scholarly journals Data Sharing Policies: Share Well and You Shall be Rewarded

2021 ◽  
Author(s):  
Jean Peccoud
Keyword(s):  
Data Sharing ◽  
Scientific Community ◽  
Computer Software ◽  
Research Data ◽  
Objective Data ◽  
Scientific Publishing ◽  
Publishing Process ◽  
Administrative Burden ◽  
Textual Description ◽  
The Impact

Abstract Sharing research data is an integral part of the scientific publishing process. By sharing data authors enable their readers to use their results in a way that the textual description of the results does not allow by itself. In order to achieve this objective, data should be shared in a way that makes it as easy as possible for readers to import them in computer software where they can be viewed, manipulated, and analyzed. Many authors and reviewers seem to misunderstand the purpose of the data sharing policies developed by journals. Rather than being an administrative burden that authors should comply with to get published, the objective of these policies is to help authors maximize the impact of their work by allowing other members of the scientific community to build upon it. Authors and reviewers need to understand the purpose of data sharing policies to assist editors and publishers in their efforts to ensure that every article published complies with them.

scholarly journals A Research data Sharing Game

2014 ◽  
Author(s):  
Tessa E Pronk ◽  
Paulien H Wiersma ◽  
Anne van Weerden
Keyword(s):  
Game Theory ◽  
Decision Theory ◽  
Data Sharing ◽  
Scientific Community ◽  
Data Retrieval ◽  
Research Data ◽  
Top Down ◽  
Average Efficiency ◽  
Natural Tendency ◽  
Share Data

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. For individuals, it is less obvious that the benefits of sharing data outweigh the associated costs, i.e. time and money. In this sense the problem of data sharing resembles a typical game in interactive decision theory, more commonly known as game theory. Within this framework we analyse in this paper how different measures to promote sharing and reuse of research data affect sharing and not sharing individuals. We find that the scientific community can benefit from top-down policies to enhance sharing data even when the act of sharing itself implies a cost. Namely, if (almost) everyone shares, many individuals can gain a higher efficiency as datasets can be reused. Additionally, measures to ensure better data retrieval and quality can compensate for sharing costs by enabling reuse. Nevertheless, an individual researcher who decides not to share omits the costs of sharing. Assuming that the natural tendency will be to use a strategy that will lead to maximisation of individual efficiency it is seen that, as more individuals decide not to share, there is a point at which average efficiency for both sharing and non-sharing researchers becomes lower than was originally the case and scientific community efficiency steadily drops. With this in mind, we conclude that the key to motivate the researcher to share data lies in reducing the costs associated with sharing, or even better, turning it into a benefit.

Beyond article publishing - support and opportunities for researchers in FAIR data sharing

2020 ◽  
Author(s):  
Graham Smith ◽  
Andrew Hufton
Keyword(s):  
Data Sharing ◽  
San Francisco ◽  
Scientific Data ◽  
Research Data ◽  
Research Assessment ◽  
Environmental Sciences ◽  
Data Repositories ◽  
The Earth ◽  
Data Citation ◽  
The Impact

<p>Researchers are increasingly expected by funders and journals to make their data available for reuse as a condition of publication. At Springer Nature, we feel that publishers must support researchers in meeting these additional requirements, and must recognise the distinct opportunities data holds as a research output. Here, we outline some of the varied ways that Springer Nature supports research data sharing and report on key outcomes.</p><p>Our staff and journals are closely involved with community-led efforts, like the Enabling FAIR Data initiative and the COPDESS 2014 Statement of Commitment <sup>1-4</sup>. The Enabling FAIR Data initiative, which was endorsed in January 2019 by <em>Nature</em> and <em>Scientific Data</em>, and by <em>Nature Geoscience</em> in January 2020, establishes a clear expectation that Earth and environmental sciences data should be deposited in FAIR<sup>5</sup> Data-aligned community repositories, when available (and in general purpose repositories otherwise). In support of this endorsement, <em>Nature</em> and <em>Nature Geoscience</em> require authors to share and deposit their Earth and environmental science data, and <em>Scientific Data</em> has committed to progressively updating its list of recommended data repositories to help authors comply with this mandate.</p><p>In addition, we offer a range of research data services, with various levels of support available to researchers in terms of data curation, expert guidance on repositories and linking research data and publications.</p><p>We appreciate that researchers face potentially challenging requirements in terms of the ‘what’, ‘where’ and ‘how’ of sharing research data. This can be particularly difficult for researchers to negotiate given that huge diversity of policies across different journals. We have therefore developed a series of standardised data policies, which have now been adopted by more than 1,600 Springer Nature journals. </p><p>We believe that these initiatives make important strides in challenging the current replication crisis and addressing the economic<sup>6</sup> and societal consequences of data unavailability. They also offer an opportunity to drive change in how academic credit is measured, through the recognition of a wider range of research outputs than articles and their citations alone. As signatories of the San Francisco Declaration on Research Assessment<sup>7</sup>, Nature Research is committed to improving the methods of evaluating scholarly research. Research data in this context offers new mechanisms to measure the impact of all research outputs. To this end, Springer Nature supports the publication of peer-reviewed data papers through journals like <em>Scientific Data</em>. Analysis of citation patterns demonstrate that data papers can be well-cited, and offer a viable way for researchers to receive credit for data sharing through traditional citation metrics. Springer Nature is also working hard to improve support for direct data citation. In 2018 a data citation roadmap developed by the Publishers Early Adopters Expert Group was published in <em>Scientific Data</em><sup>8</sup>, outlining practical steps for publishers to work with data citations and associated benefits in transparency and credit for researchers. Using examples from this roadmap, its implementation and supporting services, we outline how a FAIR-led data approach from publishers can help researchers in the Earth and environmental sciences to capitalise on new expectations around data sharing.</p><p>__</p><ol><li>https://doi.org/10.1038/d41586-019-00075-3</li> <li>https://doi.org/10.1038/s41561-019-0506-4</li> <li>https://copdess.org/enabling-fair-data-project/commitment-statement-in-the-earth-space-and-environmental-sciences/</li> <li>https://copdess.org/statement-of-commitment/</li> <li>https://www.force11.org/group/fairgroup/fairprinciples</li> <li>https://op.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1</li> <li>https://sfdora.org/read/</li> <li>https://doi.org/10.1038/sdata.2018.259</li> </ol>

scholarly journals Engaging the next generation of editorial talent through a hands-on fellowship model

2021 ◽  
Author(s):  
Bridget Deemer ◽  
Scott Hotaling ◽  
Kelsey Poulson-Ellestad ◽  
Laura Falkenberg ◽  
James Cloern ◽  
...  
Keyword(s):  
Peer Review ◽  
Subject Matter ◽  
Scientific Community ◽  
Early Career ◽  
Editorial Process ◽  
Scientific Publishing ◽  
High Quality ◽  
Publishing Process ◽  
Early Career Researchers ◽  
Hands On

Peer-review and subject-matter editing is the backbone of scientific publishing. However, early career researchers (ECRs) are given few opportunities to participate in the editorial process beyond reviewing articles. Thus, a disconnect exists: science needs high-quality editorial talent to conduct, oversee, and improve the publishing process, yet we dedicate few resources to building editorial talent nor giving ECRs formal opportunities to influence the publishing landscape from within. Here, we describe a “two-way” fellowship model that gives ECRs a “seat” at the editorial table of a field-leading journal. We describe both the necessary framework and benefits that can stem from editorial fellowships for ECRs, editors, journals, and the scientific community.

scholarly journals A research data sharing game

2014 ◽  
Author(s):  
Tessa E Pronk ◽  
Paulien H Wiersma ◽  
Anne van Weerden
Keyword(s):  
Game Theory ◽  
Decision Theory ◽  
Data Sharing ◽  
Scientific Community ◽  
Data Retrieval ◽  
Research Data ◽  
Top Down ◽  
Individual Researcher ◽  
Natural Tendency ◽  
Share Data

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. For individuals, it is less obvious that the benefits of sharing data outweigh the associated costs, for example time and money. In this sense the problem of data sharing resembles a typical game in interactive decision theory, more commonly known as game theory. Within this framework we analyse how measures to promote sharing and reuse of research data affect individuals who do and do not share data. We find that the scientific community can benefit from top-down policies to enhance sharing data even when the act of sharing itself implies a cost. Namely, if (almost) everyone shares, many individuals receive benefits, as datasets in our model can be reused to achieve a higher efficiency (i.e. more publications, higher quality papers). Surprisingly, as sharing implies a cost, even sharing individuals themselves, in a community in which sharing is common, can gain a higher efficiency than individuals who do not share, in a community in which sharing is not common. In addition to these findings, we find that measures to ensure better data retrieval and quality can compensate for sharing costs by further enabling reuse. Nevertheless, an individual researcher who decides not to share omits the costs of sharing. Assuming that the natural tendency will be to use a strategy that will lead to maximisation of individual efficiency, we see the average scientific community efficiency in our model steadily drop as more individuals decide not to share. With this in mind, we conclude that the key to motivate the researcher to share data lies in reducing the costs associated with sharing, or even better, turning it into a benefit.

scholarly journals A research data sharing game

2015 ◽  
Author(s):  
Tessa E Pronk ◽  
Paulien H Wiersma ◽  
Anne van Weerden ◽  
Feike Schieving
Keyword(s):  
Data Sharing ◽  
Scientific Community ◽  
Theoretical Framework ◽  
Research Data ◽  
Stable Strategy ◽  
Policy Measures ◽  
Conflicting Interests ◽  
Almost All

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. In this paper we analyse, within a game theoretical framework, how sharing and reuse of research data affect individuals who share or do not share their datasets. We construct a model in which there is a cost associated with sharing datasets whereas reusing such sets implies a benefit. In our calculations conflicting interests appeared for researchers. Individual researchers are always better off not sharing and omitting the sharing cost, at the same time both sharing and not sharing researchers are better off if (almost) all researchers share. Namely, the more researchers share, the more benefit can be gained by the reuse of those datasets. We simulated several policy measures to increase benefits for researchers sharing or reusing datasets. Results point out that, although policies should be able to increase the rate of sharing researchers, and increased discoverability and dataset quality could partly compensate for costs, a better measure would be to directly lower the costs for sharing, or even turn it into a (citation-) benefit. Making data available would in that case become the most profitable, and therefore stable, strategy. This means researchers would willingly make their datasets available, and arguably in the best possible way to enable reuse, making other policy measures less pressing.

scholarly journals A research data sharing game

2015 ◽  
Author(s):  
Tessa E Pronk ◽  
Paulien H Wiersma ◽  
Anne van Weerden ◽  
Feike Schieving
Keyword(s):  
Data Sharing ◽  
Scientific Community ◽  
Theoretical Framework ◽  
Research Data ◽  
Stable Strategy ◽  
Policy Measures ◽  
Conflicting Interests ◽  
Almost All

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. In this paper we analyse, within a game theoretical framework, how sharing and reuse of research data affect individuals who share or do not share their datasets. We construct a model in which there is a cost associated with sharing datasets whereas reusing such sets implies a benefit. In our calculations conflicting interests appeared for researchers. Individual researchers are always better off not sharing and omitting the sharing cost, at the same time both sharing and not sharing researchers are better off if (almost) all researchers share. Namely, the more researchers share, the more benefit can be gained by the reuse of those datasets. We simulated several policy measures to increase benefits for researchers sharing or reusing datasets. Results point out that, although policies should be able to increase the rate of sharing researchers, and increased discoverability and dataset quality could partly compensate for costs, a better measure would be to directly lower the costs for sharing, or even turn it into a (citation-) benefit. Making data available would in that case become the most profitable, and therefore stable, strategy. This means researchers would willingly make their datasets available, and arguably in the best possible way to enable reuse, making other policy measures less pressing.

scholarly journals Sharing health research data – the role of funders in improving the impact

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1641 ◽  
Author(s):  
Robert F. Terry ◽  
Katherine Littler ◽  
Piero L. Olliaro
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Ethical Issues ◽  
Good Practice ◽  
Research Data ◽  
Subsequent Publication ◽  
Share Data ◽  
Registry Entry ◽  
The Impact

Recent public health emergencies with outbreaks of influenza, Ebola and Zika revealed that the mechanisms for sharing research data are neither being used, or adequate for the purpose, particularly where data needs to be shared rapidly. A review of research papers, including completed clinical trials related to priority pathogens, found only 31% (98 out of 319 published papers, excluding case studies) provided access to all the data underlying the paper - 65% of these papers give no information on how to find or access the data. Only two clinical trials out of 58 on interventions for WHO priority pathogens provided any link in their registry entry to the background data. Interviews with researchers revealed a reluctance to share data included a lack of confidence in the utility of the data; an absence of academic-incentives for rapid dissemination that prevents subsequent publication and a disconnect between those who are collecting the data and those who wish to use it quickly.  The role of the funders of research needs to change to address this. Funders need to engage early with the researchers and related stakeholders to understand their concerns and work harder to define the more explicitly the benefits to all stakeholders.  Secondly, there needs to be a direct benefit to sharing data that is directly relevant to those people that collect and curate the data. Thirdly more work needs to be done to realise the intent of making data sharing resources more equitable, ethical and efficient.  Finally, a checklist of the issues that need to be addressed when designing new or revising existing data sharing resources should be created. This checklist would highlight the technical, cultural and ethical issues that need to be considered and point to examples of emerging good practice that can be used to address them.

scholarly journals The intertwining of reputation and sharing – The significance of standardization in preparing research data and the impact of project organization

2020 ◽  
Vol 33 ◽  
pp. 01002
Author(s):  
Saskia-Rabea Schrade
Keyword(s):  
Data Sharing ◽  
Special Form ◽  
Research Data ◽  
External Factors ◽  
Data Preparation ◽  
Project Organization ◽  
Funding Agencies ◽  
Share Data ◽  
Definition Of ◽  
The Impact

Despite efforts to increase scientists’ willingness to share research data political stakeholders and funding agencies, there is still a discrepancy between scientists’ attitude toward data sharing and their actual practice. In a first step, this paper takes a close look at scientists’ definition of research data and the influence of project organization on scientist’ willingness to share data by analyzing interviews with scientists of three different disciplines. As the analysis shows, talking about “data sharing” should always happen in the context of data preparation and its various steps. Additionally, the influence of external factors such as a special form of project organization seems to be limited.

scholarly journals Sharing health research data – the role of funders in improving the impact

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1641 ◽  
Author(s):  
Robert F. Terry ◽  
Katherine Littler ◽  
Piero L. Olliaro
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Ethical Issues ◽  
Good Practice ◽  
Research Data ◽  
Subsequent Publication ◽  
Share Data ◽  
Registry Entry ◽  
The Impact

Recent public health emergencies with outbreaks of influenza, Ebola and Zika revealed that the mechanisms for sharing research data are neither being used, or adequate for the purpose, particularly where data needs to be shared rapidly. A review of research papers, including completed clinical trials related to priority pathogens, found only 31% (98 out of 319 published papers, excluding case studies) provided access to all the data underlying the paper - 65% of these papers give no information on how to find or access the data. Only two clinical trials out of 58 on interventions for WHO priority pathogens provided any link in their registry entry to the background data. Interviews with researchers revealed a reluctance to share data included a lack of confidence in the utility of the data; an absence of academic-incentives for rapid dissemination that prevents subsequent publication and a disconnect between those who are collecting the data and those who wish to use it quickly.  The role of the funders of research needs to change to address this. Funders need to engage early with the researchers and related stakeholders to understand their concerns and work harder to define the more explicitly the benefits to all stakeholders.  Secondly, there needs to be a direct benefit to sharing data that is directly relevant to those people that collect and curate the data. Thirdly more work needs to be done to realise the intent of making data sharing resources more equitable, ethical and efficient.  Finally, a checklist of the issues that need to be addressed when designing new or revising existing data sharing resources should be created. This checklist would highlight the technical, cultural and ethical issues that need to be considered and point to examples of emerging good practice that can be used to address them.

ON THE CLASSIFICATION OF DIGITAL TECHNOLOGIES IN CIVIL PROCESS

2019 ◽  
pp. 27-35
Author(s):  
Alexandr Neznamov
Keyword(s):  
Information Processing ◽  
Scientific Community ◽  
Digital Technologies ◽  
Significant Feature ◽  
Legal Proceedings ◽  
Civil Process ◽  
Civil Proceedings ◽  
Processing Information ◽  
The Impact

Digital technologies are no longer the future but are the present of civil proceedings. That is why any research in this direction seems to be relevant. At the same time, some of the fundamental problems remain unattended by the scientific community. One of these problems is the problem of classification of digital technologies in civil proceedings. On the basis of instrumental and genetic approaches to the understanding of digital technologies, it is concluded that their most significant feature is the ability to mediate the interaction of participants in legal proceedings with information; their differentiating feature is the function performed by a particular technology in the interaction with information. On this basis, it is proposed to distinguish the following groups of digital technologies in civil proceedings: a) technologies of recording, storing and displaying (reproducing) information, b) technologies of transferring information, c) technologies of processing information. A brief description is given to each of the groups. Presented classification could serve as a basis for a more systematic discussion of the impact of digital technologies on the essence of civil proceedings. Particularly, it is pointed out that issues of recording, storing, reproducing and transferring information are traditionally more «technological» for civil process, while issues of information processing are more conceptual.

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