Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

  • Laure Perrier ,

    Contributed equally to this work with: Laure Perrier, Erik Blondal, Heather MacDonald

    Roles Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft

    l.perrier@utoronto.ca

    Affiliation University of Toronto Libraries, University of Toronto, Toronto, Ontario, Canada

  • Erik Blondal ,

    Contributed equally to this work with: Laure Perrier, Erik Blondal, Heather MacDonald

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

    Affiliation Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada

  • Heather MacDonald

    Contributed equally to this work with: Laure Perrier, Erik Blondal, Heather MacDonald

    Roles Data curation, Formal analysis, Investigation, Writing – review & editing

    Affiliation MacOdrum Library, Carleton University, Ottawa, Ontario, Canada

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

  • Laure Perrier, 
  • Erik Blondal, 
  • Heather MacDonald
PLOS
x

Abstract

Background

Funding agencies and research journals are increasingly demanding that researchers share their data in public repositories. Despite these requirements, researchers still withhold data, refuse to share, and deposit data that lacks annotation. We conducted a meta-synthesis to examine the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data.

Methods

We searched the published and unpublished literature for studies on data sharing by researchers in academic institutions. Two independent reviewers screened citations and abstracts, then full-text articles. Data abstraction was performed independently by two investigators. The abstracted data was read and reread in order to generate codes. Key concepts were identified and thematic analysis was used for data synthesis.

Results

We reviewed 2005 records and included 45 studies along with 3 companion reports. The studies were published between 2003 and 2018 and most were conducted in North America (60%) or Europe (17%). The four major themes that emerged were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Researchers lack time, resources, and skills to effectively share their data in public repositories. Data quality is affected by this, along with subjective decisions around what is considered to be worth sharing. Deficits in infrastructure also impede the availability of research data. Incentives for sharing data are lacking.

Conclusion

Researchers lack skills to share data in a manner that is efficient and effective. Improved infrastructure support would allow them to make data available quickly and seamlessly. The lack of incentives for sharing research data with regards to academic appointment, promotion, recognition, and rewards need to be addressed.

Introduction

Research communities, including funding agencies and scholarly journals, have moved towards greater access to data through the development of policies that promote data sharing [14]. Examples include the development of data sharing requirements for clinical trials by the International Committee of Medical Journal Editors [5], the creation of a data repository for all researchers working towards a solution to the Zika virus so that all data is published as soon as it becomes available [6], and large funding bodies such as the Bill & Melinda Gates Foundation implementing strong open data policies [7].

These global developments require more researchers to share their data and make it available for reuse. Proponents for open data maintain that it offers the opportunity for others to freely reuse data, makes research more reproducible, uses public funds more effectively, and expands the potential to combine data sets for increased statistical power or creating new knowledge [8]. Sharing data is routine and embedded into the research process for some disciplines such as genomics and astronomy [910]. However, in many fields data produced by researchers has traditionally only been shared at the discretion of the principal investigator upon request, and otherwise kept in filing cabinets or on hard drives. These global shifts around research data have left some feeling uneasy and argue that those who generate the data own the data, certain studies (e.g., those with human subjects) require protection that may be difficult to assure with open data, and data sharing puts an increased administrative burden upon researchers [11]. There are also concerns of the inequity of a career built on data reuse versus the hard work of writing grants, being ‘scooped’, or being falsely discredited [1112].

Although funding agencies, institutions, and journals have implemented policies on data sharing and archiving, these practices have not produced the anticipated results. Researchers still withhold data [13], refuse to share data upon request [1415], publish without data availability statements [16], and fail to put their data into repositories [16] even after agreeing to share their data when publishing a journal article. Problems encountered when data is retrieved from repositories include inadequate annotation [17], limited structured data (Marwick), and incomplete specifications for data processing and analysis [17]. To gain insights on these behaviors, it is important to understand researchers’ perspectives. In this study, we report on researchers’ views and experiences on data sharing and reuse.

Aim

Our metasynthesis focuses on the individuals conducting research, and synthesizes the available qualitative literature that examines academic researchers and data sharing. This study addresses the question: what are the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data?

Materials and methods

A protocol was developed and is available upon request to the authors. Although the PRISMA statement has not been modified for meta-syntheses, it was used to guide the reporting of this review and can be viewed in S1 Appendix.

Types of studies

This is a metasynthesis of qualitative primary studies. Qualitative research seeks to discover how people perceive and experience the world around them [18]. Direct communication (e.g., interviews, focus groups) or observation are used to explore people’s perceptions. Data is explored using qualitiatve analytical methods and findings are then presented narratively using thick description rather than through numbers [19]. Thick description presents the findings as they were interpreted or explained by the authors as opposed to simply providing descriptive summaries of each study [20]. This provides the opportunity to translate the findings into a richer, more complete understanding of a phenomenon [21]. We included studies that reported qualitative methodologies and utilized qualitative methods for data analysis. Studies that collected data using qualitative methods but did not use qualitative analysis (e.g., surveys with open-ended questions that used descriptive statistics) were excluded. Mixed methods studies were included if it was possible to retrieve findings exclusively from the qualitative research.

Identification of studies.

The studies used for our meta-synthesis were derived from two sources. The first source was a dataset [22] generated from a scoping review on research data management in academic institutions [23] which provided records from inception to April 2016. The purpose of the scoping review was to describe the volume, topics, and methodological nature of the existing research literature on research data management as it specifically related to academic institutions. The search strategy included the terms data sharing, sharing research, data reuse, and research reuse, along with spelling variations and wildcards to ensure all relevant records were captured. The second source for data came from re-running the literature searches from the scoping review with the addition of a validated qualitative search filter [24] in order to retrieve current records from April 2016 to October 2018. When the searches were conducted for the update, four of the original literature databases were unavailable and were replaced with comparable platforms upon consultation with subject matter specialists. Both the original and the updated search included a total of 40 literature databases representing a wide range of disciplines. The search strategy for MEDLINE can be found in S2 Appendix and the full search strategies for the other databases can be obtained by contacting the author. S3 Appendix lists all the literature databases searched. As well, the grey literature, conference proceedings, and a search of the reference list of all included studies were completed. No restrictions were placed on publication date or language. Thus, a comprehensive search of the literature was conducted.

Eligibility criteria.

We aimed to identify all studies that investigated the views, perspectives, and experiences of academic researchers with data sharing and reuse. Studies were included if they were original research and reported qualitative methodologies, specifically focus groups or interviews. Studies had to include researchers (full- or part-time) conducting studies in academic institutions. We defined an academic institution as a higher education degree-granting organization dedicated to education and research. If studies included a mixed population, 50% or more of the total sample had to be researchers from academic institutions in order to be eligible for inclusion. Data sharing is defined as the practice of making data available for reuse [25]; reuse is defined as the use of content outside of its original intention [25]. Examples of this include depositing data into a digital repository or publishing raw data. Mixed methods studies that used both qualitative and quantitative methods within the same study were eligible if the qualitative portion met our inclusion criteria.

Study selection.

Two investigators independently screened all records from the scoping review dataset in order to identify qualitative studies that met the eligibility criteria. The records from the updated search were assessed for eligibility by two investigators independently at level 1 (title and abstract) and level 2 (full-text) screening. Discrepancies were resolved by discussion or by a third investigator at every phase of study selection.

Quality appraisal.

The CASP (Critical Appraisal Skills Programme) Qualitative Checklist [26] was used for quality appraisal of all included studies. The CASP Qualitative Checklist is a 10-item checklist that examines three domains: validity of results, reporting of the results, and value of the research (S4 Appendix). Each study was assessed independently by two investigators and discrepancies were resolved by discussion.

Data abstraction and analysis.

The authors read and reread all articles to become familiar with each study [2728]. A data abstraction sheet was developed that included the study characteristics (e.g., year study conducted), participant characteristics (e.g., sample size), and key concepts. Key concepts or interpretations included all findings along with associated quotes from study participants [29]. Thematic analysis using constant comparison was used for data synthesis [30]. An initial set of 10 studies were coded independently by two investigators. These codes were then compared and refined in order to create an initial set of codes that were used going forward. Two investigators continued to independently code the remainder of the studies in duplicate and the team met regularly to discuss and iteratively refine the codes. All discrepancies were resolved through discussion or by a third member of the team. Finally, analytical themes were generated that offered a higher level interpretation beyond a descriptive synthesis [27,31]. The codes were grouped and categorized by making comparisons across articles in order to ensure that we appropriately captured similar themes from multiple studies. Meetings were used to review all constructs and resolve discrepancies, resulting in a refinement of the analytical themes.

Results

Forty-five studies and three companion reports were included in the review (Fig 1). Included studies are listed in S5 Appendix.

Study characteristics

The studies included in the review were published during a 15 year period between 2003 and 2018. The most studies were published in 2014 (11 out of 45) and the method for data collection were interviews (37), a combination of interviews/focus groups (5), and focus groups (4). Over half of the studies (27 out of 45) were conducted in the United States. Table 1 provides a summary of study characteristics.

Findings

We identified four major themes and several sub-themes. The four major themes were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Themes and sub-themes along with illustrative quotes are summarized in Table 2. Each theme is described below and details are provided for each sub-theme.

Data integrity.

The theme data integrity addresses researcher’s perspectives on data that are available from repositories and their expectations around the prospects of reusing this data based on the quality, documentation available, and what individual researchers deemed as worthy of sharing.

  1. Data quality: Researchers acknowledged that although there may be interest or willingness to consider using open data, there would always be people that would not trust the quality of a dataset unless they collected it themselves. They recognized the need to manage expectations with some suggesting that lowering their standards related to data quality may help with increasing the likelihood of being able to use another researcher’s dataset. Nuances around the conditions, context, or materials that are not normally recorded were identified as one of the challenges to data quality. One example was offered in the field of engineering where equipment may not be anchored solidly and produce a variation that would impact the quality of data outputs [42]. The range of skill levels of researchers (e.g., junior versus senior researchers) was flagged as potentially affecting the quality of data collected and it was noted that there was no way of knowing this when reusing a dataset. Similarly, the quality of datasets may also vary depending on the person’s intentions or purpose when collecting data. It was felt that data collected with the intention of reporting only to people within their own discipline may look different from data collected for external groups, (i.e., these datasets may include more details).
  2. Data documentation: For a dataset to be truly reusable, researchers indicated adequate documentation was necessary including a significant amount of detail and metadata. The importance of contextual information was noted as providing layers of information that offered necessary insight. Providing this was seen as a time- and energy-intensive endeavor for the researcher collecting the data and making it available for reuse. Comprehensive documentation signaled a reliable dataset to researchers that were looking for datasets to reuse.
  3. What is worth sharing: When considering their data, researchers varied on what was worth sharing. It spanned from believing the preservation of datasets to be a top priority, to complete lack of interest. For those that felt their data was not worth sharing, they believed their data would be irrelevant after a period of time and nothing more than a “historical curiosity” if it was offered for reuse [56]. Others thought their data had the potential to be useful but had clear views on what was worth sharing and felt it had to have ‘scholarly value’. As an example, researchers described biospecimens they had collected as valuable since it provided the opportunity to look at the development of a disease [64].
  4. Responsible conduct of research. The responsible conduct of research emerged as a theme that encompassed the professional standards, ethical principles, and tacit norms that researchers described when considering data sharing. The five sub-themes under this theme are the misuse of data, protecting one’s own work/intellectual property, privacy/confidentiality/ethics, control of data, and work culture.
  5. Misuse of data: Researchers expressed concern about the potential for the inappropriate use of their data. This included what was termed as ‘fishing expeditions’ which involved dredging data with no particular research question in the hopes of stumbling upon possible relationships that could be presented as convincing results. The potential for data to be misunderstood and thus produce inappropriate or misguided conclusions was also considered a possibility, even if researchers reused datasets with a focused research question. The people reusing data were given names such as ‘free riders’ [42] and it was believed that misunderstood data could lead to false conclusions and ultimately threaten the original work related to the data.
  6. Protecting one’s own work/intellectual property: Clarity around who owns data, along with intellectual property rights, were raised as issues when sharing data. Collaborations were mentioned as making it difficult to determine ownership since multiple people and institutions were involved. Licensing data was seen as both a potential solution, as well as a potential barrier (e.g., the cost could mean it would not be accessible to all) for providing access to research data. Since data had publication value, this was considered a major deterrent to sharing data. This obvious connection between publications and data made researchers feel that data needed to be protected. Publications were seen as a key research output with a relationship between this and future funding.
  7. Privacy/confidentiality/ethics: Privacy and confidentiality were taken seriously when researchers considered their data, particularly when it came to human subjects. When data was shared between institutions, the precautions undertaken were complex. This included considerations such as de-identification, re-identification risks (along with the potential for this to happen unintentionally), consent (e.g., whether re-consent was necessary), and the challenge of future use if the purpose for future use was not pre-defined. Precautions implemented included each institution independently obtaining ethics approval before data was exchanged. Views were divergent around whether data should be freely accessible with some favoring restrictions on the re-use of data and others indicating it should be freely accessible.
  8. Control of data: It was acknowledged that the relationship between research being publicly funded and making data available for public benefit had merit. However, some felt they would like to know who was using their data and for what purpose. It was considered important to have a relationship with the person who wished to reuse data. As well, access should be controlled and only given to those that could be identified as a research professional who were qualified to do research. The level of control ranged from wanting systems in place that would allow them to monitor data sharing with people who were not known to them personally (although this was acknowledged as labor-intensive), to simply wanting to have a list of who was using their data and for what purpose as a minimum level of communication. The need to protect data until publication was considered a deterrent to sharing data and it was necessary to have control over data until this was completed.
  9. Work culture: Work culture highlights the beliefs of how research should be conducted that are influenced by shared attitudes, views, and written/unwritten rules developed over time. These normative values were described as being taught to junior researchers by senior academics. In the past, the cultural norm was to rely on informal processes, such as personal relationships, for sharing data. Usually, these were people who were known and trusted either through direct contact or by reputation. As new requirements were introduced by funding agencies and journals, researchers observed changes in their practice of sharing data over time. There was an acknowledgement that a shift in culture that favored a more open view of data was needed. It was also noted that even if researchers were to understand the benefits of sharing data, this transition would not be immediate and that incentives must be identified for researchers in this process.
  10. Feasibility of sharing data: The feasibility of sharing data examines the ease with which researchers can make their data available to others, along with the related barriers and facilitators. Infrastructure, time/work required, and skills are the three sub-themes that emerged and are described below.
  11. Infrastructure: Researchers described the structures and supports that would help ensure data sharing. Infrastructure support included data storage, file migration, and funding for making data available. These challenges involved both local (e.g., individual research labs) and institution-wide settings. Data handling was a fragmented activity managed by researchers who devised their own independent strategies that generally lacked sustainability. One example was the number of people (i.e., students, staff) that rotated through a research lab where everyone was responsible for their own data [62]. Although the lab manager encouraged best practices (e.g., including sufficient documentation), this did not necessarily translate into being adopted and applied. As a result, it was not guaranteed that datasets could be easily shared or be accessible in the future. Appropriate data storage infrastructure and support were associated with good data management, which in turn laid the groundwork for data sharing. An institution-level policy to support data sharing, along with resources, were identified as important to ensure good quality data being deposited and made available.
  12. Time/work required: The effort to prepare data for sharing was seen as time-consuming, expensive, and labor-intensive. Barriers included the lack of time to organize the necessary documentation, challenges with repository interfaces, and the lack of resources. For those that chose to offer their data upon request, the administrative aspect of filling requests for data was considered an added burden.
  13. Skills: For many disciplines, data sharing was a new activity that was typically imposed by funding agencies or journals. As a result, researchers were looking for services or resources that would help with this task. The lack of technical skills and knowledge included how to anonymize data, how to create metadata, and unfamiliarity with depositing data into repositories. It was felt that providing open access to data was complex. Providing adequate support may not be feasible given that each discipline had a variety of data types, different amount of data being generated, disparities in what is considered data, and varying norms in research culture.
  14. Value of sharing data: The value of sharing data theme describes researchers’ views on the importance placed on making data available to others. While the sub-theme promote future discovery identifies a benefit to society with sharing data, researchers’ perspectives focused on the benefits to researchers themselves.
  15. Promote future discovery: The importance of making data accessible for possible use in the future was understood as a benefit. Those that described proactively sharing data (before they were required to) also noted the importance of sharing computer code as well. There was recognition that research funded by public money should be open and available. It was felt that taxpayers provided an investment and the public deserved a return on their investment. In some instances, researchers were able to identify examples of data sharing that helped promote scientific progress, such as the development of a new drug or containment of a disease. It was felt that data sharing had the potential to move a field forward by closing knowledge gaps and further opening new avenues of investigation.
  16. Researchers’ perspectives: Data was identified as a research product that helped achieve a goal such as completing a publication and there was the recognition that amongst researchers that data sharing would provide greater accountability and transparency. For those that were already reusing data, its value was recognized as helpful for writing proposals and training students. The importance of providing incentives for sharing data was emphasized with researchers unable to identify significant benefits. Suggestions included creating grants that focused specifically on the reuse of data generated from earlier grants.

Quality assessment.

The CASP tool, used to assess the quality of studies, identified 27 (out of 45) studies that had seven out of the ten items present. Most studies adequately addressed the methods (41 out of 45 studies) and aims (40 out of 45 studies) (Fig 2). Author reflexivity, which asked if the relationship between the researcher and participants was adequately considered, was not apparent in any of the studies. No studies were excluded due to a low score as this may have eliminated those with relevant and insightful results [7778].

Discussion

We conducted a comprehensive review that included 45 studies along with 3 companion reports on the views, perspectives, and experiences of academic researchers on sharing their research data. The National Institutes of Health (NIH) in the United States were one of the first funding agencies to introduce a policy on sharing research data in 2001 [4]. This aligns with beginning to see research published on this topic starting in 2003, along with over half of the studies being conducted in the United States. Our results show that some of the themes and sub-themes offer positive support for sharing data however, most highlight areas of discomfort for researchers. In particular, researchers identified concern with issues related to data quality, misuse of data, protecting data, lack of time and skills, and deficiencies in infrastructure and support.

By default, researchers believed the quality of datasets available for reuse were poor and there is support for this in the literature. Studies assessing data available in public repositories have found incomplete datasets, saved in a way that compromised reuse [7980]. Researchers who felt their data had value describe using a tacit set of criteria to determine if it had ‘scholarly value’ [38]. These criteria are based on discretion and would vary from person to person thus adding further to factors that affect the quality of datasets. For researchers who felt their data was not worth sharing, this may be reflected in how they prepare their data for depositing into repositories (e.g., providing poor documentation) and ultimately its final quality.

The lack of supportive infrastructure, lack of time, and skills deficit had an influence on data quality as well as data availability. Researchers indicated that the lack of time and skills impacted the production of sufficient data documentation, creation of suitable metadata, and appropriately anonymized data. They also lacked skills in navigating repository interfaces in order to deposit data. While training and education may address these issues [81], a more effective pathway is to focus energy and resources on creating user-friendly interfaces that allow users to accomplish their goal of depositing datasets as quickly and easily as possible [82]. At an institutional level, the lack of procedures, policies, and guidance contributed to challenges in sharing data. This was particularly true for sensitive data that requires more vetting and scrutiny before sharing. Solutions for this include using a trusted party regulated by an ethics board that manages requests and maintains the de-identified records and original identifiers [55].

Our results show that a major concern of researchers is the possibility of misuse or misinterpretation of their data, and this is reported as well in surveys [69,8384]. Traditionally, research data has been shared through professional networks and by personal request [32,36,38]. These ‘traditions’ were incorporated into research processes as early-career researchers were indoctrinated by mentors and senior researchers [52]. This approach allowed those who owned datasets to scrutinize requests and all aspects of the requestor, including the reputation of their institution, their publications, and any other factors they felt important. Data producers had a hand in assuring their work and intellectual property were protected, privacy and confidentiality were safe, and it allowed them to exercise caution if there were any concerns around the misuse of their data, including the option to decline the request to share. Currently, funding agencies and journals are moving researchers in the direction of sharing data which is not embraced by all members of the research community [12,40,69,8384]. In a recent paper, Campbell and colleagues [85] identified senior researchers as less likely to support data sharing while their early-career colleagues were more willing to make their data available for reuse. Researchers describe shifting to a culture of open data as a gradual transition in our findings, and stage of career may contribute to this need for a gradual shift.

Incentives were also identified as necessary for researchers within the research process that promoted open data [47,51,86]. In 2016, more than 500 researchers that received grants from the Wellcome Trust (welcome.ac.uk) in the United Kingdom were surveyed and although over half indicated that they made their data available for reuse, few reported direct benefits [69]. The lack of benefits appeared in our results and were identified as necessary yet lacking in the realm of data sharing. Suggestions for incentives included offering research grants that focused specifically on the reuse of data generated from earlier grants [51], and creating systems that ensure credit is awarded to data generators [8790]. In one example, Pierce and colleagues [88] proposed creating enduring links between those who generate data and any time it was used in the future. This would involve linking persistent identifier (PIDs) to all datasets and provide infrastructure to link the identifiers to publications. In this strategy, data authorship would be listed on curriculum vitae, considered in academic institutions promotions criteria, and be considered by granting agencies as an element for review for funding.

There is a global movement towards openness in research that includes open data. Data sharing and reuse is a key part of this movement and anticipated benefits include promoting research transparency, verification of findings, and gaining new insights from re-analysis [8]. Despite this, it has not become a common practice [1316]. Investing in strategies that improve skills amongst researchers that focus on improving data integrity in repositories and identifying incentives that provide motivation for data sharing are essential.

Limitations

Quality assessment indicated that some items in the CASP tool were addressed poorly in the studies. This included author reflexivity, analysis, and ethical issues. Limitations set by journals (i.e., word counts) may restrict authors from providing rich data and thick descriptions which are characteristic of qualitative studies. Studies based on low reporting quality were not excluded as this may have eliminated those with highly relevant and insightful results [77] and were used to judge the relative contribution in developing explanations in the study findings.

The qualitative data collected for this review originates from multiple disciplines and each may use a variety of data collection methods and research processes. However, when examining the studies by discipline, over a third of the studies in our review are listed as ‘combined’ (37% or 17 out of 45) [91] (i.e., participants came from multiple disciplines) yet none of the authors reported this as an issue in their analysis or impacting their results (S6 Appendix). Similarly, As well, one of the study authors (LP) conducted focus groups with academic researchers in the area of research data management (including data sharing) and found that data saturation was reached after conducting four focus groups despite collecting data from discrete disciplines (i.e., health science, humanities, natural science) [81]. While diverse tools and methods may be employed by researchers in distinct disciplines to conduct their studies, issues related to research data management were identified as a commonality [32,38,39,4445, 47,5254,56,58,5960,65,7071,75,81]

Most of the studies accepted into our review are interviews (82% or 37 out of 45 studies). While the group setting of a focus group may prompt ideas and memories from group members by listening to other participants [92], interviews provide the opportunity to go deeper into a topic and gather in-depth information [93]. When Guest and colleagues [93] performed a randomized controlled trial comparing focus groups and interviews, they found that individual interviews were more effective at generating a broad range of items at an individual level [93].

Conclusions

Misuse and misinterpretation of data is a significant concern amongst researchers when sharing their data. Preparation of data so that it is truly reusable requires an investment in time and resources as well as skills that researchers indicate they lacked. Deficiencies in infrastructure may hamper sharing data effectively, particularly sensitive data. The availability of data is marked by researchers’ decision making around what they determine is worth sharing. Currently, there is a lack of incentives for researchers to share their data with regards to academic appointment, promotion, recognition, and rewards. As such, enhancements need to be considered that focus on providing direct benefits to researchers who share their data. Identifying appropriate incentives may help improve motivation to share data and enhance the integrity of data put into repositories.

Supporting information

S4 Appendix. CASP (critical appraisal skills programme): Qualitative checklist.

https://doi.org/10.1371/journal.pone.0229182.s004

(DOCX)

Acknowledgments

We thank Jordan Raghunandan for collating information after the data was coded.

References

  1. 1. Holdren JP. Increasing access to the results of federally funded scientific research. February 22, 2013. Office of Science and Technology Policy. Executive Office of the President. United States of America. Available at: https://obamawhitehouse.archives.gov/blog/2016/02/22/increasing-access-results-federally-funded-science Accessed October 11, 2019.
  2. 2. OECD (Organization for Economic Co-Operation and Development). Declaration on access to research data from public funding. 2004. Available at: https://legalinstruments.oecd.org/en/instruments/157 Accessed October 11, 2019.
  3. 3. DCC (Digital Curation Centre). Overview of funders’ data policies. Available at: http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies Accessed October 11, 2019.
  4. 4. Shearer K. Comprehensive Brief on Research Data Management Policies. April 2015. Available at: http://web.archive.org/web/20151001135755/http://science.gc.ca/default.asp?lang=En&n=1E116DB8-1 Accessed October 11, 2019.
  5. 5. Taichman DB, Sahni P, Pinborg A, Peiperl L, Laine C, James A, et al. Data sharing statements for clinical trials: a requirement of the International Committee of Medical Journal Editors. Lancet. 2017 Jun 10;389(10086):e12–e14. pmid:28596041
  6. 6. Butler D. Zika researchers release real-time data on viral infection study in monkeys. Nature. 2016; 530:5. pmid:26842018
  7. 7. Bill & Melinda Gates Foundation. Open Access Policy. Available at: https://www.gatesfoundation.org/How-We-Work/General-Information/Open-Access-Policy Accessed October 11, 2019.
  8. 8. PolicyWise for Children and Families. The current state of data sharing. July 2016. Available at: https://policywise.com/wp-content/uploads/SAGE/Current-State-of-Data-Sharing-Funders-2016-06JUN-30.pdf Accessed October 11, 2019.
  9. 9. Hayes J. The data-sharing policy of the World Meteorological Organization: The case for international sharing of scientific data. In: Mathae KB, Uhlir PF, editors. Committee on the Case of International Sharing of Scientific Data: A Focus on Developing Countries. National Academies Press; 2012. p. 29–31.
  10. 10. Ivezic Z. Data sharing in astronomy. In: Mathae KB, Uhlir PF, editors. Committee on the Case of International Sharing of Scientific Data: A Focus on Developing Countries. National Academies Press; 2012. p. 41–45.
  11. 11. Dijkers MP. A beginner's guide to data stewardship and data sharing. Spinal Cord. 2019 Mar;57(3):169–182. pmid:30723254
  12. 12. Longo DL, Drazen JM. Data sharing. New England Journal of Medicine. 2016; 374:276. pmid:26789876
  13. 13. Naudet F, Sakarovitch C, Janiaud P, Cristea I, Fanelli D, Moher D, et al. Data sharing and reanalysis of randomized controlled trials in leading biomedical journals with a full data sharing policy: survey of studies published in The BMJ and PLOS Medicine. BMJ. 2018 Feb 13;360:k400. pmid:29440066
  14. 14. Savage CJ, Vickers AJ. Empirical study of data sharing by authors publishing in PLoS journals. PLoS One. 2009 Sep 18;4(9):e7078. pmid:19763261
  15. 15. Vanpaemel W, Vermorgen M, Deriemaecker L, Storms G. Are we wasting a good crisis? The availability of psychological research data after the storm. Collabra. 2015;1(1):1–5.
  16. 16. Federer LM, Belter CW, Joubert DJ, Livinski A, Lu YL, Snyders LN, et al. Data sharing in PLOS ONE: An analysis of Data Availability Statements. PLoS One. 2018 May 2;13(5):e0194768. pmid:29719004
  17. 17. Ioannidis JP, Allison DB, Ball CA, Coulibaly I, Cui X, Culhane AC, et al. Repeatability of published microarray gene expression analyses. Nat Genet. 2009 Feb;41(2):149–55. pmid:19174838
  18. 18. Sandelowski M, Barroso J. Classifying the findings in qualitative studies. Qual Health Res. 2003 Sep;13(7):905–23. pmid:14502957
  19. 19. Glenton C, Lewin S. Using evidence from qualitative research to develop WHO guidelines. WHO Handbook for Guideline Development. 2nd Edition. Geneva: WHO, 2014.
  20. 20. Sandelowski M, Barroso J. Reading qualitative studies. International Journal of Qualitative Methods. 2002;1(1):74–108.
  21. 21. Sherwood G. Meta-synthesis: merging qualitative studies to develop nursing knowledge. International Journal for Human Caring. 1999;3:37–42.
  22. 22. Perrier L, Blondal E, Ayala AP, Dearborn D, Kenny T, Lightfoot D, et al. Dataset for: Research data management in academic institutions: a scoping review. 2017a.
  23. 23. Perrier L, Blondal E, Ayala AP, Dearborn D, Kenny T, Lightfoot D, et al. Research data management in academic institutions: A scoping review. PLoS One. 2017b;12(5): e0178261.
  24. 24. McMaster University. McMaster PLUS Projects: Hedges. Available at: https://hiru.mcmaster.ca/hiru/hiru_hedges_home.aspx Accessed October 11, 2019.
  25. 25. CASRAI. Dictionary. Available at: http://dictionary.casrai.org Accessed October 11, 2019.
  26. 26. CASP. CASP Qualitative Checklist. Available at: https://casp-uk.net/wp-content/uploads/2018/01/CASP-Qualitative-Checklist-2018.pdf Accessed June 13, 2019.
  27. 27. Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol (2008) 8:45. pmid:18616818
  28. 28. Paterson BL, Thorne SE, Canam C, Jillings C. Meta-Study of Qualitative Health Research: A Practical Guide to Meta-Analysis and Metasynthesis. 2001, London: Sage.
  29. 29. Schütz A. Collected papers 1. The Hague, Netherlands: Martinus Nijhoff; 1962.
  30. 30. Miles MB, Huberman AM, Saldaña J. Qualitative Data Analysis: A Methods Sourcebook. Thousand Oaks, CA: SAGE, 2014.
  31. 31. Lachal J, Revah-Levy A, Orri M, Moro MR. Metasynthesis: an original method to synthesize qualitative literature in psychiatry. Frontiers in Psychiatry. 2017; 8:269 pmid:29249996
  32. 32. Allard S, Aydinoglu AU. Environmental researchers' data practices: An exploratory study in Turkey. International Symposium on Information Management in a Changing World. Springer Berlin Heidelberg. 2012; 317:13–24.
  33. 33. Bamkin M. Report of Findings from Focus Group and Online Questionnaire: The opinions of potential users of a policy databank service. JORD Project. 2014. Available at: https://jordproject.files.wordpress.com/2014/06/report-of-findings-from-focus-group-and-online-questionnaire.pdf Accessed June 19, 2019.
  34. 34. Broom A, Cheshire L, Emmison M. Qualitative researchers' understandings of their practice and the implications for data archiving and sharing. Sociology. 2009; 43(6):1163–1180.
  35. 35. Carlson J, Stowell-Bracke M. Data management and sharing from the perspective of graduate students: an examination of the culture and practice at the water quality field station. portal: Libraries and the Academy. 2013; 13(4):343–361.
  36. 36. Cheah PY, Tangseefa D, Somsaman A, Chunsuttiwat T, Nosten F, Day NPJ, et al. Perceived benefits, harms, and views about how to share data responsibly: a qualitative study of experiences with and attitudes toward data sharing among research staff and community representatives in Thailand. Journal of Empirical Research on Human Research Ethics. 2015; 10(3):278–289. pmid:26297749
  37. 37. Colledge F, Persson K, Elger B, Shaw D. Sample and data sharing barriers in biobanking: consent, committees, and compromises. Annals of Diagnostic Pathology. 2014; 18(2):78–81. pmid:24485935
  38. 38. Cragin MH, Palmer CL, Carlson JR, Witt M. Data sharing, small science and institutional repositories. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences. 2010; 368(1926):4023–4038. pmid:20679120
  39. 39. Delasalle J. Research data management at the University of Warwick: recent steps towards a joined-up approach at a UK university. LIBREAS. Library Ideas. 2013; 23. Available at: http://libreas.eu/ausgabe23/10delasalle Accessed June 19, 2019.
  40. 40. Denny SG, Silaigwana B, Wassenaar D, Bull S, Parker M. Developing ethical practices for public health research data sharing in South Africa: the views and experiences from a diverse sample of research stakeholders. Journal of Empirical Research on Human Research Ethics. 2015; 10(3):290–301. pmid:26297750
  41. 41. Diekemann AR, Wesolek A, Walters CD. The NSF/NIH effect: surveying the effect of data management requirements on faculty, sponsored programs, and institutional repositories The Journal of Academic Librarianship. 2014; 40:322–331.
  42. 42. Faniel IM, Jacobsen TE. Reusing scientific data: how earthquake engineering researchers assess the reusability of colleagues data. Computer Supported Cooperative Work. 2010; 19:355–375.
  43. 43. Faniel I, Kansa E, Kansa SW, Barrera-Gomez J, Yakel E. The challenges of digging data: A study of context in archaeological data reuse. Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries. 2013; 295–304.
  44. 44. Finn R, Wadhwa K, Taylor M, Sveinsdottir T, Noorman M, Sondervan J. Legal and ethical issues in open access and data dissemination and preservation. Recode Project. 2014. Available at: https://zenodo.org/record/1297492#.W3MvaM5KhhE Accessed June 19, 2019.
  45. 45. Frank RD, Yakel E, Faniel IM. Destruction/reconstruction: preservation of archaeological and zoological research data. Archival Science. 2015; 15(2):141–167.
  46. 46. Hall N. Environmental studies faculty attitudes towards sharing of research data. Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries. 2013; 383–384.
  47. 47. Henty M, Weaver B, Bradbury SJ, Porter S. Investigating Data Management Practices in Australian Universities. 2008. Available at: http://eprints.qut.edu.au/14549 Accessed June 19, 2019.
  48. 48. Higman R, Pinfield S. Research data management and openness: the role of data sharing in developing institutional policies and practices. Program: Electronic Library and Information Systems. 2015; 49(4):364–381.
  49. 49. Hunt SL, Bakker CJ. A qualitative analysis of the information science needs of public health researchers in an academic setting. Journal of the Medical Library Association: JMLA. 2018 Apr;106(2):184. pmid:29632441
  50. 50. Johnston L, Jeffryes J. Data management skills needed by structural engineering students: case study at the University of Minnesota. Journal of Professional Issues in Engineering Education and Practice. 2014; 140(2):05013002.
  51. 51. Johri A, Yang S, Vorvoreanu M, Madhavan K. Perceptions and practices of data sharing in engineering education. Advances in Engineering Education. 2016;5(2):n2.
  52. 52. Kervin K, Finholt T, Hedstrom M. Macro and micro pressures in data sharing. IEEE 13th International Conference. 2012; 525–32.
  53. 53. Kim Y, Stanton JM. Institutional and Individual Influences on Scientists’ Data Sharing Practices. Journal of Computational Science Education. 2012; 3(1):47–56.
  54. 54. Lage K, Losoff B, Maness J. Receptivity to library involvement in scientific data curation: a case study at the University of Colorado Boulder. portal: Libraries and the Academy. 2011; 11(4):915–937.
  55. 55. Manion FJ, Robbins RJ, Weems WA, Crowley RS. Security and privacy requirements for a multi-institutional cancer research data grid: an interview-based study. BMC Medical Informatics and Decision Making. 2009; 9:31. pmid:19527521
  56. 56. Marcus C, Ball S, Delserone L, Hribar A, Loftus W. Understanding Research Behaviors, Information Resources, and Service Needs of Scientists and Graduate Students: A Study by the University of Minnesota Libraries. 2007. Available at: https://conservancy.umn.edu/handle/11299/5546 Accessed June 19, 2019.
  57. 57. McGuire AL, Achenbaum LS, Whitney SN, Slashinski MJ, Versalovic J, Keitel WA, et al. Perspectives on human microbiome research ethics. Journal of Empirical Research on Human Research Ethics. 2012; 7(3):1–14. pmid:22850139
  58. 58. McLure M, Level AV, Cranston CL, Oehlerts B, Culbertson M. Data curation: a study of researcher practices and needs. portal: Libraries and the Academy. 2014; 14(2):139–164.
  59. 59. Murillo AP. Data at risk initiative: examining and facilitating the scientific process in relation to endangered data. Data Science Journal. 2014; 12:207–219.
  60. 60. Noorman M, Kalaitzi V, Angelaki M, Tsoukala V, Linde P, Sveinsdottir T, et al. Institutional barriers and good practice solutions. 2014. Available at: https://zenodo.org/record/1297494#.W3MwWc5KhhE Accessed June 19, 2019.
  61. 61. Ochs M, Andrews C, Downs A, Morris-Knower J, Young S. Research practices and support needs of scholars in the field of agriculture at Cornell University. Journal of Agricultural & Food Information. 2017 Jul 3;18(3–4):200–19.
  62. 62. Oleksik G, Milic-Frayling N, Jones R. Beyond data sharing: Artifact ecology of a collaborative nanophotonics research centre. Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. 2012; 1165–1174.
  63. 63. Pepe A, Goodman A, Muench A, Crosas M, Erdmann C. How do astronomers share data? Reliability and persistence of datasets linked in AAS publications and a qualitative study of data practices among US astronomers. PloS One. 2014; 9(8):e104798. pmid:25165807
  64. 64. Read KB, Surkis A, Larson C, McCrillis A, Graff A, Nicholson J, et al. Starting the data conversation: informing data services at an academic health sciences library. Journal of the Medical Library Association. 2015; 103(3):131–5. pmid:26213504
  65. 65. Stamatolos A, Neville T, Henry D. Analyzing the Data Management Environment in a Master's-level Institution. Journal of Academic Librarianship. 2016; 42(2):154–160.
  66. 66. Stapleton S, Minson V, Spears L. Investigating the research practices of agriculture scholars: findings from the University of Florida. Journal of Agricultural & Food Information. 2017 Jul 3;18(3–4):327–46.
  67. 67. Sturges P, Bamkin M, Anders JHS, Hubbard B, Hussain A, Heeley M. Research Data Sharing: Developing a Stakeholder-Driven Model for Journal Policies. Journal of the Association for Information Science and Technology. 2014; 66(12):2445–2455.
  68. 68. Valentino M, Boock M. Data Management for Graduate Students: A Case Study at Oregon State University. Practical Academic Librarianship. 2015; 5(2):77–91.
  69. 69. Van den Eynden V, Knight G, Vlad A, Radler B, Tenopir C, Leon D, et al. Towards Open Research: practices, experiences, barriers and opportunities. Wellcome Trust. 2016. Available at: https://doi.org/10.6084/m9.figshare.4055448 Accessed October 11, 2019.
  70. 70. Van Tuyl S, Michalek G. Assessing Research Data Management Practices of Faculty at Carnegie Mellon University. Journal of Librarianship and Scholarly Communication. 2015; 3(3):eP1258.
  71. 71. Wallis JC, Rolando E, Borgman CL. If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology. PLoS One. 2013; 8(7):e67332. pmid:23935830
  72. 72. Williams SC. Data sharing interviews with crop sciences faculty: why they share data and how the library can help. Issues in Science and Technology Librarianship. 2013. Available at: http://www.istl.org/13-spring/refereed2.html Accessed June 19, 2019.
  73. 73. Yatcilla JK, Bracke MS. Investigating the needs of agriculture scholars: the Purdue Case. Journal of Agricultural & Food Information. 2017 Jul 3;18(3–4):293–305.
  74. 74. Yoon A. End users’ trust in data repositories: definition and influences on trust development. Archival Science. 2014; 14(1):17–35.
  75. 75. Yoon A. Data reusers' trust development. Journal of the Association for Information Science and Technology. 2017 Apr;68(4):946–56.
  76. 76. Zimmerman AS. New knowledge from old data: the role of standards in the sharing and reuse of ecological data. 2008 Sep; 33(5):631–652.
  77. 77. Campbell R, Pound P, Morgan M, Daker-White G, Britten N, Pill R, et al. Evaluating meta ethnography: systematic analysis and synthesis of qualitative research. Health Technology Assessment. 2011;15(43):1–164 pmid:22176717
  78. 78. Atkins S, Lewin S, Smith H, Engel M, Fretheim A, Volmink J. Conducting a Meta-Ethnography of Qualitative Literature: Lessons Learnt BMC Med Res Methodol 2008;8(1):8–21.
  79. 79. Roche DG, Kruuk LE, Lanfear R, Binning SA. Public Data Archiving in Ecology and Evolution: How Well Are We Doing? PLoS Biol. 2015 Nov 10;13(11):e1002295. pmid:26556502
  80. 80. Pope LC, Liggins L, Keyse J, Carvalho SB, Riginos C. Not the time or the place: the missing spatio-temporal link in publicly available genetic data. Mol Ecol. 2015 Aug;24(15):3802–9. pmid:26033415
  81. 81. Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership: The Canadian Journal of Library and Information Practice and Research. 2018;13(1). https://doi.org/10.21083/partnership.v13i1.4115
  82. 82. Krug S. Don't make me think. Berkeley CA: New Riders, 2014.
  83. 83. Weng C, Friedman C, Rommel CA, Hurdle JF. A two-site survey of medical center personnel's willingness to share clinical data for research: implications for reproducible health NLP research. BMC Med Inform Decis Mak. 2019 Apr 4;19(Suppl 3):70. pmid:30943963
  84. 84. Stuart S, Baynes G, Hrynaszkiewicz I, Allin K, Penny D, Lucraft M, et al. Practical challenges for researching in sharing data. White paper. March 2018. Available at: https://doi.org/10.6084/m9.figshare.5975011 Accessed June 11, 2019.
  85. 85. Campbell HA, Micheli-Campbell MA, Udyawer V. Early career researchers embrace data sharing. Trends in Ecology and Evolution. 2019 Feb;34(2):98.
  86. 86. Murillo AP. Data at risk initiative: examining and facilitating the scientific process in relation to endangered data. Data Science Journal. 2014; 12:207–219.
  87. 87. Bierer BE, Crosas M, Pierce HH. Data authorship as an incentive to data sharing. N Engl J Med. 2017 Apr 27;376(17):1684–1687 pmid:28402238
  88. 88. Pierce HH, Dev A, Statham E, Bierer BE. Credit data generators for data reuse. Nature. 2019 Jun;570(7759):30–32. pmid:31164773
  89. 89. Ioannidis JP. Anticipating consequences of sharing raw data and code and of awarding badges for sharing. J Clin Epidemiol. 2016 Feb;70:258–60. pmid:26163123
  90. 90. Moher D, Naudet F, Cristea IA, Miedema F, Ioannidis JPA, Goodman SN. Assessing scientists for hiring, promotion, and tenure. PLoS Biol. 2018 Mar 29;16(3):e2004089. pmid:29596415
  91. 91. HESA (Higher Education Statistics Agency). The Higher Education Classification of Subjects. Available at: https://www.hesa.ac.uk/innovation/hecos Accessed October 11, 2019.
  92. 92. Lindlof TR, Taylor BC. Qualitative Communication Research Methods, 2nd Edition. Thousand Oaks, CA: Sage, 2002.
  93. 93. Guest G, Namey E, Taylor J, Eley N, McKenna K. Comparing focus groups and individual interviews: findings from a randomized study. International Journal of Social Research Methodology. 2017;20(6):693–708.