A dataset describing data discovery and reuse practices in research

Gregory, Kathleen

doi:10.1038/s41597-020-0569-5

Cited by 16 publications

(15 citation statements)

References 27 publications

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“…scientists in sociology, political science and education) have a "less open data culture" compared to those in nonsocial science fields (Zenk-M€ oltgen et al, 2018;Makel et al, 2021). Gregory (2020) collected approximately 1,600 international researchers' survey responses about their perception of data-related activities, making it one of the largest cross-national and cross-disciplinary surveys of its kind in recent times. The survey revealed that social scientists also had slightly lower scores than the entire sample (55 vs 64%) when being asked how they perceived the data-sharing practices in their discipline.…”

Section: Literature Reviewmentioning

confidence: 99%

Surveying research data-sharing practices in US social sciences: a knowledge infrastructure-inspired conceptual framework

Jeng

2022

OIR

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PurposeThis study develops a conceptual framework and a series of instruments for capturing researchers' data-sharing practices in the social sciences, by synergizing the theory of knowledge infrastructure and the theory of remote scientific collaboration.Design/methodology/approachThis paper triangulates the results of three studies of data sharing across the social sciences, with 144 participants in total, and classifies the confusion, “frictions” and opportunities arising from such sharing into four overarching dimensions: data characteristics, technological infrastructure, research culture and individual drivers.FindingsBased on the sample, the findings suggest that the majority of faculty and students in social science research do not share their data because many of them are unaware of the benefits and methods of doing so. Additional findings regarding social scientists' data-sharing behaviors include: (1) those who do share qualitative data in data repositories are more likely to share their research tools than their raw data; and (2) perceived technical support and extrinsic motivation are both strong predictors of qualitative data sharing (a previously underresearched subtype of social science data sharing).Originality/valueThe study confirms the previously hypothesized nature of “friction” in qualitative data sharing in the social sciences, arising chiefly from the time and labor intensiveness of ensuring data privacy.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2020-0079.

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Section: Literature Reviewmentioning

confidence: 99%

Surveying research data-sharing practices in US social sciences: a knowledge infrastructure-inspired conceptual framework

Jeng

2022

OIR

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“…Each dataset is used to set up parameters for both models. We compute the number of epochs for training, and the dimensions of the embedding vectors for each node and every individual edge type [35].…”

Section: Datasetsmentioning

confidence: 99%

Knowledge Graph Embedding for Link Prediction Models

Rehman¹,

Saeed²,

Minallah³

et al. 2022

Preprint

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For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.

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“…Indeed, in industry-based research, the likelihood of sharing research data decreases with their competitive value (Haeussler 2011). In general, then, the literature finds that appropriate guidelines, policies, and standards are effective in fostering data sharing, data management, and reuse (Gregory 2020;Gregory, Cousijn, et al 2020;Tenopir et al 2011), all of them subject to disciplinary traditions, standards, and norms (Borgman 2012). The FAIR principles (Wilkinson et al 2016) emphasise research data management and metadata, with appropriate tools for effective data management and curation being drivers for data sharing and reuse (Tenopir et al 2011), as are appropriate disciplinary practices and routines (Borgman and Pasquetto 2017;Kim and Zhang 2015).…”

Section: What Drives Academic Data Sharing? (Unspecified) Data Reuse ...mentioning

confidence: 99%

“…Data release comes with a variety of rationales: Reproduction/verification of research, making publicly funded research publicly available, enabling others to ask new questions, and advancement of research (Borgman 2012(Borgman :1066Pasquetto et al 2017). Even though researchers' support for data reuse is almost unanimous , to date, there are relatively few studies quantifying the extent of datareuse practices (Gregory 2020). In addition to assuming that data are generally shared, these studies assume, however tacitly, that data reuse will follow their availability (Borgman 2015a;; while data reuse in support of research is well-documented (Wallis et al 2013), data reuse to drive new research is not , mainly because data reuse is difficult to distinguish from data use (Borgman and Pasquetto 2017) and, because data reuse entails making the tacit dimensions of data generation explicit (Leonelli 2016).…”

Section: What Drives Academic Data Sharing? (Unspecified) Data Reuse ...mentioning

confidence: 99%

Data sharing for global, unspecified reuse

Reichmann¹

2022

Preprint

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This contribution has originally been conceived as the introduction to my dissertation, to be submitted in February 2022 to the University of Graz (Department of Sociology). In the dissertation, I take up the vast discourse on data sharing to argue that until now, the most influential positions (exemplified in the works of philosopher Sabina Leonelli and information scientists Christine L. Borgman) have construed their subject matter rather narrowly, both in terms of the dominant case studies as well as in focusing on the narrowly stated epistemic goals of the Open Science movement (of ensuring reproducibility, transparency, and data reuse, say). I take issue with both of these claims based on a series of empirical case studies, to show that data sharing is broader than has hitherto been assumed, especially in terms of its aims which have, by and large, been modelled along the lines of enabling unspecified, global data reuse. With this preprint, I explain what this means based on an analysis of three discourses, all pertaining to data sharing: Discussions predominantly found within information science documenting a tremendous variability of data types, formats, practices, and sharing behaviour, and an empirical literature that has frequently overlooked the difficulties in accounting for data practices at the level of faculties, research groups, departments, or universities, as well as a more critical discourse that has recently emerged around the factors that inhibit data sharing and reuse, such as the situatedness and contextuality of data practices. The contribution documents a growing awareness of the situated nature of data practices, in particular with respect to research activities in the global south. It further documents mounting evidence for what has been called data contextuality, discussions that ensued from the observation, already a decade old, that “data” are impossible to define, leading to a view now known as the relational view of data (data as potential evidence for knowledge claims).

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A dataset describing data discovery and reuse practices in research

Cited by 16 publications

References 27 publications

Surveying research data-sharing practices in US social sciences: a knowledge infrastructure-inspired conceptual framework

Surveying research data-sharing practices in US social sciences: a knowledge infrastructure-inspired conceptual framework

Knowledge Graph Embedding for Link Prediction Models

Data sharing for global, unspecified reuse

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