CFP – JASIST Special Issue on “Data Science in the iField”
Data and the use of data are now a critical part of our lives in an increasingly datafied society. Government agencies, private sectors, research, academic, and cultural heritage institutions produce, consume, and share massive amounts of data, which pose great challenges for organizations, individuals, and the society as a whole (Hintz, Dencik, & Wahl-Jorgensen, 2018).
Considering the far-reaching significance of data today, it is easy to understand how Data Science (DS) has generated such strong interests among researchers and educators across disciplines in recent years for both research and education. A recent bibliometric study of DS publications during 1965-2019 shows that DS is the most multi- and interdisciplinary research area across Web of Science categories (Raban & Gordon, 2020). In addition, this bibliometric study has also identified Library and Information Science (LIS) as one of the leading disciplinary areas in DS publications during that same period, along with Computer Science, Environmental Sciences, Medical Sciences, Engineering, Technology, Management, and Mathematics.
As the needs of a data-informed society grow and evolve rapidly, there is a marked shortage of workers skilled in dealing with data challenges. In response to these needs, various disciplines and units began developing data-related academic programs. A recent review of graduate-level DS education programs shows that Mathematics and Statistics, Computer Science, Business, and LIS are the leading disciplines offering such programs. Some institutions also offer an interdisciplinary data science and education program (Wu, 2019).
While DS is recognized as multi- and interdisciplinary in nature, some disciplines and academic units have started to reflect, discuss, and establish a disciplinary identity in DS research and education landscape, notably Mathematics and Statistics (Donoho, 2017), Computer Science (Siebes, 2018), Business and Management (Vicario & Coleman, 2020), and Chemistry (Szyma?ska, 2018). As a leading player in DS, LIS, broadly referred to as the Field of Information (iField), is now starting to more comprehensively explore, reflect, and position an iField perspective of DS (Virkus & Garoufallou, 2019, 2020; Shah et al., 2021). DS has been one of the most explored and discussed topics at the conferences in the field in recent years (Albright & Mehra, 2020; Anderson et al., 2019; Bishop et al., 2019; Blake & Brown, 2019; Bogers et al., 2020; Dencik, 2020; Gunderman, 2019, 2020; Hagen et al., 2019, 2020; Oh et al., 2019; Rorissa et al., 2019; Song et al., 2019; Song et al., 2020; Sundqvist et al., 2020; Taylor et al., 2019).
This special issue aims to identify and highlight the core concepts, values, expertise, and strengths that distinguish an iField approach to DS within the broader context of the DS landscape. By bringing together contributions showcasing DS research, education, and practice in this community, this special issue also aims to help establish the iField identity and articulate the ways that iField DS responds to data challenges and emerging trends. With concern for human values and a sociotechnical perspective already predominant in iField practices, we are especially interested in contributions seeking to shine a light on power inequities and social justice concerns in their exploration of data and data science practices.
Topics of Interest
The topics of this special issue tentatively include, but are not limited to:
- Foundations of Data Science for the iField
- Multidisciplinary nature of Data Science
- Data Science as a bridge between established disciplines and the iField (e.g., data science connecting social sciences to the iField)
- Data Science as a bridge between the iField and emerging disciplines (e.g., data science connecting the iField to health informatics)
- Human-centered Data Science
- Data Science for improving and clarifying human-information interaction
- Data Science for understanding information use and management in education (e.g., learning analytics)
- Data Science for understanding and promoting ethical use and management of information resources
- Data Science for sustainable and climate-friendly use and management of information resources
- Defining characteristics of Data Science in the iField
- Theories, models, and approaches
- Core competencies
- Curriculum and education models
- Job market and career pathways
We welcome submissions that are based on original, rigorous research in either long or short paper format (see JASIST submission guidelines below). Comprehensive critical literature reviews are also welcome.
Before submitting your manuscript, please ensure you have carefully read the JASIST Submission Guidelines (https://asistdl.onlinelibrary.wiley.com/hub/journal/23301643/homepage/forauthors).
The complete manuscript should be submitted through JASIST's Submission System (https://mc.manuscriptcentral.com/jasist ). To ensure that your submission is routed properly, please select "Yes" in response to "Is this submission for a special issue?" and specify "Special Issue on Data Science" when prompted later. Manuscripts of up to 10,000 words are accepted for this special issue.
- Paper submission due: June 30, 2021
- Final acceptance notification: November 30, 2021
Note: The guest editors welcome inquiries and proposals in an extended abstract for feedback on fitness of prospective submissions.