<p dir="ltr">There is a critical need for improvement in scientific data management when the big-data era arrives. Motivated by the evolution and significance of FAIR principles in contemporary research, the study focuses on the development and evaluation of a FAIR-compliant data resource portal. The challenge lies in translating the abstract FAIR principles into actionable, technological implementations and the evaluation. After baseline selection, the study aims to benchmark standards and outperform existing FAIR compliant data resource portals. The proposed approach includes an assessment of existing portals, the interpretation of FAIR principles into practical considerations, and the integration of modern technologies for the implementation. With a FAIR-ness evaluation framework designed and applied to the implementation, this study evaluated and improved the FAIR-compliance of data resource portal. Specifically, the study identified the need for improved persistent identifiers, comprehensive descriptive metadata, enhanced metadata access methods and adherence to community standards and formats. The evaluation of the FAIR-compliant data resource portal with FAIR implementation, showed a significant improvement in FAIR compliance, and eventually enhanced data discoverability, usability, and overall management in academic research.</p>

With the increasing amounts of data generated in life science, methods for data storage and sharing are being developed and implemented. Online data repositories are more and more commonly used for data sharing. The national Swedish platform Science of Life Laboratory has decided to use an institutional data repository as a mean to address the increasing amounts of data generated at the platform. In this project, the system used for the institutional repository at SciLifeLab was studied and compared to implementations of the same system at other institutions to create user documentation for the repository. This documentation was created with the FAIR principles as a guidance. Feedback on the guidelines were then sought from users and based on the received feedback, the user documentation was improved. Using a FAIR evaluation tool called FAIR evaluation services, items published on the repository were evaluated. Investigation of these results and their correlation to the items record on the repository were carried out. Out of ten evaluated datasets all except one scored exactly the same on the FAIR evaluation services tests. This could indicate that the test used is not evaluating aspects needed to encounter the differences in these published items. Based on this, conclusions as to in what extent user documentation can increase the FAIRness of data cannot be drawn.

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of "dynamic data citation". The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution.

Introduction. Research data management is a topic of ongoing discussion, particularly in academic institutions, where researchers strive to effectively handle diverse types of data. This study examines the practices of research data management in selected digital humanities projects at Uppsala University. The objective is to as- sess the impact that data management plans (DMPs) on these interdisciplinary projects and evaluate the applica- tion of the FAIR guiding principles. It is crucial to consider the researchers’ perspective on this matter. Universi- ties could invest in robust data management practices by taking into account the needs and skills of researchers. Method. Semi-structured interviews were conducted using purposive sampling targeting researchers from various departments within the Faculty of Arts who were involved in interdisciplinary digital humanities pro- jects. Eight interviews were carried out with principal investigators (PIs) and researchers. Analysis. The interviews, along with the provided DMPs, were thematically analysed to address the re- search questions regarding the effect of DMPs in the selected projects. Results. The study findings indicate that the PIs and researchers do not perceive the DMP as an integral part of their research work in digital humanities projects. Nonetheless, most participants recognise its signifi- cance and its role could be enhanced in research projects. Challenges typically arise during stages of the research data life cycle, such as data analysis, rather than in the development of the DMP itself. Moreover, the practical implementation of the FAIR principles often poses difficulties due to variations in data types and project goals. Conclusion. The results of this study highlight the need for more actionable DMPs in digital humanities projects and further training for researchers on data management issues. The interdisciplinary nature of these projects facilitates collaboration among researchers in the development of DMPs.

Die sog. FAIR-Prinzipien haben sich mittlerweile als Standard-Anforderung im Forschungsdatenmanagement etabliert. In Förderanträgen und -berichten müssen Wissenschaftler:innen darlegen, wie sie Forschungsdaten gemäß den FAIR-Prinzipien verwalten und veröffentlichen. Auch immer mehr Fachzeitschriften bzw. Verlage fordern von ihren Autor:innen, dass sie ihre Forschungsdaten gemäß den FAIR-Prinzipien teilen, um die Reproduzierbarkeit und Überprüfbarkeit ihrer Ergebnisse zu gewährleisten. Was das Akronym FAIR eigentlich bedeutet und worauf Forschende in diesem Zusammenhang achten sollten, fasst dieser Beitrag kurz zusammen.