Data Science Training for the Humanities and Social Sciences

Data Science Training for the Humanities and Social Sciences

A newly published report documents the outcomes and key learnings that stemmed from the 2020-2021 SSHRC funded Relevant Research Series, a workshop series jointly organized and hosted by the Sherman Centre for Digital Scholarship and Spark: a centre for social research innovation at McMaster University.

The recommendations can inform future interdisciplinary data science training initiatives at McMaster University and beyond. Read the complete open access report on MacSphere: Advancing Interdisciplinary Data Science Training for the Humanities and Social Sciences (

As a primer, here is the introduction of the co-written report by Dr. Andrea Zeffiro, Dr. Jason Brodeur, Dr. Sarah Whitwell, Dr. Allison Van, Dr. Michelle Dion, Evangeline Holtz-Schramek, and Dr. Veronica Litt.

“Researchers in the Humanities and Social Sciences are increasingly undertaking ‘data-driven research,’ a broad term describing research that collects, manages, evaluates, and analyzes datasets of many sizes and types as critical evidence to make informed decisions. Scholars new to data-driven research benefit from collaborating with experts from different fields to identify the methods most appropriate to support their research questions. Of course, collaboration with experts will often extend well into the life cycle of research, as scholars learn how to communicate data-driven research narratives in compelling and accessible ways. To this end, data competency training is indispensable for researchers. It allows scholars to acquire the practical knowledge and skills to work with qualitative and quantitative data critically and fluently to glean useful information and synthesize findings for academic and non-expert audiences.

In general, extra-curricular training in data-driven approaches to research at many higher education institutions (HEIs) can be dispersed among departments, institutes, centres, and central support units such as libraries and reserach computing services. In contrast, specialized intensive training opportunities can be prohibitive due to financial, physical, logistic, and temporal constraints that can make such training more difficult to access, especially when participants must be physically present. At the same time, due to workforce shortages in data literacy, building foundational and translational interdisciplinary training resources is a top priority for HEIs. Additionally, data training is critical for student learners. Extra-curricular instruction complements formal degree training and introduces data literacy fundamentals that equip learners with data fluency to meet cross-sectoral demands. Moreover, acquiring critical data literacies enables learners to become aware of their crucial role as contributors and consumers of data in their daily lives.

To begin to address the asymmetry of formal training and the shortage of data skills across sectors of the labour force, the leading research support centres within McMaster’s University Library, Faculty of Humanities, and Faculty of Social Sciences collaborated on delivering a series of complementary and interdisciplinary workshops focused on communicating data-driven research results meaningfully to a wide range of stakeholders: The Relevant Research Series. Led by the Lewis and Ruth Sherman Centre for Digital Scholarship, and Spark: a centre for social research innovation, the workshops were relevant to all empirical researchers even if they had no experience or very little interest in quantitative data-driven methods. As we detail in this report, the series was an opportunity to promote greater interdisciplinarity for data science training.

In the first part of the report, we provide pertinent background on the Relevant Research workshop series and reflect on its outcomes. The second part documents the key learnings from the 2020-2021 series, which can inform future interdisciplinary data science training initiatives at McMaster University and beyond.

Read the complete report.

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