Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan

The National Institutes of Health (NIH) Policy for Data Management and Sharing (DMS Policy) recognizes the NIH’s role as a key steward of United States biomedical research and information and seeks to enhance that stewardship through systematic recommendations for the preservation and sharing of research data generated by funded projects. The policy is effective as of January 2023. The recommendations include a requirement for the submission of a Data Management and Sharing Plan (DMSP) with funding applications, and while no strict template was provided, the NIH has released supplemental draft guidance on elements to consider when developing a plan. This article provides 10 key recommendations for creating a DMSP that is both maximally compliant and effective.     

To Apply or Not to Apply: A Survey Analysis of Grant Writing Costs and Benefits

We surveyed 113 astronomers and 82 psychologists active in applying for federally funded research on their grant-writing history between January, 2009 and November, 2012. We collected demographic data, effort levels, success rates, and perceived non-financial benefits from writing grant proposals. We find that the average proposal takes 116 PI hours and 55 CI hours to write; although time spent writing was not related to whether the grant was funded. Effort did translate into success, however, as academics who wrote more grants received more funding. Participants indicated modest non-monetary benefits from grant writing, with psychologists reporting a somewhat greater benefit overall than astronomers. These perceptions of non-financial benefits were unrelated to how many grants investigators applied for, the number of grants they received, or the amount of time they devoted to writing their proposals. We also explored the number of years an investigator can afford to apply unsuccessfully for research grants and our analyses suggest that funding rates below approximately 20%, commensurate with current NIH and NSF funding, are likely to drive at least half of the active researchers away from federally funded research. We conclude with recommendations and suggestions for individual investigators and for department heads.     

Who Shares? Who Doesn't? Factors Associated with Openly Archiving Raw Research Data

Many initiatives encourage investigators to share their raw datasets in hopes of increasing research efficiency and quality. Despite these investments of time and money, we do not have a firm grasp of who openly shares raw research data, who doesn''t, and which initiatives are correlated with high rates of data sharing. In this analysis I use bibliometric methods to identify patterns in the frequency with which investigators openly archive their raw gene expression microarray datasets after study publication.Automated methods identified 11,603 articles published between 2000 and 2009 that describe the creation of gene expression microarray data. Associated datasets in best-practice repositories were found for 25% of these articles, increasing from less than 5% in 2001 to 30%–35% in 2007–2009. Accounting for sensitivity of the automated methods, approximately 45% of recent gene expression studies made their data publicly available.First-order factor analysis on 124 diverse bibliometric attributes of the data creation articles revealed 15 factors describing authorship, funding, institution, publication, and domain environments. In multivariate regression, authors were most likely to share data if they had prior experience sharing or reusing data, if their study was published in an open access journal or a journal with a relatively strong data sharing policy, or if the study was funded by a large number of NIH grants. Authors of studies on cancer and human subjects were least likely to make their datasets available.These results suggest research data sharing levels are still low and increasing only slowly, and data is least available in areas where it could make the biggest impact. Let''s learn from those with high rates of sharing to embrace the full potential of our research output.     

生物多样性数据共享和发表: 进展和建议

生物多样性研究、保护实践、自然资源管理及科学决策等越来越依赖于大量数据的共享和整合。虽然关于数据共享的呼吁和实践越来越多, 但很多科学家仍然主动或被动地拒绝共享数据。关于数据共享, 现实中存在一些认知和技术上的障碍, 比如科学家不愿意共享数据, 担心同行竞争, 认为缺少足够的回报, 不熟悉相关数据保存机构, 缺少简便的数据提交工具, 没有足够时间和经费等。解决这些问题及改善共享文化的关键在于使共享者获得适当的回报(比如数据引用)。基于同行评审的数据发表被认为不但能够为生产、管理和共享数据的科学家提供一种激励机制, 并且能够有效地促进数据再利用。因而, 数据发表作为数据共享的方式之一, 近来引起了较多关注, 在生物多样性领域出现了专门发表数据论文的期刊。在采取数据论文的模式上, 数据保存机构和科技期刊采用联合数据政策在促进数据共享方面可能更具可行性。本文总结了数据共享和发表方面的进展, 讨论了数据论文能在何种程度上促进数据共享, 以及数据共享和数据发表的关系等问题, 提出如下建议: (1)个体科学家应努力践行数据共享; (2)使用DOI号解决数据所有权和数据引用的问题; (3)科技期刊和数据保存机构联合采用更加合理和严格的数据保存政策; (4)资助机构和研究单位应当在数据共享中起到更重要的作用。     

The Impact of National Institutes of Health Funding on U.S. Cardiovascular Disease Research

Background

Intense interest surrounds the recent expansion of US National Institutes of Health (NIH) budgets as part of economic stimulus legislation. However, the relationship between NIH funding and cardiovascular disease research is poorly understood, making the likely impact of this policy change unclear.

Methods

The National Library of Medicine''s PubMed database was searched for articles published from 1996 to 2006, originating from U.S. institutions, and containing the phrases “cardiolog,” “cardiovascular,” or “cardiac,” in the first author''s department. Research methodology, journal of publication, journal impact factor, and receipt of NIH funding were recorded. Differences in means and trends were tested with t-tests and linear regression, respectively, with P≤0.05 for significance.

Results

Of 117,643 world cardiovascular articles, 36,684 (31.2%) originated from the U.S., of which 10,293 (28.1%) received NIH funding. The NIH funded 40.1% of U.S. basic science articles, 20.3% of overall clinical trials, 18.1% of randomized-controlled, and 12.2% of multicenter clinical trials. NIH-funded and total articles grew significantly (65 articles/year, P<0.001 and 218 articles/year, P<0.001, respectively). The proportion of articles receiving NIH funding was stable, but grew significantly for basic science and clinical trials (0.87%/year, P<0.001 and 0.67%/year, P = 0.029, respectively). NIH-funded articles had greater journal impact factors than non NIH-funded articles (5.76 vs. 3.71, P<0.001).

Conclusions

NIH influence on U.S. cardiovascular research expanded in the past decade, during the period of NIH budget doubling. A substantial fraction of research is now directly funded and thus likely sensitive to budget fluctuations, particularly in basic science research. NIH funding predicts greater journal impact.     

Data sharing by scientists: practices and perceptions

Background

Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results.

Methodology/Principal Findings

A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the short- and long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region.

Conclusions/Significance

Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.     

The Leeds Evaluation of Efficacy of Detoxification Study (LEEDS) Prisons Project Study: protocol for a randomised controlled trial comparing methadone and buprenorphine for opiate detoxification

Many research-funding agencies now require open access to the results of research they have funded, and some also require that researchers make available the raw data generated from that research. Similarly, the journal Trials aims to address inadequate reporting in randomised controlled trials, and in order to fulfil this objective, the journal is working with the scientific and publishing communities to try to establish best practice for publishing raw data from clinical trials in peer-reviewed biomedical journals. Common issues encountered when considering raw data for publication include patient privacy – unless explicit consent for publication is obtained – and ownership, but agreed-upon policies for tackling these concerns do not appear to be addressed in the guidance or mandates currently established. Potential next steps for journal editors and publishers, ethics committees, research-funding agencies, and researchers are proposed, and alternatives to journal publication, such as restricted access repositories, are outlined.     

Making Research Data Repositories Visible: The re3data.org Registry

Researchers require infrastructures that ensure a maximum of accessibility, stability and reliability to facilitate working with and sharing of research data. Such infrastructures are being increasingly summarized under the term Research Data Repositories (RDR). The project re3data.org–Registry of Research Data Repositories–has begun to index research data repositories in 2012 and offers researchers, funding organizations, libraries and publishers an overview of the heterogeneous research data repository landscape. In July 2013 re3data.org lists 400 research data repositories and counting. 288 of these are described in detail using the re3data.org vocabulary. Information icons help researchers to easily identify an adequate repository for the storage and reuse of their data. This article describes the heterogeneous RDR landscape and presents a typology of institutional, disciplinary, multidisciplinary and project-specific RDR. Further the article outlines the features of re3data.org, and shows how this registry helps to identify appropriate repositories for storage and search of research data.     

Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals

Journal policy on research data and code availability is an important part of the ongoing shift toward publishing reproducible computational science. This article extends the literature by studying journal data sharing policies by year (for both 2011 and 2012) for a referent set of 170 journals. We make a further contribution by evaluating code sharing policies, supplemental materials policies, and open access status for these 170 journals for each of 2011 and 2012. We build a predictive model of open data and code policy adoption as a function of impact factor and publisher and find higher impact journals more likely to have open data and code policies and scientific societies more likely to have open data and code policies than commercial publishers. We also find open data policies tend to lead open code policies, and we find no relationship between open data and code policies and either supplemental material policies or open access journal status. Of the journals in this study, 38% had a data policy, 22% had a code policy, and 66% had a supplemental materials policy as of June 2012. This reflects a striking one year increase of 16% in the number of data policies, a 30% increase in code policies, and a 7% increase in the number of supplemental materials policies. We introduce a new dataset to the community that categorizes data and code sharing, supplemental materials, and open access policies in 2011 and 2012 for these 170 journals.     

Legislation for trial registration and data transparency

Sharing of final research data from clinical research is an essential part of the scientific method. The U.S. National Institutes of Health require some grant applications to include plans for sharing final research data, which it defines as the factual materials necessary to document, support, and validate research findings. In the U.S., however, the Privacy Rule adopted under the Health Insurance Portability and Accountability Act impedes the sharing of final research data. In most situations, final research data may be shared only where all information that could possibly be used to identify the subject has been deleted, or where the subject has given authorization for specific research, or an Institutional Review Board has granted a waiver.     

Health Research Funding in Mexico: The Need for a Long-Term Agenda

Background

The legal framework and funding mechanisms of the national health research system were recently reformed in Mexico. A study of the resource allocation for health research is still missing. We identified the health research areas funded by the National Council on Science and Technology (CONACYT) and examined whether research funding has been aligned to national health problems.

Methods and Findings

We collected the information to create a database of research grant projects supported through the three main Sectoral Funds managed by CONACYT between 2003 and 2010. The health-related projects were identified and classified according to their methodological approach and research objective. A correlation analysis was carried out to evaluate the association between disease-specific funding and two indicators of disease burden. From 2003 to 2010, research grant funding increased by 32% at a compound annual growth rate of 3.5%. By research objective, the budget fluctuated annually resulting in modest increments or even decrements during the period under analysis. The basic science category received the largest share of funding (29%) while the less funded category was violence and accidents (1.4%). The number of deaths (ρ = 0.51; P<0.001) and disability-adjusted life years (DALYs; ρ = 0.33; P = 0.004) were weakly correlated with the funding for health research. Considering the two indicators, poisonings and infectious and parasitic diseases were among the most overfunded conditions. In contrast, congenital anomalies, road traffic accidents, cerebrovascular disease, and chronic obstructive pulmonary disease were the most underfunded conditions.

Conclusions

Although the health research funding has grown since the creation of CONACYT sectoral funds, the financial effort is still low in comparison to other Latin American countries with similar development. Furthermore, the great diversity of the funded topics compromises the efficacy of the investment. Better mechanisms of research priority-setting are required to adjust the research portfolio to the new health panorama of Mexican population.     

Understanding the performance and impact of public knowledge translation funding interventions: Protocol for an evaluation of Canadian institutes of health research knowledge translation funding programs

ABSTRACT: BACKGROUND: The Canadian Institutes of Health Research (CIHR) has defined knowledge translation (KT) as a dynamic and iterative process that includes the synthesis, dissemination, exchange, and ethically-sound application of knowledge to improve the health of Canadians, provide more effective health services and products, and strengthen the healthcare system. CIHR, the national health research funding agency in Canada, has undertaken to advance this concept through direct research funding opportunities in KT. Because CIHR is recognized within Canada and internationally for leading and funding the advancement of KT science and practice, it is essential and timely to evaluate this intervention, and specifically, these funding opportunities. DESIGN: The study will employ a novel method of participatory, utilization-focused evaluation inspired by the principles of integrated KT. It will use a mixed methods approach, drawing on both quantitative and qualitative data, and will elicit participation from CIHR funded researchers, knowledge users, KT experts, as well as other health research funding agencies. Lines of inquiry will include an international environmental scan, document/data reviews, in-depth interviews, targeted surveys, case studies, and an expert review panel. The study will investigate how efficiently and effectively the CIHR model of KT funding programs operates, what immediate outcomes these funding mechanisms have produced, and what impact these programs have had on the broader state of health research, health research uptake, and health improvement. DISCUSSION: The protocol and results of this evaluation will be of interest to those engaged in the theory, practice, and evaluation of KT. The dissemination of the study protocol and results to both practitioners and theorists will help to fill a gap in knowledge in three areas: the role of a public research funding agency in facilitating KT, the outcomes and impacts KT funding interventions, and how KT can best be evaluated.     

To Crowdfund Research,Scientists Must Build an Audience for Their Work

As rates of traditional sources of scientific funding decline, scientists have become increasingly interested in crowdfunding as a means of bringing in new money for research. In fields where crowdfunding has become a major venue for fundraising such as the arts and technology, building an audience for one''s work is key for successful crowdfunding. For science, to what extent does audience building, via engagement and outreach, increase a scientist''s abilities to bring in money via crowdfunding? Here we report on an analysis of the #SciFund Challenge, a crowdfunding experiment in which 159 scientists attempted to crowdfund their research. Using data gathered from a survey of participants, internet metrics, and logs of project donations, we find that public engagement is the key to crowdfunding success. Building an audience or “fanbase” and actively engaging with that audience as well as seeking to broaden the reach of one''s audience indirectly increases levels of funding. Audience size and effort interact to bring in more people to view a scientist''s project proposal, leading to funding. We discuss how projects capable of raising levels of funds commensurate with traditional funding agencies will need to incorporate direct involvement of the public with science. We suggest that if scientists and research institutions wish to tap this new source of funds, they will need to encourage and reward activities that allow scientists to engage with the public.     

Ecological monitoring to support Water Sharing Plan evaluation and protect wetlands of inland New South Wales,Australia

Government‐funded flow response monitoring and modelling programmes (flow science) provided by the New South Wales Office of Water (NOW) have supported water resource management since 1997. Flow science has a core technical component defined by hypothesis‐driven long‐term monitoring and analysis, but it also represents many activities that support committees involved in environmental flow management. This is done through collaborations and contracting and has fostered considerable research and analysis into flow ecology, including modelling for the recent Murray–Darling Basin Plan. We describe the performance of environmental flows against legislated wetland objectives to improve wetland function and diversity using flow science. On‐ground monitoring at wetland sites has largely ceased but the flow science done so far indicates that the environmental flow rules written into Water Sharing Plans improve wetland diversity and function. Determination of the long‐term flow needs of NSW wetlands, including how well current Water Sharing Plans aid the delivery of environmental flows, requires finding the means to build on current flow science knowledge from across Australia.     

Codifying Collegiality: Recent Developments in Data Sharing Policy in the Life Sciences

Over the last decade, there have been significant changes in data sharing policies and in the data sharing environment faced by life science researchers. Using data from a 2013 survey of over 1600 life science researchers, we analyze the effects of sharing policies of funding agencies and journals. We also examine the effects of new sharing infrastructure and tools (i.e., third party repositories and online supplements). We find that recently enacted data sharing policies and new sharing infrastructure and tools have had a sizable effect on encouraging data sharing. In particular, third party repositories and online supplements as well as data sharing requirements of funding agencies, particularly the NIH and the National Human Genome Research Institute, were perceived by scientists to have had a large effect on facilitating data sharing. In addition, we found a high degree of compliance with these new policies, although noncompliance resulted in few formal or informal sanctions. Despite the overall effectiveness of data sharing policies, some significant gaps remain: about one third of grant reviewers placed no weight on data sharing plans in their reviews, and a similar percentage ignored the requirements of material transfer agreements. These patterns suggest that although most of these new policies have been effective, there is still room for policy improvement.     

Mismatched expectations

Public funding for basic research rests on a delicate balance between scientists, governments and the public. COVID could further shift this equilibrium towards translation and application.

Keeping a research laboratory well‐funded to pay for salaries, reagents, infrastructure, travel, and publications is surely a challenging task that can consume most of a PI’s time. The risk is that if the funding decreases, the laboratory will have to shrink, which means less publications and a decreased probability of getting new grants. This downward spiral is hard to reverse and can end up with a token research activity and increased teaching instead. Some would see this is as an unwelcome career change. Apart from the personal challenge for PIs to keep the income flowing, there is no guarantee that the overall funding wallet for research will continue to grow and no certainty that the covenant between the funder and the funded will remain unchanged. The COVID‐19 pandemic could in fact accelerate ongoing changes in the way public funding for research is justified and distributed.There are three legs that support the delicate stool of competitive funding. The first is the scientists or, more precisely, the primary investigators. To get to that position, they had be high achievers as they moved from primary degree to PhD to post doc to the Valhalla of their own laboratory. Along the way they showed to be hard‐working, intelligent, competitive, innovative, lucky, and something between a team player and a team leader. The judgment to grant independence is largely based on publications—and given their track record of great papers to get there, most young PIs assume they will continue to get funding. This is not a narcissistic sense of entitlement; it is a logical conclusion of their career progression.They will get started by recruiting a few PhD or higher degree students. Although this is about educating students, a PI of course hopes that their students generate the results needed for the next grant application. The minimum time for a PhD is about three years, which explains that many grants are structured around a 3‐ to 5‐year research project. The endpoints are rarely the finishing line for a group’s overall research program: Hence, the comments in reviews along the line that “this paper raises more questions than it answers and more work will be required…” Work is carried on with a relay of grants edging asymptotically to answer a question raised decades before. I recall a lecturer from my PhD days who said that he would not do an obvious experiment that would prove or disprove his hypothesis, because “If I did that experiment, it would be the end of my career”. Others are less brazen but still continue to search for the mirage of truth when they know deep in their hearts that they are in a barren desert.The funding from the competitive grants is rarely enough to feed the ever‐growing demand for more people and resources and to make provisions for a hiatus in grant income. Eventually, an additional income stream comes from industry attracted by the knowledge and skills in the laboratory. The PI signs a contract for a one‐year period and allocates some resources to deliver the answers required when due. Similarly, some other resources are shepherded to fulfill the demands of a private donor who wants rapid progress on a disease that afflicts a loved one. The research group is doing a marathon run working on their core challenges with occasional sprints to generate deliverables and satisfy funders who require rapid success—a juggling act that demands much intellectual flexibility.State funding is the second leg and governments have multiple reasons for supporting academic research, even if these are not always presented clearly. Idealistically, the public supports research to add to the pool of knowledge and to understand the world in which we live but this is not how public funding started. The first universities began as theological seminars that expanded to include the arts, law, philosophy and medicine. Naturalists and natural scientists found them a serene and welcoming place to ponder important questions, conduct experiments, and discuss with their colleagues. The influence of Wilhelm von Humboldt who championed the concept of combining teaching and research at universities was immense: both became more professional with codified ways of generating and sharing knowledge. Government funding was an inevitable consequence: initially to pay for the education of students, it expanded to provide for research activities.While that rationale for supporting teaching and research remains, additional reasons for funding research emerged, mostly in the wake of World War 2: the military, national economies, and medicine required new products and services enabled by knowledge. It also required new structures and bodies to control and distribute public resources: hence the establishment of research funding agencies to decide which projects deserve public money. The US National Science Foundation was founded in 1950 following the analysis of Vannevar Bush that the country’s economic well‐being and security relied on research. The NIH extramural program started tentatively in the late 1930s. The Deutsche Forschungsgemeinschaft (DFG) was established in 1951. The EU Framework Programmes started in 1984 with the explicit goal to strengthen the economy of the community. It was only in 2007 that the European Research Council (ERC) was established to support excellence in research rather than to look at practical benefits.But the tide is inevitably moving toward linking state research funding with return on investment measured in jobs, economic growth, or improved health. Increasingly, the rationale for government investment is not just generation of knowledge or publications, but more products and services. As science is seen as the driver of innovation and future economic growth, the goal has been to invest 3% or more of a country’s GDP into research—although almost two‐thirds of this money comes from industry in advanced economies. Even nations without a strong industrial base strive to strengthen their economies by investing in brains. This message about government’s economic expectations is not lost on the funding agencies and softly trickles down to selection committees, analysts, and program officers. The idealistic image of the independent scientist pursuing knowledge for knowledge’s sake no longer fits into this bigger picture. They are now cajoled into research collaborations and partnerships and, hooked to the laboratories’ funding habit, willingly promise that the outcome of the work will somehow have practical applications: “This work will help efforts to cure cancer”.The third leg that influences research directions is the public who pay for research through their taxes. Mostly, they do not get overly excited or concerned about those few percentages of the national budget that go to laboratories. However, the COVID‐19 crisis could change that: Now, the people in the white coats are expected to provide rapid solutions to pressing and complex problems. The scientists have so far performed extremely well: understanding SARS‐CoV‐2 pathology, genetics, and impact on the immune system along with diagnostic tests and vaccine candidates came in record time. Vaccine development moved with lightning speed from discovery of the crucial receptor proteins to mass‐producing jabs, employing many new technologies for the first time. 2020 has been a breath‐taking and successful year for scientists who delivered a great return on investment.The public have also seen what a galvanized and cooperative scientific community across disciplines can achieve. “Aha,” they may say, “why don’t you now move on to tackle triple‐negative breast cancer, Alzheimer’s or Parkinson’s?” This is a fair and challenging question. And the increasing involvement of consumers and patients in research, at the behest of funding agencies, will further this expectation until the researchers respond. And respond they will, as they have always done to every hint of what might be needed to obtain funding.The old order is changing. The days of the independent academics getting funding for life to do what they like in the manner they chose will not survive the pressures from government to show a return on investment and from society to provide solutions to their problems. The danger is that early‐stage research—I did not say “basic” as it has joined “academic” as a pejorative term—will be suffocated. Governments appoint the heads of funding agencies, and it is not surprising if the appointees share the dominant philosophy of their employer. Peer‐review committees are being discouraged, subtly, from supporting early‐stage research. Elsewhere, the guidelines for decisions on grant applications give an increasing score for implementation, translation, IP generation, and so on. Those on the panels get the message and bring it back to their institutions that slowly move away from working to understand what we are ignorant about to using our (partial) understanding to develop cures and drugs.As in all areas, balance is needed. Those at the forefront of translating knowledge into outcomes for society have to remind the public as well as the government that the practical today is only possible because of the “impractical” research of yesterday. Industry is well aware of this and has become a strong champion for excellent early‐stage research to lay the groundwork for solving the next set of hard problems in the future. The ERC and its national counterparts have a special role to play in highlighting the benefits of supporting research with excellence as the sole criterion. In the meantime, scientists have to embrace the new task of developing solutions to societal problems without abandoning the hard slog of innovative research that opens up new understanding from which flows translation into practical applications.     

Share and share alike: deciding how to distribute the scientific and social benefits of genomic data

Emerging technologies make genomic analyses more efficient and less expensive, enabling genome-wide association and gene-environment interaction studies. In anticipation of their results, funding agencies such as the US National Institutes of Health and the Wellcome Trust are formulating guidelines for sharing the large amounts of genomic data that are generated by the projects that they sponsor. Data-sharing policies can have varying implications for how disease susceptibility and drug-response research will be pursued by the scientific community, and for who will benefit from the resulting medical discoveries. We suggest that the complex interplay of stakeholders and their interests, rather than single-issue and single-stakeholder perspectives, should be considered when deciding genomic data-sharing policies.     

Mine, yours, ours? Sharing data on human genetic variation

The achievement of a robust, effective and responsible form of data sharing is currently regarded as a priority for biological and bio-medical research. Empirical evaluations of data sharing may be regarded as an indispensable first step in the identification of critical aspects and the development of strategies aimed at increasing availability of research data for the scientific community as a whole. Research concerning human genetic variation represents a potential forerunner in the establishment of widespread sharing of primary datasets. However, no specific analysis has been conducted to date in order to ascertain whether the sharing of primary datasets is common-practice in this research field. To this aim, we analyzed a total of 543 mitochondrial and Y chromosomal datasets reported in 508 papers indexed in the Pubmed database from 2008 to 2011. A substantial portion of datasets (21.9%) was found to have been withheld, while neither strong editorial policies nor high impact factor proved to be effective in increasing the sharing rate beyond the current figure of 80.5%. Disaggregating datasets for research fields, we could observe a substantially lower sharing in medical than evolutionary and forensic genetics, more evident for whole mtDNA sequences (15.0% vs 99.6%). The low rate of positive responses to e-mail requests sent to corresponding authors of withheld datasets (28.6%) suggests that sharing should be regarded as a prerequisite for final paper acceptance, while making authors deposit their results in open online databases which provide data quality control seems to provide the best-practice standard. Finally, we estimated that 29.8% to 32.9% of total resources are used to generate withheld datasets, implying that an important portion of research funding does not produce shared knowledge. By making the scientific community and the public aware of this important aspect, we may help popularize a more effective culture of data sharing.     

Research funding, partnership and strategy - a UK perspective

In July 2004, the UK government published a 10-year science and innovation framework aimed at providing a more strategic, partnership-based approach to delivering science. With the aim of creating a UK society that is confident about the governance, regulation and use of science and technology, how can we sustain or increase the supply of money for research, how should funding agencies dispense money, and how can we optimize the partnership arrangements for the funding of research?