Which DH Tools Are Actually Used in Research?

Introduction by OpenMethods Editor Ulrike Wuttke:

This short blog post written by Laure Barbot, Frank Fischer, Yoann Moranville, and Ivan Pozdniakov from 2019 sheds some light on the old question of which DH-tools are actually used in research and which of them are especially popular. The inquiry was based on an analysis of ADHO’s annual DH conference proceedings (2015-2019) from which the team automatically extracted and counted all mentions of DH-tools.This method is especially noteworthy in the light of the not yet very established software and tool citation culture within the (digital) humanities. Currently, it is not only difficult to find out which tools exist, but even more so which ones are popular, gain results, and therefore make it into scientific literature. This data could provide arguments for example on choices of which tools to teach and learn (think DH-curricula), or which tools are worth maintaining (think funding).

The blogpost is especially valuable because it discusses transparently “known issues” in the approach and analysis, e. g. that the results are dependent on the list of tools that was used (in this case the TAPoR database) and related to the general definition of a “DH-tool”. For example, the most popular “DH-tools” extracted by the team based on the TAPoR database from the ADHO-proceedings were the social network Twitter and the programming language Python. However, these two tools were then excluded by the team as being to general and subsequently Gephi was given the first prize as the first “real DH-tool”.

Ironically enough, the post was widely discussed on the social network Twitter (so maybe it is a DH-tool after all, at least a DH-communication-tool?). If you want to follow the discussion, which let to further explications in the original blog post, you may start with the threads around one of Frank Fischer’s tweets (https://twitter.com/umblaetterer/status/1202871873698193408?s=20), another of Frank Fischer’s tweets (https://twitter.com/umblaetterer/status/1202872738509197312?s=20), and Quinn Dombrowski’s tweet (https://twitter.com/quinnanya/status/1203376561526755328?s=20).

It would be very interesting to see this approach discussed further and supported (or opposed) with more theoretical foundation and extended and adapted to other data sources, e. g. proceedings from other conferences etc. This seems to me a quite easy and itself interesting DH-exercise, because all analysis and result data are available with open licenses and the tools used are Open Source.

Not that we didn’t know that, but Gephi is the most popular DH tool actually used in research work. Followed by Omeka, stylo, MALLET, Excel, D3.js, the NLTK, WordPress, Drupal, TextGrid, CollateX, GeoNames, TXM, Solr and Voyant Tools.

Source: Which DH Tools Are Actually Used in Research?

Original date of publication: 06.12.2019