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What Drives Academic Data Sharing?

  • Benedikt Fecher ,

    Affiliations: Internet-enabled Innovation, Alexander von Humboldt Institute for Internet and Society, Berlin, Germany, Research Infrastructure, German Institute for Economic Research, Berlin, Germany

  • Sascha Friesike,

    Affiliation: Internet-enabled Innovation, Alexander von Humboldt Institute for Internet and Society, Berlin, Germany

  • Marcel Hebing

    Affiliation: German Socio-Economic Panel, German Institute for Economic Research, Berlin, Germany

What Drives Academic Data Sharing?

  • Benedikt Fecher, 
  • Sascha Friesike, 
  • Marcel Hebing


Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.


The accessibility of research data has a vast potential for scientific progress. It facilitates the replication of research results and allows the application of old data in new contexts [1,2]. It is hardly surprising that the idea of shared research data finds widespread support among academic stakeholders. The European Commission, for example, proclaims that access to research data will boost Europe’s innovation capacity. To tap into this potential, data produced with EU funding should to be accessible from 2014 onwards [3]. Simultaneously, national research associations band together to promote data sharing in academia. The Knowledge Exchange Group, a joint effort of five major European funding agencies, is a good example for the cross-border effort to foster a culture of sharing and collaboration in academia [4]. Journals such as Atmospheric Chemistry and Physics, F1000Research, Nature, or PLoS One, increasingly adopt data sharing policies with the objective of promoting public access to data.

In a study among 1,329 scientists, 46% reported they do not make their data electronically available to others [5]. In the same study, around 60% of the respondents, across all disciplines, agreed that the lack of access to data generated by others is a major impediment to progress in science. Though the majority of the respondents stem from North America (75%), the results point to a striking dilemma in academic research, namely the mismatch between the general interest and the individual’s behavior. At the same time, they raise the question of what exactly prevents researchers from sharing their data with others.

Still, little research devotes itself to the issue of data sharing in a comprehensive manner. In this article we offer a cross-disciplinary analysis of prevailing barriers and enablers, and propose a conceptual framework for data sharing in academia. The results are based on a) a systematic review of 98 scholarly papers on the topic and b) a survey among 603 secondary data users who are analyzing data from the German Socio-Economic Panel Study (hereafter SOEP). With this paper we aim to contribute to research practice through policy implications and to theory by comparing our results to current organizational concepts of knowledge creation, such as commons-based peer production [6] and crowd science [7]. We show that data sharing in today’s academic world cannot be regarded a knowledge commons.

The remainder of this article is structured as follows: first, we explain how we methodologically arrived at our framework. Second, we will describe its categories and address the predominant factors for sharing research data. Drawing from these results, we will in the end discuss theory and policy implications.


In order to arrive at a framework for data sharing in academia, we used a systematic review of scholarly articles and an empirical survey among secondary data users (SOEP User survey). The first served to design a preliminary category system, the second to empirically revise it. In this section, we delineate our methodological approach as well as its limitations. Fig. 1 illustrates the research methodology.

2.1 Systematic Review

Systematic reviews have proven their value especially in evidence based medicine [8]. Here, they are used to systematically retrieve research papers from literature databases and analyze them according to a pre-defined research question. Today, systematic reviews are applied across all disciplines, reaching from educational policy [9,10] to innovation research [11]. In our view, a systematic review constitutes an elegant way to start an empirical investigation. It helps to gain an exhaustive overview of a research field, its leading scholars, and prevailing discourses and can be used to produce an analytical spadework for further inquiries.

2.1.1 Retrieval of Relevant Papers

In order to find the relevant papers for our research intent, we defined a research question (Which factors influence data sharing in academia?) as well as explicit selection criteria for the inclusion of papers [12]. According to the criteria, the papers needed to address the perspective of the primary researcher, focus on academia and stem from defined evaluation period. To ensure an as exhaustive first sample of papers as possible, we used a broad basis of multidisciplinary data banks (see Table 1) and a search term (“data sharing”) that generated a high number of search results. We did not limit our sample to research papers but also included for example discussion papers. In the first sample we included every paper that has the search term in either title or abstract. Fig. 2 summarizes the selection process of the papers.

The evaluation period spanned from December 1st 2001 to November 15th 2013, leading to a pre-sample of 9796 papers. We read the abstracts of every paper and selected only those that a) address data sharing in academia and b) deal with the perspective of the primary researcher. In terms of intersubjective comprehensibility, we decided separately for every paper if it meets the defined criteria [13]. Only those papers were included in the final analysis sample that were approved by all three coders (yes/yes/yes in the coding sheet). Papers that received a no from every coder were dismissed; the others were discussed and jointly decided upon. The most common reasons for dismissing a paper were thematic mismatch (e.g., paper focusses on commercial data), and quality issues (e.g., a letter to the editor). Additionally, we conducted a small-scale expert poll on the social network for scientists ResearchGate. The poll resulted in five additional papers, three of which were not published in the defined evaluation period. We did, however, include them in the analysis sample due to their thematic relevance. In the end, we arrived at a sample of 98 papers. Table 1 shows the selected papers and the database in which we found them.

2.1.2 Sample description

The 98 papers that made our final sample come from the following disciplines: Science, technology, engineering, and mathematics (60 papers), humanities (9), social sciences (6), law (1), interdisciplinary or no disciplinary focus (22). The distribution of the papers indicates that data sharing is an issue of relevance across all research areas, above all the STEM disciplines. The graph of our analysis sample (see Fig. 3) indicates that academic data sharing is a topic that has received a considerable increase in attention during the last decade.

Further, we analyzed the references that the 98 papers cited. Table 2 lists the most cited papers in our sample and provides an insight into which articles and authors dominate the discussion. Two of the top three most cited papers come from the journal PLoS One. Among the most cited texts, [14] is the only reference that is older than 2001.

2.1.3 Preliminary Category System

In a consecutive, we applied a qualitative content analysis in order to build a category system and to condense the content of the literature in the sample [15,16]. We defined the analytical unit compliant to our research question and copied all relevant passages in a CSV file. After, we uploaded the file to the data analysis software NVivo and coded the units of analysis inductively. We decided for inductive coding as it allows building categories and establishing novel interpretative connections based on the data material, rather than having a conceptual pre-understanding. The preliminary category system allows allocating the identified factors to the involved individuals, bodies, regulatory systems, and technical components.

2.2 Survey Among Secondary Data Users

To empirically revise our preliminary category system, we further conducted a survey among 603 secondary data users that analyze data from the German Socio-Economic Panel (SOEP). We specifically addressed secondary data users because this researcher group is familiar with the re-use of data and likely to offer informed responses.

The SOEP is a representative longitudinal study of private households in Germany [17]. It is conducted by the German Institute for Economic Research. The data is available to researchers through a research data centre. Currently, the SOEP has approximately 500 active user groups with more than 1,000 researchers per year analyzing the data. Researchers are allowed to use the data for their scientific projects and publish the results, but must neither re-publish the data nor syntax files as part of their publications.

The SOEP User Survey is a web-based usability survey among researchers who use the panel data. Beside an annually repeated module of socio-demographic and service related questions, the 2013 questionnaire included three additional questions on data sharing (see Table 3). The annual questionnaire includes the Big Five personality scale according to Richter et al. [18] that we correlated with the willingness to share (see 3.1. Data Donor). When working with panel surveys like the SOEP, researchers expend serious effort to process and prepare the data for analysis (e.g., generating new variables). Therefore the questions were designed more broadly, including the willingness to share analysis scripts.

It has to be added that different responses could have resulted if the willingness to share data sets and the willingness to share analysis scripts/code would be seperated in the survey. The web survey was conducted in November and December 2013, resulting in 603 valid response cases—of which 137 answered the open questions Q2 and Q3. We analyzed the replies to these two open questions by applying deductive coding and using the categories from the preliminary category system. We furthermore used the replies to revise categories in our category system and add empirical evidence.

The respondents are on average 37 years old, 61% of them are male. Looking at the distribution of disciplines among the researchers in our sample, the majority works in economics (46%) and sociology/social sciences (39%). For a German study it is not surprising that most respondents are German (76%). Nevertheless, 24% of the respondents are international data users. The results of the secondary data user survey, especially the statistical part, are therefore relevant for German academic institutions.

2.3 Limitations

Our methodological approach goes along with common limitations of systematic reviews and qualitative methods. The sample of papers in the systematic review is limited to journal publications in well-known databases and excludes for example monographs, grey literature, and papers from public repositories such as preprints. Our sample does in this regard draw a picture of specific scope, leaving out for instance texts from open data initiatives or blog posts. Systematic reviews are furthermore prone to publication-bias [19] the tendency to publish positive results rather than negative results. We tried to counteract a biased analysis by triangulating the derived category system with empirical data from a survey among secondary data users [20]. For the analysis, we leaned onto quality criteria of qualitative research. Regarding the validity of the identified categories, an additional quantitative survey is recommended.

2.4 Ethics Statement

The SOEP User Survey was approved by the data protection officer of the German Institute for Economic Research (DIW Berlin) and the head of the research data center DIW-SOEP. The qualitative answers have been made available without personal data to guarantee the interviewees’ anonymity.


As a result of the systematic review and the survey we arrived at a framework that depicts academic data sharing in six descriptive categories. Fig. 4 provides an overview of these six (data donor, research organization, research community, norms, data infrastructure, and data recipients) and highlights how often we found references for them in a) the literature review and b) in the survey (a/b). In total we found 541 references, 404 in the review and 137 in our survey. Furthermore, the figure shows the subcategories of each category.

  • Data donor, comprising factors regarding the individual researcher who is sharing data (e.g., invested resources, returns received for sharing)
  • Research organization, comprising factors concerning the crucial organizational entities for the donating researcher, being the own organization and funding agencies (e.g., funding policies)
  • Research community, comprising factors regarding the disciplinary data-sharing practices (e.g., formatting standards, sharing culture)
  • Norms, comprising factors concerning the legal and ethical codes for data sharing (e.g., copyright, confidentiality)
  • Data recipients, comprising factors regarding the third party reuse of shared research data (e.g., adverse use)
  • Data infrastructure, comprising factors concerning the technical infrastructure for data sharing (e.g., data management system, technical support)

In the following, we explain the hindering and enabling factors for each category. Each category is summarized by a table that lists the identified sub-categories and data sharing factors. The tables further provide text references and direct quotes for selected factors. We translated most of the direct quotes from the survey from German to English, as 76% the respondents are German.

3.1 Data Donor

The category data donor concerns the individual researcher who collects data. The sub-categories are sociodemographic factors, degree of control, resources needed, and returns (see Table 4).

3.1.1 Sociodemographic factors

Frequently mentioned in the literature were the factors age, nationality, and seniority in the academic system. Enke et al. [21], for instance, observe that German and Canadian scientists were more reluctant to share research data publicly than their US colleagues (which raises the question how national research policies influence data sharing). Tenopir et al. [5] found that there is an influence of the researcher’s age on the willingness to share data. Accordingly, younger people are less likely to make their data available to others. People over 50, on the other hand, were more likely to share research data. This result resonates with an assumed influence of seniority in the academic system and competitiveness on data sharing behavior [22]. Data sets and other subsidiary products are awarded far less credit in tenure and promotion decisions than text publications [23]. Hence does competition, especially among non-tenured researchers, go hand in hand with a reluctance to share data. The perceived right to publish first (see degree of control) with the data further indicates that publications and not (yet) data is the currency in the academic system. Tenopir et al. (2011) [5] point to an influence of the level of research activity on the willingness to share data. Individuals who work solely in research, in contrast to researchers who have time-consuming teaching obligations, are more likely to make their data available to other researchers. Acord and Harley [23] further regard character traits as an influencing factor. This conjecture is not vindicated in our questionnaire. In contrast to our initial expectations, character traits (Big Five) are not able to explain much of the variation in Q1. In a logistic regression model on the willingness to share data and scripts in general (answer categories 1–3) and controlling for age and gender, only the openness dimension shows a significant influence (positive influence with p < 0.005). All other dimensions (conscientiousness, extraversion, agreeableness, neuroticism) do not have a considerable influence on the willingness to share.

3.1.2 Degree of control

A core influential factor on the individual data sharing behavior can be subsumed under the category degree of control. It denotes the researcher’s need to have a say or at least knowledge regarding the access and use of the deposited data.

The relevance of this factor is emphasized by the results to question Q1 in our survey (see Table 5). Only a small number of researchers (18%) categorically refuses to share scripts or research data. For those who are willing to share (82%), control seems to be an important issue (summarized by the first three questions). 56% are either demanding a context with access control or would only be willing to share on request. However, it has to be said that our sample comprises mostly German-speaking researchers that are familiar with secondary data and is therefore not representative for the academia in general.

Eschenfelder and Johnson [24] suggest more control for researchers over deposited data (see also [2532]). According to some scholars, a priority right for publications, for example an embargo on data (e.g.,[33]), would enable academic data sharing. Other authors point to a researcher’s concern regarding the technical ability of the data requester to understand [29,34] and to interpret [35] a dataset (see also data recipientsadverse use). The need for control is also present in our survey among secondary data users. To the question why one would not share research data, one respondent replied: “I have doubts about others being able to use my work without control from my side” (Survey). Another respondent replied: “I want to know who uses my data.” (Survey). The results in this category indicate a perceived ownership over the data on the part of the researcher, which is legally often not the case.

3.1.3 Resources needed

Here we subsume factors relating to the researcher’s investments in terms of time and costs as well as their knowledge regarding data sharing. “Too much effort!” was a blunt answer we found in our survey as a response to the question why researchers do not share data. In the literature we found the argument time and effort 19 times and seven times in the survey. One respondent stated: “The effort to collect data yourself is immense and seems "not to be in fashion" anymore. I don't want to support this convenience” (from the survey). Another respondent said “(the) amount of extra work to respond to members of the research community who either want explanation, support, or who just want to vent." would prevent him or her from sharing data. Besides the actual sharing effort [21,23,29,33,3641], scholars utter concerns regarding the effort required to help others to make sense of the data [42]. The knowledge factor becomes apparent in Sieber’s study [43] in which most researchers stated that data sharing is advantageous for science, but that they had not thought about it until they were asked for their opinion. Missing knowledge further relates to poor curation and storing skills [33,44] and missing knowledge regarding adequate repositories [21,42]. In general, missing knowledge regarding the existence of databases and know-how to use them is described as a hindering factor for data sharing. Several scholars, for instance Piwowar et al. [45] and Teeters et al.[46], hence suggest to integrate data sharing in the curriculum. Others mention the financial effort to share data and suggest forms of financial compensation for researchers or their organizations [43,47].

3.1.4 Returns

Within the examined texts we found 26 references that highlight the issue of missing returns in exchange for sharing data, 12 more came from the survey. The basic attitude of the references describes a lack of recognition for data donors [29,31,4851]. Both sources – review and survey – argue that donors do not receive enough formal recognition to justify the individual efforts and that a safeguard against uncredited use is necessary [36,5254]. The form of attribution a donor of research data should receive remains unclear and ranges from a mentioning in the acknowledgments to citations and co-authorships [21]. One respondent stated: “"It is your own effort that is taken by others without citation or reward" (from the survey). Several authors explain that impact metrics need to be adapted to foster data sharing [34,55]. Yet, there is also literature that reports positive individual returns from shared research data. Kim and Stanton [56] for instance explain that shared data can highlight the quality of a finding and thus indicate sophistication. Piwowar et al. [45] report an increase in citation scores for papers, which feature supplementary data. Further, quality improvements in the form of professional are mentioned: “Seeing how others have solved a problem may be useful.”, “I can profit and learn from other people’s work.”, “to receive feedback from other researchers and to make my analysis repeatable.” (all quotes are from our survey). Enke et al. [21] also mention an increased visibility within the research community as a possible positive return.

3.2 Research Organization

The category research organization comprises the most relevant organizational entities for the donating researcher. These are the data donor’s own organization as well as funding agencies (see Table 6).

3.2.1 Data donor’s organization

An individual researcher is generally placed in an organizational context, for example a university, a research institute or a research and development department of a company. The respective organizational affiliation can impinge on his or her data sharing behaviour especially through internal policies, the organizational culture as well as the available data infrastructure. Huang et al. [35] for instance, in an international survey on biodiversity data sharing found out that “only one-third of the respondents reported that sharing data was encouraged by their employers or funding agencies”. The respondents whose organizations or affiliations encourage data sharing were more willing to share. Huang et al. [35] view the organizational policy as a core adjusting screw. They suggest detailed data management and archiving instructions as well as recognition for data sharing (i.e., career options). Belmonte et al. [57] and Enke et al. [21] further emphasize the importance of intra-organizational data management, for instance consistent data annotation standards in laboratories (see also 3.6. Data Infrastructure). Cragin et al. [58] see data sharing rather as a community effort in which the single organizational entity plays a minor role: “As a research group gets larger and more formally connected to other research groups, it begins to function more like big science, which requires production structures that support project coordination, resource sharing and increasingly standardized information flow”.

3.2.2 Funding agencies

Besides journal policies, the policies of funding agencies are named as a key adjusting screw for academic data sharing throughout the literature (e.g.,[21,42]). Huang et al. [35] argue that making data available is no obligation with many funding agencies and that they do not provide sufficient financial compensation for the efforts needed in order to share data. Perrino et al. [59] argue that funding policies show varying degrees of enforcement when it comes to data sharing and that binding policies are necessary to convince researchers to share. The National Science Foundation of the US, for instance, has long required data sharing in its grant contracts (see [60]), “but has not enforced the requirements consistently” [61].

3.3 Research Community

The category research community subsumes the sub-categories data sharing culture, standards, scientific value, and publications (see Table 7).

3.3.1 Data sharing culture

The literature reports a substantial variation in academic data sharing across disciplinary practices [22,62]. Even fields, which are closely related like medical genetics and evolutionary genetics show substantially different sharing rates [22]. Medical research and social sciences are reported to have an overall low data sharing culture [5], which possibly relates to the fact that these disciplines work with individual-related data. Costello [34] goes so far as to describe the data sharing culture as the main obstacle to academic data sharing (see Table 7 for quote). Some researchers see the community culture rather as a motivation. To the question what would motivate a researcher to share data, for instance, one respondent replied: “to extend the community of data users in my research community“, or „Everyone benefits from sharing data if you don't have to reinvent the wheel“(from the survey).

3.3.2 Standards

When it comes to the interoperability of data sets, many scholars see the absence of metadata standards and formatting standards as an impediment for sharing and reusing data; lacking standards hinder interoperability (e.g., [5,21,34,46,55,6268]. There were no references in the survey for the absence of formatting standards.

3.3.3. Scientific value

In this subcategory we subsume all findings that bring value to the scientific community. It is a very frequent argument that data sharing can enhance scientific progress. A contribution hereto is often considered an intrinsic motivation for participation. This is supported by our survey: We found sixty references for this subcategory, examples are “Making research better”, “Feedback and exchange”, “Consistency in measures across studies to test robustness of effect”, “Reproducibility of one’s own research”. Huang et al. [35] report that 90% of their “respondents indicated the desire to contribute to scientific progress” [69]. Tenopir et al. report that “(67%) of the respondents agreed that lack of access to data generated by other researchers or institutions is a major impediment to progress in science” [5]. Other scholars argue that data sharing accelerates scientific progress because it helps find synergies and avoid repeating work [42,7076]. It is also argued that shared data increases quality assurance and makes the review process better and that it increases the networking and the exchange with other researchers [21]. Wicherts and Bakker [77] argue that researchers who share data commit less errors and that data sharing encourages more research (see also [78]).

3.3.4 Publications

In most research communities publications are the primary currency. Promotions, grants, and recruitments are often based on publications records. The demands and offers of publication outlets therefore have an impact on the individual researcher’s data sharing disposition [79]. Enke et al. [21]describe journal policies to be the major motivator for data sharing, even before funding agencies. A study conducted by Huang et al. [69] shows that 74% of researchers would accept leading journals’ data sharing policies. However, other research indicates that today’s journal policies for data sharing are all but binding [5,27,80,81]. Several scholars argue that more stringent data sharing policies are needed to make researchers share [27,55,69,82]. At the same time they argue that publications that include or link to the used dataset receive more citations. And therefore both journals and researchers should be incentivised to follow data sharing policies[55].

3.4 Norms

In the category norms we subsume all ethical and legal codes that impact a researcher’s data sharing behaviour (see Table 8).

3.4.1 Ethical norms

As ethical norms we regard moral principles of conduct from the data collector’s perspective. Brakewood and Poldrack [83] regard the respect for persons as a core principle in data sharing and emphasize the importance of informed consent and confidentiality, which is particularly relevant in the context of individual-related data. The authors demand that a patient “needs to have the ability to think about his or her choice to participate or not and the ability to actually act on that decision”. De Wolf et al. [84], Harding et al. [85], Mennes et al. [86], Sheather [87] and Kowalczyk and Shankar [88] take the same line regarding the necessity of informed consent between researcher and study subject. Axelsson and Schroeder [63] describe the maxim to act upon public trust as an important precondition for database research. Regarding data sensitivity, Cooper [89] emphasizes the need to consider if the data being shared could harm people. Similarly, Enke et al. [21] point to the possibility that some data could be used to harm environmentally sensitive areas. Often ethical considerations in the context of data sharing concern adverse use of data, we specify that under adverse use in the category data recipients.

3.4.2 Legal norms

Legal uncertainty can deter data sharing, especially in disciplines that work with sensitive data, example are corporate or personal data [90]. Under legal norms we subsume ownership and rights of use, privacy, contractual consent and copyright. These are the most common legal issues regarding data sharing. The sharing of data is restricted by the national privacy acts. In this regard, Freyman et al. [91] and Pitt and Tang [30] emphasize the necessity for de-identification as a pre-condition for sharing individual-related data. Pearce and Smith [27] on the other hand state that getting rid of identifiers is often not enough and pleads for restricted access. Many authors point to the necessity of contractual consent between data collector and study participant regarding the terms of use of personal data [25,47,84,86,89,9296]. While privacy issues apply to individual-related data, issues of ownership and rights of use concern all kinds of data. Enke et al. [21] states that the legal framework concerning the ownership of research data before and after deposition in a database is complex and involves many uncertainties that deter data sharing (see also [34,97]). Eisenberg [98] even regards the absence of adequate intellectual property rights, especially in the case of patent-relevant research, as a barrier for data sharing and therefore innovation (see also [22,99]). Chandramohan et al. [100] emphasize that data collection financed by tax money is or should be a public good.

3.5 Data Recipient

In the category data recipients, we subsumed influencing factors regarding the use of data by the data recipient and the recipient’s organizational context (see Table 9).

3.5.1 Adverse use

A multitude of hindering factors for data sharing in academia can be assigned to a presumed adverse use on the part of the data recipient. In detail, these are falsification, commercial misuse, competitive misuse, flawed interpretation, and unclear intent. For all of these factors, references can be found in both, the literature and the survey. Regarding the fear of falsification, one respondent states: “I am afraid that I made a mistake somewhere that I didn’t find myself and someone else finds.” In the same line, Costello [34] argues that “[a]uthors may fear that their selective use of data, or possible errors in analysis, may be revealed by data publication”. Many authors describe the fear of falsification as a reason to withhold data [23,27,29,34,43,101]. Few authors see a potential “commercialization of research findings” [93] as a reason not to share data (see also: [34,36,92]). The most frequently mentioned withholding reason regarding the third party use of data is competitive misuse; the fear that someone else publishes with my data before I can (16 survey references, 13 text references). This indicates that at least from the primary researcher’s point of view, withholding data is a common competitive strategy in a publication-driven system. To the question, what concern would prevent one from sharing data, one researcher stated: "I don't want to give competing institutions such far-reaching support." Costello [34] encapsulates this issue: “If I release data, then I may be scooped by somebody else producing papers from them.” Many other authors in our sample examine the issue of competitive misuse [5,22,23,46,48,102104]. Another issue regarding the recipient’s use of data concerns a possible flawed interpretation (e.g., [21,59,89,105,106]). Perrino et al. [59], regarding a dataset from psychological studies, state: “The correct interpretation of data has been another concern of investigators. This included the possibility that the [data recipient] might not fully understand assessment measures, interventions, and populations being studied and might misinterpret the effect of the intervention”. The issue of flawed interpretation is closely related to the factor data documentation (see 3.6 data infrastructure). Associated with the need for control (as outlined in data donor), authors and respondents alike mention a declaration of intent as an enabling factor, as one of the respondents states “missing knowledge regarding the purpose and the recipient” is a reason not to share (see also). Respondents in the survey stated that sharing data would lead to more transparency: “(I would share data) to benefit from others' scripts and for transparency in research“(from the survey).

3.5.2 Recipient’s organization

According to Fernandez et al. [107] the recipient’s organization, its type (commercial or public) and data security conditions, have some impact on academic data sharing. Fernandez et al. [107] summarize the potential uncertainties: “Do the lab facilities of the receiving researcher allow for the proper containment and protection of the data? Do the physical, logical and personnel security policies of the receiving lab/organization adequately reduce the risk that [someone] release[s] the data in an unauthorized fashion?” (see also [5]).

3.6 Data Infrastructure

In the category data infrastructure we subsume all factors concerning the technical infrastructure to store and retrieve data. It is comprised of the sub-categories architecture, usability, and management software (see Table 10).

3.6.1 Architecture

A common rationale within the surveyed literature is that restricted access, for example through a registration system, would contribute to data security and counter the perceived loss of control ([47,87,89,94,108]). Some authors even emphasize the necessity to edit the data after it has been stored [21]. There is however disunity if the infrastructure should be centralized (e.g., [65]) or decentralized (e.g.,[109]). Another issue is that data quality is maintained after it has been archived [83]. In this respect, Teeters et al. [46] suggest that data infrastructure should provide technical support and means of indexing.

3.6.2 Usability

The topic of usability comes up multiple times in the literature. The authors argue that service providers need to make an effort to simplify the sharing process and the involved tools [46,86]. Authors also argue that guidelines are needed besides a technical support that make it easy for researchers to share [110,111].

3.6.3 Management system

We found 24 references for the management system of the data infrastructure, these were concerned with data documentation and metadata standards [5,63,65,112]. The documentation of data remains a troubling issue and many disciplines complain about missing standards [23,113,114]. At the same time other authors explain that detailed metadata is needed to prevent misinterpretation [36].

Discussion and Conclusion

The accessibility of research data holds great potential for scientific progress. It allows the verification of study results and the reuse of data in new contexts ([1,2]). Despite its potential and prominent support, sharing data is not yet common practice in academia. With the present paper we explain that data sharing in academia is a multidimensional effort that includes a diverse set of stakeholders, entities and individual interests. To our knowledge, there is no overarching framework, which puts the involved parties and interests in relation to one another. In our view, the conceptual framework with its empirically revised categories has theoretical and practical use. In the remaining discussion we will elaborate possible implications for theory, and research practice. We will further address the need for future research.

4.1 Theoretical Implications: Data is Not a Knowledge Commons

Concepts for the production of immaterial goods in a networked society frequently involve the dissolution of formal entities, modularization of tasks, intrinsic motivation of participants, and the absence of ownership. Benkler’s [6] commons-based peer production, to a certain degree wisdom of the crowds [115] and collective intelligence [116] are examples for organizational theories that embraces novel forms of networked collaboration. Frequently mentioned empirical cases for these forms of collaboration are the open source software community [117] or the online encyclopedia Wikipedia [118,119]. In both cases, the product of the collaboration can be considered a commons. The production process is inherently inclusive.

In many respects, Franzoni and Sauermann’s [7] theory for crowd science resembles the concepts commons-based peer production and crowd intelligence. The authors dissociate crowd science from traditional science, which they describe as largely closed. In traditional science, researchers retain exclusive use of key intermediate inputs, such as data. Crowd science on the other hand is characterized by its inherent openness with respect to participation and the disclosure of intermediate inputs such as data. Following that line of thought, data could be considered a knowledge commons, too. A good that can be accessed by everyone and whose consumption is non-rivalry [120]. Crowd-science is in that regard a commons-driven principle for scholarly knowledge. In many respects, academia indeed fulfils the requirements for crowd science, be it the immateriality of knowledge products, the modularity of research, and public interest.

In the case of data sharing in academia, however, the theoretical depiction of a crowd science [7] or an open science [121], and with both the accessibility of data, does not meet the empirical reality. The core difference to the model of a commons-based peer production like we see it in open source software or crowd science lies in the motivation for participation. Programmers do not have to release their code under an open source licence. And many do not. The same is true for Wikipedia, where a rather small but active community edits pages. Both systems run on voluntariness, self-organization, and intrinsic motivation. Academia however, contradicts to different degrees these characteristics of a knowledge exchange system.

In an ideal situation every researcher would publish data alongside a research paper to make sure the results are reproducible and the data is reusable in a new context. Yet today, most researchers remain reserved to share their research data. This indicates that their efforts and perceived risks outweigh the potential individual benefits they expect from data sharing. Research data is in large parts not a knowledge commons. Instead, our results points to a perceived ownership of data (reflected in the right to publish first) and a need for control (reflected in the fear of data misuse). Both impede a commons-based exchange of research data. When it comes to data, academia remains neither accessible nor participatory. As data publications lack sufficient formal recognition (e.g., citations, co-authorship) in comparison to text publications, researchers find furthermore too few incentives to share data. While altruism and with it the idea to contribute to a common good, is a sufficient driver for some researchers, the majority currently remains incentivised not to share. If data sharing leads to better science and simultaneously, researchers are hesitant to share, the question arises how research policies can foster data sharing among academic researchers.

4.2 Policy Implications: Towards more Data Sharing

Worldwide, research policy makers support the accessibility of research data. This can be seen in the US with efforts by the National Institutes of Health [122,123] and also in Europe, with the EU’s Horizon 2020 programme [3]. In order to develop consequential policies for data sharing, policy makers need to understand and address the involved parties and their perspectives. The framework that we present in this paper helps to gain a better understanding of the prevailing issues and provides insights into the underlying dynamics of academic data sharing. Considering that research data is far from being a commons, we believe that research policies should work towards an efficient exchange system in which as much data is shared as possible. Strategic policy measures could therefore go into two directions: First, they could provide incentives for sharing data and second impede researchers not to share. Possible incentives could include adequate formal recognition in the form of data citation and career prospects. In academia, a largely non-monetary knowledge exchange system, research policy should be geared towards making intermediate products count more. Furthermore could forms of financial reimbursement, for example through additional person hours in funding, help to increase the individual effort to make data available. As long as academia remains a publication-centred business, journal policies further need to adopt mandatory data sharing policies [2,124] and provide easy-to use data management systems. Impediments regarding sharing supplementary data could include clear and elaborate reasons to opt out. In order to remove risk aversion and ambiguity, an understandable and clear legal basis regarding the rights of use is needed to inform researchers on what they can and cannot do with data they collected. This is especially important in medicine and in the social sciences where much data comes from individuals. Clear guidelines that explain how consent can be obtained and how data can be anonymized are needed. Educational efforts within data-driven research fields on proper data curation, sharing culture, data documentation, and security could be fruitful in the intermediate-term. Ideally these become part of the curriculum for university students. Infrastructure investments are needed to develop efficient and easy-to-use data repositories and data management software, for instance as part of the Horizon 2020 research infrastructure endeavors.

4.3 Future Research

We believe that more research needs to address the discipline-specific barriers and enablers for data sharing in academia in order to make informed policy decisions. Regarding the framework that we introduce in this paper, the identified factors need further empirical revision. In particular, we regard the intersection between academia and industry worth investigating. For instance: A study among German life scientists showed that those who receive industry funding are more likely to deny others’ requests for access to research materials [125]. In the same line, Haeussler [126] in a comparative study on information sharing among scientists, finds that the likelihood of sharing information decreases with the competitive value of the requested information. It increases when the inquirer is an academic researcher. Following this, future research could address data sharing between industry and academia. Open enterprise data, for example, appears to be a relevant topic for legal scholars as well as innovation research.

Author Contributions

Conceived and designed the experiments: BF SF. Analyzed the data: BF SF MH. Wrote the paper: BF SF MH. Performed the study: BF SF MH.


  1. 1. Dewald W, Thursby JG, Anderson RG (1986) Replication in empirical economics: The Journal of Money, Credit and Banking Project. Am Econ Rev: 587–603.
  2. 2. McCullough BD (2009) Open Access Economics Journals and the Market for Reproducible Economic Research. Econ Anal Policy 39: 118–126. doi: 10.1016/s0313-5926(09)50047-1
  3. 3. European Commission (2012) Scientific data: open access to research results will boost Europe’s innovation capacity. Httpeuropaeurapidpress-ReleaseIP-12–790enhtm.
  4. 4. Knowledge Exchange (2013) Knowledge Exchange Annual Report and Briefing 2013. Annual Report. Copenhagen, Denmark. Available:
  5. 5. Tenopir C, Allard S, Douglass K, Aydinoglu AU, Wu L, et al. (2011) Data Sharing by Scientists: Practices and Perceptions. PLoS ONE 6: e21101. doi: 10.1371/journal.pone.0021101. pmid:21738610
  6. 6. Benkler Y (2002) Coase’s Penguin, or, Linux and “The Nature of the Firm.” Yale Law J 112: 369. doi: 10.2307/1562247.
  7. 7. Franzoni C, Sauermann H (2014) Crowd science: The organization of scientific research in open collaborative projects. Res Policy 43: 1–20. doi: 10.1016/j.respol.2013.07.005.
  8. 8. Higging JP., Green S (2008) Cochrane handbook for systematic reviews of interventions. Chichester, England; Hoboken, NJ: Wiley-Blackwell.
  9. 9. Davies P (2000) The Relevance of Systematic Reviews to Educational Policy and Practice. Oxf Rev Educ 26: 365–378. doi: 10.1080/713688543
  10. 10. Hammersley M (2001) On “Systematic” Reviews of Research Literatures: A “narrative” response to Evans &amp; Benefield. Br Educ Res J 27: 543–554. doi: 10.1080/01411920120095726.
  11. 11. Dahlander L, Gann DM (2010) How open is innovation? Res Policy 39: 699–709. doi: 10.1016/j.respol.2010.01.013.
  12. 12. Booth A, Papaioannou D, Sutton A (2012) Systematic approaches to a successful literature review. Los Angeles; Thousand Oaks, Calif.: Sage.
  13. 13. Given LM (2008) The Sage encyclopedia of qualitative research methods. Thousand Oaks, Calif.: Sage Publications. Available: Accessed 31 March 2014.
  14. 14. Feinberg SE, Martin ME, Straf ML (1985) Sharing Research Data. Washington, DC: National Academy Press.
  15. 15. Mayring P (2000) Qualitative Content Analysis. Forum Qual Soc Res 1. Available:
  16. 16. Schreier M (2012) Qualitative content analysis in practice. London; Thousand Oaks, Calif.: Sage Publications.
  17. 17. Wagner G, Frick JR, Schupp J (2007) The German Socio-Economic Panel Study (SOEP)—Evolution, Scope and Enhancements. Schmollers Jahrb 127: 139–169. doi: 10.2139/ssrn.1028709
  18. 18. Richter D, Metzing M, Weinhardt M, Schupp J (2013) SOEP scales manual. Berlin. 75 p.
  19. 19. Torgerson CJ (2006) Publication Bias: The Achilles’ Heel of Systematic Reviews? Br J Educ Stud 54: 89–102. doi: 10.1111/j.1467–8527.2006.00332.x.
  20. 20. Patton MQ (2002) Qualitative research and evaluation methods. 3 ed. Thousand Oaks, Calif: Sage Publications. 598 p.
  21. 21. Enke N, Thessen A, Bach K, Bendix J, Seeger B, et al. (2012) The user’s view on biodiversity data sharing—Investigating facts of acceptance and requirements to realize a sustainable use of research data—. Ecol Inform 11: 25–33. doi: 10.1016/j.ecoinf.2012.03.004. pmid:23072676
  22. 22. Milia N, Congiu A, Anagnostou P, Montinaro F, Capocasa M, et al. (2012) Mine, Yours, Ours? Sharing Data on Human Genetic Variation. PLoS ONE 7: e37552. doi: 10.1371/journal.pone.0037552. pmid:22679483
  23. 23. Acord SK, Harley D (2012) Credit, time, and personality: The human challenges to sharing scholarly work using Web 2.0. New Media Soc 15: 379–397. doi: 10.1177/1461444812465140.
  24. 24. Eschenfelder K, Johnson A (2011) The Limits of sharing: Controlled data collections. Proc Am Soc Inf Sci Technol 48: 1–10. doi: 10.1002/meet.2011.14504801062.
  25. 25. Haddow G, Bruce A, Sathanandam S, Wyatt JC (2011) “Nothing is really safe”: a focus group study on the processes of anonymizing and sharing of health data for research purposes. J Eval Clin Pract 17: 1140–1146. doi: 10.1111/j.1365-2753.2010.01488.x. pmid:20629997
  26. 26. Jarnevich C, Graham J, Newman G, Crall A, Stohlgren T (2007) Balancing data sharing requirements for analyses with data sensitivity. Biol Invasions 9: 597–599. doi: 10.1007/s10530-006-9042-4.
  27. 27. Pearce N, Smith A (2011) Data sharing: not as simple as it seems. Environ Health 10: 1–7. doi: 10.1186/1476-069X-10-107. pmid:21205326
  28. 28. Bezuidenhout L (2013) Data Sharing and Dual-Use Issues. Sci Eng Ethics 19: 83–92. doi: 10.1007/s11948-011-9298-7. pmid:21805213
  29. 29. Stanley B, Stanley M (1988) Data Sharing: The Primary Researcher’s Perspective. Law Hum Behav 12: 173–180. doi: 10.1007/bf01073125
  30. 30. Pitt MA, Tang Y (2013) What Should Be the Data Sharing Policy of Cognitive Science? Top Cogn Sci 5: 214–221. doi: 10.1111/tops.12006. pmid:23335581
  31. 31. Whitlock MC, McPeek MA, Rausher MD, Rieseberg L, Moore AJ (2010) Data Archiving. Am Nat 175: 145–146. doi: 10.1086/650340. pmid:20073990
  32. 32. Fisher JB, Fortmann L (2010) Governing the data commons: Policy, practice, and the advancement of science. Inf Manage 47: 237–245. doi: 10.1016/
  33. 33. Van Horn JD, Gazzaniga MS (2013) Why share data? Lessons learned from the fMRIDC. NeuroImage 82: 677–682. doi: 10.1016/j.neuroimage.2012.11.010. pmid:23160115
  34. 34. Costello MJ (2009) Motivating Online Publication of Data. BioScience 59: 418–427. doi: 10.1525/bio.2009.59.5.9
  35. 35. Huang X, Hawkins BA, Lei F, Miller GL, Favret C, et al. (2012) Willing or unwilling to share primary biodiversity data: results and implications of an international survey. Conserv Lett 5: 399–406. doi: 10.1111/j.1755-263X.2012.00259.x.
  36. 36. Gardner D, Toga AW, et al. (2003) Towards Effective and Rewarding Data Sharing. Neuroinformatics 1: 289–295. pmid:15046250 doi: 10.1385/ni:1:3:289
  37. 37. Campbell EG, Clarridge BR, Gokhale M, Birenbaum L, Hilgartner S, et al. (2012) Data withholding in academic genetics: evidence from a national survey. JAMA 287: 473–480. doi: 10.1007/s00438-012-0693-9. pmid:22552803
  38. 38. Piwowar HA, Becich MJ, Bilofsky H, Crowley RS, on behalf of the caBIG Data Sharing and Intellectual Capital Workspace (2008) Towards a Data Sharing Culture: Recommendations for Leadership from Academic Health Centers. PLoS Med 5: e183. doi: 10.1371/journal.pmed.0050183. pmid:18767901
  39. 39. Piwowar HA, Day RS, Fridsma DB (2007) Sharing Detailed Research Data Is Associated with Increased Citation Rate. PLoS ONE 2: e308. doi: 10.1371/journal.pone.0000308. pmid:17375194
  40. 40. Rushby N (2013) Editorial: Data-sharing. Br J Educ Technol 44: 675–676. doi: 10.1111/bjet.12091.
  41. 41. Reidpath DD, Allotey PA (2001) Data Sharing in Medical Research: An Empirical Investigation. Bioethics 15: 125–134. doi: 10.1111/1467-8519.00220. pmid:11697377
  42. 42. Wallis JC, Rolando E, Borgman CL (2013) If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS ONE 8: e67332. doi: 10.1371/journal.pone.0067332. pmid:23935830
  43. 43. Sieber JE (1988) Data sharing: Defining problems and seeking solutions. Law Hum Behav 12: 199–206. doi: 10.1007/BF01073128.
  44. 44. Haendel MA, Vasilevsky NA, Wirz JA (2012) Dealing with Data: A Case Study on Information and Data Management Literacy. PLoS Biol 10: e1001339. doi: 10.1371/journal.pbio.1001339. pmid:22666180
  45. 45. Piwowar HA (2010) Who shares? Who doesn’t? Bibliometric factors associated with open archiving of biomedical datasets. ASIST 2010 Oct 22–27 2010 Pittsburgh PA USA: 1–2.
  46. 46. Teeters JL, Harris KD, Millman KJ, Olshausen BA, Sommer FT (2008) Data Sharing for Computational Neuroscience. Neuroinformatics 6: 47–55. doi: 10.1007/s12021-008-9009-y. pmid:18259695
  47. 47. De Wolf VA, Sieber JE, Steel PM, Zarate AO (2006) Part III: Meeting the Challenge When Data Sharing Is Required. IRB Ethics Hum Res 28: 10–15. pmid:16770883
  48. 48. Dalgleish R, Molero E, Kidd R, Jansen M, Past D, et al. (2012) Solving bottlenecks in data sharing in the life sciences. Hum Mutat 33: 1494–1496. doi: 10.1002/humu.22123. pmid:22623360
  49. 49. Parr C, Cummings M (2005) Data sharing in ecology and evolution. Trends Ecol Evol 20: 362–363. doi: 10.1016/j.tree.2005.04.023. pmid:16701396
  50. 50. Elman C, Kapiszewski D, Vinuela L (2010) Qualitative Data Archiving: Rewards and Challenges. PS Polit Sci Polit 43: 23–27. doi: 10.1017/S104909651099077X.
  51. 51. Whitlock MC (2011) Data archiving in ecology and evolution: best practices. Trends Ecol Evol 26: 61–65. doi: 10.1016/j.tree.2010.11.006. pmid:21159406
  52. 52. Ostell J (2009) Data Sharing: Standards for Bioinformatic Cross-Talk. Hum Mutat 30: vii–vii. doi: 10.1002/humu.21013.
  53. 53. Tucker J (2009) Motivating Subjects: Data Sharing in Cancer Research.
  54. 54. Rohlfing T, Poline J-B (2012) Why shared data should not be acknowledged on the author byline. NeuroImage 59: 4189–4195. doi: 10.1016/j.neuroimage.2011.09.080. pmid:22008368
  55. 55. Parr CS (2007) Open Sourcing Ecological Data. BioScience 57: 309. doi: 10.1641/B570402.
  56. 56. Kim Y, Stanton JM (2012) Institutional and Individual Influences on Scientists’ Data Sharing Practices. J Comput Sci Educ 3: 47–56.
  57. 57. Belmonte MK, Mazziotta JC, Minshew NJ, Evans AC, Courchesne E, et al. (2007) Offering to Share: How to Put Heads Together in Autism Neuroimaging. J Autism Dev Disord 38: 2–13. doi: 10.1007/s10803-006-0352-2. pmid:17347882
  58. 58. Cragin MH, Palmer CL, Carlson JR, Witt M (2010) Data sharing, small science and institutional repositories. Philos Trans R Soc Math Phys Eng Sci 368: 4023–4038. doi: 10.1098/rsta.2010.0165. pmid:20679120
  59. 59. Perrino T, Howe G, Sperling A, Beardslee W, Sandler I, et al. (2013) Advancing Science Through Collaborative Data Sharing and Synthesis. Perspect Psychol Sci 8: 433–444. doi: 10.1177/1745691613491579
  60. 60. Cohn JP (2012) DataONE Opens Doors to Scientists across Disciplines. BioScience 62: 1004. doi: 10.1525/bio.2012.62.11.16.
  61. 61. Borgman CL (2012) The conundrum of sharing research data. J Am Soc Inf Sci Technol 63: 1059–1078. doi: 10.1002/asi.22634.
  62. 62. Nelson B (2009) Data sharing: Empty archives. Nature 461: 160–163. doi: 10.1038/461160a. pmid:19741679
  63. 63. Axelsson A-S, Schroeder R (2009) Making it Open and Keeping it Safe: e-Enabled Data-Sharing in Sweden. Acta Sociol 52: 213–226. doi: 10.2307/25652127.
  64. 64. Jiang X, Sarwate AD, Ohno-Machado L (2013) Privacy Technology to Support Data Sharing for Comparative Effectiveness Research: A Systematic Review. Med Care 51: S58–S65. doi: 10.1097/MLR.0b013e31829b1d10. pmid:23774511
  65. 65. Linkert M, Curtis T., Burel J-M, Moore W, Patterson A, et al. (2010) Metadata matters: access to image data in the real world. Metadata Matters Access Image Data Real World 189: 777–782. doi: 10.1083/jcb.201004104. pmid:20513764
  66. 66. Edwards PN, Mayernik MS, Batcheller AL, Bowker GC, Borgman CL (2011) Science friction: Data, metadata, and collaboration. Soc Stud Sci 41: 667–690. doi: 10.1177/0306312711413314. pmid:22164720
  67. 67. Anagnostou P, Capocasa M, Milia N, Bisol GD (2013) Research data sharing: Lessons from forensic genetics. Forensic Sci Int Genet. Available: Accessed 8 October 2013.
  68. 68. Sansone S-A, Rocca-Serra P (2012) On the evolving portfolio of community-standards and data sharing policies: turning challenges into new opportunities. GigaScience 1: 1–3. doi: 10.1186/2047-217X-1-10. pmid:23587310
  69. 69. Huang X, Hawkins BA, Gexia Qiao (2013) Biodiversity Data Sharing: Will Peer-Reviewed Data Papers Work? BioScience 63: 5–6. doi: 10.1525/bio.2013.63.1.2
  70. 70. Butler D (2007) Data sharing: the next generation. Nature 446: 10–11. doi: 10.1038/446010b. pmid:17330012
  71. 71. Chokshi DA, Parker M, Kwiatkowski DP (2006) Data sharing and intellectual property in a genomic epidemiology network: policies for large-scale research collaboration. Bull World Health Organ 84. doi: 10.2471/blt.06.029843
  72. 72. Feldman L, Patel D, Ortmann L, Robinson K, Popovic T (2012) Educating for the future: another important benefit of data sharing. The Lancet 379: 1877–1878. doi: 10.1016/S0140-6736(12)60809-5.
  73. 73. Drew BT, Gazis R, Cabezas P, Swithers KS, Deng J, et al. (2013) Lost Branches on the Tree of Life. PLoS Biol 11: e1001636. doi: 10.1371/journal.pbio.1001636. pmid:24019756
  74. 74. Weber NM (2013) The relevance of research data sharing and reuse studies. Bull Am Soc Inf Sci Technol 39: 23–26. doi: 10.1002/bult.2013.1720390609.
  75. 75. Piwowar HA (2011) Who Shares? Who Doesn’t? Factors Associated with Openly Archiving Raw Research Data. PLoS ONE 6: e18657. doi: 10.1371/journal.pone.0018657. pmid:21765886
  76. 76. Samson K (2008) NerveCenter: Data sharing: Making headway in a competitive research milieu. Ann Neurol 64: A13–A16. doi: 10.1002/ana.21478. pmid:18991356
  77. 77. Wicherts JM, Bakker M (2012) Publish (your data) or (let the data) perish! Why not publish your data too? Intelligence 40: 73–76. doi: 10.1016/j.intell.2012.01.004.
  78. 78. Wicherts JM, Borsboom D, Kats J, Molenaar D (2006) The poor availability of psychological research data for reanalysis. Am Psychol 61: 726–728. doi: 10.1037/0003-066X.61.7.726. pmid:17032082
  79. 79. Breeze J, Poline J-B, Kennedy D (2012) Data sharing and publishing in the field of neuroimaging. GigaScience 1: 1–3. doi: 10.1186/2047-217X-1-9. pmid:23587310
  80. 80. Alsheikh-Ali AA, Qureshi W, Al-Mallah MH, Ioannidis JPA (2011) Public Availability of Published Research Data in High-Impact Journals. PLoS ONE 6: e24357. doi: 10.1371/journal.pone.0024357. pmid:21915316
  81. 81. Noor MAF, Zimmerman KJ, Teeter KC (2006) Data Sharing: How Much Doesn’t Get Submitted to GenBank? PLoS Biol 4: e228. doi: 10.1371/journal.pbio.0040228. pmid:16822095
  82. 82. Piwowar HA, Chapman WW (2010) Public sharing of research datasets: A pilot study of associations. J Informetr 4: 148–156. doi: 10.1016/j.joi.2009.11.010. pmid:21339841
  83. 83. Brakewood B, Poldrack RA (2013) The ethics of secondary data analysis: Considering the application of Belmont principles to the sharing of neuroimaging data. NeuroImage 82: 671–676. doi: 10.1016/j.neuroimage.2013.02.040. pmid:23466937
  84. 84. De Wolf VA, Sieber JE, Steel PM, Zarate AO (2005) Part I: What Is the Requirement for Data Sharing? IRB Ethics Hum Res 27: 12–16. doi: 10.2307/3563537
  85. 85. Harding A, Harper B, Stone D, O’Neill C, Berger P, et al. (2011) Conducting Research with Tribal Communities: Sovereignty, Ethics, and Data-Sharing Issues. Environ Health Perspect 120: 6–10. doi: 10.1289/ehp.1103904. pmid:21890450
  86. 86. Mennes M, Biswal BB, Castellanos FX, Milham MP (2013) Making data sharing work: The FCP/INDI experience. NeuroImage 82: 683–691. doi: 10.1016/j.neuroimage.2012.10.064. pmid:23123682
  87. 87. Sheather J (2009) Confidentiality and sharing health information. Br Med J 338. doi: 10.1136/bmj.b2160
  88. 88. Kowalczyk S, Shankar K (2011) Data sharing in the sciences. Annu Rev Inf Sci Technol 45: 247–294. doi: 10.1002/aris.2011.1440450113.
  89. 89. Cooper M (2007) Sharing Data and Results in Ethnographic Research: Why This Should not be an Ethical Imperative. J Empir Res Hum Res Ethics Int J 2: 3–19. doi: 10.1525/jer.2007.2.1.3
  90. 90. Sayogo DS, Pardo TA (2013) Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data. Gov Inf Q 30: S19–S31. doi: 10.1016/j.giq.2012.06.011.
  91. 91. Freymann J, Kirby J, Perry J, Clunie D, Jaffe CC (2012) Image Data Sharing for Biomedical Research—Meeting HIPAA Requirements for De-identification. J Digit Imaging 25: 14–24. doi: 10.1007/s10278-011-9422-x. pmid:22038512
  92. 92. Anderson JR, Toby L. Schonfeld (2009) Data-Sharing Dilemmas: Allowing Pharmaceutical Company Access to Research Data. IRB Ethics Hum Res 31: 17–19. doi: 10.2307/25594876.
  93. 93. Ludman EJ, Fullerton SM, Spangler L, Trinidad SB, Fujii MM, et al. (2010) Glad You Asked: Participants’ Opinions Of Re-Consent for dbGap Data Submission. J Empir Res Hum Res Ethics 5: 9–16. doi: 10.1525/jer.2010.5.3.9. pmid:20831417
  94. 94. Resnik DB (2010) Genomic Research Data: Open vs. Restricted Access. IRB Ethics Hum Res 32: 1–6. doi: 10.2307/25703686.
  95. 95. Rai AK, Eisenberg RS (2006) Harnessing and Sharing the Benefits of State-Sponsored Research: Intellectual Property Rights and Data Sharing in California’s Stem Cell Initiative. Duke Law Sch Sci Technol Innov Res Pap Ser: 1187–1214.
  96. 96. Levenson D (2010) When should pediatric biobanks share data? Am J Med Genet A 152A: fm vii–fm viii. doi: 10.1002/ajmg.a.33287.
  97. 97. Delson E, Harcourt-Smith WEH, Frost SR, Norris CA (2007) Databases, Data Access, and Data Sharing in Paleoanthropology: First Steps. Evol Anthropol 16: 161–163. doi: 10.1002/evan.20141
  98. 98. Eisenberg RS (2006) Patents and data-sharing in public science. Ind Corp Change 15: 1013–1031. doi: 10.1093/icc/dtl025.
  99. 99. Cahill JM, Passamano JA (2007) Full Disclosure Matters. East Archaeol 70: 194–196. doi: 10.2307/20361332.
  100. 100. Chandramohan D, Shibuya K, Setel P, Cairncross S, Lopez AD, et al. (2008) Should Data from Demographic Surveillance Systems Be Made More Widely Available to Researchers? PLoS Med 5: e57. doi: 10.1371/journal.pmed.0050057. pmid:18303944
  101. 101. Hayman B, Wilkes L, Jackson D, Halcomb E (2012) Story-sharing as a method of data collection in qualitative research. J Clin Nurs 21: 285–287. doi: 10.1111/j.1365-2702.2011.04002.x. pmid:22150861
  102. 102. Masum H, Rao A, Good BM, Todd MH, Edwards AM, et al. (2013) Ten Simple Rules for Cultivating Open Science and Collaborative R&amp;D. PLoS Comput Biol 9: e1003244. doi: 10.1371/journal.pcbi.1003244. pmid:24086123
  103. 103. Molloy JC (2011) The Open Knowledge Foundation: Open Data Means Better Science. PLoS Biol 9: e1001195. doi: 10.1371/journal.pbio.1001195. pmid:22162946
  104. 104. Overbey MM (1999) Data Sharing Dilemma. Anthropol Newsl April.
  105. 105. Zimmerman AS (2008) New Knowledge from Old Data: The Role of Standards in the Sharing and Reuse of Ecological Data. Sci Technol Hum Values 33: 631–652. doi: 10.2307/29734058.
  106. 106. Albert H (2012) Scientists encourage genetic data sharing. Springer Healthc News 1: 1–2. doi: 10.1007/s40014-012-1434-z.
  107. 107. Fernandez J, Patrick A, Zuck L (2012) Ethical and Secure Data Sharing across Borders. In: Blyth J, Dietrich S, Camp LJ, editors. Financial Cryptography and Data Security. Lecture Notes in Computer Science. Springer Berlin Heidelberg, Vol. 7398. pp. 136–140. Available:
  108. 108. Rodgers W, Nolte M (2006) Solving Problems of Disclosure Risk in An Academic Setting: Using A Combination of Restrictred Data and Restricted Access Methods. J Empir Res Hum Res Ethics Int J 1. doi: 10.1525/jer.2006.1.3.85
  109. 109. Constable H, Guralnick R, Wieczorek J, Spencer C, Peterson AT, et al. (2010) VertNet: A New Model for Biodiversity Data Sharing. PLoS Biol 8: e1000309. doi: 10.1371/journal.pbio.1000309. pmid:20169109
  110. 110. Nicholson SW, Bennett TB (2011) Data Sharing: Academic Libraries and the Scholarly Enterprise. Portal Libr Acad 11: 505–516.
  111. 111. Fulk J, Heino R, Flanagin AJ, Monge PR, Bar F (2004) A Test of the Individual Action Model for Organizational Information Commons. Organ Sci 15: 569–585. doi: 10.2307/30034758.
  112. 112. Jones R, Reeves D, Martinez C (2012) Overview of Electronic Data Sharing: Why, How, and Impact. Curr Oncol Rep 14: 486–493. doi: 10.1007/s11912-012-0271-7. pmid:22976780
  113. 113. Myneni S, Patel VL (2010) Organization of biomedical data for collaborative scientific research: A research information management system. Int J Inf Manag 30: 256–264. doi: 10.1016/j.ijinfomgt.2009.09.005. pmid:20543892
  114. 114. Karami M, Rangzan K, Saberi A (2013) Using GIS servers and interactive maps in spectral data sharing and administration: Case study of Ahvaz Spectral Geodatabase Platform (ASGP). Comput Geosci 60: 23–33. doi: 10.1016/j.cageo.2013.06.007.
  115. 115. Surowiecki J (2004) The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. 1st ed. New York: Doubleday. 296 p.
  116. 116. Lévy P (1997) Collective intelligence: mankind’s emerging world in cyberspace. Cambridge, Mass: Perseus Books. 277 p.
  117. 117. von Hippel E, von Krogh G (2003) Open Source Software and the “Private-Collective” Innovation Model: Issues for Organization Science. Organ Sci 14: 209–223. doi: 10.1287/orsc.
  118. 118. Benkler Y (2006) The wealth of networks: how social production transforms markets and freedom. New Haven [Conn.]: Yale University Press.
  119. 119. Kittur A, Kraut RE (2008) Harnessing the wisdom of crowds in wikipedia: quality through coordination ACM Press. p. 37. Available: Accessed 2 April 2014.
  120. 120. Hess C, Ostrom E, editors (2007) Understanding knowledge as a commons: from theory to practice. Cambridge, Mass: MIT Press. 367 p.
  121. 121. Nielsen MA (2012) Reinventing discovery: the new era of networked science. Princeton, N.J: Princeton University Press. 264 p.
  122. 122. NIH (2002) NIH Data-Sharing Plan. Anthropol News 43: 30–30. doi: 10.1111/an.2002.
  123. 123. NIH (2003) NIH Posts Research Data Sharing Policy. Anthropol News 44: 25–25. doi: 10.1111/an.2003.
  124. 124. Savage CJ, Vickers AJ (2009) Empirical Study of Data Sharing by Authors Publishing in PLoS Journals. PLoS ONE 4: e7078. doi: 10.1371/journal.pone.0007078. pmid:19763261
  125. 125. Czarnitzki D, Grimpe C, Pellens M (2014) Access to Research Inputs: Open Science Versus the Entrepreneurial University.
  126. 126. Haeussler C (2011) Information-sharing in academia and the industry: A comparative study. Res Policy 40: 105–122. doi: 10.1016/j.respol.2010.08.007.