Data curation in scientific teams

Hinnant, Charles C.; Stvilia, Besiki; Wu, Shuheng; Worrall, Adam; Burnett, Kathleen; Burnett, Gary; Kazmer, Michelle M.; Marty, Paul F.

doi:10.1145/2132176.2132263

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Data curation is about maintaining and adding value to a trusted body and appraisal of digital information over its entire life (Pennock, 2007). Data curation is defined as the activity of managing and promoting the use of data from its point of creation, to ensure it is fit for contemporary purpose and available for discovery and reuse (Hinnant, Stvilia, Wu, Worrall and Burnett, 2012;Lord, MacDonald, Lyon and Giaretta, 2004;Laughton and Du Plessis, 2013). In the same vein, Yakel (2007:335) defines data curation as the active involvement of information professional in the management, including the preservation, of digital data for future use.…”

Section: Data Curation Centre (Dcc) Lifecycle Modelmentioning

confidence: 99%

“…Data curation is the activity of managing and promoting the use of data from its point of creation to ensure it is fit for contemporary purpose and available for discovery and use (Hinnant et al, 2012;Walton, 2010;and Lord et al, 2004). Data curation consists of a range of activities and processes focused on capture, appraisal, description, preservation, access and reuse which adds value to research data throughout its lifecycle.…”

Section: Research Data Managementmentioning

confidence: 99%

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Research Data Management in Kenya's Agricultural Research Institutes

Ng’eno¹

2022

Advances in Library and Information Science

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The purpose of this study was to examine research data management (RDM) in Kenya's agricultural research institutes with the view to proposing interventions to improve management, sharing, and reuse of agricultural research output. The objectives of the study were twofold: to assess the status of research data management in Kenya's agricultural research institutes and to determine the legal and policy framework, ICT infrastructure, and human capital available to facilitate RDM in Kenya's agricultural research institutes. The findings of the study revealed that RDM legal framework did not exist in the institutes surveyed; the RDM policies and regulations were not updated as necessary; the institutes suffered from inadequacies of technical infrastructure, skills, and collaborative partnerships. The study recommended, among others, the establishment of a formal data governance structure to address RDM issues, institutional and policy framework for RDM, capacity building programs, incentives for researchers, and a sound technical infrastructure.

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Section: Data Curation Centre (Dcc) Lifecycle Modelmentioning

confidence: 99%

Section: Research Data Managementmentioning

confidence: 99%

Research Data Management in Kenya's Agricultural Research Institutes

Ng’eno¹

2022

Advances in Library and Information Science

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Research project tasks, data, and perceptions of data quality in a condensed matter physics community

Stvilia

Hinnant

et al. 2014

Asso for Info Science & Tech

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To be effective and at the same time sustainable, a community data curation model needs to be aligned with the community's current data practices, including research project activities, data types, and perceptions of data quality. Based on a survey of members of the condensed matter physics (CMP) community gathered around the National High Magnetic Field Laboratory, a large national laboratory, this article defines a model of CMP research project tasks consisting of 10 task constructs. In addition, the study develops a model of data quality perceptions by CMP scientists consisting of four data quality constructs. The paper also discusses relationships among the data quality perceptions, project roles, and demographic characteristics of CMP scientists. The findings of the study can inform the design of a CMP data curation model that is aligned and harmonized with the community's research work structure and data practices.

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Curating Big Data Made Simple: Perspectives from Scientific Communities

Sowe

Zettsu

2014

Big Data

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The digital universe is exponentially producing an unprecedented volume of data that has brought benefits as well as fundamental challenges for enterprises and scientific communities alike. This trend is inherently exciting for the development and deployment of cloud platforms to support scientific communities curating big data. The excitement stems from the fact that scientists can now access and extract value from the big data corpus, establish relationships between bits and pieces of information from many types of data, and collaborate with a diverse community of researchers from various domains. However, despite these perceived benefits, to date, little attention is focused on the people or communities who are both beneficiaries and, at the same time, producers of big data. The technical challenges posed by big data are as big as understanding the dynamics of communities working with big data, whether scientific or otherwise. Furthermore, the big data era also means that big data platforms for data-intensive research must be designed in such a way that research scientists can easily search and find data for their research, upload and download datasets for onsite/offsite use, perform computations and analysis, share their findings and research experience, and seamlessly collaborate with their colleagues. In this article, we present the architecture and design of a cloud platform that meets some of these requirements, and a big data curation model that describes how a community of earth and environmental scientists is using the platform to curate data. Motivation for developing the platform, lessons learnt in overcoming some challenges associated with supporting scientists to curate big data, and future research directions are also presented.

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Data curation in scientific teams

Cited by 5 publications

References 9 publications

Research Data Management in Kenya's Agricultural Research Institutes

Research Data Management in Kenya's Agricultural Research Institutes

Research project tasks, data, and perceptions of data quality in a condensed matter physics community

Curating Big Data Made Simple: Perspectives from Scientific Communities

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