This page contains files and further information associated with "The Transparency of Empirical Legal Research (2018–2020)". This is now published on F1000: https://f1000research.com/articles/12-144 Full details of our methods can be found in our preprint (https://osf.io/svrhy/). **Creating a search string and assembling the sample** First, we assembled a database of law journal articles published by journals ranked in the top 25 of the Washington and Lee rankings (https://managementtools4.wlu.edu/LawJournals/). We coded them to determine how to create a search string to find empirical articles. This script explains how we analysed that data (https://osf.io/9q47g/). We then downloaded lists of all the articles published in Washington & Lee top 25 journals that were student-edited and in the Web of Science top 25 journals that were peer-reviewed from 2018-2020. We randomized the order of those lists and selected the first 150 journals that we confirmed were empirical from each list. This code explains this process and produces all of the exclusions and reasons for exclusion (https://osf.io/9q47g/). **Coding** These articles were then coded by two coders (https://osf.io/2nk4j; https://osf.io/eyxp3), with disagreements resolved by a third coder (structured coding form: https://osf.io/4btxv/). This code (https://osf.io/7q32m/) flags disagreements, which were then reviewed by the three coders. It then takes the files containing the third coder's decisions (https://osf.io/ktpcd/, https://osf.io/jx7fe/), combines them, and produces the raw data file used for the analysis below. **Data analysis** This code (https://osf.io/v6rek/) takes the raw data files above and performs all the analysis reported in the manuscript.