When sharing data and code when submitting papers to a journal, you need to make sure that the reviewers can test and inspect your data as conveniently as possible. In between reviews, you should also be maximally transparent on any changes that have been made to the data or the code underlying your study. When using data that was published elsewhere, this means you should pay specific attention to the versions you have used and make sure they are readily accessible.
The scientific culture in linguistics has been changing recently, and more and more papers are published with code and data accompanying them. What is still often forgotten, however, is that code and data should also be shared with the reviewers during the first submission of a paper in order to guarantee a maximally transparent review process that includes also a thorough inspection of the data and the code. This calls for attention from two sides: Reviewers should make sure that they receive data and code if they are needed to replicate the results reported in a paper, while authors should make sure to submit them to the reviewers in a way that they can easily inspect them. In this new blog post series, I want to summarize what authors should keep in mind when preparing their data and code for submission to a journal. On the one hand, I hope that this post will increase awareness among colleagues that data and code should be shared upon submission. On the other hand, I hope it also provides active help to all colleagues who plan to submit an article to a journal and are not sure how to share their data in the best form.
In 2016, the Concepticon was introduced as a reference catalog for linguistic data of various kinds (List et al. 2016). The aim of the Concepticon project is to collect concept lists and link the glosses in the lists to unified concept sets (List et al. 2016; List et al. 2020). The project is a collaborative effort and the group of editors is constantly adding new data to the Concepticon (https://concepticon.clld.org/). In 2019, we implemented a review process that works similar to a submission process at an academic journal and is improving the quality of the resource in many ways. This blog post describes our effort to improve data validity by reviewing every single concept list that is added to the Concepticon. The database is curated openly on GitHub, so you can follow our review process by looking at several examples here: https://github.com/concepticon/concepticon-data
Over the past year, the doctoral students of our department have been organizing a weekly journal club. For me as a linguist, the discussions of various research articles opened up a whole new perspective on science in general and linguistics in particular. I learned about the interests and viewpoints of my fellow doctoral students and other researchers in our department working on historical linguistics, language documentation, and cultural evolution. In addition, organizing a journal club also helped me learn how to spark and lead a discussion. I hope the following personal insights offer some ideas and guidelines on how you can organize a journal club of your own.
Writing reviews for a journal is one of those things which most scientists never actively learn. For laypeople, this may be surprising, given how often the scientific method with its rigorous peer review procedure is being mentioned in the news nowadays. How can it be, one may ask oneself, that this procedure that is usually presented as the core principle of scientific reasoning, is never really actively taught? If the review by experts is the core of the scientific method and what decides about the acceptance of an article, how can it be that scientists do never take a course on article reviewing, and how can it be that reviewers are (as I have previously discussed in a German blogpost) themselves never reviewed or graded?