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The study aims to shed light on the current state of “Dataset repositories” indexed in Directory of Open Access Repositories (OpenDOAR).

From each repository/record information, the Open-Access Policies, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), year of creation and the number of data sets archived in the repositories were manually searched, documented and analyzed.

Developed countries like the United Kingdom and the USA are primarily involved in the development of institutional open-access repositories comprising significant components of OpenDOAR. The most extensively used software is DSpace. Most data set archives are OAI-PMH compliant but do not follow open-access rules. The study also highlights the sites’ embrace of Web 2.0 capabilities and discovers really simple syndication feeds and Atom integration. The use of social media has made its presence known. Furthermore, the study concludes that the number of data sets kept in repositories is insufficient, although the expansion of such repositories has been consistent over the years.

The work has the potential to benefit both researchers in general and policymakers in particular. Scholars interested in research data, data sharing and data reuse can learn about the present state of repositories that preserve data sets in OpenDOAR. At the same time, policymakers can develop recommendations and policies to assist in the construction and maintenance of repositories for data sets.

According to the literature, there have been numerous studies on open-access repositories and OpenDOAR internationally, but no research has focused on repositories preserving content-type data sets. As a result, the study attempts to uncover various characteristics of OpenDOAR Data set repositories.

This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

The purpose of this study was to understand the landscape of Indian research data repositories (RDRs) indexed in the re3data.org. The study analysed the metadata elements of Indian RDRs to identify their disciplinary orientations, typology, standards adopted, foreign collaborations, etc. The study ascertained the current status of the Indian RDRs by visiting their respective websites and tried to identify and map the exact disciplinary orientation of each RDR.

The study used “content analysis” of the metadata elements extracted from re3data.org along with the information analysis of the respective websites of the registered RDRs.

The study identified that only 80% of the Indian RDRs listed by the re3data.org is currently active. Most of the Indian RDRs are hosted by the central and state governments and are almost equally distributed among Life Sciences, Natural Sciences and Social Sciences domains. The data provided by the re3data.org for the Indian RDRs are not complete and up-to-date.

The findings indicate the presence of a good number of inactive RDRs in the re3data.org. The study suggests using a revised version of the DFG subject classification scheme or considering a standard classification scheme for subject indexing.

To the best of the authors’ knowledge, this study is the first of its kind that critically analysed the metadata values extracted and moved further to identify the current status of Indian RDRs.

This study aims to identify the readiness of institutional repositories in Indonesia to implement digital humanities (DH) data curation. Data curation is a method of managing research data that maintains the data’s accuracy and makes it available for reuse. It requires controlled data management.

The study uses a qualitative approach. Data collection was carried out through a focus group discussion in September–October 2022, interviews and document analysis. The informants came from four institutions in Indonesia.

The findings reveal that the national research repository has implemented data curation, albeit not optimally. Within the case study, one of the university repositories diligently curates its humanities data and has established networks extending to various ASEAN countries. Both the national archive repository and the other university repository have implemented rudimentary data curation practices but have not prioritized them. In conclusion, the readiness of the national research repository and the university repository stand at the high-capacity stage, while the national archive repository and the other university repository are at the established and early stages of data curation, respectively.

This study examined only four repositories due to time constraints. Nonetheless, the four institutions were able to provide a comprehensive picture of their readiness for DH data curation management.

This study provides insight into strategies for developing DH data curation activities in institutional repositories. It also highlights the need for professional development for curators so they can devise and implement stronger ownership policies and data privacy to support a data-driven research agenda.

This study describes the preparations that must be considered by institutional repositories in the development of DH data curation activities.

Data sharing is increasingly being recognized as an essential component of scholarly research and publishing. Sharing data improves results and propels research and discovery forward. Given the importance of data sharing, the purpose of the study is to unveil the present scenario of research data repositories (RDR) and sheds light on strategies and tactics followed by different countries for efficient organization and optimal use of scientific literature.

The data for the study is collected from registry of RDR (re3data registry) (re3data.org), which covers RDR from different academic disciplines and provides filtration options “Search” and “Browse” to access the repositories. Using these filtration options, the researchers collected metadata of repositories i.e. country wise contribution, content-type data, repository language interface, software usage, metadata standards and data access type. Furthermore, the data was exported to Google Sheets for analysis and visualization.

The re3data registry holds a rich and diverse collection of data repositories from the majority of countries all over the world. It is revealed that English is the dominant language, and the most widely used software for the creation of data repositories are “DataVerse”, followed by “Dspace” and “MySQL”. The most frequently used metadata standards are “Dublin Core” and “Datacite metadata schema”. The majority of repositories are open, with more than half of the repositories being “disciplinary” in nature, and the most significant data sources include “scientific and statistical data” followed by “standard office documents”.

The main limitation of the study is that the findings are based on the data collected through a single registry of repositories, and only a few characteristic features were investigated.

The study will benefit all countries with a small number of data repositories or no repositories at all, with tools and techniques used by the top repositories to ensure long-term storage and accessibility to research data. In addition to this, the study provides a global overview of RDR and its characteristic features.

The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.

A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.

Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.

Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.

This study aims to highlight the many distinguishing characteristics of open-access repositories that archive “Patents” in the Directory of Open-Access Repositories (OpenDOAR) provided by Asian nations.

The OpenDOAR was chosen as a data collection tool that provides a quality-assured list of repositories indexed globally. The data was extracted on 28 March 2023.

The study found that only eight Asian countries contributed to open-access repositories on OpenDOAR, with China being the highest contributor. These countries mainly focused on institutional repositories, primarily using DSpace and English as the main language interface. Web 2.0 tools, especially RSS and Atom, were commonly used, along with some presence of social media platforms on the sites, although to a lesser extent. While many repositories followed the OAI-PMH protocol, a considerable portion did not adopt open-access policies.

To the best of the authors’ knowledge, this study is the first one that brings to light the different features of repositories archiving one of the important content types, i.e. “Patents” in the OpenDOAR by Asian countries.

The purpose of this study is to investigate the current research data management practices among researchers in Ghana and their impact on data reuse and collaborative research. The study aims to identify the methods used by researchers to store and preserve their research data, as well as to determine the extent to which researchers share their data with others.

The study uses a mixed-method research strategy to blend qualitative and quantitative data and is conducted at two public and two private universities in Ghana.

The study revealed that researchers in Ghana currently store and preserve their research data using personal devices, such as laptops, CDs and external flash drives, rather than keeping the data in university data repositories. They also do not share their research data with others, which negatively affects collaborative research. The current practice of storing data on personal devices and not sharing data with others hinders collaborative research. The study recommends that universities in Ghana revise their research policy documents to address RDM-related issues such as data storage, data preservation, data sharing and data reuse.

The study was conducted at two public and two private universities in Ghana, but the findings were placed in a wider context through appropriate references.

This study emphasises the need for sound research data management procedures to support research collaboration and data reuse in Ghana. Universities should provide incentives to academics to disclose their data to encourage data sharing and collaboration.

The government and management of universities should consciously invest in the needed technologies and equipment to implement research data management in their universities.

This study looks at how researchers in Ghana manage their research data and how it affects data reuse and collaborative research.

The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.

The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.

The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.

Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.