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Despite wide acceptance in medical research, implementation of the FAIR (findability, accessibility, interoperability, and reusability) principles in certain health domains and interoperability across data sources remain a challenge. While clinical trial registries collect metadata about clinical studies, numerous epidemiological and public health studies remain unregistered or lack detailed information about relevant study documents. Making valuable data from these studies available to the research community could improve our understanding of various diseases and their risk factors. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) seeks to optimize data sharing among the clinical, epidemiological, and public health research communities while preserving privacy and ethical regulations. We aimed to develop a tailored metadata schema (MDS) to support the standardized publication of health studies' metadata in NFDI4Health services and beyond. This study describes the development, structure, and implementation of this MDS designed to improve the FAIRness of metadata from clinical, epidemiological, and public health research while maintaining compatibility with metadata models of other resources to ease interoperability. Based on the models of DataCite, ClinicalTrials.gov, and other data models and international standards, the first MDS version was developed by the NFDI4Health Task Force COVID-19. It was later extended in a modular fashion, combining generic and NFDI4Health use case-specific metadata items relevant to domains of nutritional epidemiology, chronic diseases, and record linkage. Mappings to schemas of clinical trial registries and international and local initiatives were performed to enable interfacing with external resources. The MDS is represented in Microsoft Excel spreadsheets. A transformation into an improved and interactive machine-readable format was completed using the ART-DECOR (Advanced Requirement Tooling-Data Elements, Codes, OIDs, and Rules) tool to facilitate editing, maintenance, and versioning. The MDS is implemented in NFDI4Health services (eg, the German Central Health Study Hub and the Local Data Hub) to structure and exchange study-related metadata. Its current version (3.3) comprises 220 metadata items in 5 modules. The core and design modules cover generic metadata, including bibliographic information, study design details, and data access information. Domain-specific metadata are included in use case-specific modules, currently comprising nutritional epidemiology, chronic diseases, and record linkage. All modules incorporate mandatory, optional, and conditional items. Mappings to the schemas of clinical trial registries and other resources enable integrating their study metadata in the NFDI4Health services. The current MDS version is available in both Excel and ART-DECOR formats. With its implementation in the German Central Health Study Hub and the Local Data Hub, the MDS improves the FAIRness of data from clinical, epidemiological, and public health research. Due to its generic nature and interoperability through mappings to other schemas, it is transferable to services from adjacent domains, making it useful for a broader user community.

Computers increasingly collect, manage, and analyse data for scholarly research. Linked data gives libraries the ability to support this e-research, making it a powerful tool. Libraries are at a tipping point in adoption of linked data, and this issue ofLibrary Technology Reportsexplores current research in linked open data, explaining concepts and pioneering services, such as Five building blocks of metadata data model, content rules, metadata schema, data serialisation, and data exchange Three case studies Europeana, Digital Public Library of America, and BIBFRAME How libraries, archives and museums are currently addressing such issues as metadata quality, open data and business models, cross community engagement, and implementation

This systematic review synthesised existing research papers that explore the available metadata standards to enable researchers to preserve, discover, and reuse research data in repositories. This review provides a broad overview of certain aspects that must be taken into consideration when creating and assessing metadata standards to enhance research data preservation discoverability and reusability strategies. Research papers on metadata standards, research data preservation, discovery and reuse, and repositories published between January 2003 and April 2023 were reviewed from a total of five databases. The review retrieved 1597 papers, and 13 papers were selected in this review. We revealed 13 research articles that explained the creation and application of metadata standards to enhance preservation, discovery, and reuse of research data in repositories. Among them, eight presented the three main types of metadata, descriptive, structural, and administrative, to enable the preservation of research data in data repositories. We noted limited evidence on how these metadata standards can be used to enhance the discovery and reuse of research data in repositories to enable the preservation, discovery, and reuse of research data in repositories. No reviews indicated specific higher education institutions employing metadata standards for the research data created by their researchers. Repository designs and a lack of expertise and technology know-how were among the challenges identified from the reviewed papers. The review has the potential to influence professional practice and decision-making by stakeholders, including researchers, students, librarians, information communication technologists, data managers, private and public organisations, intermediaries, research institutions, and non-profit organizations.

All disciplines should follow the geosciences and demand best practice for publishing and sharing data, argue Shelley Stall and colleagues. All disciplines should follow the geosciences and demand best practice for publishing and sharing data, argue Shelley Stall and colleagues. Researchers repairs a broken GPS module at a research station in Greenland

Purpose The purpose of this study was to investigate metadata creation practices in a functional academic institution repository in Malawi, with a specific focus on the Lilongwe University of Agriculture and Natural Resources (LUANAR) library. Design/methodology/approach The study used a qualitative approach with a case study design. The study adopted a case study strategy that focuses on the in-depth, holistic and in-context examination of one or more cases. The researcher used non-probability purposive sampling to include all three LUANAR Digital Repository (LDR) staff at LUANAR library because they were thought to be knowledgeable about the LDR metadata work. The three library staff members directly involved in repository metadata were investigated for the study. Data collection techniques used in a case study approach included semi-structuring face-to-face interviews and documentary analysis. Data from interviews and documentary reviews were manually analyzed and presented in thematic categories based on the study’s objectives. Findings Qualified Dublin Core (DC) was chosen by all participants as the only metadata structure scheme that they will use to create and implement metadata in the repository. DC application profile was the only scheme used to enforce uniform naming and capitalization conventions in the application of Qualified DC element definitions. The scheme, however, was discovered to be the Qualified DC default format in the DSpace system. All participants indicated that the Agricultural Organization of the United Nations Vocabulary is used. Participants highlighted that institutional repository system compatibility, the subject matter of the resources, resource types and staff expertise influenced the selection criteria for the metadata schemes. The repository policy had been developed but had yet to be adopted by the LUANAR management. Research limitations/implications The current study was limited to LUANAR library. A wider study across public and private universities in Malawi is needed to ascertain the role of metadata policy, technical knowledge and metadata specialist institutional repositories. Practical implications Metadata policy is to aid in the understanding of the data, ensuring that appropriate security measures are used to protect the data and for metadata harvesting purposes. Social implications Academic libraries should lobby for management support towards metadata policy for institutional repositories. Originality/value Very little is known about challenges affecting the growth of institutional repositories and standards adopted, including metadata harvesting. This paper bridges the gap in metadata standards for institutional repositories in developing countries.

Imaging technologies are used throughout the life and biomedical sciences to understand mechanisms in biology and diagnosis and therapy in animal and human medicine. We present criteria for globally applicable guidelines for open image data tools and resources for the rapidly developing fields of biological and biomedical imaging.

The Findable, Accessible, Interoperable, and Reusable guiding principles promote Findability, Accessibility, Interoperability, and Reuse of data to enhance data management and stewardship. In biomedicine, particular ethical, legal, and technical barriers complicate research data sharing. To help researchers overcome these challenges, we propose a framework of FAIRification from three dimensions – scientific, technical, and legal/ethical. We advocate for prospective FAIRification of study data, starting with a strong emphasis on planning for data-sharing from the beginning. Reflective questions throughout the process guide researchers to reflect on their situation. Researchers should assess resources and feasibility, secure technical and legal support, consider stakeholder needs, and devise an appropriate data sharing process. Given the sensitivity of biomedical data, confidentiality and security require careful attention. The data sharing strategy should be finalized before the study starts and documented in relevant study materials. Technical preparation for data sharing follows planning. Data should be well-documented with a data dictionary and metadata to facilitate reuse and provided in an accessible format. The data can be hosted on a repository to promote sharing and reuse. While a secure repository provides the technical foundation for data protection, effective administration is required to enforce data use agreements and licensing. We also discuss the importance of subsequent management upon data upload. Continued support for researchers and data maintenance are essential for effective reuse. Examples and resources to facilitate FAIRification are included to help researchers navigate challenges and ensure biomedical data are FAIR and reusable.

Open up, share and network information so that marine stewardship can mitigate climate change, overfishing and pollution. Open up, share and network information so that marine stewardship can mitigate climate change, overfishing and pollution.

The Findable, Accessible, Interoperable, and Reusable guiding principles promote Findability, Accessibility, Interoperability, and Reuse of data to enhance data management and stewardship. In biomedicine, particular ethical, legal, and technical barriers complicate research data sharing. To help researchers overcome these challenges, we propose a framework of FAIRification from three dimensions - scientific, technical, and legal/ethical. We advocate for prospective FAIRification of study data, starting with a strong emphasis on planning for data-sharing from the beginning. Reflective questions throughout the process guide researchers to reflect on their situation. Researchers should assess resources and feasibility, secure technical and legal support, consider stakeholder needs, and devise an appropriate data sharing process. Given the sensitivity of biomedical data, confidentiality and security require careful attention. The data sharing strategy should be finalized before the study starts and documented in relevant study materials. Technical preparation for data sharing follows planning. Data should be well-documented with a data dictionary and metadata to facilitate reuse and provided in an accessible format. The data can be hosted on a repository to promote sharing and reuse. While a secure repository provides the technical foundation for data protection, effective administration is required to enforce data use agreements and licensing. We also discuss the importance of subsequent management upon data upload. Continued support for researchers and data maintenance are essential for effective reuse. Examples and resources to facilitate FAIRification are included to help researchers navigate challenges and ensure biomedical data are FAIR and reusable.The Findable, Accessible, Interoperable, and Reusable guiding principles promote Findability, Accessibility, Interoperability, and Reuse of data to enhance data management and stewardship. In biomedicine, particular ethical, legal, and technical barriers complicate research data sharing. To help researchers overcome these challenges, we propose a framework of FAIRification from three dimensions - scientific, technical, and legal/ethical. We advocate for prospective FAIRification of study data, starting with a strong emphasis on planning for data-sharing from the beginning. Reflective questions throughout the process guide researchers to reflect on their situation. Researchers should assess resources and feasibility, secure technical and legal support, consider stakeholder needs, and devise an appropriate data sharing process. Given the sensitivity of biomedical data, confidentiality and security require careful attention. The data sharing strategy should be finalized before the study starts and documented in relevant study materials. Technical preparation for data sharing follows planning. Data should be well-documented with a data dictionary and metadata to facilitate reuse and provided in an accessible format. The data can be hosted on a repository to promote sharing and reuse. While a secure repository provides the technical foundation for data protection, effective administration is required to enforce data use agreements and licensing. We also discuss the importance of subsequent management upon data upload. Continued support for researchers and data maintenance are essential for effective reuse. Examples and resources to facilitate FAIRification are included to help researchers navigate challenges and ensure biomedical data are FAIR and reusable.