FAQ

What is CHASS?

CHASS is the acronym for Computational Humanities, Arts, and Social Sciences, and refers to the application of computational and data-science tools within the humanities, arts, and social sciences.

What is the CHASS toolkit?

The computing and data sciences have generated a host of tools that have proved useful for supporting the home-grown research goals of the humanities, arts, and social sciences (including public health). These methods are highly adaptable and the toolkit of methods is constantly expanding as computing power increases and novel techniques emerge. The Resources page presents some of the techniques within the CHASS toolkit.

Isn’t CHASS all quantitative, with no room for qualitative and humanities modes of research?

No. The publication history of CHASS indicates that qualitative, quantitative, ethnographic, and interpretative modes of research are all alive and well within CHASS. For example, a computational social simulation usually consists of a virtual society populated by AI agents in interactive social networks. This is an important technique for understanding human societies and for evaluating policy proposals. Computational social simulation critically depends on stakeholders and subject-matter experts to define what’s important and to help validate a model. Quantitative data may play a role in validating a model at the level of emergent population behavior, but model design depends heavily on qualitative, ethnographic, and interpretative skills, typically accessed through a participatory modeling process. The lesson here is that computing and data-science techniques have progressed to the point that they critically require input from humanities, arts, interpretative social sciences, and empirical social sciences sciences to realize their potential.

Is computational humanities the same as digital humanities?

No. Digital humanities is older than CHASS, geared into earlier computing and data-science technologies, and originally focused on digital access to resources needed for humanities research, especially through digitization, database storage, tagging methodologies, and information-retrieval technologies. As the computing and data-science toolkit expanded and transformed, some have tried to stretch the meaning of “digital humanities” to accommodate the host of new techniques in the CHASS toolkit. CHASS prefers a different approach to naming, honoring digital humanities as the first domain to recognize what computers could do within the humanities, and speaking of “computational humanities” to indicate the wealth of new techniques that go well beyond digitization and database technologies. Of course, CHASS also goes will beyond the humanities to the arts and social sciences, including public health and policy research.

I’ve heard about the CHASS toolkit. How do I access expertise to help my research in humanities, arts, social sciences, or public health?

Almost all CHASS work is collaborative, combining the various kinds of expertise needed for a given project, which might be computational, data-science, software engineering, qualitative, quantitative, ethnographic, interpretative, or artistic. Few scholars possess the skills to undertake a CHASS project solo so teamwork is essential. Unfortunately, many scholars in the humanities, arts, and social sciences are not trained in collaborative research methods, but that’s what’s needed. The CHASS Initiative is dedicated to maintaining resources so people can learn about the CHASS toolkit and get their creative imaginations going, and also to helping researchers forge collaborative links among people having the right kinds of expertise.

I want to work on a project that requires a technique from the CHASS toolkit but the technical demands seem too elementary to interest researchers in computing and data sciences. What should I do?

Like every other part of the university, specialized research in computing and data sciences is often extremely technical and focusing on such research is important for career success. Application of CHASS methods often does not require advanced expertise in computing and data sciences; well-trained graduate students, and even undergraduate students, may have the relevant skills. Students certainly benefit from engagement with experts in the humanities, arts, social sciences, and public health, and typically deeply appreciate the opportunity to do something practical with their developing skillset. Thus, the solution may involve partnering not with people doing leading-edge research, but with people who are still learning but are already competent in the application of specific techniques in the CHASS toolkit.