Open data platforms are interfaces between data demand of and supply from their users. Yet, data platform providers frequently struggle to aggregate data to suit their users’ needs and to establish a high intensity of data exchange in a collaborative environment. Here, using open life science data platforms as an example for a diverse data structure, we systematically categorize these platforms based on their technology intermediation and the range of domains they cover to derive general and specific success factors for their management instruments. Our qualitative content analysis is based on 39 in-depth interviews with experts employed by data platforms and external stakeholders. We thus complement peer initiatives which focus solely on data quality, by additionally highlighting the data platforms’ role to enable data utilization for innovative output. Based on our analysis, we propose a clearly structured and detailed guideline for seven management instruments. This guideline helps to establish and operationalize data platforms and to best exploit the data provided. Our findings support further exploitation of the open innovation potential in the life sciences and beyond.

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Data platforms for open life sciences–A systematic analysis of management instruments

In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content.

https://www.mdpi.com/2504-2289/7/1/24/pdf?version=1674902464

GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism

The data ecosystem is complex and involves multiple stakeholders. Researchers and scientists engaging in data-intensive research collect, analyse, store, manage and share large volumes of data. Consequently, capturing researchers’ and scientists’ views from multidisciplinary fields on data use, sharing and governance adds an important African perspective to emerging debates. We conducted a descriptive cross-sectional survey and received 160 responses from researchers and scientists representing 43 sub-Saharan African countries. Whilst most respondents were satisfied with institutional data storage processes, 40% indicated that their organisations or institutions did not have a formally established process for storing data beyond the life cycle of the project. Willingness to share data was generally high, but increased when data privacy was ensured. Robust governance frameworks increased the willingness to share, as did the regulation of access to data on shared platforms. Incentivising data sharing remains controversial. Respondents were satisfied with exchanging their data for co-authorship on publications (89.4%) and collaboration on projects (77.6%). However, respondents were split almost equally in terms of sharing their data for commercial gain. Regarding the process of managing data, 40.6% indicated that their organisations do not provide training on best practices for data management. This could be related to a lack of resources, chronic institutional under-investment, and suboptimal research training and mentorship in sub-Saharan Africa. The sustainability of data sharing may require ethical incentive structures to further encourage researchers and scientists. Tangible infrastructure to facilitate such sharing is a prerequisite. Capacity development in data governance for researchers and scientists is sorely needed.

Data sharing and data governance in sub-Saharan Africa: Perspectives from researchers and scientists engaged in data-intensive research

  
Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.

https://zenodo.org/record/7240925/files/Research%20Data%20Preservation%20Practices%20of%20Library%20and%20Information%20Science%20Faculties.pdf

Research Data Preservation Practices of Library and Information Science Faculties

<sec> <title>BACKGROUND</title> Background: The rapid growth in human research data, storage, and analysis tools, and skilled researchers combined with the declining costs and new federal policies are creating an immense drive for research data sharing. At the same, triangulating various types of human research data proportionately increase the risks of re-identification when used for secondary research purposes. Various institutional gatekeepers are involved in the governance of human data for research. Of those with data governance responsibilities, signing officials (SOs) most often serve as signatories on research data submission and access request agreements between institutions. Although SOs play important leadership roles in research data compliance, we know surprisingly little about the ways in which new data management and sharing (DMS) requirements for researchers adopted at the National Institutes of Health (NIH) affects SO’s data governance responsibilities. </sec> <sec> <title>OBJECTIVE</title> The purpose of this study was to understand the potential impacts of new DMS policies on secondary data governance of genomic and related health data among SOs in the United States </sec> <sec> <title>METHODS</title> We administered online surveys to 56 institutional SOs working at biomedical research institutions across the United States. We investigated their understanding about where research data supported with extramural funding are deposited, how researchers outside the institution access these data, and what happens to the data after grant funding ends. </sec> <sec> <title>RESULTS</title> We found SOs frequently approve duplicate data deposits and impose stricter access controls when data use limitations are unclear or unspecified. In addition, only 23.08% of SOs reported that their institutions track where data from federally funded projects are deposited after funding for the project sunsets. Consequently, investigators often deposit their scientific data into “an NIH-funded repository” to meet the DMS requirements, but also within the “institution’s own repository” or “a third-party repository. </sec> <sec> <title>CONCLUSIONS</title> Our findings inform five policy recommendations and best practices for researchers, institutional authorities, and institutional review boards to improve coordination and develop comprehensive and consistent data governance policies that balance the need for scientific progress with effective human data protections. </sec> <sec> <title>CLINICALTRIAL</title> N/A </sec> 

Survey of U.S. institutional signing officials (SOs) on data sharing policies and practices (Preprint)

Data sharing can accelerate scientific discovery while increasing return on investment beyond the researcher or group that produced them. Data repositories enable data sharing and preservation over the long term, but little is known about scientists' perceptions of them and their perspectives on data management and sharing practices. Using focus groups with scientists from five disciplines (atmospheric and earth science, computer science, chemistry, ecology, and neuroscience), we asked questions about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans. Participants identified metadata quality control and training as problem areas in data management. Additionally, participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. We present their desired repository features as a rubric for the research community to encourage repository utilization. Future directions for research are discussed. 

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A focus groups study on data sharing and research data management

/pdf/a-focus-groups-study-on-data-sharing-and-research-data-2oi9i78w.pdf

Joshua Wolfgang Koepke

Papers

A focus groups study on data sharing and research data management

A focus groups study on data sharing and research data management