Archives des mots-clés : research data

Research Data Management in the Humanities: Challenges and Opportunities in the Canadian Context

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Authors : Stefan Higgins, Lisa Goddard, Shahira Khair

In recent years, research funders across the world have implemented mandates for research data management (RDM) that introduce new obligations for researchers seeking funding. Although data work is not new in the humanities, digital research infrastructures, best practices, and the development of highly qualified personnel to support humanist researchers are all still nascent.

Responding to these changes, this article offers four contributions to how humanists can consider the role of “data” in their research and succeed in its management. First, we define RDM and data management plans (DMP) and raise some exigent questions regarding their development and maintenance.

Second, acknowledging the unsettled status of “data” in the humanities, we offer some conceptual explanations of what data are, and gesture to some ways in which humanists are already (and have always been) engaged in data work.

Third, we argue that data work requires conscious design—attention to how data are produced—and that thinking of data work as involving design (e.g., experimental and interpretive work) can help humanists engage more fruitfully in RDM.

Fourth, we argue that RDM (and data work, generally) is labour that requires compensation in the form of funding, support, and tools, as well as accreditation and recognition that incentivizes researchers to make RDM an integral part of their research.

Finally, we offer a set of concrete recommendations to support humanist RDM in the Canadian context.

URL : Research Data Management in the Humanities: Challenges and Opportunities in the Canadian Context

DOI : https://doi.org/10.16995/dscn.9956

An analysis of the effects of sharing research data, code, and preprints on citations

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Authors : Giovanni Colavizza, Lauren Cadwallader, Marcel LaFlamme, Grégory Dozot, Stéphane Lecorney, Daniel Rappo, Iain Hrynaszkiewicz

Calls to make scientific research more open have gained traction with a range of societal stakeholders. Open Science practices include but are not limited to the early sharing of results via preprints and openly sharing outputs such as data and code to make research more reproducible and extensible. Existing evidence shows that adopting Open Science practices has effects in several domains.

In this study, we investigate whether adopting one or more Open Science practices leads to significantly higher citations for an associated publication, which is one form of academic impact. We use a novel dataset known as Open Science Indicators, produced by PLOS and DataSeer, which includes all PLOS publications from 2018 to 2023 as well as a comparison group sampled from the PMC Open Access Subset. In total, we analyze circa 122’000 publications. We calculate publication and author-level citation indicators and use a broad set of control variables to isolate the effect of Open Science Indicators on received citations.

We show that Open Science practices are adopted to different degrees across scientific disciplines. We find that the early release of a publication as a preprint correlates with a significant positive citation advantage of about 20.2% on average. We also find that sharing data in an online repository correlates with a smaller yet still positive citation advantage of 4.3% on average.

However, we do not find a significant citation advantage for sharing code. Further research is needed on additional or alternative measures of impact beyond citations. Our results are likely to be of interest to researchers, as well as publishers, research funders, and policymakers.

Arxiv : https://arxiv.org/abs/2404.16171

Assessing Quality Variations in Early Career Researchers’ Data Management Plans

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Author : Jukka Rantasaari

This paper aims to better understand early career researchers’ (ECRs’) research data management (RDM) competencies by assessing the contents and quality of data management plans (DMPs) developed during a multi-stakeholder RDM course. We also aim to identify differences between DMPs in relation to several background variables (e.g., discipline, course track).

The Basics of Research Data Management (BRDM) course has been held in two multi-faculty, research-intensive universities in Finland since 2020. In this study, 223 ECRs’ DMPs created in the BRDM of 2020 – 2022 were assessed, using the recommendations and criteria of the Finnish DMP Evaluation Guide + General Finnish DMP Guidance (FDEG).

The median quality of DMPs appeared to be satisfactory. The differences in rating according to FDEG’s three-point performance criteria were statistically insignificant between DMPs developed in separate years, course tracks or disciplines. However, using content analysis, differences were found between disciplines or course tracks regarding DMP’s key characteristics such as sharing, storing, and preserving data.

DMPs that contained a data table (DtDMPs) also differed highly significantly from prose DMPs. DtDMPs better acknowledged the data handling needs of different data types and improved the overall quality of a DMP.

The results illustrated that the ECRs had learned the basic RDM competencies and grasped their significance to the integrity, reliability, and reusability of data. However, more focused, further training to reach the advanced competency is needed, especially in areas of handling and sharing personal data, legal issues, long-term preserving, and funders’ data policies.

Equally important to the cultural change when RDM is an organic part of the research practices is to merge research support services, processes, and infrastructure into the research projects’ processes. Additionally, incentives are needed for sharing and reusing data.

URL : Assessing Quality Variations in Early Career Researchers’ Data Management Plans

DOI : https://doi.org/10.2218/ijdc.v18i1.873

FAIRness of Research Data in the European Humanities Landscape

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Authors : Ljiljana Poljak Bilić, Kristina Posavec

This paper explores the landscape of research data in the humanities in the European context, delving into their diversity and the challenges of defining and sharing them. It investigates three aspects: the types of data in the humanities, their representation in repositories, and their alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable).

By reviewing datasets in repositories, this research determines the dominant data types, their openness, licensing, and compliance with the FAIR principles. This research provides important insight into the heterogeneous nature of humanities data, their representation in the repository, and their alignment with FAIR principles, highlighting the need for improved accessibility and reusability to improve the overall quality and utility of humanities research data.

URL : FAIRness of Research Data in the European Humanities Landscape

DOI : https://doi.org/10.3390/publications12010006

Analysis on open data as a foundation for data-driven research

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Authors : Honami Numajiri, Takayuki Hayashi

Open Data, one of the key elements of Open Science, serves as a foundation for “data-driven research” and has been promoted in many countries. However, the current status of the use of publicly available data consisting of Open Data in new research styles and the impact of such use remains unclear.

Following a comparative analysis in terms of the coverage with the OpenAIRE Graph, we analyzed the Data Citation Index, a comprehensive collection of research datasets and repositories with information of citation from articles. The results reveal that different countries and disciplines tend to show different trends in Open Data.

In recent years, the number of data sets in repositories where researchers publish their data, regardless of the discipline, has increased dramatically, and researchers are publishing more data. Furthermore, there are some disciplines where data citation rates are not high, but the databases used are diverse.

URL : Analysis on open data as a foundation for data-driven research

DOI : https://doi.org/10.1007/s11192-024-04956-x

Handling Open Research Data within the Max Planck Society — Looking Closer at the Year 2020

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Authors : Martin Boosen, Michael Franke, Yves Vincent Grossmann, Sy Dat Ho, Larissa Leiminger, Jan Matthiesen

This paper analyses the practice of publishing research data within the Max Planck Society in the year 2020. The central finding of the study is that up to 40\% of the empirical text publications had research data available. The aggregation of the available data is predominantly analysed.

There are differences between the sections of the Max Planck Society but they are not as great as one might expect. In the case of the journals, it is also apparent that a data policy can increase the availability of data related to textual publications.

Finally, we found that the statement on data availability “upon (reasonable) request” does not work.

URL : Handling Open Research Data within the Max Planck Society — Looking Closer at the Year 2020

Arxiv : https://arxiv.org/abs/2402.18182

From Data Creator to Data Reuser: Distance Matters

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Authors : Christine L. Borgman, Paul T. Groth

Sharing research data is complex, labor-intensive, expensive, and requires infrastructure investments by multiple stakeholders. Open science policies focus on data release rather than on data reuse, yet reuse is also difficult, expensive, and may never occur. Investments in data management could be made more wisely by considering who might reuse data, how, why, for what purposes, and when.

Data creators cannot anticipate all possible reuses or reusers; our goal is to identify factors that may aid stakeholders in deciding how to invest in research data, how to identify potential reuses and reusers, and how to improve data exchange processes.

Drawing upon empirical studies of data sharing and reuse, we develop the theoretical construct of distance between data creator and data reuser, identifying six distance dimensions that influence the ability to transfer knowledge effectively: domain, methods, collaboration, curation, purposes, and time and temporality.

These dimensions are primarily social in character, with associated technical aspects that can decrease – or increase – distances between creators and reusers. We identify the order of expected influence on data reuse and ways in which the six dimensions are interdependent.

Our theoretical framing of the distance between data creators and prospective reusers leads to recommendations to four categories of stakeholders on how to make data sharing and reuse more effective: data creators, data reusers, data archivists, and funding agencies.

URL : From Data Creator to Data Reuser: Distance Matters

arXiv : https://arxiv.org/abs/2402.07926