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This article discusses the involvement of Polish medical libraries in the field of research data management (RDM). It provides an overview of RDM’s development and its introduction in medical libraries. On the basis of a survey conducted among the staff of Polish medical libraries, the authors outline the librarians’ level of competence, the extent of services they provide and the organisational context of their work. It is stressed that it is crucial to further develop librarians’ qualifications related to RDM, as well as to increase the awareness of libraries’ role in this area among researchers. The authors present conclusions concerning the formal acknowledgement of librarians’ role in the process of RDM, which contributes to the development of both libraries and research institutions.

Objective: Increases in data-intensive research at colleges and universities is driving demand for data services provided by academic libraries. The current work investigates the distribution of library data services, how such services are offered, and the effect of resourcing on the amount of services offered by a library. Methods: We used a web-based inventory of 25 academic libraries at U.S. Research 1 (R1) Carnegie institutions to assess the state of data services at university libraries. We categorized and quantified services, and tested for an effect of library resourcing on the size of library data service portfolios.Results: Support for data management and geospatial services was relatively widespread, with increasing support in areas of data analyses and data visualization. There was significant variation among services in the modality in which they were offered (web, consult, instruction) and library resourcing had a significant effect on the number of data services a library offered.Conclusions: While a core subset of these data services are offered at most academic libraries, more specialized topics are restricted to well-resourced libraries. In light of the influence of resource scarcity on the number of services a library can offer, intra- and inter-campus partnerships will be critical to ensure campus support for data service needs.

Objective: This study was aimed at analyzing and disseminating the fossil wood collection data stored in the thin slide sections of the paleobotany collection of the Department of Paleontology and Stratigraphy of the Institute of Geosciences of a university in Southern Brazil. Thin sheets of petrified wood are described. Research data used in geosciences specifically seek to develop a model for the use of this type of sheet in paleobotany, enabling visualization of its representativeness in studies published over 40 years to obtain the anatomical characteristics of fossil woods and define their systematic affinities as a specific typology of research data in geosciences. Methods: The methodology involved interviewing a paleobotanical specialist and using different techniques applied in metric studies to map scientific production. Thus, a dataset of (20) thin slide sections of petrified fossil wood used in “Stressing environmental conditions in the “petrified forest” from the Mata Sequence in the Triassic context of the Paraná Basin,” a paper published by the Journal of South American Earth Sciences (DOI:10.1016/j.jsames.2023.104415), was used. Depending on the methodology, these thin petrified wood sheets can reveal paleoclimatic signatures based on the anatomical characteristics of the fossil wood. In addition to this case study, which represents a collection of more than (2.000) thousand blades of fossil wood and approximately 40 years of research, the paleobotanical collection of the department is reused in methodology classes. The collection comprises a database created by research physicists that provides information on anatomical features, systematic affinities, paleoclimatic conditions, and paleoenvironmental insights. Potential for reuse: The reuse, registration, storage, identification, and preservation of thin slide sections as a type of research data used in paleobotany aims to improve the methodology associated with the organization of the physical database of the institution. The research data and blades of fossil wood are being digitized, and soon will be available under the CC BY 4.0 license in the ZENODO repository, according to the sample described here in this data paper (DOI Mendeley:10.17632/b8phfcsync.1). Furthermore, the data can be reused in optical and electronic scanning microscopy software.

Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search.

Virtual platforms have revolutionized distance education, making it accessible worldwide and empowering scientists, academicians, and researchers to access knowledge effortlessly. These platforms provide flexibility, allowing the users to tailor their learning experience to their needs and integrating knowledge into their work. Reproducibility is crucial in cancer research, and researchers integrate data analysis into virtual or electronic learning (e-Learning) platforms to facilitate replication and verification, promoting transparency and reliability. This integration enhances accessibility and enables collaboration among scientists and stakeholders in the fight against cancer. Virtual learning offers written and audio-visual communication benefits facilitated by electronic and web-enabling advancements. In the dynamic virtual realm, researchers transcend limitations, exchange knowledge, and push the boundaries of cancer research. Virtual platforms provide time efficiency and financial freedom, while advanced tools support data analysis and facilitate new insights. These tools unlock hidden patterns and accelerate the pace of discovery. The digital ecosystem generates new ideas, improves research methodology, and enhances research quality. Limitless collaboration and advanced tools propel cancer research, unravelling complex data with precision and innovation. The potential of cyberspace to revolutionize scientific research in the future, therefore, is promising.

Data gaps are a significant lack of data about marginalized groups existing due to unequal power relations (D’Ignazio and Klein, 2020). They both perpetuate and result in a dominance of male, white, hetero, and cis perspectives in how we make sense of and interact with the world. The most prominent data gap is the gender data gap notably described by Criado-Perez (2020). However, not only women, but all marginalized groups are affected by such gaps, as data about them are frequently not collected due to a disregard on behalf of those in power of the need to do so. LGBTIQ+ people, considered a ‘hidden population’ by demographers, are a case in point. The acronym is used to refer to lesbian, gay, bisexual, trans, intersex, and queer people, as well as all people with non-normative sexual or gender identities, including asexual and agender people, who do not consider themselves as falling under one of these labels. A first step towards identifying and closing data gaps is to take stock of data that already exist. In this paper we give an overview of LGBTIQ+ data in European social science archives. We researched all data archives of CESSDA ERIC, the Consortium of European Social Science Data Archives, and found 66 LGBTIQ+ datasets in 9 of the 34 member and associated archives and 1 former member archive. We discuss characteristics, coverages, and findability of the identified datasets and approach the question of potential data gaps by analyzing the keywords assigned to each dataset by the archive.

IntroductionThe COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency.MethodsAt the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output.ResultsOver the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020–2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19.DiscussionResearch is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.

As research misconduct has created reproducibility and researcher reputation concerns, there is an opportunity to recommend data management techniques to assist researchers as they seek to prevent these issues. Also central to the discussion are issues of power in the conduct of research, particularly in upholding the values of honesty and accountability. This commentary discusses how data professionals can engage in practical strategies to protect against allegations of misconduct.

[Purpose/Significance] In 2021, the Ministry of Education of China issued the "Norms for the Construction of Digital Campuses in Higher Education Institutions (Trial)", which mentioned that universities should attempt to form a new model of academic exchange and resource sharing through the construction of institutional repositories (IRs). The construction of knowledge resources in university IRs is gradually developing towards the next generation IRs. Research data resources are an important component of the future resources. From the perspective of the generation, acquisition, and existence of information resources in universities, the institutional resources in universities primarily consist of resources generated by "industry, academia, research and application" activities, including teaching resources, scientific research resources, design resources, scientific activity resources, etc., which are specifically reflected in the activity process such as scholar resources, research activity resources, research process resources, and research achievements resources. Research data resources are studied as the research object in this article. [Method/Process] This paper analyzes the formation mechanism of institutional knowledge resources, and conduct research on the integration, management and reuse of research data resources, including: 1) internal factors of research data, data generation, source, type, structure, and dependency mode; 2) research on external elements of data, such as data standards, unique identifiers, data registration, data protocols, data rights, data reuse, and data sharing Through the study of internal and external factors, the influencing factors, functions and roles, responsibilities and rights, permissions and attribution of data integration management and data reuse were sorted and interpreted and feasible methods were designed for research data management, collection, implementation, and registration. In order to ensure the effectiveness of data management, a series of standards and schemes have been developed, such as data type and format standards, metadata schemes, and data guardianship demand survey templates. The purpose is to achieve data discovery, interoperability, and reuse through continuous monitoring of scientific data. The basic rules of reuse are mainly divided into: 1) reuse: the concept of reuse, reuse, sharing, and incomplete equivalence in reuse; 2) sharing: possible to be used; 3) protocol usage: discussion about how to use it; and 4) rights use: complying with the data copyright agreement. [Results/Conclusions] Through research courses, the development and implementation of favorable data management and reuse strategies have clarified the data objects, management set management services, and data reuse permissions of research data in university IRs. We have clarified the current situation and urgent issues to integrate research data into the long-term preservation, management, and sharing and reuse system of knowledge resources in university IRs. We have defined the responsibilities, management mechanisms, standardized business processes, permission attributes, data exchange platforms, data registration, and data reuse of research data subjects by category, region, and field, and provided some suggestions and guarantee measures for the establishment of a data management center in China.

This paper presents the results of an empirical research study of Croatian scientists’ use and management of research data. This research study was carried out from 28 June 2023 until 31 August 2023 using an online questionnaire consisting of 28 questions. The answers of 584 respondents working in science were filtered out for further analysis. About three-quarters of the respondents used the research data of other scientists successfully. Research data were mostly acquired from colleagues from the same department or institution. Roughly half of the respondents did not ask other scientists directly for their research data. Research data are important to the respondents mostly for raising the quality of research. Repeating someone else’s research by using their research data is still a problem. Less than one-third of the respondents provided full access to their research data mostly due to their fear of misuse. The benefits of research data sharing were recognized but few of the respondents received any reward for it. Archiving research data is a significant problem for the respondents as they dominantly use their own computers prone to failure for that activity and do not think about long-term preservation. Finally, the respondents lacked deeper knowledge of research data management.

The special collection “Data for Psychological Research on COVID-19” presents selected datasets collected during the COVID-19 pandemic. In selecting the data papers, care was taken to ensure that the described datasets not only represent the full range of psychological topics addressed during the pandemic but also reflect its global nature, in that diverse nationalities were included in the investigated samples. As these datasets are shared according to the FAIR Principles (Wilkinson et al., 2016), they are findable, accessible, interoperable and reusable. The special collection comprises 12 data papers presenting quantitative studies on the impact of COVID-19 on various psychological constructs, including socio-political attitudes, beliefs in conspiracy theories, emotional reactions, and control behaviour.

In medical research, there are vast amounts of health data that can be used in a variety of ways. But what about this kind of data at Helios? This article describes the existing infrastructure and the institutions who are important for the research at Helios. Furthermore, it will be shown what the library of Helios Kliniken is currently doing for researchers and how it wants to position itself as a partner for research data.

Today, one can observe shifts in the research landscape, which is formed by digitization and open science principles. The open science movement continues to gain momentum, attention and debate. In parallel with the principle of unity, open science gives rise to a taxonomy of several related ideas, guidelines and concepts, such as open access, open replicable research and open data. Over the past fifteen years, research institutions have focused on open access to publications. However, recently the focus of attention has shifted to research data as a "new currency" in research activities and their distribution in open access, and the guiding principles of data management are becoming crucial for the wide implementation of open science practices and the effective use of data in research, industry, business and other sectors of the economy. In this context, it is relevant to carry out a thorough study of primary scientific works on open science issues and to study the role of the concept of "open research data" in the paradigm of a holistic ecosystem of open science and business ecosystem. In this work, it is proposed to use the methods of quantitative and qualitative bibliometric analysis, which allows to identify the main trends and form the basis for further research. The information base for this work was the international scientometric database Scopus, which enables to analyze bibliographic data using built-in tools and import them for external use in the VOSviewer software. The study revealed an increasing trend in the number of publications on the subject under study, with the highest annual growth rate in 2017 (76%) and 2019 (66%). Qualitative bibliographic analysis made it possible to analyze the most cited and, therefore, trending works on the selected topic. In terms of the number of citations per year, the results show that the studies with such directions in open science as open program code (open source); data/research reproducibility, research data management; open access to publications (open access) are most popular. In addition, a cluster analysis of the co-prevalence of keywords was conducted. It formed clusters dedicated to both institutional and infrastructural problems of the development of open science and research data. Separately, the results of the analysis create a scientific basis for further research into the key determinants of the effectiveness of the implementation of a proper research data management system at the micro, meso, and macro levels. It will improve the effectiveness of the implementation of scientific developments from one field of knowledge to another, while achieving increased interdisciplinary research. In parallel with this, interested persons of the real sector of the economy get the opportunity to analyze scientific results, determining the possibility of their adoption in their own activities.

Open data offer the opportunity to economically combine data into large-scale datasets, fostering collaboration and re-use in the interest of treating researchers’ resources as well as study participants with care. Whereas advantages of utilising open data might be self-evident, the production of open datasets also challenges individual researchers. This is especially true for open data that include personal data, for which higher requirements have been legislated. Mainly building on our own experience as scholars from different research traditions (life sciences, social sciences and humanities), we describe best-practice approaches for opening up research data. We reflect on common barriers and strategies to overcome them, condensed into a step-by-step guide focused on actionable advice in order to mitigate the costs and promote the benefit of open data on three levels at once: society, the disciplines and individual researchers. Our contribution may prevent researchers and research units from re-inventing the wheel when opening data and enable them to learn from our experience.

Data's role in a variety of technical and research areas is undeniably growing. This can be seen, for example, in the increased investments in the development of data-intensive analytical methods such as artificial intelligence (Zhang 2022), as well as in the rising rate of data generation which is expected to continue into the near future (Rydning and Shirer 2021). Academic research is one of the areas, where data is the lifeblood of generating hypotheses, creating new knowledge, and reporting results. Unlike proprietary industry data, academic research data is often subjected to stricter requirements regarding transparency, and accessibility. This is in part due to the public funding which many research institutions receive. One way to fulfil these requirements is by observing the FAIR (Findability, Accessibility, Interoperability, Reusability) principles for scientific data (Wilkinson et al. 2016). These introduce a variety of benefits, such as increased research reproducibility, a more transparent use of public funding, and environmental sustainability. A way of implementing the FAIR principles in practice is with the help of FAIR Digital Objects (FDOs) (European Commission: Directorate-General for Research and Innovation 2018). A FDO consists of data, an accompanying Persistent Identifier (PID), and rich metadata which describes the context of the data. Additionally, the data format contained in an FDO should be widely used, and ideally open. Our presentation is focused on the third of FDO's components mentioned previously – metadata. It outlines the concept for a framework which enables the collaborative definition of metadata fields which can be used to annotate FDO-encapsulated data for a given domain of research.The first component of the presented framework is a controlled vocabulary of the domain related to the data which needs to be annotated. A controlled vocabulary is a collective that denotes a controlled list of terms, their definitions, and the relations between them. In the framework presented in this contribution, the terms correspond to the metadata fields used in the data annotation process. Formally, the type of controlled vocabularies used in the framework is a thesaurus (National Information Standards Organization 2010). Thesauri consist not only of the elements mentioned previously, but also allow for the inclusion of synonyms for every defined term. This eliminates the ambiguity which can occur when using terms with similar definitions. Additionally, thesauri specify simple hierarchical relations between the terms in the vocabulary, which can provide an explicit structure to the set of defined metadata fields. The most important feature of our framework, however, is that the controlled vocabularies can be developed in a collaborative fashion by the domain experts of a given research field. Specifically, people are able to propose term definitions and edits, as well as cast votes on the appropriateness of terms which have already been proposed.Despite their advantages, one limit of thesauri is their lacking capability of relating metadata fields to each other in a more semantically rich fashion. This motivated the use of the second component of the framework, namely ontologies. An ontology can be defined as “a specification of a conceptualization” (Gruber 1995). More precisely, it is a data structure which represents entities in a given domain, as well as various relations between them. After a set of metadata fields has been defined within a controlled vocabulary, that vocabulary can be transformed into an ontology which contains additional relations between the fields. These can extend beyond the hierarchical structure of a thesaurus and can contain domain-specific information about the metadata fields. For example, one such relation can denote the data type of the value which a given field must take. Furthermore, ontologies can be used to link not only metadata, but also data, as well as individual FDOs themselves. This can contribute to the Reusability aspect of FAIR Data Objects. For example, an FDO generated by a research group in a given domain can be linked to an existing domain ontology. Afterwards, the FDO can be reused more easily by researchers from the same scientific field, because the ontology will have already specified the FDO's relation to the subject area. Additionally, cross-domain ontologies can be combined with each other which can increase the reusability of FDOs beyond their domain boundaries.The components described above are being implemented in the form of multiple software tools related to the framework. The first one, a controlled vocabulary editor written as a Python-based web application called VocPopuli, is the entry point for domain experts who want to develop a metadata vocabulary for their field of research or lab. The software, whose first version is already being tested internally, enables the collaborative definition, and editing of metadata terms. Additionally, it annotates each term, as well as the entire vocabulary, with the help of the PROV Data Model (PROV-DM) (Moreau and Missier 2013) - a schema used to describe the provenance of a given object. Finally, it assigns a PID to each term in the vocabulary, as well as the vocabulary itself. It is worth noting that the generated vocabularies themselves can be seen through the prism of FDOs: they contain data (the defined terms) which is annotated with metadata (e.g., the terms' authors) and provided with a PID.The second software solution will facilitate the transformation of the vocabularies developed with the help of VocPopuli into ontologies. It will handle two distinct use cases – the from-scratch conversion of vocabularies into ontologies, and the augmentation of existing ontologies with the terms from a given thesaurus. As is the case with VocPopuli, the second tool is being developed in the Python programming language. The software solutions will be finally tested by two semi-overlapping groups of users from materials science. On the one hand, domain experts will input, edit, and discuss vocabulary terms in their area of interest, and thus create vocabularies. On the other hand, vocabulary and ontology administrators will oversee the vocabulary creation, and ontology transformation processes in a semi-automatic fashion.After development is complete, the tools will be used in the creation of controlled vocabularies for various experimental procedures, as well as their transformation and/or integration into semantically richer ontologies. This will augment our already published work in the area (Garabedian et al. 2022) and will thereby test the integration of the new framework with already existing resources. The new vocabularies will describe processes in multiple domains, such as materials science, tribology, and metalworking. Afterwards, the developed thesauri will be used in the creation of metadata templates which can be used to annotate experimental data generated in the procedures mentioned above.

Purpose: Advances in information and communication technologies have facilitated the research and production of large volumes of data, and this has led to a new paradigm in scientific research called the fourth paradigm of science. The purpose of this study was to evaluate the status of research data sharing among researchers at Kerman University of Medical Sciences. Methodology: This study is a descriptive survey research. The study population consisted of all faculty members of Kerman University of Medical Sciences with 521 members. The sample size was estimated 220 using Cochran formula. Data collection tool was a researcher-built questionnaire. Cronbach's alpha coefficient was used to calculate the reliability of different parts of questionnaire. Data were analyzed using descriptive statistics including frequency, percentage, mean and standard deviation. Findings: The status of research data sharing in terms of human factors with an average of 3.67 is in relatively desirable condition. Among the human factor variables, the factor of understanding the importance and necessity of data sharing with an average of 4.21 is in a better position than other items. Also, the status of research data sharing of technical factors with an average of 2.58 and in terms of organizational factors with an average of 2.35 is in a relatively desirable condition. Among the items of organizational factors, respectively, holding training courses, support of senior managers of the organization, standards and regulations, the existence of a special program for data sharing and communication channels between researchers are in an unfavorable condition. Also, the sharing of research data in terms of legal factors with an average of 2.36 is relatively desirable. Legal frameworks, control of data access, and the organization's obligation to share data in research projects are in poor condition. Cultural factors related to data sharing, with an average of 3.95 are in good condition. Among the items of this dimension, only two items of incentive and motivation mechanisms and reducing unsound competition are in a relatively desirable condition. Results: The results indicated that Data management programs will enable communication with the areas of production, organization, management and preparation of specialized medical data, providing a sharing process for future exploitation. Encouraging the culture of data sharing, holding training courses, designing data sharing agreements, protecting privacy and data confidentiality, financial support, and strengthening the technology infrastructure are among the suggestions of this research.

Data sharing is considered as a hot topic and a norm in many scientific fields. But it seems that, it was neglected in Iranian Medical fields. This research aiming to identify relevant factors in data sharing. With using semi-structured interview, survey and DEMATEL techniques, the present study identified the influencing factors affecting data sharing. The sample population consisted of 14 experienced Iranian medical researchers in data sharing. The results showed that, education, budgeting, attitudes, and institutional requirements were the most influential factors among others and were pure casual factors. In view of Iranian medical researchers, budgeting and organizational supports had the highest effect on authorship, structures, attitudes, ownership and their trust. Co-authorship, trust among the researchers and research system, legal issues like ownership, and the conditions of data access were not strong casual nor strong effect variables in this research, but had important role in data sharing and should be more considered. Identifying factors influencing data sharing enhance and strengthen data sharing and facilitate movement towards data-driven researches, and scientific communities could apply the advantages of data sharing.

The article encourages representatives of the academic community to exploit the potential of the library and “advertises” the knowledge and versatile skills of librarians. The paper systemises and classifies the fields of activity where the potential of libraries can contribute to the delivery of a wide spectrum of research projects. Library activities are presented from a new perspective, with a focus on new quality of services provided by libraries, which can use both their own infrastructure and the potential of their staff to support and co-deliver projects. In addition to the services that are undoubtedly part of libraries’ mission, a new and very important aspect of their activities was described. In fact, libraries can carry out research projects themselves and even play the role of a project leader, which gives them a new place in the academic community. Research projects are understood as projects that involve basic research - funded mainly by the National Science Centre, the Ministry of Education and Science and from EU funds under Horizon 2020. In the case of EU-funded projects that do not involve basic research (Operational Programme Digital Poland), it is difficult to determine whether the research aspect - if any - is in line with the definition of basic research, unless it is clearly evident from the project description made available to the public.

As funder, journal, and disciplinary norms and mandates have foregrounded obligations of data sharing and opportunities for data reuse, the need to plan for and curate data sets that can reach researchers and end-users with disabilities has become even more urgent. We begin by exploring the disability studies literature, describing the need for advocacy and representation of disabled scholars as data creators, subjects, and users. We then survey the landscape of data repositories, curation guidelines, and research-data-related standards, finding little consideration of accessibility for people with disabilities. We suggest three sets of minimal good practices for moving toward truly accessible research data: 1) ensuring Web accessibility for data repositories; 2) ensuring accessibility of common text formats, including those used in documentation; and 3) enhancement of visual and audiovisual materials. We point to some signs of progress in regard to truly accessible data by highlighting exemplary practices by repositories, standards, and data professionals. Accessibility needs to become a mainstream component of curation practice included in every training, manual, and primer.

Objective: Research data repositories are an evolution of document repositories that aim to access and preserve all materials used before, during, and after scientific research. In this context, this study aims to conduct an exploratory and descriptive investigation of the international scenario of data repositories by monitoring the descriptive metadata of the international register of this type of repositories in the Registry of Research Data Repositories (re3data.org). Methods: The process requires applying knowledge inherent to the techniques and technologies used for descriptive data analysis, information retrieval, manipulation, analysis, and data visualization. Consequently, three scripts in Python 3.11 are provided for collecting metadata from re3data and scripts and converting the metadata to enable visualization in software such as VOSviewer, a dataset with metadata descriptions of repositories and conversions for visualization of networks. The datasets produced in this study can be found in the ZENODO Data Repository (https://doi.org/10.5281/zenodo.7903109). In a collection on (05/05/2023), 3108 links to the repository descriptions were retrieved. Data and scripts were created for this methodological experiment and shared at (DOI: doi.org/10.5281/zenodo.7903109). The dataset contains a root directory with three subdirectories: (scripts) with (.py) Python codes, another directory called (data) with textual files containing tab-separated values (.TSV), and the file (Information Systems Research, RIS). The third directory (env) contains the Python libraries required to run the scripts. Potential for reuse: The research method applied to manipulate this dataset is based on automated re3data metadata extraction and network visualization; after the data collection and analysis process, it is possible to trigger a study based on the descriptions extracted from the Registry of Research Data Repositories (re3data), researchers can visualize the international scenario of research data repositories, verified by re3data, which allows ethical monitoring of the number of research data repositories that are registered in re3data, what are their areas, institutions, countries, the language of research data, the typology of repositories and deposited data, their themes, areas of knowledge, types of access, licenses and software used. In addition, other issues can be raised while interpreting the data. The community of Librarianship and Information Science professionals need to share data and the extraction technique these research data. Finally, it can be concluded whether information about research data repositories allows us to state that they are heterogeneous data sources that enable access and preservation of a wide range of research data types

Background: The work of data research infrastructure operators is poorly understood, yet the services they provide are used by millions of scientists across the planet. Policy and implications: As the data services and the underlying infrastructure are typically funded through the public purse, it is essential that policymakers, research funders, experts reviewing funding proposals, and possibly even end-users are equipped with a good understanding of the daily tasks of service providers. Recommendations: We suggest drawing parallels between research data infrastructure and road infrastructure. To trigger the imagination and foster understanding, this policy brief contains a table of corresponding aspects of the two classes of infrastructure, and a table of policy implications. Conclusions: Just as economists and specialist evaluators are typically brought in to inform policies and funding decisions for road infrastructure, we encourage this to also be done for research infrastructures

Collaborations between scientists from the global north and global south (N-S collaborations) are a key driver of the “fourth paradigm of science” and have proven crucial to addressing global crises like COVID-19 and climate change. However, despite their critical role, N-S collaborations on datasets are not well understood. Science of science studies tend to rely on publications and patents to examine N-S collaboration patterns. To this end, the rise of global crises requiring N-S collaborations to produce and share data presents an urgent need to understand the prevalence, dynamics, and political economy of N-S collaborations on research datasets. In this paper, we employ a mixed methods case study research approach to analyze the frequency of and division of labor in N-S collaborations on datasets submitted to GenBank over 29 years (1992–2021). We find: (1) there is a low representation of N-S collaborations over the 29-year period. When they do occur, N-S collaborations display “burstiness” patterns, suggesting that N-S collaborations on datasets are formed and maintained reactively in the wake of global health crises such as infectious disease outbreaks; (2) The division of labor between datasets and publications is disproportionate to the global south in the early years, but becomes more overlapping after 2003. An exception in the case of countries with lower S&T capacity but high income, where these countries have a higher prevalence on datasets (e.g., United Arab Emirates). We qualitatively inspect a sample of N-S dataset collaborations to identify leadership patterns in dataset and publication authorship. The findings lead us to argue there is a need to include N-S dataset collaborations in measures of research outputs to nuance the current models and assessment tools of equity in N-S collaborations. The paper contributes to the SGDs objectives to develop data-driven metrics that can inform scientific collaborations on research datasets.

As the FAIR Principles about findability, accessiblity, interoperability and reusability of research (Wilkinson et al. 2016) reach further and deeper into the research ecosystem, they are increasingly reflected in research policies, research infrastructures, data management plans and other elements of the research landscape. Yet many of these elements are themselves limited in their FAIRness, which hinders the FAIRification - adaptation to the FAIR Principles - of elements that depend on them, e.g. datasets, software, reviews, replication attempts or research evaluation. This can cause friction in alignment with current practices, thereby leading to missed educational and community engagement opportunities and hampering efficient monitoring of compliance or systematic identification of potential improvements.This poster looks at how the FAIRness of FAIR Digital Objects is affected by the FAIRness of their dependencies, focusing on two types of examples - research data policies and research ethics workflows.In the first part, the poster explores how the role of research data-related policies and regulations would change if theiy would increasingly involve FAIR Digital Objects, e.g. if policies and their key stipulations would have persistable identifiers linked to well-defined and machine-actionable schemas. These explorations will touch upon both technical and social aspects: what mechanisms are available and already used to increase the FAIRness of policies? Does it help or hinder if certain aspects of the transition to a FAIRer ecosystem are shared in a more or less FAIR way or with shorter or longer delays? Does having more FAIR policies themselves provide funders, institutions, publishers or other organizations with more of an edge or a handicap in terms of assisting their respective communities in the transition towards more FAIRness in their respective corner of the research ecosystem? How can the design of FAIR policy elements be tailored to optimize learning opportunities for specific stakeholder groups pertaining to specific types of collections of FAIR Digital Objects?In the second part, the poster explores what the benefits and risks would be of making more use of FAIR Digital Objects in research ethics workflows (Hegde et al. 2022). The components considered include the circumstances suggesting or even requiring an ethical review, the types of information that need to be exchanged during the process, the types of communications set up to convey said information, the stakeholders involved in any part of the process, the ways in which metadata about the process is stored and shared, and rules that govern any of these aspects and related matters. These questions will be discussed from the perspectives of several stakeholder groups, e.g. researchers, research subjects, research administrators, reviewers (on ethics committees or during manuscript or grant proposal review), data stewards, tool developers, science journalists, ethics educators and others. Another aspect considered is the potential of a more FAIR ethics process to reduce the burden on the stakeholders involved and to make their participation more meaningful, while raising compliance with applicable regulations, increasing the speed and transparency of the process and improving documentation and standardization.Generalizing based on these two examples, the poster concludes with a depiction of how to include dependencies of research-related FAIR Digital Objects in FAIR Digital workflows and assessments or reuses thereof.

Although data reusers request information about how research data was created and curated, this information is often non-existent or only briefly covered in data descriptions. The need for such contextual information is particularly critical in fields like archaeology, where old legacy data created during different time periods and through varying methodological framings and fieldwork documentation practices retains its value as an important information source. This article explores the presence of contextual information in archaeological data with a specific focus on data provenance and processing information, i.e., paradata. The purpose of the article is to identify and explicate types of paradata in field observation documentation. The method used is an explorative close reading of field data from an archaeological excavation enriched with geographical metadata. The analysis covers technical and epistemological challenges and opportunities in paradata identification, and discusses the possibility of using identified paradata in data descriptions and for data reliability assessments. Results show that it is possible to identify both knowledge organisation paradata (KOP) relating to data structuring and knowledge-making paradata (KMP) relating to fieldwork methods and interpretative processes. However, while the data contains many traces of the research process, there is an uneven and, in some categories, low level of structure and systematicity that complicates automated metadata and paradata identification and extraction. The results show a need to broaden the understanding of how structure and systematicity are used and how they impact research data in archaeology and comparable field sciences. The insight into how a dataset's KOP and KMP can be read is also a methodological contribution to data literacy research and practice development. On a repository level, the results underline the need to include paradata about dataset creation, purpose, terminology, dataset internal and external relations, and eventual data colloquialisms that require explanation to reusers.

The paper is devoted to the problem of generating artificial datasets for data envelopment analysis (DEA), which can be used for testing DEA models and methods. In particular, the papers that applied DEA to big data often used synthetic data generation to obtain large-scale datasets because real datasets of large size, available in the public domain, are extremely rare. This paper proposes the algorithm which takes as input some real dataset and complements it by artificial efficient and inefficient units. The generation process extends the efficient part of the frontier by inserting artificial efficient units, keeping the original efficient frontier unchanged. For this purpose, the algorithm uses the assurance region method and consistently relaxes weight restrictions during the iterations. This approach produces synthetic datasets that are closer to real ones, compared to other algorithms that generate data from scratch. The proposed algorithm is applied to a pair of small real-life datasets. As a result, the datasets were expanded to 50K units. Computational experiments show that artificially generated DMUs preserve isotonicity and do not increase the collinearity of the original data as a whole.

The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners. Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.

Abstract Background Electronic Laboratory Notebooks (ELNs) are used to document experiments and investigations in the wet-lab. Protocols in ELNs contain a detailed description of the conducted steps including the necessary information to understand the procedure and the raised research data as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create semantic documentation of the provenance of research data by the use of ontologies and linked data methodologies. Methods Based on an ELN protocol of a biomedical wet-lab experiment, a retrospective provenance model of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment’s description such as headings, tables, and links, to translate the ELN protocol into a semantic knowledge representation. To satisfy the Findable, Accessible, Interoperable, and Reuseable (FAIR) guiding principles, a ready-to-publish bundle is created that contains the research data together with their semantic documentation. Results While the manual modelling efforts serve as proof of concept by employing one protocol, the automated structure-based approach demonstrates the potential generalisation with seven ELN protocols. For each of those protocols, a ready-to-publish bundle is created and, by employing the SPARQL query language, it is illustrated that questions about the processes and the obtained research data can be answered. Conclusions The semantic documentation of research data obtained from the ELN protocols allows for the representation of the retrospective provenance of research data in a machine-interpretable way. Research Object Crate (RO-Crate) bundles including these models enable researchers to easily share the research data including the corresponding documentation, but also to search and relate the experiment to each other.

Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here, we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

Data discovery is important to facilitate data re-use. In order to help frame the development and improvement of data discovery tools, we collected a list of requirements and users’ wishes. This paper presents the analysis of these 101 use cases to examine data discovery requirements; these cases were collected between 2019 and 2020. We categorized the information across 12 ‘topics’ and eight types of users. While the availability of metadata was an expected topic of importance, users were also keen on receiving more information on data citation and a better overview of their field. We conducted and analysed a survey among data infrastructure specialists in a first attempt at ranking the requirements. Between these data professionals, these rankings were very different, excepting the availability of metadata and data quality assessment.

On October 5, 2022, the “Workshop on Quality Management at Research Data Repositories” – jointly organized by re3data COREF and CoreTrustSeal – was held online with more than 70 participants attending. The objective of the workshop was to discuss activities research data repositories perform to assure, assess, and improve data quality. The workshop started with input from the workshop organizers: re3data COREF presented results of a recent suvey on quality management at repositories, and CoreTrustSeal shared the perspective of a certification organization. Then, repositories from different disciplinary backgrounds presented their approaches to quality management. The workshop concluded with a breakout session and a plenary discussion on options for making information on data quality assurance more visible.

Background: The work of data research infrastructure operators is poorly understood, yet the services they provide are used by millions of scientists across the planet. Policy and implications: As the data services and the underlying infrastructure are typically funded through the public purse, it is essential that policymakers, research funders, experts reviewing funding proposals, and possibly even end-users are equipped with a good understanding of the daily tasks of service providers. Recommendations: We suggest drawing parallels between research data infrastructure and road infrastructure. To trigger the imagination and foster understanding, this policy brief contains a table of corresponding aspects of the two classes of infrastructure. Conclusions: Just as economists and specialist evaluators are typically brought in to inform policies and funding decisions for road infrastructure, we encourage this to also be done for research infrastructures

El artículo presenta el relevamiento realizado con el fin de localizar los portales y repositorios que contienen datos primarios de investigación en la Argentina. Para llevarlo a cabo, se consultaron directorios y registros de repositorios, tanto de datos como de publicaciones, se revisaron los repositorios incluidos en el Sistema Nacional de Repositorios Digitales y aquellos cosechados por el Sistema de Información Universitaria del Consejo Interuniversitario Nacional en su servicio SIU-BDU 2. Además, se realizaron búsquedas en el motor de búsqueda Google y se hicieron relevamientos de fuentes documentales sobre la temática. Se hallaron 25 repositorios y portales que contienen datos y se analizaron algunas características generales de estos. = This article presents the survey to locate the portals and repositories that contain primary research data in Argentina. For this purpose, directories and repositories records, both for data and publications, the repositories included in the Sistema Nacional de Repositorios Digitales, and those harvested by the Sistema de Información Universitaria del Consejo Interuniversitario Nacional in its SIU-BDU 2 service were consulted. Besides, Google search engine and documentary sources on the subject were used. We found 25 repositories containing data and we analyzed some general their characteristics.

Recordings of musical practices are kept in various public institutions and private depositories around the world. They constitute valuable data for ethnomusicological research and are substantial for the world's musical heritage. At the moment, there are no commonly used systems and standards for organizing, describing or categorizing these data, which makes their use difficult. In this paper, we discuss the required steps to make them findable, accessible, interoperable and reusable (FAIR), and outline action items to reach these goals. We show solutions that help researchers to manage their data over the whole research lifecycle and discuss the benefits of combining technologies from information science, music information retrieval, and linked data, with the aim of giving incentives for the ethnomusicology research community to actively participate in these developments in the future.

Verschiedene Stakeholder fordern eine bessere Verfügbarkeit von Forschungsdaten. Der Erfolg dieser Initiativen hängt wesentlich von einer guten Auffindbarkeit der publizierten Datensätze ab, weshalb Dataset Retrieval an Bedeutung gewinnt. Dataset Retrieval ist eine Sonderform von Information Retrieval, die sich mit dem Auffinden von Datensätzen befasst. Dieser Beitrag fasst aktuelle Forschungsergebnisse über das Informationsverhalten von Datensuchenden zusammen. Anschließend werden beispielhaft zwei Suchdienste verschiedener Ausrichtung vorgestellt und verglichen. Um darzulegen, wie diese Dienste ineinandergreifen, werden inhaltliche Überschneidungen von Datenbeständen genutzt, um den Metadatenaustausch zu analysieren.

This paper presents findings from a survey on the status quo of data quality assurance practices at research data repositories. The personalised online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information and yielded 332 complete responses. The results demonstrate that most repositories perform data quality assurance measures, and overall, research data repositories significantly contribute to data quality. Quality assurance at research data repositories is multifaceted and nonlinear, and although there are some common patterns, individual approaches to ensuring data quality are diverse. The survey showed that data quality assurance sets high expectations for repositories and requires a lot of resources. Several challenges were discovered: for example, the adequate recognition of the contribution of data reviewers and repositories, the path dependence of data review on review processes for text publications, and the lack of data quality information. The study could not confirm that the certification status of a repository is a clear indicator of whether a repository conducts in-depth quality assurance.

Background: Research Software is a concept that has been only recently clarified. In this paper we address the need for a similar enlightenment concerning the Research Data concept. Methods: Our contribution begins by reviewing the Research Software definition, which includes the analysis of software as a legal concept, followed by the study of its production in the research environment and within the Open Science framework. Then we explore the challenges of a data definition and some of the Research Data definitions proposed in the literature. Results: We propose a Research Data concept featuring three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind. Conclusions: The analysis of this definition and the context in which it is proposed provides some answers to the Borgman’s conundrum challenges, that is, which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. They are completed with answers to the questions: how? and where?

Background: Open Science seeks to render research outputs visible, accessible and reusable. In this context, Research Data and Research Software sharing and dissemination issues provide real challenges to the scientific community, as consequence of recent progress in political, legal and funding requirements. Methods: We take advantage from the approach we have developed in a precedent publication, in which we have highlighted the similarities between the Research Data and Research Software definitions. Results: The similarities between Research Data and Research Software definitions can be extended to propose protocols for Research Data dissemination and evaluation derived from those already proposed for Research Software dissemination and evaluation. We also analyze FAIR principles for these outputs. Conclusions: Our proposals here provide concrete instructions for Research Data and Research Software producers to make them more findable and accessible, as well as arguments to choose suitable dissemination platforms to complete the FAIR framework. Future work could analyze the potential extension of this parallelism to other kinds of research outputs that are disseminated under similar conditions to those of Research Data and Research Software, that is, without widely accepted publication procedures involving editors or other external actors and where the dissemination is usually restricted through the hands of the production team.

Resumen: Los datos abiertos (open data) son datos que pueden ser utilizados, reutilizados y redistribuidos libremente por cualquier persona, y que se encuentran sujetos, cuando más, al requerimiento de atribución y de compartirse de la misma manera en que aparecen. En este trabajo se describe el concepto de datos abiertos y de datos de investigación y por qué es necesario compartirlos. Se exponen las ventajas e inconvenientes de compartir datos y el procedimiento para llevar a cabo esta práctica. Se especifican los elementos que deben constituir el plan de gestión de los datos o documento que describe el tratamiento que van a recibir los datos de investigación recopilados o generados en el curso de un estudio o proyecto. Por último, se exponen las vías verde y dorada para la publicación de los datos y la necesidad de poner al servicio de los investigadores el asesoramiento técnico y las infraestructuras necesarias para que puedan llevar a cabo estas prácticas. Abstract: Open data are data that can be freely used, reused and redistributed by any person, and that are, at most, subject to the attribution requirement and shared in the same way in which they appear. This paper describes the concept of open data and research data and why it is important to share them. The advantages and disadvantages of data sharing and the procedure for carrying out this practice are discussed. We specifies the elements that should constitute the data management plan or document that describes the treatment that will receive the research data collected or generated in the course of a study or project. Finally, the green and gold routes for the publication of the data and the need to put at the service of the researchers the technical advice and the infrastructures necessary to carry out these practices are presented.

Objective: Data-driven research, given the hidden and vital value of research data, is an emerging paradigm that has led to the need for effective and proper management of research data. Research data needs to be stored and retrieved using appropriate methods to make it easy to discover, retrieve, and reuse to add value. Accordingly, it is important to manage research data that includes the organization of data, from entering the research cycle to publishing and sharing that data, recording and maintaining valuable research results. The aim of this study is to identify the dimensions of research data management development by emphasizing the methods of providing research data management services that have been implemented in scientific and research environments around the world. Methodology: This research is a systematic review with a qualitative approach to identify the dimensions of research data management development, using MAXQDA10 software to analyze the content of scientific resources and documents. Accordingly, the process of collecting documents using the library method and searching for scientific databases has continued to reaching the point of theoretical saturation and the repetition of dimensions and components. Also, in order to ensure that the theoretical saturation point is reached and the validity of the research are guaranteed, the websites of the top ten universities in the world in the field of research activities in 2019 were observed. With the open coding method, the concepts were extracted from the texts, and then with the central coding and searching for the commonality of the concepts, the graph of the categories was drawn. In total, 403 concepts were extracted from the collected documents, of which 321 concepts are in the field of research data management and 82 concepts are related to the areas of research data sharing, advantages, challenges and solutions. Then, it was ensured that basic concepts in research data management were identified. Findings: It is apparent that the implementation of research data management in universities and scientific centers depends on five basic components: politics, planning, services, stakeholders and technology, which each have a wide range of dimensions and multiple processes. Understanding needs, being aware of existing challenges, developing the necessary knowledge and skills will help determine appropriate policies and planning to best use existing networks and technical infrastructure, which are now available to most universities and research centers, to provide data management services. Conclusion: In this research, while presenting the basic categories of research data management, dimensions and new methods in the field have been clearly identified. It is expected that Iranian universities and research centers will also work integrated to implement new policies and programs needed to provide and develop research data management services to prevent redundancies and additional costs in the research process.

Organizing research data is very important for data and information managers through a research data management mechanism (research data management/MDP) in a repository system. In this mechanism, research data must be organized and described as an effort to provide access. One important aspect of organizing is the availability of metadata. This Study was supported by the Institute of Sciences of Indonesia (LIPI) and the SAINTEK Scholarship from the Ministry of Research and Technology/National Research and Innovation Agency of the Republic of Indonesia (KEMENRISTEK/BRIN) in 2020 and it’s purpose is to identify and describe metadata standards and metadata elements used in research data management in the National Scientific Repository (RIN) system. This study uses a qualitative approach with a case study method. Sources of data come from literature / document studies and direct observation. The results of the study show that the RIN system adopts descriptive metadata from three main standards, they are DublinCore, DataCite, and DDI. As a medium for describing research data in general, the metadata sections provided by the RIN system in the dataset folder are quite large and complete. Of the 35 metadata fields available in the dataset folder in this system, the three metadata standards complement each other with an adaptation of the dominant DDI standard with 32 metadata fields. However, the fields that are available can also be found in other standards, such as the title, subject, or keyword fields that are also found in the DublinCore and DataCite standards. Thus, the metadata fields provided in the RIN system is good enough and sufficient for research data management needs.

In this commentary on Hofmann et al. (2021), the notion of ethnomusicology and some of its underlying biases are questioned and reflected in the light of applying FAIR data principles to musicological research data from outside a Western canon and its musical practices.

Objectives Pre-surgical ‘health optimisation’ programmes aim to improve patients’ health and to reduce their need for surgery. Routinely-collected data does not provide sufficient insight into whether or how these programmes meet their intended outcomes. The aim was to link routinely-collected NHS data with bespoke research questionnaire data to address this insufficiency. Approach A study was designed to identify and collect data from a patient cohort eligible for a health optimisation programme (offering weight management and smoking cessation support) to determine programme uptake and patient outcomes. Patient and public involvement (PPI) group engagement revealed support for the use of data linkage between routinely-collected NHS data and study questionnaires, and shaped the study proposal. The HDRUK-supported regional ‘Systemwide Dataset’ of NHS routinely-collected data would provide demographics, healthcare usage and clinical outcomes. Questionnaires would provide data on patient decision-making and engagement with weight management or smoking cessation and detailed pain, function, wellbeing and activation measures. Results The study received Health Research Authority approval and recruited six Participant Identification Centres. PPI co-production of the study’s participant-facing documents addressed the need for appropriate description of the novel data linkage process for potential participants. The data linkage design will offer an important advantage of a reduction in the participants’ time and recall accuracy needed in completing questionnaires due to the ability to omit duplicate demographic and healthcare usage fields. Further potential advantages include the ability to compare participants with patients who decline participation in the questionnaires, and long-term follow up of patient outcomes due to the continuation of routinely-collected data. Challenges have been faced in operationalizing the data collection and linkage processes due to decisions over data controllership of the combined dataset. Conclusion Linkage between routinely-collected NHS data and bespoke research questionnaires was deemed ethically acceptable, and could offer significant advantages in combining demographics, healthcare usage, outcomes and experience data of a cohort. Systemic decision-making to specify and streamline acceptable processes for this data linkage are important next steps.

Research data means data used or created in the course of research or experiments. Research data is very important for validation of research conducted and for use in future research and projects. Recently, convergence research between various fields and international cooperation has been continuously done due to the explosive increase of research data and the increase in the complexity of science and technology. Developed countries are actively promoting open science policies that share research results and processes to create new knowledge and values through convergence research. Communities to promote the sharing and utilization of research data such as RDA (Research Data Alliance) and COAR (Confederation of Open Access Repositories) are active, and various platforms for managing and sharing research data are being developed and used. OpenAIRE (Open Access Infrastructure for Research In Europe), a research data platform in Europe, ARDC (Australian Research Data Commons) in Australia, and IRDB (Institutional Repositories DataBase) in Japan provide research data or research data related services. Korea has been establishing and implementing a research data sharing and utilization strategy to promote the sharing and utilization of research data at the national level, led by the central government. Based on this strategy, KISTI has been building a Korean research data platform (DataON) since 2018, and has been providing research data sharing and utilization services to users since January 2020. This paper reviews the characteristics of DataON and how it is used for research by showing its applications.

[Purpose/Significance] Data security is an important cornerstone of national security. The promulgation of the data security law of the People's Republic of China (Draft) points out the development direction for the establishment and improvement of data security governance system in China. [Methods/Process] Through text analysis, the paper analyzes the content of the data security law of the People's Republic of China, mainly to interpret the legislative spirit and principles, content and characteristics of the law and its deficiencies. [Results/Conclusions] The draft not only pays attention to the confidentiality of data itself, but also takes into account the availability of data. It provides legal protection for safeguarding national data sovereignty and legitimate rights and interests of data stakeholders. In addition, relevant supporting measures should be formulated as soon as possible.

Arguments for the FAIR (Findable, Accesible, Inter-operable and Reusable) principles of science have mostly been based on appeals to values. However, the work of onboarding diverse researchers to make efficient and effective implementations of FAIR requires different appeals. In our recent effort to transform the institution into a FAIR University by 2025, here we report on the experiences of the Community of Data Driven Insights (CDDI), a interfaculty initiative where all university-wide research data service providers are joined together to support researchers and research groups (e.g. see research showcase example here) with all aspects concerning research data management. CDDI aims to turn all digital objects within Maastricht University (UM) into FAIR Digital Objects (FDO) and by disclosing the progress and challenges of implementing FDOs (e.g. see CDDI OSF repo: https://osf.io/398cz/), we hope to shed light on the process in a way that might be useful for other institutions in Europe and elsewhere. We initially identified 5 challenges for FDO implementation. These challenges were first a matter of reshaping the culture of science making practices to fit the FAIR principles. Additionally, it required an educational awareness within the scientific communities, and finally financial and technical tools to actually facilitate the transition to FAIR practices of science making. These perspectives show the complex dimensions of FAIR principles and FDO implementation to researchers across disciplines in a single university.