The ability to visualize data in an intelligible way is an important skill for scientists. In linguistics, especially in lexical semantics, data are often visualized using graphs, i.e., networks. For example, in the web app for the Database of Cross-Linguistic Colexifications (CLICS), we use networks to illustrate that a lexical form refers to two different concepts by connecting the concepts (i.e., nodes) with a line (i.e., edge). When identifying the colexifications between concepts across a large number of languages, the network grows and a tool to visualize multiple data points becomes necessary. Here, I present a tutorial for the first steps to visualize a colexification network with Cytoscape. The tutorial is intended for beginners who want to learn how the tool works and serves as a starting point for further skill development.
Reading, summarizing, and citing literature is an essential part of every student and researcher’s life. While there are many books and workshops on academic writing, there is less information on how to efficiently organize literature. For this reason, I offer an overview of how to organize literature and notes in Zotero. Specifically, I illustrate some of my own workflows for organizing the literature for my dissertation. I also discuss general features of Zotero. The interested reader who wants to learn more will benefit from the related links.
A colexification network is a network consisting of concepts as nodes with weighted edges drawn between the nodes, indicating how often the concepts colexify across the data in a given sample of languages. Having seen how individual colexifications can be computed with the help of the CL Toolkit package in an earlier blog post, we will now see how this code needs to be extended in order to compute colexification networks.
Writing a term paper requires the same scrutiny as writing an article for a journal. As a result, the techniques which apply when writing term papers are very similar to those which apply when writing a journal article, and students should feel encouraged to take the task as seriously as a journal article that scientists send off to peer review. In the following, I will briefly introduce major techniques that help to structure one’s work when writing a term paper and which also help to interact well with one’s supervisor during the writing process.
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
With an increasing amount of data being available in Cross-Linguistic Formats, it is becoming more and more important to know the basics underlying the Python packages designed by the CLDF initiative in order to allow interested users a quick access to the data. This very short tutorial illustrates how the CLDF data underlying the World Atlas of Language structures can be accessed and written to a table in which each individual values for all WALS parameters are for each language variety in one row.
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.
Semantic data are notoriously difficult to handle. In contrast to the form-part of the linguistic sign, meanings are not organized sequentially, but rather network-like (List 2014: 34f). As a result, we often encounter problems when trying to model complex relations between different meanings, specifically in those cases, where we have only tables as our base material. This blog post tries to summarize how major types of semantic data are handled in the Concepticon project and how they can be accessed in code.
I noticed that scientists deal with digital communication very differently: some avoid all sorts of platforms, others are much more present and involved in the discussions that are taking place in the online world. Digital communication as a linguist (or scientist in general) also includes sharing your research output. This does not have to be an article in a high-ranking journal. As a student, you can start by publishing your thesis, conference presentation slides, or a preprint. In this post, I’ll illustrate some of the possibilities that linguists and other researchers have to discuss and share their work.
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?
I cannot remember when I decided to become a linguist. I cannot even remember when I first called myself a linguist (as opposed to a student, a Sinologist, or a scientist). But I can remember when I wrote my first review for a linguistics journal, and I also remember that it came close to a catastrophe, since I maintained a very hostile tone, I didn’t like the paper, thought the authors were badly informed, and didn’t want to allow the paper to be published.