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This handbook aims to support higher education institutions with the integration of FAIR-related content in their curricula and teaching. It was written and edited by a group of about 40 collaborators in a series of six book sprint events that took place between 1 and 10 June 2021. The document provides practical material, such as competence profil...
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... Despite these advantages, reusing others' data is challenging because it requires researchers to locate, access, and understand the data, often relying on metadata, documentation, contact with data creators, or their own expertise (Borgman, 2015;Pasquetto et al., 2019). The current digital ecosystem for data publications often falls short of meeting reusers' information needs (Engelhardt, 2022;Wilkinson et al., 2016), despite efforts to build infrastructures and progress in some fields (Klingner et al., 2023;Ugochukwu & Phillips, 2024). ...
Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered system research, and enhance the replicability of evaluation experiments. Building a sustainable data reuse process and culture relies on frameworks that encompass policies, standards, roles, and responsibilities, all of which must address the diverse needs of data providers, curators, and reusers. To advance the knowledge and accumulate empirical understandings on data reuse, this study investigated the data reuse practices of experienced researchers from the area of Interactive Information Retrieval (IIR) studies, where data reuse has been strongly advocated but still remains a challenge. To enhance the knowledge on data reuse behavior and reusability assessment strategies within IIR community, we conducted 21 semi-structured in-depth interviews with IIR researchers from varying demographic backgrounds, institutions, and stages of careers on their motivations, experiences, and concerns over data reuse. We uncovered the reasons, strategies of reusability assessments, and challenges faced by data reusers within the field of IIR as they attempt to reuse researcher data in their studies. The empirical finding improves our understanding of researchers' motivations for reusing data, their approaches to discovering reusable research data, as well as their concerns and criteria for assessing data reusability, and also enriches the on-going discussions on evaluating user-generated data and research resources and promoting community-level data reuse culture and standards.
... The learning block consists of six units supplemented by a glossary. The learning outcomes of each unit target basic research data management and FAIR skills inspired by the FAIRsFAIR Teaching and Training Handbook for Higher Education Institutions (Engelhardt et al., 2022) and The Open Handbook of Linguistic Data Management (Berez-Kroeker et al., 2022). By integrating research infrastructures, language data and tools into teaching, educators can bridge the gap between theoretical knowledge and practical aspects of linguistic research data management, equipping students with the necessary skills and competences to thrive in the evolving landscape of open science and data-driven research. ...
To help realise its potential as the research infrastructure for language as social and cultural data, CLARIN is supporting the training of students and scholars in using its language data, tools and services. Lecturers and teachers in the CLARIN network have integrated CLARIN language resources into higher education programmes and other training activities. This paper showcases some recent courses and training initiatives, along with inventories and new learning materials, partly developed in EU-funded projects, which are accessible through the CLARIN Learning Hub. Each section briefly describes the motivation behind the initiative, the authors’ experience, related efforts in the field, and future perspectives.
... Avoimesti saatavilla oleva FAIR teaching and training -käsikirja liber-konferenssin arvokasta antia olivat siellä esitellyt uraa uurtavat välineet ja materiaalit, joita voi hyödyntää käytännön työssä. Konferenssissa esiteltiin avoimesti saatavilla oleva How to Be FAIR with Your Data -käsikirja opetuksen ja ohjauksen tueksi (Engelhardt et al. 2021). Mari Elisa Kuusniemi on suomalaisena asiantuntijana ollut mukana laatimassa käsikirjaa. ...
LIBER-konferenssin teema oli tänä vuonna kirjastot tutkimus- ja innovaatiotoiminnassa. Tieteellisten kirjastojen asiantuntijaroolit eivät rajoitutukipalveluihin, vaan ne laajentuvat kohti tutkimuksen kumppanuutta ja johtajuutta. Ukrainan kirjastojen tilanne, kansalaistiede ja tutkimusdata nousivat vahvoiksi puheenaiheiksi niin pääpuheenvuoroissa kuin työpajoissa. Suomalaisten esitykset olivat konferenssin kärkeä.
(1) Background: Each year, significant volumes of healthcare data are generated through both research and care. Since fundamental digital processes cannot function effectively without essential data competencies, the challenge lies in enhancing the quality of data management by establishing data literacy among professionals in outpatient healthcare and research. (2) Methods: Within the DIM.RUHR project (Data Competence Center for Interprofessional Use of Health Data in the Ruhr Metropolis), a didactic concept for interprofessional data literacy education is developed, structured as a learning objectives matrix. Initially conceived through a literature review, this concept has been continually developed through collaboration with interprofessional project partners. The study was conducted between February 2023 and June 2024. (3) Results: The foundational structure and content of the didactic concept are based on various scientific studies related to general data literacy and the outcomes of an interactive workshop with project partners. Eight distinct subject areas have been developed to encompass the data literacy required in healthcare professions: (1) Fundamentals and general concepts, (2) ethical, legal, and social considerations, (3) establishing a data culture, (4) acquiring data, (5) managing data, (6) analyzing data, (7) interpreting data, and (8) deriving actions. Within these, learners’ data literacy is assessed across the four competency areas: basic, intermediate, advanced, and highly specialized. (4) Conclusions: The learning objectives matrix is anticipated to serve as a solid foundation for the development of teaching and learning modules aimed at enhancing data literacy across healthcare professions, enabling them to effectively manage data processes while addressing the challenges associated with digital transformation.
