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This paper aims to provide statistical information on the worldwide spread of the open-source research data management application, the Dataverse Project, to librarians, data managers and information managers who are considering using the application at their own institution.

To produce a list of dataverse repositories, the official Dataverse website was evaluated, and JSON data were downloaded and parsed. Data standardisation was performed to assess the state of installations in various nations and continents across the world.

Globally, the Dataverse repositories have seen a rise in overall installations. The year 2020 alone saw a 23.21% rise. In a country-by-country comparison, the USA (13) has the most dataverse installations, while Europe (25) has the highest number of installations worldwide.

This research will be useful to librarians, data managers and information managers, among others, who want to learn more about Dataverse repositories throughout the world before deploying at their local level.

The purpose of this study is to examine the development of Dataverse, a global research data management consortium. The authors examine specifically the institutional characteristics, the utilization of the associated data sets and the relevant research data management services at its participating university libraries. This evidence-based approach is essential for understanding the current state of research data management practices in the global context.

The data was collected from 67 participants’ data portals between December 1, 2020, and January 31, 2021.

Over 80% of its current participants joined the group in the past five years, 2016–2020. Thirty-three Dataverse portals have had less than 10,000 total downloads since their inception. Twenty-nine participating universities are included in three major global university ranking systems, and 18 of those university libraries offer research data services.

This project is an explorative study on Dataverse, an international research data management consortium. The findings contribute to the understanding of the current development of the Dataverse project as well as the practices at the participating institutions. Moreover, they offer insights to other global higher education institutions and research organizations regarding research data management. While this study is practical, its findings and observations could be of use to future researchers interested in developing a framework for data work in academic libraries.

The progress of life science and social science research is contingent on effective modes of data storage, data sharing and data reproducibility. In the present digital era, data storage and data sharing play a vital role. For productive data-centric tasks, findable, accessible, interoperable and reusable (FAIR) principles have been developed as a standard convention. However, FAIR principles have specific challenges from computational implementation perspectives. The purpose of this paper is to identify the challenges related to computational implementations of FAIR principles. After identification of challenges, this paper aims to solve the identified challenges.

This paper deploys Petri net-based formal model and Petri net algebra to implement and analyze FAIR principles. The proposed Petri net-based model, theorems and corollaries may assist computer system architects in implementing and analyzing FAIR principles.

To demonstrate the use of derived petri net-based theorems and corollaries, existing data stewardship platforms – FAIRDOM and Dataverse – have been analyzed in this paper. Moreover, a data stewardship model – “Datalection” has been developed and conversed about in the present paper. Datalection has been designed based on the petri net-based theorems and corollaries.

This paper aims to bridge information science and life science using the formalism of data stewardship principles. This paper not only provides new dimensions to data stewardship but also systematically analyzes two existing data stewardship platforms FAIRDOM and Dataverse.

Research data management (RDM) is gaining a lot of momentum in the present day and rightly so. Research data are the core of any research study. The findings and conclusions of a study are entirely dependent on the research data. Traditional publishing did not focus on the presentation of data, along with the publications such as research monographs and especially journal articles, probably because of the difficulties involved in managing the research data sets. The current day technology, however, has helped in making this task easier. The purpose of this paper is to present a conceptual framework for managing research data at the institutional level.

This paper discusses the significance and advantages of sharing research data. In the spirit of open access to publications, freeing research data and making it available openly, with minimal restrictions, will help in not only furthering research and development but also avoiding duplication of efforts. The issues and challenges involved in RDM at the institutional level are discussed.

A conceptual framework for RDM at the institutional level is presented. A model for a National Repository of Open Research Data (NRORD) is also proposed, and the workflow of the functioning of NRORD is also presented.

The framework clearly presents the workflow of the data life-cycle in its various phases right from its creation, storage, organization and sharing. It also attempts to address crucial issues in RDM such as data privacy, data security, copyright and licensing. The framework may help the institutions in managing the research data life-cycle in a more efficient and effective manner.

To outline the current trajectories in digital social research and to highlight the roles of qualitative research in those trajectories.

A secondary analysis of the primary literature.

Qualitative research has shifted over time in relation to rapidly changing digital phenomena, but arguably finds itself in ‘crisis’ when faced with algorithms and ubiquitous digital data. However, there are many highly significant qualitative approaches that are being pursued and have the potential to contextualize, situate and critique narratives and practices of data.

To situate current debates around methods within longer trajectories of digital social research, recognizing their conceptual, disciplinary and empirical commitments.

The purpose of this study is to explore current practices, challenges and technological needs of different data repositories.

An online survey was designed for data repository managers, and contact information from the re3data, a data repository registry, was collected to disseminate the survey.

In total, 189 responses were received, including 47% discipline specific and 34% institutional data repositories. A total of 71% of the repositories reporting their software used bespoke technical frameworks, with DSpace, EPrint and Dataverse being commonly used by institutional repositories. Of repository managers, 32% reported tracking secondary data reuse while 50% would like to. Among data reuse metrics, citation counts were considered extremely important by the majority, followed by links to the data from other websites and download counts. Despite their perceived usefulness, repository managers struggle to track dataset citations. Most repository managers support dataset and metadata quality checks via librarians, subject specialists or information professionals. A lack of engagement from users and a lack of human resources are the top two challenges, and outreach is the most common motivator mentioned by repositories across all groups. Ensuring findable, accessible, interoperable and reusable (FAIR) data (49%), providing user support for research (36%) and developing best practices (29%) are the top three priorities for repository managers. The main recommendations for future repository systems are as follows: integration and interoperability between data and systems (30%), better research data management (RDM) tools (19%), tools that allow computation without downloading datasets (16%) and automated systems (16%).

This study identifies the current challenges and needs for improving data repository functionalities and user experiences.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0204

This chapter describes how the technologies of big data might apply to rural contexts. It considers the relative advantages and disadvantages of such ‘new’ innovations.

It uses two case studies, one of online community specialist groups linked to rural activities and a second from a policy shift relating to firearm legislation in the English context.

The chapter suggests that digital data in the forms discussed here can be both benign and underutilised in its potential. In relation to the management of datasets holding information on firearm owners, these need careful reflection regarding their establishment, access and general use.

The chapter provides insight into the rural context and makes a case that such locales are not immune from the influence of the dataverse. The appearance of ‘big data’ here is not without political implications. The case of UK firearm legislation reform demonstrates the implications of policy falling short of its potential and how a social science analysis can unpack the operation of power as well as position the debate more broadly.

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.

How does university-firm collaboration affect the performance of both universities and firms? The purpose of this paper is to evaluate university-firm collaborations aimed at expanding the treatment effects of collaboration ambition on university academic performance as well as collaboration ambition focused on the firm’s production of innovation and financial performance for the top 110 US universities and the top 200 US R&D performing firms.

“Two studies, based on the three archival data sets (National Bureau of Economic Research-Rensselaer Scientific Papers Database and the Harvard Dataverse Network (DVN) US Patent Citations database and Compustat database), are undertaken in the top 110 US universities and the top 200 US R&D performing firms.” The study introduces a theoretical model that explicitly addresses collaboration diversity, number of collaborations, knowledge stock and the endogeneity problem that is generated by self-selection of collaboration ambition in university and firm’s performance.

The results suggest that the effects of adopting proactive collaboration decision on academic performance are insignificant in the firm subsample. However, more interestingly, the authors find supporting evidence of the negative impact of collaboration on university groups. The authors also find that collaboration diversity, knowledge stock and collaboration ambition lead to stronger firm performance but the number of collaborations is smaller on firm performance. Furthermore, the authors find that collaboration ambition moderates the positive effect of the number of collaborations on firm performance.

University-firm collaboration is a multifaceted relationship, suggesting that the empirical analysis can be interpreted through the university and the firm view to enhance the understanding of the collaboration for performance creation. This study articulates the positive role of collaboration diversity, knowledge stock and collaboration ambition and the negative role of the number of collaborations on university-firm collaboration in terms of university and firm performance. Moreover, proactive collaboration ambition has the positive effect of a higher number of collaborations on firm performance. The authors conclude that policy should refrain from overly focusing on collaboration diversity, number of collaborations, knowledge stock and collaboration ambition, and the authors consider the interactions between the number of collaborations and collaboration ambition on university-firm collaboration when discussing their effects on mutual performance.

This study demonstrates the effects of university-firm collaboration on academic performance. In addition, the authors discuss the factors that influence collaboration to help the firm to increase its innovation and financial performance. Therefore, it would be interesting to see simultaneously how university-firm collaboration affects the performance of both partners.

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.