Category: Data Curation, Open Data, and Research Data Management

"Synchronic Curation for Assessing Reuse and Integration Fitness of Multiple Data Collections"


SC is a framework that can be implemented to curate data collections to solve multiple research use cases in different scientific fields. SC fills an urgent need in data driven research that requires usage of large and diverse data collections. To reuse data, the first step is to assess its quality and its fitness to address the research use case at hand. SC proposes modelling data collections to research questions to enable targeted analyses and comparisons that can help users identify which collections are more reliable and adequate to solve them. Importantly, SC enables curators and researchers to assess multiple datasets at the same time.

https://doi.org/10.2218/ijdc.v17i1.847

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"Boundaries, Extensions, and Challenges of Visualization for Humanities Data: Reflections on Three Cases"


This paper discusses problems of visualizing humanities data of various forms, such as video data, archival data, and numeric-oriented social science data, with three distinct case studies. By describing the visualization practices and the issues that emerged from the process, this paper uses the three cases to each identify a pertinent question for reflection. More specifically, I reflect on the difficulty, thoughts, and considerations of choosing the most effective and sufficient forms of visualization to enhance the expression of specific cultural and humanities data in the projects. Discussions in this paper concern some questions, such as, how do the multi-modality of humanities and cultural data challenge the understanding, roles, and functions of visualizations, and more broadly, visual representations in humanities research? What do we lose of the original data by visualizing them in those projects? How to balance the benefits and disadvantages of visual technologies to display complex, unique, and often culturally saturated humanities datasets.

https://arxiv.org/abs/2210.03630

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"Increasing the Reuse of Data through FAIR-enabling the Certification of Trustworthy Digital Repositories"


To address this gap the FAIRsFAIR project developed a number of tools and resources that facilitate the assessment of FAIR-enabling practices at the repository level as well as the FAIRness of datasets within them. These include the CoreTrustSeal+FAIRenabling Capability Maturity model (CTS+FAIR CapMat), a FAIR-Enabling Trustworthy Digital Repositories-Capability Maturity Self-Assessment template, and F-UJI, a web-based tool designed to assess the FAIRness of research data objects.

https://doi.org/10.2218/ijdc.v17i1.852

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"Uncommon Commons? Creative Commons Licencing in Horizon 2020 Data Management Plans"


I find that 36% of DMPs mention creative commons and among those a number of different approaches towards licencing exist (overall policy per project, licencing decisions per dataset, licencing decisions per partner, licensing decision per data format, licensing decision per perceived stakeholder interest), often clad in rather vague language with CC licences being “recommended” or “suggested”.

https://doi.org/10.2218/ijdc.v17i1.840

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"FAIREST: A Framework for Assessing Research Repositories "

"In this article, we introduce the FAIREST principles, a framework inspired by the well-known FAIR principles, but designed to provide a set of metrics for assessing and selecting solutions for creating digital repositories for research artefacts. The goal is to support decision makers in choosing such a solution when planning for a repository, especially at an institutional level.. . . We further describe an assessment of 11 widespread solutions, with the goal to provide an overview of the current landscape of research data repository solutions, identifying gaps and research challenges to be addressed."

https://doi.org/10.1162/dint_a_00159

"Many Researchers Were Not Compliant with Their Published Data Sharing Statement: A Mixed-Methods Study – Journal of Clinical Epidemiology"

https://doi.org/10.1016/j.jclinepi.2022.05.019

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"Surveying Research Data-Sharing Practices in Us Social Sciences: A Knowledge Infrastructure-Inspired Conceptual Framework"

https://doi.org/10.1108/OIR-03-2020-0079

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"Are We Building the Data Discovery Infrastructure Researchers Want? Comparing Perspectives of Support Specialists and Researchers"

"This is a meta-synthesis of work the authors have conducted over the last six years investigating the data discovery practices of researchers and support specialists, like data librarians. We bring together data collected from in-depth interview studies with 6 support specialists in the field of social science in Germany, with 21 social scientists in Singapore, an interview with 10 researchers and 3 support specialists from multiple disciplines, a global survey with 1630 researchers and 47 support specialists from multiple disciplines, an observational study with 12 researchers from the field of social science and a use case analysis of 25 support specialists from multiple disciplines."

https://arxiv.org/abs/2209.14655

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"Going Qual In: Towards Methodologically Inclusive Data Work in Academic Libraries"

https://cutt.ly/EV9wSla

"In this paper, we report on the results of interviews with academic librarians about their understanding of data literacy, qualitative research, and academic library infrastructure around qualitative research. From the interviews, we propose a model of data literacy that incorporates both interpretive and instrumental elements. We conclude with suggestions for incorporating qualitative data and analysis methods into academic library programming and services around data literacy and research data."

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"Developing Data Literacy: How Data Services and Data Fellowships Are Creating Data Skilled Social Researchers"

https://cutt.ly/VV9wBEF

"This paper describes two successful approaches to quantitative data literacy training within the UK and the synergies and collaborations between these two programmes. The first is a data literacy training programme, being delivered by the UK Data Service, which focuses on training in basic data literacy skills. The second is a Data Fellows programme that has been developed to help undergraduate social science students gain real-world experience by applying their classroom skills in the workplace."

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"Factors Contributing to Repository Success in Recruiting Data Deposits"

https://doi.org/10.29173/iq1037

"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."

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Fostering Data Literacy: Teaching with Quantitative Data in the Social Sciences

https://doi.org/10.18665/sr.317506

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"NIH Launches Bridge2AI Program to Expand the Use of Artificial Intelligence in Biomedical and Behavioral Research"

https://cutt.ly/gVPCJVw

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"The Connection of Open Science Practices and the Methodological Approach of Researchers"

https://doi.org/10.1007/s11135-022-01524-4

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"Evaluating the (In)Accessibility of Data behind Papers in Astronomy"

https://arxiv.org/abs/2209.04543

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Paywall: "From Zero to Python in 10.5 Hours: Building Foundational Programming Skills with Marine Biology Graduate Students and Researchers in an Introductory Workshop Series"

https://doi.org/10.1080/0194262X.2022.2116143

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"Neither Carrots nor Sticks? Challenges Surrounding Data Sharing from the Perspective of Research Funding Agencies — a Qualitative Expert Interview Study"

https://doi.org/10.1371/journal.pone.0273259

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