Browsing by Subject "data curation"

Now showing 1 - 10 of 15
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    Big Data Curation Framework: Curation Actions and Challenges
    (Sage, 2022) Yoon, Ayoung; Kim, Jihyun; Donaldson, Devan Ray; Library and Information Science, School of Computing and Informatics
    Big data curation represents an emerging topic of inquiry but still in an early phase along its adoption curve. The term big data itself is a nebulous concept, and the differences between small data curation and big data curation are nuanced. The goal of this research is to provide a theoretical framework that identifies big data curation actions and associated curation challenges. This study is based on the practices of big data research and data curation by systematically examining literature. The outcome of the study includes the big data curation framework that provides overview of curation activities and concerns that are essential to perform such activities. The study also provides practical implications for libraries, archives, data repositories and other information organisations that concerns the issue of big data curation as big data presents a multidimensional array of exigencies in relation to the mission of those organisations.
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    Big data researchers’ perceived value of big data curation
    (2023) Yoon, Ayoung
    This study aims to understand the value of big data curation in a professional context. Researchers' understanding of big data curation is critical to promptly preparing data for future use and curating professionals preparing. The literature analysis suggests that big data researchers acknowledge the value of curation in staying abreast of technology and data quality, but social aspects (e.g., legal and ethical issues) are less recognized.
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    Data curation as collective action during COVID-19
    (Wiley, 2021-03) Shankar, Kalpana; Jeng, Wei; Thomer, Andrea; Weber, Nicholas; Yoon, Ayoung; Library and Information Science, School of Informatics and Computing
    In this commentary, the authors, an international group data curation researchers and educators, reflect on some of the challenges and opportunities for data curation in the wake of the COVID-19 pandemic. We focus on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of data curation and visualization for public-facing “dashboards,” and human subjects research and policies. We conclude with some areas of opportunity and need, including broader and richer data curation education in the information schools, the establishment of better data management policy implementations by research funders, the award of formal academic credit for data curation activities and data sharing, and engagement in cooperative action around data ethics and security.
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    Data Curation for Community Science Project: CHIME Pilot Study
    (Digital Curation Centre, 2018-04-25) Yoon, Ayoung; Spotts, Lydia; Copeland, Andrea; Library and Information Science, School of Informatics
    This paper introduces a community science project, Citizen Data Harvest in Motion Everywhere (CHIME), and the findings from our pilot study, which investigated potential concerns regarding data curation. The CHIME project aims to build a cyclist community–driven data archive that citizens, community scientists, and governments can use and reuse. While citizens’ involvement in the project enables data collection on a massive, unprecedented scale, the citizen-generated data (cyclists’ video data recorded with wearable cameras in the CHIME context) also presents several concerns regarding curation due to the grassroots nature of the data. Learning from our examination of cyclists’ video data and interviews with them, we will discuss the curation concerns and challenges we identified in our pilot study and introduce our approach to addressing these issues. Our study will provide insights into data curation concerns, to which other citizen science projects can refer. As a next step, we are in the process of developing a data curation model that will consider other factors related to this community science project and can be implemented in future community science projects.
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    Data reusers’ trust development.
    (2017) Yoon, Ayoung
    Data reuse refers to the secondary use of data—not for its original purpose but for studying new problems. Although reusing data might not yet be the norm in every discipline, the benefits of reusing shared data have been asserted by a number of researchers, and data reuse has been a major concern in many disciplines. Assessing data for trustworthiness becomes important in data reuse with the growth in data creation because of the lack of standards for ensuring data quality and potential harm from using poor-quality data. This research explores many facets of data reusers’ trust in data generated by other researchers focusing on the trust judgment process with influential factors that determine reusers’ trust. The author took an interpretive qualitative approach by using in-depth semistructured interviews as the primary research method. The study results suggest different stages of trust development associated with the process of data reuse. Data reusers’ trust may remain the same throughout their experiences, but it can also be formed, lost, declined, and recovered during their data reuse experiences. These various stages reflect the dynamic nature of trust.
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    Factors of trust in data reuse
    (Emerald, 2019) Yoon, Ayoung; Lee, Yoo Young; Library and Information Science, School of Informatics and Computing
    Purpose The purpose of this paper is to quantitatively examine factors of trust in data reuse from the reusers’ perspectives. Design/methodology/approach This study utilized a survey method to test the proposed hypotheses and to empirically evaluate the research model, which was developed to examine the relationship each factor of trust has with reusers’ actual trust during data reuse. Findings This study found that the data producer (H1) and data quality (H3) were significant, as predicted, while scholarly community (H3) and data intermediary (H4) were not significantly related to reusers’ trust in data. Research limitations/implications Further disciplinary specific examinations should be conducted to complement the study findings and fully generalize the study findings. Practical implications The study finding presents the need for engaging data producers in the process of data curation, preferably beginning in the early stages and encouraging them to work with curation professionals to ensure data management quality. The study finding also suggests the need for re-defining the boundaries of current curation work or collaborating with other professionals who can perform data quality assessment that is related to scientific and methodological rigor. Originality/value By analyzing theoretical concepts in empirical research and validating the factors of trust, this study fills this gap in the data reuse literature.
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    In between data sharing and reuse: Shareability, availability and reusability in diverse contexts
    (2017-11) Yoon, Ayoung; Jeng, Wei; Curty, Renata; Murillo, Angela
    Although data availability cannot be considered the sole predictor of effective reuse, because only accessible and well-managed data can make reuse possible, data reuse is contingent on the availability of data. It is critical to understand the nature of shareability, availability, and reusability, and their synergy and relationships, to further understand the dynamics of data reuse practices in multiple environments and contexts. This panel aims to closely examine aspects related to data shareability, availability and reusability, based on the assumption that each condition poses a cumulative effect on each other and impacts the efficiency and efficacy of the data reuse process. The panelists will present their findings and perspectives in a diverse context regarding data availability, between academic and non-academic; data shareability and data reusability, social sciences and earth science, researchers’ and journal publishers’ perspectives. Presentations will be followed by an interactive session taking the team-based approach, with the expectation to engage participants in discussion and experience-sharing, and to contribute in terms of practice and research with the current knowledge and applications.
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    Librarian roles in data curation
    (2013-01-28) Coates, Heather L.
    This presentation is aimed at SLIS students and practicing academic librarians interested in learning more about data curation and the potential roles for librarians in this emerging field. Throughout, background information and relevant literature are discussed in terms of pragmatic librarian knowledge and expertise. Specific topics addressed include the roles and responsibilities of various individuals and organizations involved in research, the activities that take place across the research life cycle, as well as opportunities for librarians to fill existing service and expertise gaps.
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    Library capacity for data curation services: a US national survey
    (2019) Yoon, Ayoung; Donaldson, Devan Ray
    Purpose – The purpose of this paper is to understand the landscape of data curation services among public and academic libraries in the USA, with a focus on library capacity for providing data curation services. Design/methodology/approach – The authors conducted an online survey by employing stratified sampling from the American Library Directory. A total of 198 responses were analyzed. Findings – The authors’ findings provide insight into the current landscape of libraries’ data curation services. The survey participants evaluated six capacity dimensions for both public and academic libraries – value, financial, administrative, technical infrastructure, human resources and network. The ratings the participants gave to these capacity dimensions were significantly different between academic and public libraries. Practical implications – This study suggests several areas in which libraries will benefit from further developing their capacity to successfully run data curation services. Originality/value – This is among the first research study to address the concept of capacity in the context of libraries’ data curation services.
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    Love Data Week website 2016 - 2020
    (2020-06) Coates, Heather L.; Atwood, Thea; Bass, Michelle; Condon, Patricia; Foster, Erin D.; Graebner, Carla; Ippoliti, Cynthia; Julian, Renaine; Karcher, Sebastian; Kouper, Inna; Neeser, Amy; Ratajeski, Melissa; Beke-Harrigan, Heidi; Hardeman, Megan; Vecchio, Julie; Wright, Stephanie; Yin, Wei; Glusker, Ann; Sahadath, Catie; Chaput, Jennifer; Hannan, Katie; Woodbrook, Rachel; Adamus, Trisha
    All pages from the Love Data Week event website are archived here in PDF. Love Data Week was established in 2016 as Love Your Data week. Originally created in the USA, it quickly grew to an international event in which a wide range of institutions, organizations, scholars, students, and other data lovers could celebrate their data. Coordinated by Heather Coates, the planning committee developed themes, wrote, curated content, developed activities, all to celebrate data in all its forms, promote good research data management strategies, ask hard questions about the role of data in our lives, and share data success and horror stories. Though the website is defunct, the event lives on, driven by the community.