Adaptable Methods for Training in Research Data Management

Biernacka, Katarzyna; Helbig, Kerstin; Buchholz, Petra

doi:10.5334/dsj-2021-014

Cited by 2 publications

(5 citation statements)

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“…A similar finding emerged from the study done by Wiljes and Cimiano (2019), namely that the competency teaching technique that relies on practical exercise is useful for teaching RDM courses in HLIs. The study is inconsistent with the study done by Biernacka et al (2021), which proposed ways of teaching and learning data mining and learning analytical skills by using adaptive learning (adaptive teaching technique) to orchestrate the interaction with learners and deliver customised resources and learning activities to address the unique need of each learner. Whitmire (2015) added that the active learning exercises were successful and generally enjoyed by students.…”

Section: Discussionmentioning

confidence: 73%

“…Wiljes and Cimiano (2019) add that hands-on demonstrations, group discussions and individual presentations allow students to link the acquired knowledge to their research works. HLIs should use diverse teaching and activation methods to increase engagement and active participation among students (Biernacka et al, 2021). In addition, application, knowledge transfer, and replication methods need to be integrated to facilitate the learning process (Biernacka et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…HLIs should use diverse teaching and activation methods to increase engagement and active participation among students (Biernacka et al, 2021). In addition, application, knowledge transfer, and replication methods need to be integrated to facilitate the learning process (Biernacka et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…, 2021). In addition, application, knowledge transfer, and replication methods need to be integrated to facilitate the learning process (Biernacka et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

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Teaching research data management courses in higher learning institutions in Tanzania

Mosha

Ngulube

2022

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PurposeThe study investigated teaching research data management (RDM) courses in higher learning institutions (HLIs) in Tanzania to enable postgraduate students to work with their research data.Design/methodology/approachThe study triangulated research methods. Postgraduate students were investigated using survey questionnaires to learn about their needs and perceptions of the teaching RDM courses in HLIs. Key informants (academicians, information and communication technologists and library staff) were also investigated using in-depth interviews to explore their experiences and knowledge of teaching RDM courses. SPSS statistical software was used for analysing quantitative data; qualitative data were analysed thematically.FindingsA total of 70 questionnaires were distributed to postgraduate students with a returning rate of 44 (69%). On the other hand, 12 key informants were interviewed. A low level of RDM literacy was revealed among 38 (86%) respondents. Most respondents 40 (91%) reported the need for HLIs to start teaching RDM courses. A lack of skills and knowledge in teaching RDM courses was revealed among key informants. The competency-based, adaptive and constructive teaching techniques were selected for teaching RDM courses, whereas intensive training and online tutorials were revealed as teaching formats.Research limitations/implicationsThis study focused on teaching RDM courses in HLIs. The survey questionnaires were distributed to all 2nd year postgraduate students, however, the findings cannot be generalised to all postgraduate students due to the response rate obtained. The findings obtained from key informants can also not be used as a basis for generalization across HLIs.Practical implicationsThis study concluded that postgraduate students need to be well equipped with skills and knowledge on RDM and its related concepts; teaching RDM courses should be regarded as a continuous programme for benefit of students, researchers and the community at large.Social implicationsAppropriate teaching of RDM courses among students not only ensures that students meet the funders’ and publishers’ requirements, but also encourages students to store and share their research among researchers worldwide; thus increasing collaboration and visibility of the datasets and data owners through data citations and acknowledgements.Originality/valueThis is a comprehensive study that provides findings for HLIs to teach RDM courses in HLIs, especially for postgraduate students. The findings revealed the need for teaching RDM courses in HLIs. The study provides the basis for further RDM research in HLIs and research institutions.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…, 2021). In addition, application, knowledge transfer, and replication methods need to be integrated to facilitate the learning process (Biernacka et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

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Teaching research data management courses in higher learning institutions in Tanzania

Mosha

Ngulube

2022

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“…Is the required expertise for this process available? Are there enough time and finances to train a select few to become FAIR data stewards within the organization with the intent of nurturing skills and expertise within the organization, for example, by implementing a train the trainer concept [68]? Do time and financial constraints necessitate that it is better to outsource FAIR expertise?…”

Section: Authors' Reactionsmentioning

confidence: 99%

Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review

Inau¹,

Sack²,

Waltemath³

et al. 2023

J Med Internet Res

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Background Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains. Objective This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data. Methods The Arksey and O’Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic. Conclusions This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing. International Registered Report Identifier (IRRID) RR2-10.2196/22505

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Adaptable Methods for Training in Research Data Management

Cited by 2 publications

References 3 publications

Teaching research data management courses in higher learning institutions in Tanzania

Teaching research data management courses in higher learning institutions in Tanzania

Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review

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