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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.

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.

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 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.

In today's global and competitive corporate environment characterised by rapidly changing information, knowledge and technology (IKT), researchers must be upskilled in all aspects of research data management (RDM). This study investigates a set of capabilities and competencies required by researchers at selected South African public universities, using the community capability model framework (CCMF) in conjunction with the digital curation centre (DCC) lifecycle model.

The post-positivist paradigm was used in the study, which used both qualitative and quantitative methodologies. Case studies, both qualitative and quantitative, were used as research methods. Because of the COVID-19 pandemic rules and regulations, semi-structured interviews with 23 study participants were conducted online via Microsoft Teams to collect qualitative data, and questionnaires were converted into Google Forms and emailed to 30 National Research Foundation (NRF)-rated researchers to collect quantitative data.

Participating institutions are still in the initial stages of providing RDM services. Most researchers are unaware of how long their institutions retain research data, and they store and backup their research data on personal computers, emails and external storage devices. Data management, research methodology, data curation, metadata skills and technical skills are critically important RDM competency requirements for both staff and researchers. Adequate infrastructure, as well as human resources and capital, are in short supply. There are no specific capacity-building programmes or strategies for developing RDM skills at the moment, and a lack of data curation skills is a major challenge in providing RDM.

The findings of the study can be applied widely in research, teaching and learning. Furthermore, the research could help shape RDM strategy and policy in South Africa and elsewhere.

The scope, subject matter and application of this study contribute to its originality and novelty.

Open-access repositories (OARs) are essential for openly disseminating intellectual knowledge on the internet and providing free access to it. The current study aims to evaluate the growth and development of OARs in the field of technology by investigating several characteristics such as coverage, OA policies, software type, content type, yearly growth, repository type and geographic contribution.

The directory of OARs acts as the source for data harvesting, which provides a quality-assured list of OARs across the globe.

The study found that 125 nations contributed a total of 4,045 repositories in the field of research, with the USA leading the list with the most repositories. Maximum repositories were operated by institutions having multidisciplinary approaches. The DSpace and Eprints were the preferred software types for repositories. The preferred upload content by contributors was “research articles” and “electronic thesis and dissertations”.

The study is limited to the subject area technology as listed in OpenDOAR; therefore, the results may differ in other subject areas.

The work can benefit researchers across disciplines and, interested researchers can take this study as a base for evaluating online repositories. Moreover, policymakers and repository managers could also get benefitted from this study.

The study is the first of its kind, to the best of the authors’ knowledge, to investigate the repositories of subject technology in the open-access platform.

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.

This study aims to investigate the facilitating conditions (FCs) and how these FC affect institutional repository (IR) sustainability practices in public universities in Nigeria.

A survey research design was adopted in this study. The study population comprised 542 librarians from public universities that have IRs across Nigeria. A sample size of 230 librarians was determined using Taro Yamane’s formula. A multi-stage sampling technique was used to select the respondents in three stages, which were purposive, stratified and purposive sampling. A structured, validated questionnaire was used for data collection. Data were analyzed using descriptive and inferential (simple and multiple regression) statistics at a 5% level of significance.

The result revealed that the availability of FCs (ßeta = 0.459, t(211) = 7.719, p = 0.000) has a positive and significant influence on IR sustainability in public university libraries in Nigeria. The F-test (1, 223) value of 59.582 shows that there is sufficient evidence to substantiate the model’s usefulness in explaining IR sustainability. The R² (0.211) indicates that 21.1% of the variation in IR sustainability is explained by the availability of FCs in public university libraries in Nigeria. The finding suggests that the availability of FCs is a vital predictor of IR sustainability in public university libraries in Nigeria. The result also depicts that out of the eight parameters that measure the availability of FCs, it was current awareness of IR that had a positive and significant influence on IR sustainability.

This study concluded that ICT skills and FCs are contributory factors to IR sustainability practices by librarians in public university libraries in Nigeria. It was recommended that university administrators formulate policies that promote the sustainability of IR and provide adequate funds to support IR sustainability. Furthermore, the library management in public university libraries in Nigeria should drive content recruitment and create awareness of the IRs among students and faculty to ensure continued use.

Digital innovation requires organizations to reconfigure their information technology infrastructure (ITI) to cultivate creativity and implement fast experimentation. This research inquiries into ITI generativity, an emerging concept demoting a critical ITI capability for organizational digital innovation. More specifically, it conceptualizes ITI generativity across two dimensions—namely, systems and applications infrastructure (SAI) generativity and data analytics infrastructure (DAI) generativity—and examines their respective social and technical antecedents and their impact on digital innovation.

This research formulates a theoretical model to investigate the social and technical antecedents along with innovation outcomes of ITI generativity. To test this model and its associated hypotheses, a survey was administered to IT professionals possessing knowledge of their organization's IT architecture and digital innovation performance. The dataset, comprising responses from 140 organizations, was analyzed using the partial least squares technique.

Results reveal that both dimensions of ITI generativity contribute to digital innovation performance, with the effect of DAI generativity being more pronounced. In addition, SAI and DAI generativities are driven by social and technical factors within an organization. More specifically, SAI generativity is positively associated with the usage of a digital application services platform and IT human resources, whereas DAI generativity is positively linked to the usage of a data analytics services platform, data analytics services usability and data analytics human resources.

This research contributes to the literature on digital innovation by introducing ITI generativity as a crucial ITI capability and deciphering its role in digital innovation. It also offers useful insights and guidance for practitioners on how to build ITIs to achieve better digital innovation performance.