case is that the scientist who uncovered Wang’s  fraud was not driven by the usual academic incentives. He was being paid by Wall Street short sellers who were betting against the drug company!

This was not an isolated incident. The most notorious example of Alzheimer’s research misconduct – doctored images in Sylvain Lesné’s papers – was also discovered with the help of short sellers. And as reported in Science, Lesné’s “paper has been cited in about 2,300 scholarly articles—more than all but four other Alzheimer’s basic research reports published since 2006, according to the Web of Science database. Since then, annual NIH support for studies labeled ‘amyloid, oligomer, and Alzheimer’s’ has risen from near zero to $287 million in 2021.” While not all of that research was motivated by Lesné’s paper, it’s inconceivable that a paper with that many citations could not have had some effect on the direction of the field.

These cases show how a critical part of the scientific ecosystem – the exposure of faked research – can be undersupplied by ordinary science. Unmasking fraud is a difficult and awkward task, and few people want to do it. But financial incentives can help close those gaps.

Plan of Action

People who witness scientific fraud often stay silent due to perceived pressure from their colleagues and institutions. Whistleblowing is an undersupplied part of the scientific ecosystem.

We can correct these incentives by borrowing an idea from the Securities and Exchange Commission, whose bounty program around financial fraud pays whistleblowers 10-30% of the fines imposed by the government. The program has been a huge success, catching dozens of fraudsters and reducing the stigma around whistleblowing. The Department of Justice has recently copied the model for other types of fraud, such as healthcare fraud. The model should be extended to scientific fraud.

• Funder: Any U.S. government funding agency, such as NIH or NSF
• Eligibility: Research employees with insider knowledge from having worked in a particular lab
• Cost: The program should ultimately pay for itself, both through the recoupment of grant expenditures and through the impacts on future funding, including, potentially, the trajectory of entire academic fields.

The amount of the bounty should vary with the scientific field and the nature of the whistleblower in question. For example, compare the following two situations: 

• An undergraduate whistleblower who identifies a problem in a psychology or education study that hardly anyone had cited, let alone implemented in the real world 
• A graduate student or postdoc who calls out their own mentor for academic fraud related to influential papers on Alzheimer’s disease or cancer. 

The stakes are higher in the latter case. Few graduate students or post-docs will ever be willing to make the intense personal sacrifice of whistleblowing on their own mentor and adviser, potentially forgoing approval of their dissertation or future recommendation letters for jobs. If we want such people to be empowered to come forward despite the personal stakes, we need to make it worth their while. 

Suppose that one of Lesné’s students in 2006 had been rewarded with a significant bounty for direct testimony about the image manipulation and fraud that was occurring. That reward might have saved tens of millions in future NIH spending, and would have been more than worth it. In actuality, as we know, none of Lesné’s students or postdocs ever had the courage to come forward in the face of such immense personal risk. 

The Office of Research Integrity at the Department of Health and Human Services should be funded to create a bounty program for all HHS-funded research at NIH, CDC, FDA, or elsewhere. ORI’s budget is currently around $15 million per year. That should be increased by at least $1 million to account for a significant number of bounties plus at least one full-time employee to administer the program. 

Conclusion

Some critics might say that science works best when it’s driven by people who are passionate about truth for truth’s sake, not for the money. But by this point it’s clear that like anyone else, scientists can be driven by incentives that are not always aligned with the truth. Where those incentives fall short, bounty programs can help.

This memo produced as part of the Federation of American Scientists and Good Science Project sprint. Find more ideas at Good Science Project x FAS