Sukjin You

Bio: Sukjin You is an academic researcher from University of Wisconsin–Milwaukee. The author has contributed to research in topics: Ranking (information retrieval) & Data sharing. The author has an hindex of 4, co-authored 13 publications receiving 64 citations.

Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields

Abstract: Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation—where data references are included alongside bibliographic references in the reference section of a publication—is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.

Enhancing usability of digital libraries: Designing help features to support blind and visually impaired users

Abstract: Blind and visually impaired (BVI) users experience vulnerabilities in digital library (DL) environments largely due to limitations in DL design that prevent them from effectively interacting with DL content and features. Existing research has not adequately examined how BVI users interact with DLs, nor the typical problems encountered during interactions. This is the first study conducted to test whether implementing help features corresponding to BVI users’ needs can reduce five critical help-seeking situations they typically encounter, with the goal to further enhance usability of DLs. Multiple data collection methods including pre-questionnaires, think-aloud protocols, transaction logs, and pre and post search interviews, were employed in an experimental design. Forty subjects were divided into two groups with similar demographic data based on data generated from pre-questionnaires. The findings of this study show that the experimental group encountered fewer number of help-seeking situations than the control group when interacting with the experimental and baseline versions of a DL. Moreover, the experimental group outperformed the control group on perceived usefulness of the DL features, ease of use of the DL, and DL satisfaction. This study provides theoretical and practical contributions to the field of library and information science. Theoretically, this study frames vulnerabilities of BVI users within the social model of disability in which improper DL design impairs their ability to effectively access and use DLs. Practically, this study takes into account BVI users’ critical help-seeking situations and further translates these into the design of help features to improve the usability of DLs.

Open Peer Review in Scientific Publishing: A Web Mining Study of PeerJ Authors and Reviewers

Abstract: Purpose: To understand how authors and reviewers are accepting and embracing Open Peer Review (OPR), one of the newest innovations in the Open Science movement. Design/methodology/approach: This research collected and analyzed data from the Open Access journal PeerJ over its first three years (2013-2016). Web data were scraped, cleaned, and structured using several Web tools and programs. The structured data were imported into a relational database. Data analyses were conducted using analytical tools as well as programs developed by the researchers. Findings: PeerJ, which supports optional OPR, has a broad international representation of authors and referees. Approximately 73.89% of articles provide full review histories. Of the articles with published review histories, 17.61% had identities of all reviewers and 52.57% had at least one signed reviewer. In total, 43.23% of all reviews were signed. The observed proportions of signed reviews have been relatively stable over the period since the Journal's inception. Research limitations: This research is constrained by the availability of the peer review history data. Some peer reviews were not available when the authors opted out of publishing their review histories. The anonymity of reviewers made it impossible to give an accurate count of reviewers who contributed to the review process. Practical implications: These findings shed light on the current characteristics of OPR. Given the policy that authors are encouraged to make their articles' review history public and referees are encouraged to sign their review reports, the three years of PeerJ review data demonstrate that there is still some reluctance by authors to make their reviews public and by reviewers to identify themselves. Originality/value: This is the first study to closely examine PeerJ as an example of an OPR model journal. As Open Science moves further towards open research, OPR is a final and critical component. Research in this area must identify the best policies and paths towards a transparent and open peer review process for scientific communication.

Measuring happiness of US cities by mining user-generated text in Flickr.com: a pilot analysis

Abstract: This poster describes a methodology to numerically represent the happiness of a city by mining user generated terms in Flickr.com. As a pilot analysis, we collected 15,000 text records consisting of titles, tags, descriptions, and comments for the thirty most populous cities in the United States. Parsed text was utilized to calculate happiness scores (H-Score) by matching text extracted from Flickr.com with a happiness index dictionary. In addition, we examined the relationships between the calculated H-scores and real world phenomena including population, crime rate, and climate. Based on this pilot analysis, a future study is planed that involves a large dataset with prediction analysis.

Assessing the Impact

Abstract: This section offers suggestions related to helping a student deal with bullying in schools, as well as creating an environment where that individual can easily return to the school community. It also mentions the significance of the method 'Shared Responsibility' in dealing with the situation.

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

Abstract: We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

The trouble with computers: Usefulness, usability and productivity

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Big data, little data, no data : scholarship in the networkedworld

Abstract: "Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data -- because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure -- an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation -- six "provocations" meant to inspire discussion about the uses of data in scholarship -- Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

What Drives Academic Data Sharing

Abstract: Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded a knowledge commons, but research policies that better incentivize data sharing are needed to improve the quality of research results and foster scientific progress.