Leveraging Machine Learning to Detect Data Curation Activities

Lafia, Sara; Thomer, Andrea K.; Bleckley, David; Akmon, Dharma; Hemphill, Libby

doi:10.1109/escience51609.2021.00025

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…Definitions of data quality assurance and data curation also partially intersect; for example, data curation is also often tied to the idea of producing data that are fit for a specific purpose (CASRAI 2022a). Aspects of quality assurance are sometimes subsumed under data curation activities (Lafia et al 2021). However, conceptualising data quality assurance as simply an aspect of data stewardship or data curation makes it difficult to analyse and understand specific characteristics of data quality assurance.…”

Section: Data Quality Assurancementioning

confidence: 99%

Data Quality Assurance at Research Data Repositories

Kindling

Strecker

2022

Data Science Journal

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This paper presents findings from a survey on the status quo of data quality assurance practices at research data repositories.The personalised online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information and yielded 332 complete responses.The results demonstrate that most repositories perform data quality assurance measures, and overall, research data repositories significantly contribute to data quality. Quality assurance at research data repositories is multifaceted and nonlinear, and although there are some common patterns, individual approaches to ensuring data quality are diverse. The survey showed that data quality assurance sets high expectations for repositories and requires a lot of resources. Several challenges were discovered: for example, the adequate recognition of the contribution of data reviewers and repositories, the path dependence of data review on review processes for text publications, and the lack of data quality information. The study could not confirm that the certification status of a repository is a clear indicator of whether a repository conducts in-depth quality assurance.

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Section: Data Quality Assurancementioning

confidence: 99%

Data Quality Assurance at Research Data Repositories

Kindling

Strecker

2022

Data Science Journal

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“…In 2018, ICPSR implemented standardized curation levels and terminology (ICPSR, 2020); we have harmonized curation level information from 2017 to the 2018 levels. We understand that higher levels of data curation at ICPSR are more extensive, demanding more effort and staff time spent on curation activities (Lafia et al, 2021). Level 1 studies receive ICPSR's base level of curation and can generally be disseminated more quickly, while Level 3 is ICPSR's most extensive level of curation.…”

Section: Data Overviewmentioning

confidence: 99%

How do properties of data, their curation, and their funding relate to reuse?

Hemphill

Pienta

Lafia

et al. 2022

Asso for Info Science & Tech

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Despite large public investments in facilitating the secondary use of data, there is little information about the specific factors that predict data's reuse. Using data download logs from the Inter-university Consortium for Political and Social Research (ICPSR), this study examines how data properties, curation decisions, and repository funding models relate to data reuse. We find that datasets deposited by institutions, subject to many curatorial tasks, and whose access and preservation is funded externally are used more often. Our findings confirm that investments in data collection, curation, and preservation are associated with more data reuse.

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“…IS scholars and practioners define data curation as the "the active and on-going management of data through its life cycle of interest and usefulness to scholarship, science, and education" [Cragin et al 2010] (see also Lafia et al [2021]; Palmer et al [2013]; Yakel [2007]). 3 Research in data curation has included substantial scholarship on data practices (the ways in which people work with, share, and reuse data; examples include Crooks and Currie [2021]; Stvilia et al [2013]; Thomer [2022]; Yan et al [2020]; Zimmerman [2008]) and on understanding the full "life cycle" of data use (e.g., Ball [2012]; ; Tenopir et al [2015]).…”

Section: Prior Workmentioning

confidence: 99%

“…We manually coded a randomly selected proportional sample of Jira ticket worklog entries stratified by curation level. These were coded in brat software [Stenetorp et al 2012] Lafia et al [2021]). We trained a computational model with 0.75 accuracy to assign each worklog entry one of the eight categories of curatorial actions (summarized in Figure 3).…”

Section: Research Sitementioning

confidence: 99%

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The Craft and Coordination of Data Curation: Complicating Workflow Views of Data Science

Thomer

Akmon²,

York

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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Data curation is the process of making a dataset fit-for-use and archivable. It is critical to data-intensive science because it makes complex data pipelines possible, studies reproducible, and data reusable. Yet the complexities of the hands-on, technical, and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of data curators invisible but also hides the impact that curators' work has on the later usability, reliability, and reproducibility of data. To better understand the work and impact of data curation, we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium for Political and Social Research (ICPSR). We asked: What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulated accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. While we identified numerous curatorial actions performed by ICPSR curators, we also found that curators rely on a number of craft practices to perform their jobs. The reality of their work practices defies the rote sequence of events implied by many life cycle or workflow models. Further, we show that craft practices are needed to enact data curation best practices and standards. The craft that goes into data curation is often invisible to end users, but it is well recognized by ICPSR curators and their supervisors. Explicitly acknowledging and supporting data curators as craftspeople is important in creating sustainable and successful curatorial infrastructures.

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Leveraging Machine Learning to Detect Data Curation Activities

Cited by 13 publications

References 26 publications

Data Quality Assurance at Research Data Repositories

Data Quality Assurance at Research Data Repositories

How do properties of data, their curation, and their funding relate to reuse?

The Craft and Coordination of Data Curation: Complicating Workflow Views of Data Science

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