Uses and Reuses of Scientific Data: The Data Creators’ Advantage

Pasquetto, Irene V.; Borgman, Christine L.; Wofford, Morgan F.

doi:10.1162/99608f92.fc14bf2d

Cited by 97 publications

(134 citation statements)

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“…The latter contains the same metadata descriptions plus one more field (propagated abstracts) for datasets in order to amass the abstracts coming from publications via context propagation. In order to evaluate the results, the user can play with a simple search interface 10 and explore the saved queries as examples (refer to Section 5.2), or query the indexes from scratch.…”

Section: Methodsmentioning

confidence: 99%

“…Over the last few years, research data have gained unprecedented importance and are now considered as central as traditional publications. Being able to search, find, access, and reuse such research products helps to accelerate scientific progress [3,11], and cross-pollinate research by potentially fostering multidisciplinarity [10]. However, despite the extensive literature in the field of metadata-driven discovery technologies for scholarly communication, research data discovery still remains an open field of research.…”

Section: Introductionmentioning

confidence: 99%

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Context-Driven Discoverability of Research Data

Baglioni¹,

Manghi²,

Mannocci³

2020

Digital Libraries for Open Knowledge

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Research data sharing has been proved to be key for accelerating scientific progress and fostering interdisciplinary research; hence, the ability to search, discover and reuse data items is nowadays vital in doing science. However, research data discovery is yet an open challenge. In many cases, descriptive metadata exhibit poor quality, and the ability to automatically enrich metadata with semantic information is limited by the data files format, which is typically not textual and hard to mine. More generally, however, researchers would like to find data used across different research experiments or even disciplines. Such needs are not met by traditional metadata description schemata, which are designed to freeze research data features at deposition time. In this paper, we propose a methodology that enables "context-driven discovery" for research data thanks to their proven usage across research activities that might differ from the original one, potentially across diverse disciplines. The methodology exploits the collection of publicationdataset and dataset-dataset links provided by OpenAIRE Scholexplorer data citation index so to propagate articles metadata into related research datasets by leveraging semantic relatedness. Such "context propagation" process enables the construction of "context-enriched" metadata of datasets, which enables "context-driven" discoverability of research data. To this end, we provide a real-case evaluation of this technique applied to Scholexplorer. Due to the broad coverage of Scholexplorer, the evaluation documents the effectiveness of this technique at improving data discovery on a variety of research data repositories and databases.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Context-Driven Discoverability of Research Data

Baglioni¹,

Manghi²,

Mannocci³

2020

Digital Libraries for Open Knowledge

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“…Stars are shown next to repositories to show an approximate level of interest and have no effect on notifications or the activity feed. 27 Table 4 summarizes the basic data for the four engagement metrics in our corpus.…”

Section: Reuse Featuresmentioning

confidence: 99%

Dataset Reuse: Toward Translating Principles to Practice

et al. 2020

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“…Pre-registration and subsequent data sharing practices are expected to reduce the prevalence of post-hoc hypothesis (HARKing;Forstmeier et al, 2017;Kerr, 1998), and falsifying or misrepresenting data in results (Banks et al, 2019;Miyakawa, 2020). Doing so can reduce duplication of effort and introduce findings to the research community sooner, and ensure that all findings (beneficial and adverse) are reported, results that can prevent costly mistakes (Gupta et al, 2015) and accelerate the impact of prevention scientists (Pasquetto et al, 2019).…”

Section: Potential Advantages Of Synthesis-ready Researchmentioning

confidence: 99%

“…Ensuring that all types of research data are reused appropriately is a warranted concern (Bishop, 2009;Yardley et al, 2014); yet, evidence suggests that individuals reusing data seek to collaborate with the original data creators to harness their extensive knowledge about the data, rather than using it to scoop the data or to disprove the original publication's findings, resulting in improved and more accurate data reuse and increased opportunities for publication (Pasquetto et al, 2019). As well, providing clearly detailed documentation in the data and metadata files (discussed in Section 5) should reduce inappropriate use of shared data.…”

Section: Potential Challenges and Future Considerationsmentioning

confidence: 99%

Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact

Hennessy¹,

Acabchuk²,

Arnold³

et al. 2020

Preprint

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Synthesis of evidence from the totality of relevant research is essential to inform and improve prevention efforts and policy. Given the large and usually heterogeneous evidence available,reaching a thorough understanding of what works, for whom, and in what contexts, can only be achieved through a systematic and comprehensive synthesis of evidence. Many barriers impede comprehensive evidence synthesis, which leads to uncertainty about the generalizability of intervention effectiveness, including: inaccurate terminology titles/abstracts/keywords (hampering literature search efforts); ambiguous reporting of study methods (resulting ininaccurate assessments of study rigor); and poorly reported participant characteristics, outcomes, and key variables (obstructing the calculation of an overall effect or the examination of effect modifiers). To address these issues and improve the reach of primary studies through theirinclusion in evidence syntheses, we provide a set of practical guidelines to help prevention scientists prepare synthesis-ready research. We use a recent mindfulness trial as an empirical example to ground the discussion and demonstrate ways to ensure: (1) primary studies are discoverable; (2) the types of data needed for synthesis are present; and (3) these data are readily synthesizable. We highlight several tools and practices that can aid authors in these efforts, such as creating a repository for each project to host all study-related data files. We also provide step-by-step guidance and software suggestions for standardizing data design and public archiving to facilitate synthesis-ready research.

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Uses and Reuses of Scientific Data: The Data Creators’ Advantage

Cited by 97 publications

References 0 publications

Context-Driven Discoverability of Research Data

Context-Driven Discoverability of Research Data

Dataset Reuse: Toward Translating Principles to Practice

Ensuring Prevention Science Research is Synthesis-Ready for Immediate and Lasting Scientific Impact

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