Search

Purpose This study aimed to ascertain the attitudes of Korean scholarly journal editors and publishers toward research data sharing policies and the publication of data papers through a survey. Methods Between May 16 and June 16, 2023, a SurveyMonkey survey link was distributed to 388 societies, including 270 member societies of the Korean Council of Science Editors and 118 societies that used an e-submission system operated by the Korea Institute of Science and Technology Information. A total of 78 societies (20.1%) responded, from which 72 responses (18.6%) were analyzed after excluding invalid responses. Results Out of the representatives of 72 journals, 20 editors or publishers (27.8%) declared a data sharing policy. Those journals that did not have such a policy often expressed uncertainty about their future plans regarding this issue. A common concern was a potential decrease in manuscript submissions, primarily due to the increased workload this policy might impose on editors and manuscript editors. Four respondents (5.6%) had published data papers, with two of them including this as a publication type in their author guidelines. Concerns about copyright and data licensing were cited as drawbacks to publishing data papers. However, the expansion of publication types and the promotion of data reuse were viewed as benefits. Conclusion Korean scholarly journal editors’ and publishers’ attitudes toward data sharing policy and publishing data papers are not yet favorable. More training courses are needed to raise awareness of data sharing platforms and emphasize the need for research data sharing and data papers.

Purpose The present study analyzed the current status of the data sharing policies of journals published in Brazil, France, and Korea that were listed in the 2018 Scimago Journal and Country Ranking and Web of Science Core Collection. Methods Web of Science journals were selected from the 2018 Scimago Journal and Country Ranking. The homepages of all target journals were searched for the presence of statements on data sharing policies, including clinical trial data sharing policies, the level of the policies, and actual statements of data availability in articles. Results Out of 565 journals from these three countries, 118 (20.9%) had an optional data sharing policy, and one had a mandatory data sharing policy. Harvard Dataverse was the repository of one journal. The number of journals that had adopted a data sharing policy was 11 (6.7%) for Brazil, 64 (27.6%) for France, and 44 (25.9%) for Korea. One journal from Brazil and 20 journals from Korea had adopted clinical trial data sharing policies in accordance with the International Committee of Medical Journal Editors. Statements of data sharing were found in articles from two journals. Conclusion Journals from France and Korea adopted data sharing policies more actively than those from Brazil. However, the actual implementation of these policies through descriptions of data availability in articles remains rare. In many journals that appear to have data sharing policies, those policies may just reflect a standard description by the publisher, especially in France. Actual data sharing was not found to be frequent.

Abstract Background Health-related research funders, regulators and journals expect that de-identified individual-level health data be shared widely, with as few restrictions as possible; yet, in reality, the volume of shared data remains low. Main body Health researchers and other data producers are reluctant to share their data unless they are confident that their datasets are of high quality and reliable, and that they are used in accordance with the values and aims of their institutions. We argue that having an institutional, departmental or group data management and sharing policy is the first step towards encouraging researchers and healthcare professionals to share their data more widely. Our paper outlines the elements of a data management and sharing policy, which should include aims consistent with those of the institution as well as with data management procedures, models of data sharing, request procedures, consent models and cost recovery mechanisms. A policy would help an institution, department or group maximise the use of its data and protect the interests of the institution and its members. We base our recommendations on our experience collecting and curating data for large clinical trials conducted in low- and middle-income countries, facilitating the sharing of datasets with secondary users, whilst teaching data management and conducting empirical research on data sharing. Although the fundamentals of a policy are general, the paper is focused on the low- and middle-income country context. Conclusion We argue that having an institutional, departmental or group data management and sharing policy is the first step in promoting data sharing.

This paper aims at analysing the extent to which spatial data are accessed and shared among stakeholders. It also uses Social Network Analysis to investigate institutional and individual behaviour in that process. Finally, it investigates the level of cooperation of all involved actors towards Spatial Data Infrastructure development. Results showed that public and private organizations have been individually engaged data collection and management. However, they are still using different standards and this has led to some disparities in terms of spatial data quality. Results also revealed the existing cooperation in data sharing that follow informal relationships through friendship or goodwill. In this case, organizations that use those data for public interest get them for free, while those use them for income generation pay access fees. At the end, spatial data providers and users suggested the establishment of a spatial data sharing policy that will harmonize procedures in spatial data collection and dissemination.

Abstract Background With the goal of realizing genome-based personalized healthcare, we have developed a biobank that integrates personal health, genome, and omics data along with biospecimens donated by volunteers of 150,000. Such a large-scale of data integration involves obvious risks of privacy violation. The research use of personal genome and health information is a topic of global discussion with regard to the protection of privacy while promoting scientific advancement. The present paper reports on our plans, current attempts, and accomplishments in addressing security problems involved in data sharing to ensure donor privacy while promoting scientific advancement. Methods Biospecimens and data have been collected in prospective cohort studies with the comprehensive agreement. The sample size of 150,000 participants was required for multiple researches including genome-wide screening of gene by environment interactions, haplotype phasing, and parametric linkage analysis. Results We established the T ohoku M edical M egabank (TMM) data sharing policy: a privacy protection rule that requires physical, personnel, and technological safeguards against privacy violation regarding the use and sharing of data. The proposed policy refers to that of NCBI and that of the Sanger Institute. The proposed policy classifies shared data according to the strength of re-identification risks. Local committees organized by TMM evaluate re-identification risk and assign a security category to a dataset. Every dataset is stored in an assigned segment of a supercomputer in accordance with its security category. A security manager should be designated to handle all security problems at individual data use locations. The proposed policy requires closed networks and IP-VPN remote connections. Conclusion The mission of the biobank is to distribute biological resources most productively. This mission motivated us to collect biospecimens and health data and simultaneously analyze genome/omics data in-house. The biobank also has the mission of improving the quality and quantity of the contents of the biobank. This motivated us to request users to share the results of their research as feedback to the biobank. The TMM data sharing policy has tackled every security problem originating with the missions. We believe our current implementation to be the best way to protect privacy in data sharing.

Surface observations provide ground evidence of climate change to support the scientific guidance paving the way to better adaptation and mitigation actions. The West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL) has designed a multi-stakeholder initiative to rescue the deteriorated near-surface weather, climate and hydrological equipment of West African countries. The main goal for this multi-stakeholder framework was to monitor the climate and collect long term and high-quality records of essential climate variables in support of research, education, capacity building, and climate services provision. Proactive and inclusive partnership initiatives were developed to jointly (re)design and (re)implement near surface observatiories with the national meteorological and hydrological services or agencies (NMHS/As) in West Africa. The co-production scheme used by this framework succeeded in evaluating the existing observations networks, to modernizing sensors and field equipment, and densifying the sites in order to improve the quality of data collection, transmission, archiving, processing and sharing policies. After more than four years of community-of-practice, the existing regional basic hydroclimatic was increased/upgraded by 45% with automatic weather observing systems while fifty automatic water level, ten water quality sensors, three mesoscale research catchments, and several pilot sites to benefit countries’ services provision, research infrastructure, education, and capacity building. Country-specific data sharing policies were harmonized and signed to support data services delivery. This practice paper exposes the concepts, outcomes, challenges, lessons learned and the ways forward in setting-up the framework and keeping it on working to leverage the co-production of data & information services for better-informed decision-making in the field of sustainable development in West Africa.

This paper attempts to present a brief overview of several Research Data Management (RDM) issues and a detailed literature review regarding the RDM aspects adopted in libraries globally. Furthermore, it will describe several tendencies concerning the management of repository tools for research data, as well as the challenges in implementing the RDM. The proper planned training and skill development for all stakeholders by mentors to train both staff and users are some of the issues that need to be considered to enhance the RDM process. An effort will be also made to present the suitable policies and workflows along with the adoption of best practices in RDM, so as to boost the research process in an organisation. This study will showcase the implementation of RDM processes in the Higher Educational Institute of India, referring particularly to the Central Library @ NIT Rourkela in Odisha, India with a proposed framework. Finally, this study will also propose an area of opportunities that can boost research activities in the Institute. [ABSTRACT FROM AUTHOR]

The National Institutes of Health (NIH) implemented a policy on data sharing in 2003. The policy reaffirmed the principle that data should be made as widely and freely available as possible while safeguarding the privacy of research participants, and protecting confidential and proprietary data. Restricted availability of unique resources upon which further studies are dependent can impede the advancement of research and the delivery of medical care. Therefore, research data supported with NIH funds should be made readily available for research purposes to qualified individuals within the scientific community. One approach to sharing data is to establish a network of databases. However, there are a number of barriers to creating successful networks, which can include fundamental differences in informatics infrastructure and communication tools used at various research sites. Solutions will entail standards for data collection, processing, and archiving to allow interoperability among the databases and the ability to query data across databases. Open architectures for data collection as well as software to facilitate communication across different databases are needed.

Qualidata was launched in 1994 as a proactive service for the location, documentation and preservation of qualitative social science research data. Over the course of its relatively short history, Qualidata has succeeded in gaining acceptance for the deposit and re-use of qualitative data material amongst the academic community. Initially concentrating on acquiring important early social studies, then working with the UK's Economic and Social Research Council (ESRC) to operate a "datasets policy," ESDS Qualidata, now merged into UK Data Archive, is in a good position to look back and review the progress made in acquiring data. This paper draws on ESDS Qualidata's pioneering experiences in acquiring and making available qualitative data. It reflects on strategies that have proved successful and comment on others that have been less productive, developing these into guidance for others wishing to embark on the acquisition and deposit of qualitative social science research data. URN: urn:nbn:de:0114-fqs0502361

Qualidata was launched in 1994 as a pro-active service for the location, documentation and preservation of qualitative social science research data. Over the course of its relatively short history, Qualidata has succeeded in gaining acceptance for the deposit and re-use of qualitative data material amongst the academic community. Initially concentrating on acquiring important early social studies, then working with the UK's Economic and Social Research Council (ESRC) to operate a "datasets policy," ESDS Qualidata, now merged into UK Data Archive, is in a good position to look back and review the progress made in acquiring data. This paper draws on ESDS Qualidata's pioneering experiences in acquiring and making available qualitative data. It reflects on strategies that have proved successful and comment on others that have been less productive, developing these into guidance for others wishing to embark on the acquisition and deposit of qualitative social science research data. [ABSTRACT FROM AUTHOR]

Background: With the goal of realizing genome-based personalized healthcare, we have developed a biobank that integrates personal health, genome, and omics data along with biospecimens donated by volunteers of 150,000. Such a large-scale of data integration involves obvious risks of privacy violation. The research use of personal genome and health information is a topic of global discussion with regard to the protection of privacy while promoting scientific advancement. The present paper reports on our plans, current attempts, and accomplishments in addressing security problems involved in data sharing to ensure donor privacy while promoting scientific advancement., Methods: Biospecimens and data have been collected in prospective cohort studies with the comprehensive agreement. The sample size of 150,000 participants was required for multiple researches including genome-wide screening of gene by environment interactions, haplotype phasing, and parametric linkage analysis., Results: We established the T ohoku M edical M egabank (TMM) data sharing policy: a privacy protection rule that requires physical, personnel, and technological safeguards against privacy violation regarding the use and sharing of data. The proposed policy refers to that of NCBI and that of the Sanger Institute. The proposed policy classifies shared data according to the strength of re-identification risks. Local committees organized by TMM evaluate re-identification risk and assign a security category to a dataset. Every dataset is stored in an assigned segment of a supercomputer in accordance with its security category. A security manager should be designated to handle all security problems at individual data use locations. The proposed policy requires closed networks and IP-VPN remote connections., Conclusion: The mission of the biobank is to distribute biological resources most productively. This mission motivated us to collect biospecimens and health data and simultaneously analyze genome/omics data in-house. The biobank also has the mission of improving the quality and quantity of the contents of the biobank. This motivated us to request users to share the results of their research as feedback to the biobank. The TMM data sharing policy has tackled every security problem originating with the missions. We believe our current implementation to be the best way to protect privacy in data sharing.