Automating Research Data Management Using Machine-Actionable Data Management Plans

Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.

Intelligent Information System for Academic Institutions

Research and publication is considered an authenticated certificate of innovative work done by researchers in various fields. In research, new scientific results may be assessed, corrected, and further built up by the scientific neighborhood only if they are available in published form. Guidelines on accountable research and publication are currently set to encourage and promote high ethical standards in the conduct of research and in biomedical publications. They address various aspects of the research and publishing including duties of editors and authorship determination. The chapter presents research and publication system using big data analytics and research data management techniques with a background of information systems and need of information in research data management.

Publishing trends on research data management in Sub-Saharan Africa: A bibliometrics analysis

Research data management is an umbrella term used to describe activities related to the creation, organisation, structuring, naming, backing up, storage, conservation, and sharing of research data as well as all actions that guarantee security of research data. As is often the case, researchers from Sub-Saharan Africa are lagging behind their counterparts in developed countries in embracing the best practices of research data management. One of the factors to which this slow pace of adoption of research data management could be attributed, is inadequate research on the subject. The purpose of this paper is to analyse the quantity, quality, visibility and authorship of publications on research data management in Sub-Saharan Africa. Bibliometrics approaches were used to analyse publications on research data management from, and on, Sub-Saharan Africa which are currently indexed in Google Scholar. The index was chosen because it is free and is reputed to have liberal selection criteria which do not favour, or discriminate, any discipline or geographic regions. Data was retrieved from Google Scholar using Harzing’s “Publish or Perish” software and analysed using VOSviewer. The findings of the study revealed that the quantity, quality, visibility and authorship collaboration of scholarly publications on research data management in Sub-Saharan Africa is low. The findings may be used by libraries and research institutions in Sub-Saharan Africa to develop and promote best practices in research data management as a means of enhancing their research output and impact.

Research data management and services: Resources for different data practitioners

The emergence of data-driven research and demands for the establishment of Research Data Management (RDM) has created interest in academic institutions and research organizations globally. Some of the libraries especially in developed countries have started offering RDM services to their communities. Although lagging behind, some academic libraries in developing countries are at the stage of planning or implementing the service. However, the level of RDM awareness is very low among researchers, librarians and other data practitioners. The objective of this paper is to present available open resources for different data practitioners particularly researchers and librarians. It includes training resources for both researchers and librarians, Data Management Plan (DMP) tool for researchers; data repositories available for researchers to freely archive and share their research data to the local and international communities. A case study with a survey was conducted at the University of Dodoma to identify relevant RDM services so that librarians could assist researchers to make their data accessible to the local and international community. The study findings revealed a low level of RDM awareness among researchers and librarians. Over 50% of the respondent indicated their perceived knowledge as poor in the following RDM knowledge areas; DMP, data repository, long term digital preservation, funders RDM mandates, metadata standards describing data and general awareness of RDM. Therefore, this paper presents available open resources for different data practitioners to improve RDM knowledge and boost the confidence of academic and research libraries in establishing the service.

Research data management: a bibliometric and altmetric study based on Dimensions

Objective. This study aims to analyze the scientific production on research data management indexed in the Dimensions database. Design/Methodology/Approach. Using the term “research data management” in the Dimensions database, 677 articles were retrieved and analyzed employing bibliometric and altmetric indicators. The Altmetrics.com system was used to collect data from alternative virtual sources to measure the online attention received by the retrieved articles. Bibliometric networks from journals bibliographic coupling and keywords co-occurrence were generated using the VOSviewer software. Results/Discussion. Growth in scientific production over the period 1970-2021 was observed. The countries/regions with the highest rates of publications were the USA, Germany, and the United Kingdom. Among the most productive authors were Andrew Martin Cox, Stephen Pinfield, Marta Teperek, Mary Anne Kennan, and Amanda L. Whitmire. The most productive journals were the International Journal of Digital Curation, Journal of eScience Librarianship, and Data Science Journal, while the most representative research areas were Information and Computing Sciences, Information Systems, and Library and Information Studies. Conclusions. The multidisciplinarity in research data management was demonstrated by publications occurring in different fields of research, such as Information and Computing Sciences, Information Systems, Library and Information Studies, Medical and Health Sciences, and History and Archeology. About 60% of the publications had at least one citation, with a total of 3,598 citations found, featuring a growing academic impact. Originality/Value. This bibliometric and altmetric study allowed the analysis of the literature on research data management. The theme was investigated in the Dimensions database and analyzed using productivity, impact, and online attention indicators.

The Notion of "Research Data": Types and Kinds of Research Data in the Context of Data Management Practice

With the rapid development of the concept of Open Science, the quantitative growth of data obtained during the research, scientific attention to the practice of research data management (research data management) increases, which actualizes the definition of “research data” and identifying types of research data within the practice of their management, justification and coverage of the specifics of such data. The methodological tools of the study are based on the terminological method, the use of which was due to the need to identify relevant interpretations of the concept of “research data”, as well as analysis of repositories for data from various fields of science, indexed by re3data.org., in the general areas presented in the register, namely: descriptions of repositories, including information on the types of data deposited by scientists and data curators. The analysis made it possible to define research data as materials obtained and collected to substantiate the scientific results of research in any field and in any form: numerical, textual, computer code, etc., as well as to identify types of data specific to different branches of science, which, in turn, allowed us to conclude the existing data formats, the most common among both natural and human sciences: text, numerical and graphic formats. As a result of the analysis, it was found that research data can be considered textual, numerical, software, archival, graphic and other objects (files) that serve as the basis of the study and the factual basis for scientific conclusions in a particular field of science. It was found that the type of data directly depends on the nature of the study and the characteristics of the discipline or field of research.

Getting a Grip on Research Data Management

In March 2021 the Tri-Agency released its Research Data Management Policy, including its three pillar requirements. This article reviews some key points from the Alliance RDM (Portage Network) workshop "Putting the Tri-Agency Policy into Practice: Workshopping Your Institutional Research Data Management Strategy."

An initial evaluation of research data management online training at the University of New South Wales

In response to low research data management engagement at the University of New South Wales, Australia, an introductory research data management online training was developed and rolled out to all newly enrolled Higher Degree Research candidates. This article outlines the development process of the research data management online training and provides an initial evaluation of the training from the perspectives of the candidates and the university. As such, this article joins up with existing literature on research data management training to assist institutions and research data management stakeholders with the development of research data management training to help researchers and research students enact research data management best practices. Overall, the majority of the candidates ( n = 643) were satisfied with the quality of the training and found it helpful. The benefits the training brought to the university are mapped out by linking institutional research data management problems, the research data management online training’s design and findings into a coherent narrative.