Conceptualizing Data Curation Activities Within Two Academic Libraries

Lafferty-Hess, Sophia; Rudder, Julie; Downey, Moira; Ivey, Susan; Darragh, Jennifer; Kati, Rebekah

doi:10.7710/2162-3309.2347

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Assemblage modeling also highlights the human labor involved in data cleaning and curation pipelines (e.g. Nordstrom, 2015; Lafferty-Hess et al. , 2020; Plantin, 2019).…”

Section: Discussionmentioning

confidence: 99%

Data as assemblage

Boyd

2022

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PurposeA definition of data called data as assemblage is presented. The definition accommodates different forms and meanings of data; emphasizes data subjects and data workers; and reflects the sociotechnical aspects of data throughout its lifecycle of creation and use. A scalable assemblage model describing the anatomy and behavior of data, datasets and data infrastructures is also introduced.Design/methodology/approachData as assemblage is compared to common meanings of data. The assemblage model's elements and relationships also are defined, mapped to the anatomy of a US Census dataset and used to describe the structure of research data repositories.FindingsReplacing common data definitions with data as assemblage enriches information science and research data management (RDM) frameworks. Also, the assemblage model is shown to describe datasets and data infrastructures despite their differences in scale, composition and outward appearance.Originality/valueData as assemblage contributes a definition of data as mutable, portable, sociotechnical arrangements of material and symbolic components that serve as evidence. The definition is useful in information science and research data management contexts. The assemblage model contributes a scale-independent way to describe the structure and behavior of data, datasets and data infrastructures and supports analyses and comparisons involving them.

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“…Assemblage modeling also highlights the human labor involved in data cleaning and curation pipelines (e.g. Nordstrom, 2015; Lafferty-Hess et al. , 2020; Plantin, 2019).…”

Section: Discussionmentioning

confidence: 99%

Data as assemblage

Boyd

2022

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“…Essential for this migration are data curation tasks that ensure information flows between the four different domains (Lafferty-Hess et al. , 2020).…”

Section: Approaches To Measuring Rdm Costsmentioning

confidence: 99%

“…The labour costs of digital curation, however, are often underestimated (National Research Council, 2015). For this paper, we narrow in on data cleaning and data documentation and go, for these two curation tasks, beyond the work by Lafferty-Hess et al. (2020) by analysing the individual steps involved.…”

Section: Approaches To Measuring Rdm Costsmentioning

confidence: 99%

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Measuring the time spent on data curation

Perry

Netscher

2022

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PurposeBudgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management.Design/methodology/approachWe make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work.FindingsWe identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics.Originality/valueThe different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed.

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“…Therefore, we do not discuss the rationale for each step. For such discussions, the reader is referred to other sources that have described curation in more detail (Palumbo et al 2015;Johnston 2017;Hudson-Vitale et al 2017;Lafferty-Hess et al 2020;Gerlach, Färber, and König-Ries 2020). Nevertheless, it bears mentioning that the workflow was informed by work with early adopters who are researchers prior to launching publicly.…”

Section: Workflowmentioning

confidence: 99%

Implementing and Managing a Data Curation Workflow in the Cloud

Rios

2021

JeSLIB

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Objective: To increase data quality and ensure compliance with appropriate policies, many institutional data repositories curate data that is deposited into their systems. Here, we present our experience as an academic library implementing and managing a semi-automated, cloud-based data curation workflow for a recently launched institutional data repository. Based on our experiences we then present management observations intended for data repository managers and technical staff looking to move some or all of their curation services to the cloud. Methods: We implemented tooling for our curation workflow in a service-oriented manner, making significant use of our data repository platform’s application programming interface (API). With an eye towards sustainability, a guiding development philosophy has been to automate processes following industry best practices while avoiding solutions with high resource needs (e.g., maintenance), and minimizing the risk of becoming locked-in to specific tooling. Results: The initial barrier for implementing a data curation workflow in the cloud was high in comparison to on-premises curation, mainly due to the need to develop in-house cloud expertise. However, compared to the cost for on-premises servers and storage, infrastructure costs have been substantially lower. Furthermore, in our particular case, once the foundation had been established, a cloud approach resulted in increased agility allowing us to quickly automate our workflow as needed. Conclusions: Workflow automation has put us on a path toward scaling the service and a cloud based-approach has helped with reduced initial costs. However, because cloud-based workflows and automation come with a maintenance overhead, it is important to build tooling that follows software development best practices and can be decoupled from curation workflows to avoid lock-in.

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Conceptualizing Data Curation Activities Within Two Academic Libraries

Abstract: This article underwent semi-anonymous peer review in accordance with JLSC's peer review policy.

Cited by 8 publications

References 9 publications

Data as assemblage

Data as assemblage

Measuring the time spent on data curation

Implementing and Managing a Data Curation Workflow in the Cloud

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