scholarly journals Data Management and Data Sharing in Psychological Science: Revision of the DGPs Recommendations

2020 ◽  
Author(s):  
Mario Gollwitzer ◽  
Andrea Abele-Brehm ◽  
Christian Fiebach ◽  
Roland Ramthun ◽  
Anne M. Scheel ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Data Protection ◽  
Scientific Practice ◽  
Data Access ◽  
Scientific Data ◽  
Research Data ◽  
Primary Data ◽  
Practical Implementation ◽  
Data Repositories

Providing access to research data collected as part of scientific publications and publicly funded research projects is now regarded as a central aspect of an open and transparent scientific practice and is increasingly being called for by funding institutions and scientific journals. To this end, researchers should strive to comply with the so-called FAIR principles (of scientific data management), that is, research data should be findable, accessible, interoperable, and reusable. Systematic data management supports these goals and, at the same time, makes it possible to achieve them efficiently. With these revised recommendations on data management and data sharing, which also draw on feedback from a 2018 survey of its members, the German Psychological Society (Deutsche Gesellschaft für Psychologie; DGPs) specifies important basic principles of data management in psychology. Initially, based on discipline-specific definitions of raw data, primary data, secondary data, and metadata, we provide recommendations on the degree of data processing necessary when publishing data. We then discuss data protection as well as aspects of copyright and data usage before defining the qualitative requirements for trustworthy research data repositories. This is followed by a detailed discussion of pragmatic aspects of data sharing, such as the differences between Type 1 and Type 2 data publications, restrictions on use (embargo period), the definition of "scientific use" by secondary users of shared data, and recommendations on how to resolve potential disputes. Particularly noteworthy is the new recommendation of distinct "access categories" for data, each with different requirements in terms of data protection or research ethics. These range from completely open data without usage restrictions ("access category 0") to data shared under a set of standardized conditions (e.g., reuse restricted to scientific purposes; "access category 1"), individualized usage agreements ("access category 2"), and secure data access under strictly controlled conditions (e.g., in a research data center; “access category 3"). The practical implementation of this important innovation, however, will require data repositories to provide the necessary technical functionalities. In summary, the revised recommendations aim to present pragmatic guidelines for researchers to handle psychological research data in an open and transparent manner, while addressing structural challenges to data sharing solutions that are beneficial for all involved parties.

scholarly journals Perceptions and Attitudes toward Data Sharing among Dental Researchers

2018 ◽  
Vol 4 (1) ◽  
pp. 68-75 ◽  
Author(s):  
H. Spallek ◽  
S.M. Weinberg ◽  
M. Manz ◽  
S. Nanayakkara ◽  
X. Zhou ◽  
...  
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
International Association ◽  
Improve Patient Care ◽  
Research Data ◽  
Research Subjects ◽  
Data Repositories ◽  
Use Of Data ◽  
Share Data ◽  
Past Experiences

Introduction: Increasing attention is being given to the roles of data management and data sharing in the advancement of research. This study was undertaken to explore opinions and past experiences of established dental researchers as related to data sharing and data management. Methods: Researchers were recruited from the International Association for Dental Research scientific groups to complete a survey consisting of Likert-type, multiple-choice, and open-ended questions. Results: All 42 respondents indicated that data sharing should be promoted and facilitated, but many indicated reservations or concerns about the proper use of data and the protection of research subjects. Many had used data from data repositories and received requests for data originating from their studies. Opinions varied regarding restrictions such as requirements to share data and the time limits of investigator rights to keep data. Respondents also varied in their methods of data management and storage, with younger respondents and those with higher direct costs of their research tending to use dedicated experts to manage their data. Discussion: The expressed respondent support for research data sharing, with the noted concerns, complements the idea of developing managed data clearinghouses capable of promoting, managing, and overseeing the data-sharing process. Knowledge Transfer Statement: Researchers can use the results of this study to evaluate and improve management and sharing of research data. By encouraging and facilitating the data-sharing process, research can advance more efficiently, and research findings can be implemented into practice more rapidly to improve patient care and the overall oral health of populations.

scholarly journals Publishing descriptions of non-public clinical datasets: guidance for researchers, repositories, editors and funding organisations

2015 ◽  
Author(s):  
Iain Hrynaszkiewicz ◽  
Varsha Khodiyar ◽  
Andrew L Hufton ◽  
Susanna-Assunta Sansone
Keyword(s):  
Clinical Research ◽  
Data Sharing ◽  
Peer Review Process ◽  
Open Data ◽  
Data Access ◽  
Research Data ◽  
Future Research ◽  
Patient Privacy ◽  
Journal Articles ◽  
Data Repositories

AbstractSharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles – where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. We propose changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).

scholarly journals Archaeological Analysis in the Information Age: Guidelines for Maximizing the Reach, Comprehensiveness, and Longevity of Data

2019 ◽  
Vol 8 (1) ◽  
pp. 40-52 ◽  
Author(s):  
Sarah W. Kansa ◽  
Levent Atici ◽  
Eric C. Kansa ◽  
Richard H. Meadow
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Information Age ◽  
Research Data ◽  
Primary Data ◽  
Reproducible Research ◽  
Research Data Management ◽  
The Past ◽  
Archaeological Analysis ◽  
The Web

ABSTRACTWith the advent of the Web, increased emphasis on “research data management,” and innovations in reproducible research practices, scholars have more incentives and opportunities to document and disseminate their primary data. This article seeks to guide archaeologists in data sharing by highlighting recurring challenges in reusing archived data gleaned from observations on workflows and reanalysis efforts involving datasets published over the past 15 years by Open Context. Based on our findings, we propose specific guidelines to improve data management, documentation, and publishing practices so that primary data can be more efficiently discovered, understood, aggregated, and synthesized by wider research communities.

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>

Research Data Access and Management in National Libraries

2019 ◽  
pp. 1-28
Author(s):  
Enrique Wulff
Keyword(s):  
Data Management ◽  
Data Access ◽  
Research Data ◽  
British Library ◽  
National Library ◽  
Data Repositories ◽  
Data Archiving ◽  
National Libraries ◽  
Synergistic Relationship ◽  
Publication Standards

National libraries have developed research data responsibilities for reasons of data ownership and cost-efficiency. Due to their multi-faceted and synergistic relationship with research data actors (publishers and researchers), their leadership in publication standards makes them a unique participant as advisors on research data archiving and citation, as much as for their discovery and licensing expertise. National libraries engage with the data community to raise awareness of the relevance of data management and so promote themselves as an essential place for data repositories and the researcher community. This chapter introduces a framework of five national libraries: the British Library, the Library of Congress, the National Library of Medicine, the German National Library of Science and Technology, and the German National Library of Medicine.

The Indonesian National Scientific Repository: A Case Study of Research Data Sharing

2020 ◽  
Vol 49 (1) ◽  
pp. 14-25
Author(s):  
Deka Anggawira ◽  
Nina Mayesti
Keyword(s):  
Data Sharing ◽  
Data Access ◽  
Scientific Data ◽  
Research Data ◽  
Case Study Method ◽  
Structured Interviews ◽  
Staff Members ◽  
Institutional Goals ◽  
Development Teams

AbstractThis study discusses the sharing of research data through the Repositori Ilmiah Nasional, the Indonesian national scientific repository, which is managed by the Center for Scientific Data and Documentation, Indonesian Institute of Sciences (Pusat Dokumentasi dan Informasi Ilmiah, Lembaga Ilmu Pengetahuan Indonesia, known by the abbreviation PDDI-LIPI). The purpose of this study is to describe the process of research data sharing and identify supporting factors and obstacles faced in that process. This study uses a qualitative approach, with a case study method. Data collection techniques included field observations and observations on the repository system; semi-structured interviews with several informants, including researchers as well as development and librarian teams; and, analysis of policy documents and guidelines. Through these investigations, we discovered that while the Center has developed a new DataVerse repository system to enable research data sharing, there are still several issues that impede the repository from meeting institutional goals for increased data access. There is a need for additional training and socialization of researchers, to encourage and motivate them to share their research data through this service. Additionally, staff members need to gain competence in the management and curation of data. Researchers and librarians involved in research data sharing activities still face various obstacles in the areas of policy, service visibility, and promotion. This research is expected to increase the awareness of researchers, librarians, and repository development teams about each other’s needs and to aid them in collaborating with each other to optimize the sharing of research of data through the repository.

scholarly journals A necessidade de uma política nacional para a gestão de dados de pesquisa no Brasil | The need for a national policy for the management of research data in Brazil

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Maíra Murrieta Costa ◽  
Murilo Bastos da Cunha
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Technological Development ◽  
Scientific Information ◽  
Policy Research ◽  
National Policy ◽  
Legal Framework ◽  
Scientific Data ◽  
Research Data ◽  
National Information

RESUMO Este trabalho discute os conceitos e características da ciência produzida no século XXI. Traz considerações sobre a ciberinfraestrutura necessária à colaboração e compartilhamento de dados de pesquisa. Contextualiza a gestão de dados e os motivos que podem causar impacto no compartilhamento de dados, dentre eles pesquisa em temas de soberania nacional, ou mesmo pesquisas que culminaram como desenvolvimento tecnológico. São apresentados exemplos sobre o processo de compartilhamento de dados em alguns países no exterior. No contexto brasileiro, apresenta e discute o marco legal sobre dados abertos governamentais e científicos. Além disso, apresenta algumas políticas institucionais brasileiras sobre gestão de dados. Ao final do artigo é apresentado um conjunto de diretrizes que pode servir de subsídio para a elaboração de uma política nacional para a gestão de dados de pesquisa.Palavras-chave: Brasil; Gestão de dados de pesquisa; gestão de dados científicos, Informação científica; Política nacional de informação.ABSTRACT This article discusses the concepts and characteristics of science produced in the 21st century. It presents considerations about the necessary cyberinfrastructure for research data collaboration and sharing.  It contextualizes data management and the reasons that may impact data sharing, such as research on national sovereignty issues or research that culminated as technological development. Examples are given about the process of data sharing in some countries abroad. In the Brazilian context, it presents and discusses the legal framework on open governmental and scientific data. In addition, it presents some Brazilian institutional policies on data management. At the end presents a set of guidelines that can subsidy the creation of a national policy for research data management.Keywords: Brazil; National information policy; Research data management; Data scholarship, Scientific information.

Research Data Access and Management in National Libraries

2021 ◽  
pp. 885-906
Author(s):  
Enrique Wulff
Keyword(s):  
Data Management ◽  
Data Access ◽  
Research Data ◽  
British Library ◽  
National Library ◽  
Data Repositories ◽  
Data Archiving ◽  
National Libraries ◽  
Synergistic Relationship ◽  
Publication Standards

National libraries have developed research data responsibilities for reasons of data ownership and cost-efficiency. Due to their multi-faceted and synergistic relationship with research data actors (publishers and researchers), their leadership in publication standards makes them a unique participant as advisors on research data archiving and citation, as much as for their discovery and licensing expertise. National libraries engage with the data community to raise awareness of the relevance of data management and so promote themselves as an essential place for data repositories and the researcher community. This chapter introduces a framework of five national libraries: the British Library, the Library of Congress, the National Library of Medicine, the German National Library of Science and Technology, and the German National Library of Medicine.

scholarly journals Potential opportunities and risks of sharing agricultural research data in Tanzania

2021 ◽  
Vol 45 (3-4) ◽  
Author(s):  
Anajoyce Samuel Katabalwa ◽  
Jo Bates ◽  
Pamela Abbott
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Agricultural Research ◽  
Research Output ◽  
Open Data ◽  
Research Literature ◽  
Research Data ◽  
Data Repositories ◽  
The Government ◽  
Support Research

Purpose: The purpose of this paper was to examine the potential opportunities and risks of sharing agricultural research data in Tanzania identified in the existing research literature. Design/methodology/approach: The study involved a review of the literature on research data sharing practices. Findings: The findings indicate that, research data sharing have significant positive benefits among researchers such as increase high research impact; enhancing international community collaboration among researchers with same interests; improving scientific transparency and accuracy of data (Rappert and Bezuidenhout, 2016); increasing research output whereby a single dataset can be used to generate more than one article by different authors; and many more. The risks hampering data sharing practices includes researchers’ fears that data will be scooped, poached or misused (Onyancha, 2016); unreliable electric power; lack of fund to support research data sharing activities; absence of institutional governmental support for data management; perceived lack of evidence benefits (Leonelli, Rappert and Bezuidenhout, 2018); and others. However, in Tanzania research data sharing is relatively new, thus, no any governmental agency mandating or encouraging research data sharing; therefore, there is no research data management; no research open data repositories and no research data sharing policy at any agricultural institution in Tanzania. The study recommends that agricultural researchers should be sensitized to share their data, research data policy and data repositories should also be established to support data sharing practices in Tanzania. Originality and usefulness: From the available literature, this has been the first time that an effort has been made to examine the potential opportunities and risks of sharing agricultural research data in Tanzania. The study could be used by agricultural institutions and other institutions to assess the researchers’ needs in supporting research data sharing. Also, it can be used by the government and institutions to see the need of establishing open data repositories and open data policies to support research data sharing.

scholarly journals The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Daniel Arend ◽  
Patrick König ◽  
Astrid Junker ◽  
Uwe Scholz ◽  
Matthias Lange
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Best Practice ◽  
Research Process ◽  
Research Data ◽  
Primary Data ◽  
Data Repository ◽  
Plant Genomics ◽  
Quality Of Data

Abstract Background The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. Although the ELIXIR Core Data Resources and other established infrastructures provide comprehensive and long-term stable services and platforms for FAIR data management, a large quantity of research data is still hidden or at risk of getting lost. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, e.g., time series of images or high-resolution hyper-spectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institional boundaries. Results To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements a “bring the infrastructure to the data” approach, which allows research data to be kept in place and wrapped in a FAIR-aware software infrastructure. This article presents new features of the e!DAL infrastructure software and the PGP repository as a best practice on how to easily set up FAIR-compliant and intuitive research data services. Furthermore, the integration of the ELIXIR Authentication and Authorization Infrastructure (AAI) and data discovery services are introduced as means to lower technical barriers and to increase the visibility of research data. Conclusion The e!DAL software matured to a powerful and FAIR-compliant infrastructure, while keeping the focus on flexible setup and integration into existing infrastructures and into the daily research process.

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