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.     

Effects of stratification in the analysis of affected-sib-pair data: benefits and costs

The benefits and costs of stratification of affected-sib-pair (ASP) data were examined in three situations: (1) when there is no difference in identity-by-descent (IBD) allele sharing between stratified and unstratified ASP data sets; (2) when there is an increase in IBD allele sharing in one of the stratified groups; and (3) when the data are stratified on the basis of IBD allele-sharing status at one locus, and the stratified ASPs are then analyzed for linkage at a second locus. When there is no difference in IBD sharing between strata, a penalty is always paid for stratifying the data. The loss of power to detect linkage in the stratified ASP data sets is the result of multiple testing and the smaller sample size within individual strata. In the case in which etiologic heterogeneity (i.e., severity of phenotype, age at onset) represents genetic heterogeneity, the power to detect linkage can be increased by stratifying the ASP data. This benefit is obtained when there is sufficient IBD allele sharing and sample sizes. Once linkage has been established for a given locus, data can be stratified on the basis of IBD status at this locus and can be tested for linkage at a second locus. When the relative risk is in the vicinity of 1, the power to detect linkage at the second locus is always greater for the unstratified ASP data set. Even for values of the relative risk that diverge sufficiently from 1, with adequate sample sizes and IBD allele sharing, the benefits of stratifying ASP data are minimal.     

Assessing and managing risk when sharing aggregate genetic variant data

Access to genetic data across studies is an important aspect of identifying new genetic associations through genome-wide association studies (GWASs). Meta-analysis across multiple GWASs with combined cohort sizes of tens of thousands of individuals often uncovers many more genome-wide associated loci than the original individual studies; this emphasizes the importance of tools and mechanisms for data sharing. However, even sharing summary-level data, such as allele frequencies, inherently carries some degree of privacy risk to study participants. Here we discuss mechanisms and resources for sharing data from GWASs, particularly focusing on approaches for assessing and quantifying the privacy risks to participants that result from the sharing of summary-level data.     

Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff

Background

Significant efforts are underway within the biomedical research community to encourage sharing and reuse of research data in order to enhance research reproducibility and enable scientific discovery. While some technological challenges do exist, many of the barriers to sharing and reuse are social in nature, arising from researchers’ concerns about and attitudes toward sharing their data. In addition, clinical and basic science researchers face their own unique sets of challenges to sharing data within their communities. This study investigates these differences in experiences with and perceptions about sharing data, as well as barriers to sharing among clinical and basic science researchers.

Methods

Clinical and basic science researchers in the Intramural Research Program at the National Institutes of Health were surveyed about their attitudes toward and experiences with sharing and reusing research data. Of 190 respondents to the survey, the 135 respondents who identified themselves as clinical or basic science researchers were included in this analysis. Odds ratio and Fisher’s exact tests were the primary methods to examine potential relationships between variables. Worst-case scenario sensitivity tests were conducted when necessary.

Results and Discussion

While most respondents considered data sharing and reuse important to their work, they generally rated their expertise as low. Sharing data directly with other researchers was common, but most respondents did not have experience with uploading data to a repository. A number of significant differences exist between the attitudes and practices of clinical and basic science researchers, including their motivations for sharing, their reasons for not sharing, and the amount of work required to prepare their data.

Conclusions

Even within the scope of biomedical research, addressing the unique concerns of diverse research communities is important to encouraging researchers to share and reuse data. Efforts at promoting data sharing and reuse should be aimed at solving not only technological problems, but also addressing researchers’ concerns about sharing their data. Given the varied practices of individual researchers and research communities, standardizing data practices like data citation and repository upload could make sharing and reuse easier.     

A human rights approach to an international code of conduct for genomic and clinical data sharing

Fostering data sharing is a scientific and ethical imperative. Health gains can be achieved more comprehensively and quickly by combining large, information-rich datasets from across conventionally siloed disciplines and geographic areas. While collaboration for data sharing is increasingly embraced by policymakers and the international biomedical community, we lack a common ethical and legal framework to connect regulators, funders, consortia, and research projects so as to facilitate genomic and clinical data linkage, global science collaboration, and responsible research conduct. Governance tools can be used to responsibly steer the sharing of data for proper stewardship of research discovery, genomics research resources, and their clinical applications. In this article, we propose that an international code of conduct be designed to enable global genomic and clinical data sharing for biomedical research. To give this proposed code universal application and accountability, however, we propose to position it within a human rights framework. This proposition is not without precedent: international treaties have long recognized that everyone has a right to the benefits of scientific progress and its applications, and a right to the protection of the moral and material interests resulting from scientific productions. It is time to apply these twin rights to internationally collaborative genomic and clinical data sharing.     

When Data Sharing Gets Close to 100%: What Human Paleogenetics Can Teach the Open Science Movement

This study analyzes data sharing regarding mitochondrial, Y chromosomal and autosomal polymorphisms in a total of 162 papers on ancient human DNA published between 1988 and 2013. The estimated sharing rate was not far from totality (97.6% ± 2.1%) and substantially higher than observed in other fields of genetic research (evolutionary, medical and forensic genetics). Both a questionnaire-based survey and the examination of Journals’ editorial policies suggest that this high sharing rate cannot be simply explained by the need to comply with stakeholders requests. Most data were made available through body text, but the use of primary databases increased in coincidence with the introduction of complete mitochondrial and next-generation sequencing methods. Our study highlights three important aspects. First, our results imply that researchers’ awareness of the importance of openness and transparency for scientific progress may complement stakeholders’ policies in achieving very high sharing rates. Second, widespread data sharing does not necessarily coincide with a prevalent use of practices which maximize data findability, accessibility, useability and preservation. A detailed look at the different ways in which data are released can be very useful to detect failures to adopt the best sharing modalities and understand how to correct them. Third and finally, the case of human paleogenetics tells us that a widespread awareness of the importance of Open Science may be important to build reliable scientific practices even in the presence of complex experimental challenges.     

Application of genome-wide SNP data for uncovering pairwise relationships and quantitative trait loci

The genetic analysis of quantitative traits in humans is changing as a result of the availability of whole-genome SNP data. Heritability analysis can make use of actual genetic sharing between pairs of individuals estimated from the genotype data, rather than the expected genetic sharing implied by their family relationship. This could provide more accurate heritability estimates and help to overcome the equal environment assumption. Quantitative trait locus (QTL) linkage mapping can make use of local genetic sharing inferred from very dense local genotype data from pedigree members or individuals not previously known to be related. This approach may be particularly suited for detecting loci that contain rare variants with major effect on the phenotype. Finally, whole-genome SNP data can be used to measure the genetic similarity between individuals to provide matched sets for association studies, in order to avoid spurious association from population stratification.     

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。     

Water,Water, Everywhere: Defining and Assessing Data Sharing in Academia

Sharing of research data has begun to gain traction in many areas of the sciences in the past few years because of changing expectations from the scientific community, funding agencies, and academic journals. National Science Foundation (NSF) requirements for a data management plan (DMP) went into effect in 2011, with the intent of facilitating the dissemination and sharing of research results. Many projects that were funded during 2011 and 2012 should now have implemented the elements of the data management plans required for their grant proposals. In this paper we define ‘data sharing’ and present a protocol for assessing whether data have been shared and how effective the sharing was. We then evaluate the data sharing practices of researchers funded by the NSF at Oregon State University in two ways: by attempting to discover project-level research data using the associated DMP as a starting point, and by examining data sharing associated with journal articles that acknowledge NSF support. Sharing at both the project level and the journal article level was not carried out in the majority of cases, and when sharing was accomplished, the shared data were often of questionable usability due to access, documentation, and formatting issues. We close the article by offering recommendations for how data producers, journal publishers, data repositories, and funding agencies can facilitate the process of sharing data in a meaningful way.     

Microarrays in brain research: the good, the bad and the ugly

Making sense of microarray data is a complex process, in which the interpretation of findings will depend on the overall experimental design and judgement of the investigator performing the analysis. As a result, differences in tissue harvesting, microarray types, sample labelling and data analysis procedures make post hoc sharing of microarray data a great challenge. To ensure rapid and meaningful data exchange, we need to create some order out of the existing chaos. In these ground-breaking microarray standardization and data sharing efforts, NIH agencies should take a leading role     

生物多样性数据论文发表趋势分析

近年来有关科学数据共享的呼声越来越高, 基于同行评审的生物多样性数据论文也受到越来越多的关注, 并出现了一些专门发表数据论文的数据期刊。本文总结了近年来生物多样性数据发表方面的进展, 选择两本代表性数据期刊(Biodiversity Data Journal和Scientific Data), 分析了它们自创刊以来的发文数量、涉及生物类群、文章浏览量和被引次数等指标。结果显示两本数据期刊的发文量都呈稳步增长趋势, 其生物多样性数据论文覆盖了包括动物界、植物界、真菌界在内的众多生物类群, 文章浏览量和被引次数方面也有可喜的表现, 说明数据论文正在被越来越多的研究者所接受。对文章作者国别的分析则显示了不同地区的研究者在发表生物多样性数据论文或数据共享方面的不均衡。建议相关领域的中国研究者和期刊关注生物多样性数据论文和数据共享政策, 更多地践行数据共享。     

Balancing data sharing requirements for analyses with data sensitivity

Data sensitivity can pose a formidable barrier to data sharing. Knowledge of species current distributions from data sharing is critical for the creation of watch lists and an early warning/rapid response system and for model generation for the spread of invasive species. We have created an on-line system to synthesize disparate datasets of non-native species locations that includes a mechanism to account for data sensitivity. Data contributors are able to mark their data as sensitive. This data is then ‘fuzzed’ in mapping applications and downloaded files to quarter-quadrangle grid cells, but the actual locations are available for analyses. We propose that this system overcomes the hurdles to data sharing posed by sensitive data.     

The role of production frequency in the sharing of simian immunodeficiency virus-specific CD8+ TCRs between macaques

In some epitope-specific responses, T cells bearing identical TCRs occur in many MHC-matched individuals. The sharing of public TCRs is unexpected, given the enormous potential diversity of the TCR repertoire. We have previously studied the sharing of TCR beta-chains in the CD8(+) T cell responses to two influenza epitopes in mice. Analysis of these TCRbeta repertoires suggests that, even with unbiased V(D)J recombination mechanisms, some TCRbetas can be produced more frequently than others, by a process of convergent recombination. The TCRbeta production frequency was shown to be a good predictor of the observed sharing of epitope-specific TCRbetas between mice. However, this study was limited to immune responses in an inbred population. In this study, we investigated TCRbeta sharing in CD8(+) T cell responses specific for the immunodominant Mamu-A*01-restricted Tat-SL8/TL8 and Gag-CM9 epitopes of SIV in rhesus macaques. Multiple data sets were used, comprising a total of approximately 6000 TCRbetas sampled from 20 macaques. We observed a spectrum in the number of macaques sharing epitope-specific TCRbetas in this outbred population. This spectrum of TCRbeta sharing was negatively correlated with the minimum number of nucleotide additions required to produce the sequences and strongly positively correlated with the number of observed nucleotide sequences encoding the amino acid sequences. We also found that TCRbeta sharing was correlated with the number of times, and the variety of different ways, the sequences were produced in silico via random gene recombination. Thus, convergent recombination is a major determinant of the extent of TCRbeta sharing.     

The user's view on biodiversity data sharing — Investigating facts of acceptance and requirements to realize a sustainable use of research data —

Data sharing has become an important issue in modern biodiversity research to address large scale questions. Despite the steadily growing scientific demand, data are not easily accessed. Why is this the case? This study explores the reasons for the reluctance to share data on the one hand and the motivations for sharing on the other by summarising results from > 60 interviews and > 700 survey participants within the biodiversity science community. As result, there is a clear commitment to share biodiversity data, but also a reluctance to actually do so due to a mixture of social and technical impediments, such as loss of control over data and lack of professional reward for sharing. This exploratory study summarises the formal and technical requirements for data sharing and reuse, stated by voluntarily participating scientists worldwide. To ensure sustainable data use, user friendly data infrastructure have to be expanded or newly designed, data management plans for all scientific investigations have to be promoted, training for the users has to be provided and motivational aspects at all stages of data submission and re-use have to be considered.     

To share or not to share: Incentivizing data sharing in life science communities

Most scientists recognize the importance of sharing data online in an open fashion. Nonetheless, many studies have documented the concerns that accompany data sharing activities, including loss of credit or IP, misuse and the time needed to curate interoperable data. To this end, discussions around data sharing often identify incentives that could potentially ameliorate these disincentivising concerns. Nonetheless, current Open Data discussions often rely on evidence‐based studies to identify the disincentives to overcome. This results in highly specific and directed interventions. In contrast, this paper offers a different interpretation of these concerns. To do so, it makes use of the Thomas Theorem which suggests that: “If men define situations as real, they are real in their consequences”. Using empirical evidence from sub‐Saharan African (bio)chemistry laboratories, this paper illustrates how individual perceptions of research environments – whether associated with evidence or not – are highly influential in shaping data sharing practices. It concludes with the suggestion that discussion on incentivising data sharing amongst scientific communities need to take a broader set of concerns into account and offer a more creative approach to ameliorating environmental disincentives.     

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.     

Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide

The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3–4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.     

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.     

A Game Theoretic Framework for Analyzing Re-Identification Risk

Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.     

Ethical principles for promoting health research data sharing with sub‐Saharan Africa

A powerful feature of global health research is data‐sharing with regions which bear the heaviest burden of disease. It offers novel opportunities for aggregating data to address critical global health challenges in ways higher than relying on individual studies. Yet there exist important stratifiers of the capacity to share data, particularly across the Global North‐South divide. Systemic challenges that characterize sub‐Saharan Africa and disadvantage the region's scientific productivity threaten the burgeoning data‐sharing culture too. Like all endeavors requiring equal commitments under unequal circumstances, a strong ethical impetus is needed to help reduce inequities and imbalances to encourage adherence. This article discusses mandatory data‐sharing in relation to peculiar challenges faced by sub‐Saharan African scientists to suggest ethical principles for rethinking and reframing solutions. We propose six principles which mirror guidelines from the Institute of Medicine and encapsulate principles from the Emanuel Framework, Nairobi Data Sharing Principles, and the COHRED guidelines.