Academic Libraries as Data Quality Hubs

Giarlo, Michael J.

doi:10.7710/2162-3309.1059

Cited by 40 publications

(33 citation statements)

References 10 publications

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“…For instance, the generation and extraction of contextual data information (metadata), which are key parts of data management (Giarlo, 2012), are considered to be one quality dimension (Marchionini et al, 2012); actions such as checksum, replication, media refreshment, version management, and prevention of unauthorized access and corruption can ensure that data retains its integrity and is not altered or destroyed in unsanctioned ways (Giarlo, 2012). Fixity computation, auditing, and the detection of storage corruption are equally important because it affects data quality properties (Altman, 2012).…”

Section: Data Management and Qualitymentioning

confidence: 99%

“…Discussions on quality attributes have been recently initiated using the influence of information quality research (e.g., Arazy & Kopak, 2011;Knight & Burn, 2005;Lee at al., 2002) and MIS research (e.g., Madnick et al, 2009;Fox et al, 1994;Wang & Strong, 2006). Examples of the adopted or identified attributes of quality, as taken from recent discussions, include integrity, usability, objectivity, understandability, authenticity, accuracy, comprehensiveness, utility, transparency, and accessibility (Giarlo, 2012;OMB, 2002;Sticco, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

Yoon¹

iConference 2016 Proceedings

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Past research has shown that data reusers are concerned with the issue of data quality and the identified attributes of quality. While data reusers find evidence of the attributes of data quality during their assessment of data for reuse, there may be other dimensions of data quality that reusers are concerned about but that are not always visible to them. This study explores these invisible dimensions of data quality that have been identified by data reusers. The findings of this study indicate that data reusers are concerned with two kinds of invisible characteristics for assessing the data: the efforts put on data, and the ethics behind the data. While these quality dimensions cannot be easily measured at face-level, data reusers find proxy evidence that indicates the presence of these invisibilities. This finding signifies the role of data management that can make these invisible data qualities visible.

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Section: Data Management and Qualitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

Yoon¹

iConference 2016 Proceedings

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“…Libraries are a natural place for these new practices to emerge, but implementation is not trivial. Beyond new roles and practices, institutional changes may be required such as the reorganization of roles, positions, and the development of library infrastructure to support research data curation (Giarlo, 2013). …”

Section: Literature Reviewmentioning

confidence: 99%

“…Databrary supports after-the-fact and active curation , or what Giarlo (2013) refers to as post hoc and sheer curation , respectively. After-the-fact curation consists of ingesting datasets after all data collection is complete, typically after all study products (research papers, analyses, etc.)…”

Section: Description Of Servicesmentioning

confidence: 99%

“…Thus, researchers naturally balk at the prospect of preparing data for sharing after a study has ended. Researchers who invest a lot of time in interpreting and processing their data are less likely to share (Borgman, 2012) and after-the-fact data curation does not scale well (Giarlo, 2013). From the beginning, Databrary was intended to be an active repository in which users browse, comment on, excerpt, cite, modify, deposit, and reuse data.…”

Section: Description Of Servicesmentioning

confidence: 99%

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Researcher-Library Collaborations: Data Repositories as a Service for Researchers

Gordon

Millman

Steiger

et al. 2015

Journal of Librarianship and Scholarly Communication

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Introduction New interest has arisen in organizing, preserving, and sharing the raw materials—the data and metadata—that undergird the published products of research. Library and information scientists have valuable expertise to bring to bear in the effort to create larger, more diverse, and more widely used data repositories. However, for libraries to be maximally successful in providing the research data management and preservation services required of a successful data repository, librarians must work closely with researchers and learn about their data management workflows. Description of Services Databrary is a data repository that is closely linked to the needs of a specific scholarly community—researchers who use video as a main source of data to study child development and learning. The project's success to date is a result of its focus on community outreach and providing services for scholarly communication, engaging institutional partners, offering services for data curation with the guidance of closely involved information professionals, and the creation of a strong technical infrastructure. Next Steps Databrary plans to improve its curation tools that allow researchers to deposit their own data, enhance the user-facing feature set, increase integration with library systems, and implement strategies for long-term sustainability.

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“A greatly unexplored area”: Digital curation and innovation in digital humanities

Poole

2017

Asso for Info Science & Tech

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New types of digital data, tools, and methods, for instance those that cross academic disciplines and domains, those that feature teams instead of single scholars, and those that involve individuals from outside the academy, enables new forms of scholarship and teaching in digital humanities. Such scholarship promotes reuse of digital data, provokes new research questions, and cultivates new audiences. Digital curation, the process of managing a trusted body of information for current and future use, helps maximize the value of research in digital humanities. Predicated on semistructured interviews, this naturalistic case study explores the creation, use, storage, and planned reuse of data by 45 interviewees involved with 19 Office of Digital Humanities Start‐Up Grant (SUG) projects. Interviewees grappled with challenges surrounding data, collaboration and communication, planning and project management, awareness and outreach, resources, and technology. Overall this study explores the existing digital curation practices and needs of scholars engaged in innovative digital humanities work and to discern how closely these practices and needs align with the digital curation literature.

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Academic Libraries as Data Quality Hubs

Cited by 40 publications

References 10 publications

Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

Researcher-Library Collaborations: Data Repositories as a Service for Researchers

“A greatly unexplored area”: Digital curation and innovation in digital humanities

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