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The Benefits of Research Collaboration

Posted by on Wednesday, April 7, 2021 in DH Center Blog.

The Benefits of Research Collaboration

By Holly McCammon, Cornelius Vanderbilt Chair of Sociology and Professor of Sociology, Mellon Faculty Fellow

Drop-down menu reading software update

I can imagine your frustration as you realize that the user-friendly and, yes, simplified software you’ve been using is no longer meeting your research needs.  Previously, it had been so helpful, as the software guided you (that is, held your hand) through the computerized process.  You’ve leaned heavily on this digital tool, but now your research question is compelling you to take a more customized approach.  You can see that you really do need to learn [fill in the name of the programming language or statistical package here].  This can be a challenging moment in research, and tough moments require creative solutions.

I want to make the case here that collaborative research relationships can be highly worthwhile.  Not only can they allow you to accomplish the goals of your project, but they can help you learn new skills.  A collaboration can provide an applied learning setting, where you see firsthand how the sophisticated new approach works.

In Digital Humanities research, academic collaboration can be quite fruitful, especially in circumstances where a scholar is moving beyond software basics to more intensively utilize a research methodology.  A knowledgeable collaborator can help you take those programming steps, say, to employ computerized text analysis.  A collaborator can allow you to pursue a quantitative approach more vigorously, say, in network analysis, to name just some examples of methods increasingly deployed in the Digital Humanities.

Imagine now that your new collaborator has the very methods know-how that your research needs.  You’re back on track again!  And, in the process of working with this collaborator, you’re going to learn a lot.

We know that disciplines vary in the prevalence of collaborative research, with the natural sciences experiencing high levels, the humanities lower levels, and the social sciences often falling somewhere in between.  Collaboration in academic scholarship involving researchers with different skill sets, however, can provide a critically important opportunity for researchers to work together and utilize their combined knowledge and expertise in pursuit of a joint project.

Learning how to utilize a new research method can take considerable time.  If a humanities scholar, who has invested perhaps years learning how to conduct textual criticism, now desires, say, to learn Python programming to perform topic modeling, the time commitment can be substantial, especially when we go it alone.  And we know in academics that time is always scarce.

I often think of these moments as “fork in the road” moments:  which way should the person go?  Should they learn how to program?  Or could they take the other road?  What if “the other road” is a research collaboration?  What if the qualitative scholar cultivates a research collaboration with someone well-versed in computerized analysis?  If that humanities scholar establishes a research relationship with a data scientist, the research process may well proceed much more rapidly and the qualitative scholar is likely to learn much about programming and text analysis in the process (and the data scientist is likely to learn about textual criticism or synoptic methods as well!).

The collaborators, who come from different disciplines and training backgrounds, in their joint project, will first simply need to learn how to speak to one another; that is, they will need to learn at least some of the other’s disciplinary language, much like learning the language in a new country you may visit.  And the more you learn, the more you learn.

Working with a data scientist, for instance, provides opportunities to read their code and understand its logic.  Working with a network analyst can teach you which software package has the most utility and the best user interface and, importantly, how to make the software work for you.  Learning in an application setting (that is, seeing the code being developed and applied and viewing the output) can offer a very rich environment for developing new skills.  Reading about riding a bike is one thing, but watching someone ride can be enormously helpful … and it may put you on the path to ride one yourself.

In the Digital Humanities today, scholars, both new ones and more experienced scholars, can find themselves confronting sizeable skill gaps.  One way to bridge those gaps is to seek a research collaborator, someone whose skills complement your own.  If you are a qualitative researcher pursuing a digital or statistical project, you might seek a partner who is skilled quantitatively or who knows programming.  Such a division of labor in the research—a bit of what Émile Durkheim referred to as “organic solidarity”—can be highly fruitful, not only in achieving your research goals but in allowing scholars to learn from one another in the collaborative endeavor.