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In this panel, we will discuss how "data misuse" is understood across different disciplines, and in particular digital curation, critical data studies, scholarly communication, and algorithmic fairness. The audience will be invited to contribute to the discussion by reporting on their own experience with data misuse, and brainstorming potential interventions to prevent misuse. Controversial reuses of open research data are emerging, including exploitation of marginalized communities, geo privacy violations, and perpetuation of harmful stereotypes. Incidents of data misuse hinder scientific progress and erode public trust, yet defining misuse remains challenging as one community's misuse might be another's best practice. The development of a shared framework to understand when, how, and why misuse of research data occurs can help science stakeholders decide when and how to release crucial research data, evaluate the potential for misuse, and tailor documentation of research data to prevent misuse. Our goal for this panel discussion is to take us a step closer to the development of such a theoretical framework for defining data misuse. [ABSTRACT FROM AUTHOR]

While long recognized in the humanities, there is growing recognition in the sciences and social sciences that primary sources—as diverse as manuscripts, photographs, cultural belongings, and specimens—hold vast data about scientific and human knowledge for use in scholarship, community research, and global knowledge. Yet, data embedded in these sources are largely disconnected from the systems of discovery, access, and structured data that support reuse and insights across globally dispersed repositories. In this paper, we share select findings of a systematic review to explore the use of primary sources, and the data embedded in them, via linked data across the sciences and social sciences. Our results confirm the use of a variety of primary source data across diverse disciplines, particularly those requiring longitudinal studies and data integration from diverse repositories and contexts. We highlight how linked data are understood to: connect collections to communities; support highly granular credit, attribution, and assessment of impact; and interrelate diverse sources of knowledge. While these results suggest the value of linked data for the specific research needs of anthropology, the effectiveness of linked data in achieving these objectives and the suitability of this approach for a diversity of institutions and communities need further study. [ABSTRACT FROM AUTHOR]

Between 1996 and 2018, there was little federally funded research on gun violence, even though it remains a perennial issue, resulting in considerable loss of life. Advocacy and academic groups piece together data to conduct their research, but the U.S. firearms data infrastructure is limited. A current National Institute of Health (NIH)‐funded coordinating center led by researchers at the University of Michigan aims to help confront that problem. This Quality Improvement (QI) study seeks to uncover the information needs of researchers in firearm injury prevention and community‐based interventions through interviews with public health, medical, and criminal justice researchers. Interviews reveal problems associated with existing databases and infrastructures, as well as what researchers need to support their research. We find fragmented data resources and a lag in public health data availability create obstacles to obtaining many datasets. The researchers also describe problems and opportunities for data harmonization across intervention‐focused studies. [ABSTRACT FROM AUTHOR]

The data‐intensive research paradigm is sweeping through the scientific community, and new interaction challenges for interacting with data have emerged. Data reuse emerges as a critical driver of human‐data interaction, positioning data repositories at the forefront as the optimal solution for facilitating this process. This study adopts a diary study methodology to analyze the data repository‐supported human‐data interaction behaviors driven. The findings reveal that human‐data interaction extends beyond mere engagement with data, encapsulating elements of human‐computer interaction, engagement with literature, interactions with agent systems, and interpersonal communication. [ABSTRACT FROM AUTHOR]

The open data movement is often touted as a sweeping strategy to democratize science, promote diverse data reuse, facilitate reproducibility, accelerate innovation, and much more. However, the potential perils of open data are seldom examined and discussed in equal measure to these promises. As we continue to invest in open data, we need to study the full spectrum of what open data facilitates in practice, which can then inform future policy and design decisions. This paper aims to address this gap by presenting an investigative digital ethnography of one contrarian community, anthropogenic climate change (ACC) skeptics, to describe how they process, analyze, preserve, and share data. Skeptics often engage in data reuse similar to conventional data reusers, albeit for unconventional purposes and with varying degrees of trust and expertise. The data practices of ACC skeptics challenge the assumption that open data is universally beneficial. These findings carry implications for data repositories and how they might curate data and design databases with this type of reuse in mind. [ABSTRACT FROM AUTHOR]

Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter‐university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine‐readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot‐based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data. [ABSTRACT FROM AUTHOR]

This paper explores the social and technical perceptions of physical and digital formats as they relate to work in the recovery and reuse of scientific data, specifically historical, archival, and defunct data sources. Proprietary and obsolete formats, or formats that need significant transformation work, stand out as central challenges for scientists and data curators who are recovering reusable data from archival or legacy data sources. The challenges confronting data sharing and reuse of contemporary scientific data are already known to be myriad; formats often pose a major, compounding challenge to retrospective data curation research and practice. Based on 23 qualitative interviews with practitioners conducting data recovery and reuse, ranging from marine biologists to data librarians, we study how they understand, engage with, and utilize formats within their data curation work. This paper enumerates the formats deployed throughout the scientific data curation process and explores how practitioners creating and curating scientific data based on historical and archival materials encounter, make sense of, and utilize formats. The paper focuses on practitioner perceptions of formats around the following themes: how practitioners' historical relationships to certain challenging formats inform their ongoing curation practices; the importance of contexts in prioritizing or ignoring formats within scientific curation work; and how formats reveal larger sociotechnical issues. The paper concludes by with practical and theoretical implications of navigating formats within the recovery and reuse of scientific data and offers suggestions for reconfiguring formats within broader data curation lifecycles. [ABSTRACT FROM AUTHOR]

The value of geologic data is well established and demonstrated by efforts such as EarthChem and EarthCube. Although these communities are active in the documentation and preservation of geologic data, more work is needed to understand how geologists use this data to address specific problems. In this preliminary analysis, we focus on the information behaviors of professional geologists as they engage with multiple data streams to make decisions. Using semi‐structured interviews and grounded theory, our findings document how a single data point can drive changes to existing models. Responses also show that geologists view their experiences in data collection as critical and they use their knowledge and experience to iteratively re‐assess the context and fitness of their data as they search for coherent interpretations that resolve data‐model conflicts. [ABSTRACT FROM AUTHOR]

In recent years, the amount of open data from governments and academic communities have increased rapidly. Open data are expected to promote the transparency and accountability of governments and academic communities, enable public participation, and facilitate digital innovation. However, open data are still facing problems such as unsatisfactory quality, insufficient data governance, increasing preservation cost and ineffective data reuse at present. This panel will invite experts in related fields to discuss the quality, reuse, and governance of open data, and propose feasible solutions from an international perspective. We will verify and promote the action plan in practice, and have more academic discussion with ASIS&T and relevant academic communities. [ABSTRACT FROM AUTHOR]

Data sharing and reuse promise many benefits to science, but many researchers are reluctant to share and reuse data. Data papers, published as peer‐reviewed articles that provide descriptive information about specific datasets, are a potential solution as they may incentivize sharing by providing a mechanism for data producers to get citation credit and support reuse by providing contextual information about dataset production. Data papers can receive many citations. However, does citation of a data paper mean reuse of the underlying dataset? This paper presents preliminary findings from a content‐based citation analysis of data papers (n = 103) published in two specialized data journals, one in earth sciences and one in physical and chemical sciences. We conclude that while the genre of data papers facilitates some data sharing and reuse, they fail to live up to their full potential. Further, practices of reuse of datasets from data papers vary considerably between disciplines. We propose measures for academic publishers to enhance the data paper's role in scholarly communication to attract more attention from researchers and to inform discipline‐specific policy and practices related to data publication. [ABSTRACT FROM AUTHOR]

As a data intensive field that unites researchers from many disciplines, Earth System Science (ESS) is an ideal site for examining evolving cross‐disciplinary data practices. This paper reports on results from a survey examining data sharing, data reuse, and research reproducibility practices of ESS researchers, aimed at informing improvements in data services for interdisciplinary sciences. Data reuse was found to be very high for new and comparative analyses but very limited for reproducing research. Data sharing was also strong, mostly through supplements to published papers, with moderate use of open access repositories. At the same time, there was interesting variability in both data sharing and reuse among ESS disciplines. The most pronounced challenges to reuse and reproducibility stem from limited documentation on how data are collected and managed, practices that are poorly supported by institutions, funders, and publishers. A more refined approach to "reproducibility" is needed that aligns with priorities and practices within the research community. Just as importantly, advances in data service models for ESS and other interdisciplinary fields need to account for the diverse and distributed system of repositories and build a workforce with deeper knowledge of the complex data and methods that drive integrative systems science. [ABSTRACT FROM AUTHOR]

Data sharing has been emphasized in scientific research in the era of open science; however, the concept of data sharing is ambiguous, and the real situation of data sharing is unclear. One reason is that most studies focused on researchers' recognition, motivation, and other contributing factors and did not thoroughly discuss the actual practice of data sharing for preparing research articles. To explicitly understand the current status of data sharing, the authors considered and investigated primary reuse, data integration, and data release distinctively as the actual practices of data sharing. A total of 237 articles published in 2018 and registered in the Web of Science global citation database were manually checked. Unlike the scarcity of data reuse (8.0%) or data release (5.1%), existing data were most frequently reused in the form of data integration (20.7%). The result implies that further studies on detailed data reuse are required to clarify the concept of data sharing. [ABSTRACT FROM AUTHOR]

Data have significant potential to address current societal problems not only at the federal and state levels, but also in smaller communities, in neighborhoods, and in the lives of individuals. While the proposition for this potential is that data are and will be shared with and reused by and for communities at different levels, not all data are systematically or routinely shared for reuse with communities due to social, structural and technical infrastructure barriers. Data intermediary organizations can play a significant role in removing existing barriers while unlocking the potential of data for all, particularly for communities with limited human or financial resources, limited access to existing data infrastructures, and underserved populations. Considering the significance of the data intermediary organizations on local communities, this study aims to explore the role of intermediaries that usually facilitate community members/organizations' data utilization. The findings of this study reveal that data intermediary organizations play four major roles that are crucial in communities' data utilization: (1) democratizing data, (2) adding value to existing data, (3) enhancing communities' data literacy, and (4) building communities' data capacity. This study has several important implications to offer as a solution to overcome the challenges of data reuse at the local level. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper applies Pirson and Malhotra's (2011) framework for organizational trust to data reuse in an educational context. Their framework delineates stakeholders along two axes: depth of the interactions (e.g., shallow or deep) and the locus of the relationship (e.g., internal or external). We analyzed 139 survey responses and 44 in-depth interviews with users of repositories holding video records of practice. We found that factors such as data quality, co-production of data between the repository and data producer, responsiveness of staff, and transparency of curation processes influenced trust, and that responsiveness and transparency were of particular importance for trust development for users with deeper interactions and a more internal loci of relationships to repositories. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 research data reuse process. The panelists will present their findings and perspectives 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. [ABSTRACT FROM AUTHOR]

ABSTRACT In acknowledging the potentials of existing data, researchers' interests in sharing and reusing data have recently emerged. However, sharing and reusing data is not a simple one-step process for researchers. Because data reusers build their work on other researchers' findings, the process of data reuse involves various interactions and communications with other relevant parties. Exploring the nature of communications around data is thus important to fully understand data reuse practices and to support smoother processes of data reuse. This study investigates communications occurring around data during data reusers' experiences through qualitative interview studies involving this group. This study's results show that the communications with different stakeholders mainly support data reuse in three areas: searching, learning, and problem solving. The findings provide valuable insights into the domain of scholarly communication, data reuse, and data services. [ABSTRACT FROM AUTHOR]

ABSTRACT The discipline of social science has unique research norms and cultures regarding data sharing and reuse that can be affected by complex factors related to context, time and dependence on human subjects. Compared with STEM disciplines, social sciences emphasize the protection of study participants and observees. Extra effort is required from reusers to preserve data interconnectedness in order to guarantee the data's understandability and informative value. In this panel, the panelists will present their research findings and provide perspective on social science data sharing and reuse, including factors that may influence data reuse behavior, researchers' trust judgment in data for data reuse, and infrastructural barriers and incentives for data sharing among social scientists. This panel aims to provide an overview of the current state of social science data reuse and sharing, and, in collaboration with panel participants, elicit topics for future research. It also proposes a practical agenda to develop alternative incentives for individual researchers, and potential ways in which data sharing and reuse can be improved, coordinated, and encouraged among social scientists. [ABSTRACT FROM AUTHOR]

ABSTRACT This poster presents preliminary findings of a quasi-experiment think-aloud study where scientists were presented four canned results of information regarding earth science data in a counter-balanced design. Scientists were asked to think-aloud regarding what information about the data assisted them in their ability to determine reusability of that dataset. Sixteen scientists from various earth science fields participated in the study. Each scientist responded to four canned results, a post-result usefulness survey, a post-search rank-order survey, and a post-search survey. Participants stated that concise data descriptions, attribute and unit lists, as well as research methods steps were particularly important in their ability to determine reusability of data. Participants preferred more robust results over less robust results, and stated that they would rather have too much information than to request the data to find out it actually did not serve their needs. [ABSTRACT FROM AUTHOR]

ABSTRACT This study examined the data reusers' failed or unsuccessful experience to understand what constituted reusers' failure. Learning from failed experiences is necessary to understand why the failure occurred and to prevent the failure or convert the failure to success. This study offers an alternative view on data reuse practices and provides insights for facilitating data reuse processes by eliminating core components of failure. From the interviews with 23 quantitative social science data reusers who had failed data reuse experiences, the study findings suggest: (a) ease of reuse, particularly the issue of access and interoperability, is the important initial condition for a successful data reuse experience; (b) understanding data through documentation may be less of an issue, at least for experienced researchers to make their data reuse unsuccessful, although the process can still be challenging; and (c) the major component of failed experience is the lack of support in reusing data, which emphasizes the need to develop a support system for data reusers. [ABSTRACT FROM AUTHOR]

ABSTRACT Sharing and reuse of scientific data, which can enhance the transparency and reproducibility of research and lead to the creation of new knowledge from existing data, is both a growing scholarly communication practice and an expanding area of interest in information science. However, much of the literature to date has focused on the data practices of scientists working in academic environments, with less research done on understanding the practices of scientists working in other types of environments, such as government or industry. This poster presents the results of a study in which data from a worldwide survey of scientists were analyzed to determine if differences in data practices, perceptions, and access to resources for data sharing existed between scientists who reported their primary work sector as academic and those who reported a non-academic primary work sector. Researchers' perceptions of data sharing and reuse were generally positive and did not differ significantly by work sector. However, differences were found in actual reported data sharing practices, even when controlling for researchers' age, geographic location, and subject discipline. Researchers outside of academia had lesser odds of reporting sharing all their data. Differences were also found in reported barriers to data sharing, as well as in reported access to and use of data sharing resources, suggesting that data sharing challenges faced by scientists working outside of academia may differ from those faced by their academic peers. Implications for the adoption of data sharing practices and technologies, as well as for knowledge sharing and creation across work sectors, are discussed, and suggestions are offered for further research. [ABSTRACT FROM AUTHOR]

Computational reproducibility is a critical first step to supporting data reuse. Incentives that encourage data reuse within an academic setting are unlikely to be compelling for students who decide not to continue in academe. The goal of this work is to identify key barriers in the most likely of scenarios with computational reuse, when a doctoral student reproduces work done by another student from the same lab. [ABSTRACT FROM AUTHOR]

Researchers in the social and health sciences are used to dealing with confidential data, and repositories in these areas have developed mechanisms to prevent unethical or illegal disclosure of this data. However, other scientific communities also collect data whose disclosure may cause harm to communities, cultures, or the environment. This paper presents results from 62 interviews and observations with archaeologists and zoologists. It focuses on how researchers' perceptions of potential harm influence attitudes about data confidentiality, and how these, in turn, influence opinions about who should be responsible for managing access to data. This is particularly problematic in archaeology when harm is not to a living individual but is targeted at a community or culture that may or may not have living representatives, and in zoology when an environment or a species may be at risk. We find that while both archaeologists and zoologists view location information as highly important and valuable in facilitating use and reuse of data, they also acknowledge that location should at times be considered confidential information since it can be used to facilitate the destruction of cultural property through looting or decimation of endangered species through poaching. While researchers in both disciplines understand the potential dangers of allowing disclosure of this information, they disagree about who should take responsibility for access decisions and conditions. [ABSTRACT FROM AUTHOR]

As STEM areas increasingly rely on pre-existing data, either to validate or extend the scientific body of knowledge, students who have baseline knowledge of how to find, evaluate, and access data will have an advantage. Accordingly, undergraduate STEM curricula is increasingly focused on research-based group projects that develop professional skills, building the professional portfolio needed for early career scientists, technologists, and engineers. This project works to develop new tools to implement basic data skills within the undergraduate disciplinary curricula. The first step in this process was to identify the competencies that are likely to be needed by those seeking data for reuse. Using competencies identified in the literature and via brainstorming, formatted similarly to the traditional model used by librarians to teach assessment of publication quality, a data credibility checklist was developed as the first tool in an emerging toolkit. [ABSTRACT FROM AUTHOR]

In this panel session we will discuss approaches to data curation for data reuse, with particular emphasis on the contextual information needed for reuse. We invite three panelists to present perspectives from communities that are 1) conducting scholarly research in the area of data reuse, 2) performing data curation and preservation work, and 3) educating information professionals. Given the shared interests among the three communities, one of the major objectives of the panel session is to deepen the engagement and connection among these groups. We have planned interaction with the audience to integrate the panelists' different perspectives and approaches, outline common objectives, and explore ways we might create a common agenda in the area of data curation for data reuse. [ABSTRACT FROM AUTHOR]

This poster reports a qualitative exploratory study of data reuse processes, and factors scientists consider when deciding whether or not to reuse data. Findings from this study revealed six core theoretical variables along with 28 influential factors that were salient across interviews and supported by research literature. The synthesis of findings led to the development of a model that draws upon the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate scientists' intentions and behavior towards the reuse of research data. The proposed model offers a novel approach to the current base of knowledge that has been essentially exploratory and atheoretical in nature. It also serves as the foundation for a future larger scale research that expects to provide guidelines for policymakers, open data advocates, and data repository stakeholders to better attune policies and repositories to scientists' expectations and needs. [ABSTRACT FROM AUTHOR]

ABSTRACT Data reuse is an emerging academic practice in information sciences studies, and there are diverse studies focusing on this topic. The concept of open health data is becoming a main stream in government administration, academic fields and business/industry. In this study, we analyzed 36 medical journal articles that used data from National Health Insurance Research Database through qualitative method to illustrate open health data reuse behavior in the medical field. The results indicated five stages of data reuse procedure: data re-appropriating, data recreating, data re-segmenting, data reconstructing and data revalidating. This paper demonstrates not only medical researchers' data reuse behavior with a practical perspective, but also how they transform quantitative data to valuable clinical discoveries. Moreover, the implications suggest different functionalities for an ideal open health data platform to support researchers' data reuse behavior in each research stage. [ABSTRACT FROM AUTHOR]

The Enron Email Corpus is one of the most utilized collections of documents in Natural Language Processing, Machine Learning, and Network Analysis. Different groups of researchers have transformed the corpus, changing the content and format to meet their needs. The many distinct versions can all claim to be the Enron Email Corpus, though they are as distinct from the original publicly available collection as they are from each other. Researchers then have to determine the usefulness of a particular version in comparison to the many others available, as well as ascertain what has been done to the collection and how it would affect their specific research goal. This is especially important for reproducing a particular research method onto a different corpus, as transposing a method necessitates a deep understanding of the original data in the experiment. This project models the various transformations performed on different versions of the collection to form a network of connected datasets, highlighting the most important nodes as well as the most common transformations. Traversing different paths between nodes offers the community a way to model and reproduce the necessary data work performed on one collection that can be transposed onto other collections. [ABSTRACT FROM AUTHOR]

In this paper we describe an analysis of the most frequently used data types intersecting data sources in quantitative data re‐ uses in six major social science subject disciplines. Using a sample of 1,319 papers from 40 social sciences journals in Taiwan, this study reveals the reuse patterns among the subjects for a time span of 15 years. The finding shows that the six sub‐ jects formed three groups, each with similar data reuse patterns. It also shows that business data from governments, series surveys from academic institutions, and series surveys from governments are the three most important kinds of data for social scientists, but the major users of those data differed. [ABSTRACT FROM AUTHOR]

ABSTRACT The panel aims to update ASIS&T members and information professionals about important standards, specifications and best practice guidelines that have been developed or initiated by the international standards institutions and communities in dealing with open data. The openness and flexibility of the web have created both new opportunities and new challenges for data publishers and data consumers, such as how to represent, describe and make data available (by publishers) in a way that it will be easily found, understood, used and re-used (by consumers). The panelists will introduce related standards, bring insight to the adoption and implementation of new standards, comment on success and progress in the realm of certain areas such as research data, data on the web (in terms of publishing, annotation and use), metadata in for special domains (such as electronic health records, space debris and genealogical archives) and look at the raising questions and issues. Using the chart from the 2017-01 W3C recommendations, Data on the Web Best Practices, as context for the panel's presentations, the benchmarks to be used by the panel include the comprehension, processability, discoverability, reuse possibility and effectiveness, trust, linkability, accessibility and interoperability. [ABSTRACT FROM AUTHOR]