In 2023, a team from a local grant-funded medical data repository requested guidance from Penn Libraries on evaluating the extent to which their repository was FAIR-enabling. After a consultation with the repository team, our research data experts discovered that many of the current self-assessments of the FAIR guidelines were for data creators rather than data repository managers. In addition, we wanted a self-assessment tool similar to the process and guidance created by CoreTrustSeal but focusing explicitly on the FAIR Principles. In answer to their request, the Penn Libraries Research Data Engineer conducted a literature review and coalesced current guidance and assessment tools on the principles. After this review of the existing documentation, a small team developed a FAIR Principles self-assessment tool for repository teams. In addition to several iterations of the tool, we also met with the repository team for feedback on making the tool more understandable. Our conversation provided insights into the challenges of explaining the FAIR Principles to those without information or data science backgrounds. The discussion and creation of this self-assessment tool helped develop a more transparent and trustworthy repository. This paper will discuss our process for developing the assessment, the goals for utilizing the tool, and the lessons learned. Reporting our findings as they currently stand will prompt the research data management field to ruminate on the adoption of FAIR Principles for data repositories. We also intend to encourage conversation on the usability of the FAIR Principles for professionals without an information or data science background.

In today’s data-driven world, it is crucial for students to be data literate; able to view, understand, and reason with data in multimodal forms representing real-world phenomena. Despite its importance, data literacy is rarely integrated into K-12 curricula, and its definition remains unclear for this age group. This paper reviews existing literature to define the competencies relevant to adolescent learners and highlights those crucial for middle-grade students. A literature review of theoretical and empirical discussions on data literacy concepts, instructional practices, and assessments revealed eight key competencies. Among these, two were identified as most critical for middle-grade students: interpreting data representations and evaluating claims based on data representations. This paper aims to serve as a conceptual and practical guide to enhance data literacy in educational settings, providing a foundation for educators and researchers to collaboratively support middle-grade learners.

The Institute for Social Research and Data Innovation (ISRDI) at the University of Minnesota submitted an application for approval to the Core Trust Seal (CTS) in June 2022. In the course of the protracted process to prepare ISRDI materials for the application, we learned five lessons that expanded our perspective on the role of the archive within our organization and committed our institute to building a sustainable metadata culture. By taking the time to review the specialized nature of ISRDI as it developed over time, clarify and document the processes that developed as the intitute matured and expanded, and applying the standards and guidelines supported by the CTS, ISDRI is now in a position to more easily identify areas of future process development and address outstanding needs in terms of our documentation and preservation of our work. These lessons are applicable to research organizations that include the responsibility to preserve a record of their work in the mid- and long-term.

Given the capitalist model of higher education that has developed since the 1980s, the data collected by institutions of higher education on students is based on micro-targeting to understand and retain students as consumers, and to retain that customer base (i.e. to prevent attrition/dropouts). Institutional data has long been collected but the authors will question how, why, and for whom the data is collected in the current higher education model. The authors will then turn to the current higher education focus on equity, diversity, inclusion, and particularly on the concept of belongingness in higher education. The authors question the collective and local purposes of institutional data collection and the fallout of the current practices and will argue that using existing institutional data to facilitate student belongingness is impossible with current practices. We will propose a new framework of asset-minded institutional data practices that centers the student as a whole person and recenters data collection away from the concept of students as commodities. We propose a new framework based on data feminism that intends to elevate qualitative data and all persons/experiences along the bell-shaped curve, not just the middle two quadrants.

What factors make data repositories successful in recruiting research data deposits from scholars? While quite a few studies outline researchers’ data management needs and how repositories can meet those needs, few have assessed the success of various approaches. This study examines infrastructure for accepting data into repositories and identifies factors influential in recruiting data deposits.

This article reports the results of a survey conducted between 16th November and 8th December 2020 among African data curators and data experts about different aspects of data sharing. The sample of respondents has been extracted from participants to the 1st IASSIST Africa Regional Workshop held on 11th -13th January 2021, Kampala, UGANDA and other data experts and practitioners. First, we recall the main results of a previous article published by IQ about the same argument in order to introduce the new survey. After that we analyse the new findings comparing them with the previous results, splitting the samples between Africans and not Africans.

Purpose: The purpose of this paper was to examine the potential opportunities and risks of sharing agricultural research data in Tanzania identified in the existing research literature. Design/methodology/approach: The study involved a review of the literature on research data sharing practices. Findings: The findings indicate that, research data sharing have significant positive benefits among researchers such as increase high research impact; enhancing international community collaboration among researchers with same interests; improving scientific transparency and accuracy of data (Rappert and Bezuidenhout, 2016); increasing research output whereby a single dataset can be used to generate more than one article by different authors; and many more. The risks hampering data sharing practices includes researchers’ fears that data will be scooped, poached or misused (Onyancha, 2016); unreliable electric power; lack of fund to support research data sharing activities; absence of institutional governmental support for data management; perceived lack of evidence benefits (Leonelli, Rappert and Bezuidenhout, 2018); and others. However, in Tanzania research data sharing is relatively new, thus, no any governmental agency mandating or encouraging research data sharing; therefore, there is no research data management; no research open data repositories and no research data sharing policy at any agricultural institution in Tanzania. The study recommends that agricultural researchers should be sensitized to share their data, research data policy and data repositories should also be established to support data sharing practices in Tanzania. Originality and usefulness: From the available literature, this has been the first time that an effort has been made to examine the potential opportunities and risks of sharing agricultural research data in Tanzania. The study could be used by agricultural institutions and other institutions to assess the researchers’ needs in supporting research data sharing. Also, it can be used by the government and institutions to see the need of establishing open data repositories and open data policies to support research data sharing.

Accompanying the growing importance of research data management, the provision and maintenance of metadata – understood as data about (research) data – have obtained a key role in contextualizing, understanding, and preserving research data. Acknowledging the importance of metadata in the social sciences, the Consortium of European Social Science Data Archives started the Metadata Office project in 2019. This project report presents the various activities of the Metadata Office (MDO). Metadata models, schema, controlled vocabularies and thesauri are covered, including the MDO’s collaboration with the DDI Alliance on multilingual translations of DDI vocabularies for CESSDA Service Providers. The report also summarizes the communication, training and advice provided by MDO, including DDI use across CESSDA, illustrates the impact of the project for the social science and research data management community, and offers an outline regarding future plans of the project.

Preservation of 3D research data is a present and emerging need. An increasing number of researchers are generating, capturing and/or analyzing 3D data, but are rarely focused on preservation or reuse. This paper and presentation will describe models of 3D data creation and use, outline the specific concerns for this data type, unpack complexities and challenges of preserving it and examine existing 3D data preservation resources while working through local case studies from the field of anthropology. Directions to move digital 3D data preservation forward will be discussed.