Call for papers: 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
– Practices
– Core competencies
– Curriculum and education models
– Job market and career pathways
– Stakeholders

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.

Submission Guidelines

Before submitting your manuscript, please ensure you have carefully read the JASIST Submission Guidelines (…).


The complete manuscript should be submitted through JASIST’s Submission System ( ). 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.


Submission Deadlines

  • 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.


Guest Editors

Yin Zhang, Kent State University, U.S.A,

Il-Yeol Song, Drexel University, U.S.A.,

Theresa Anderson, Women in Data Science (WIDS), Australia,

Dan Wu, Wuhan University, China,

Consulting Editor: Javed Mostafa, University of North Carolina at Chapel Hill, U.S.A.,



Albright, K. & Mehra, B. (2020). Information for a sustainable world: Addressing society’s grand challenges: 83rd annual meeting, ASIS&T 2020, 22 October – 1 November 2020, Proceedings.


Anderson, T.D., Blake, C., Hutchinson, Ben, & Fraher, R. (2019, October 19-23). Human centered data science [Conference incubation session]. 82nd Annual Meeting, ASIS&T 2019, Melbourne, Australia.


Bishop, B., Allard, S., Benedict, K., Greenberg, J., Hoebelheinrich, N., Lin, X., & Wilson, B. (2019, September 24-26). Curricula models and resources along the data continuum: Lessons learned in the development and delivery of research data management and data science education. [Conference panel]. ALISE 2019, Knoxville, Tennessee, U.S.A.


Blake, C. & Brown, C. (2019). Information anyone, anywhere, any time, any way: 82nd annual meeting, ASIS&T 2019, Melbourne, Australia, 19-23 October 2019, Proceedings.


Bogers, T., Eckert, K., Gäde, M., Mostafa, J.M., Petras, V., & Song, I. (2020, March 23–26). The Challenges of teaching data science in iSchools. [Conference workshop]. iConference 2020.


Dencik, L. (2020, March 23–26). Civic participation in the datafied society. [Conference keynote]. iConference 2020.


Donoho, D. (2017). 50 years of data science. Journal of Computational & Graphical Statistics, 26(4), 745–766.


Gunderman, H. C. (2019). Exploring learning in a global information context: Association for Library and Information Science Education annual conference: ALISE 2019, Knoxville, Tennessee, September 24-26, 2019, Proceedings.


Gunderman, H. C. (2020). Transforming LIS education in an interconnected world: Association for Library and Information Science Education annual conference, ALISE 2020, Virtual Format, October 13-23, 2020, Proceedings.


Hagen, L., Zamir, M., Andrews, J., & Hamerly, D. (2019, March 31 – April 3). Undergraduate data science education in iSchools: Current practices and future directions. [Conference panel presentation]. iConference 2019, Washington DC, United States.


Hagen, L., M. Abdul-Muhammad, M., Andrews, J., Zamir, H., Hamerly, D., & Clifford-Bova, S. (2020, March 23–26). Undergraduate data science education in iSchools: Optics and politics [Conference panel presentation]. iConference 2020.


Hintz, A., Dencik, L., & Wahl-Jorgensen, K. (2019). Digital citizenship in a datafied society. Polity Press.


Oh, S., Song, I.-Y., Mostafa, J., Zhang, Y., & Wu, D. (2019, October 19-23). Data science education in the iSchool context [Conference presentation]. 82nd Annual Meeting, ASIS&T 2019, Melbourne, Australia.


Raban, D. R., & Gordon, A. (2020). The evolution of data science and big data research: A bibliometric analysis. Scientometrics, 122(3), 1563–1581.


Rorissa, A., Federer, L., Hagen, L., Kim, J., Andrews, J. (2019, October 19-23). Data science research and practice: High time for synergy [Conference panel]. 82nd Annual Meeting, ASIS&T 2019, Melbourne, Australia.


Shah, C. Anderson, T., Hagen, L., & Zhang, Y. (2021). An iSchool approach to data science: Human-centered, socially responsible, and context-driven – A position paper. Journal of the Association for Information Science and Technology.


Siebes, A. (2018). Data science as a language: Challenges for computer science-a position paper. International Journal of Data Science and Analytics, 6(3), 177–187.


Song, I.-Y., Mostafa, J., & Wu, D. (2019, March 31 – April 3). Model data science curriculum for iSchools: The iSchool Data Science Committee (iDSCC) update [Conference presentation]. iConference 2019, Washington DC, United States.


Song, I.-Y., Mostafa, J., Zhang, Y., & Wu, D. (2020, March 23–26). iSchool Data Science Curriculum Committee (iDSCC) update [Conference presentation]. iConference 2020.


Sundqvist, A., Berget, G., Nolin, J., & Skjerdingstad, K. I. (2020). Sustainable digital communities: 15th international conference, iConference 2020, Boras, Sweden, March 23–26, 2020, Proceedings.


Szymaska, E. (2018). Modern data science for analytical chemical data – A comprehensive review. Analytica Chimica Acta, 1028, 1–10.


Taylor, N. G., Christian-Lamb, C., Martin, M. H., & Nardi, B. (2019). Information in contemporary society: 14th international conference, iConference 2019, Washington, DC, USA, March 31 April 3, 2019, Proceedings.


Vicario, G., & Coleman, S. (2020). A review of data science in business and industry and a future view. Applied Stochastic Models in Business & Industry, 36(1), 6–18.


Virkus, S., & Garoufallou, E. (2019). Data science from a library and information science perspective. Data Technologies and Applications, 53(4), 422-441.


Virkus, S., & Garoufallou, E. (2020). Data science and its relationship to library and information science: A content analysis. Data Technologies and Applications, 54 (5), 643-663.


Wu, D. (2019, October 19-23). Research on data science curriculum [Conference presentation]. 2019 Association for Information Science and Technology Annual Meeting, Melbourne, Australia.

Source: Yin Zhang
Kent State University