A few days ago, Sidwell and Alwes submitted a very nice dataset on Vietic languages to Zenodo (10.5281/zenodo.5263194). When inspecting the data, I realized that this dataset could be easily converted to our CLDF formats in our new Lexibank standards. Since both authors explicitly invited for discussions of the data and the testing of the results, I thought it would even be better to quickly illustrate the CLDF conversion in a blog post, as this may enable colleagues to do the same with their datasets in the future.
There has been much discussion about the reproducibility of research and how it can be improved (Munafò et al. 2017). One antidote to the “reproducibility crisis” seems obvious: data sharing. However, one additional point that is not mentioned as often is the standardization of shared data to make them comparable. Especially for cross-linguistic studies, this is an important step that needs to take place so that we can conduct reproducible studies in different languages. The NoRaRe database (Tjuka et al. 2021a) is a resource that provides standardized cross-linguistic data on norms, ratings, and relations published in psychology and linguistics. In this blog post, I describe a beginner’s guide to adding data sets to NoRaRe.
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.
In this study, we discuss the sparsely studied Gelong language of Hainan Island and its affiliation to the Hlai languages. Our work is based on Andy Chin’s article “The Gelong Language in the Multilingual Hub of Hainan”. We extracted Chin’s data and processed it with the help of various computer-assisted methods in order to make it more accessible, machine-readable, and comparable with other datasets.
In the summer of 2018, I set out to collect data for my master’s thesis (Tjuka 2019). The goal was to elicit body part terms that can also refer to object and landscape features. This was BC (before COVID-19), so I was able to meet with informants living in Berlin at the time and conduct my urban fieldwork study. The informants who participated in the study spoke one of 13 different languages (e.g., Wolof, Vietnamese, Czech). As a first task, I elicited 28 body part terms to get a sense of the naming patterns in each language. This blog post provides background information and introduces the resulting multilingual body part concept list.
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.
While searching for the topic of a small research project about the linguistic history of South America, I realized that a lot of data that is crucial for assessing central arguments is not openly available, but new data is difficult to come by these days. And when it is, it is not usually presented in data format that allows for easy reuse. Guided by these thoughts, I decided to turn towards the upcycling of previously published data (also called retro-standardization, see for example Geisler et al. (forthcoming) on the upcycling of the TPPSR dataset, https://tppsr.clld.org). The dataset I chose was previously published by Adolfo Constenla Umaña (2005). In this article, the author investigated comparatively the long-claimed genealogical relationship of three families of Central and South America, Chibchan, Lencan and Misumalpam (Lehmann 1920).
With the recent publication of the new version of the EDICTOR application for the curation and creation of etymological dictionaries, several new features were introduced which target specifically the annoation of language-internal word families opposed to cross-linguistic cognates. While working on the EDICTOR update, I carried out intensive tests of the new features by annotating a German wordlist for language-internal cognates. In this post, I will quickly discuss some of the new features in EDICTOR 2.0 by showing some examples of the freshly annotated wordlist for German.
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
Multi-SimLex (https://multisimlex.com) is a multilingual resource which provides user ratings for word pairs translated into different languages. The data is important for the evaluation of methods that derive word embeddings from large corpora. While it is on the one hand desirable to link such a large dataset to Concepticon, it is difficult to do so in concrete, given that the datasets represents word similarity ratings withouth any clear reference to concepts. In this post, I will show how the data can nevertheless be linked to Concepticon, and how the original Multi-SimLex data can be represented without losing any information in the form of a Concepticon Concept List.
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.
In the last two weeks we had a renewed interest in colexifications, especially in the third generation of the “Database of Cross-Linguistic Colexifications” (Rzymski, Tresoldi, et al., 2020). The attention was due to two different and independent requests in few days. For those unfamiliar, the concept of “colexification” (François, 2008) refers to instances in which a language uses the same lexeme to express more than one comparable concept (e.g., Russian де́рево, which can mean both “tree” and “wood”). The CLICS project, first developed by List et al. (2014), is an offspring of the transparent approaches to standardization, aggregation, and curation of linguistic data that have been promoted within the CLDF framework (Forkel et al., 2018). It uses standardized lexical databases to identify “colexification networks”.