DOI: 10.5061/dryad.bnzs7h4h1
title: Computational phylogenetics reveal the history of sign languages

description: Sign languages are naturally occurring languages. As such, their emergence
 and spread reflect the histories of their communities. However,
 limitations in historical recordkeeping and linguistic documentation have
 hindered diachronic analysis of sign languages. Here, we use computational
 phylogenetic methods to study family structure among 19 sign languages
 from deaf communities worldwide. We use phonologically coded lexical data
 from contemporary languages to infer relatedness, and suggest these
 methods can help study regular form changes in sign languages. The
 inferred trees are consistent in key respects with known historical
 information, but challenge certain assumed groupings and surpass analyses
 made available by traditional methods. Moreover, the phylogenetic
 inferences are not reducible to geographic distribution, but do affirm the
 importance of geopolitical forces in the histories of human languages.
description: Data set comprises coded vocabulary data from 19 sign languages.
 Vocabulary items were sourced from freely available online sign language
 dictionaries and were annotated using a web-based interface developed for
 the project. The categories and category values used in the coding system
 are compatible with and informed by leading contemporary theories of sign
 language phonology. Additional information about data collection and
 coding is available in Section 2 and Section 4 of the supplementary
 materials text.
description: Proprietary and open-source software capable of working with .CSV
 files, including Microsoft Excel and Google Sheets.
description: # Phylogenies with Matricial Datasets This is the implementation of the
 numerical methods described in the paper "Computational phylogenetics
 reveal histories of sign languages". Instructions are given below to
 reproduce all the results from the paper, and to re-use the methodology on
 other data sets. The code uses R version 4.1.2, with the
 "parallel" library. The code is also working on 4.1.2. **All
 files and code are compressed in the ZIP (including a copy of the README);
 files are also available at
 ** ## functions
 This folder contains all the core functions needed to run the analysis.
 Users wishing to reproduce the results from the paper, or to make small
 changes (parameter values, different data) will not need to open these
 files. These files may be of use to researchers wishing to make more
 substantial changes to the model. In case of need, you can contact
 Grégoire Clarté. ## datasets This folder contains the datasets used for
 the experiments presented in the papers. The data consists in a csv file,
 where each line corresponds to a word in a language. The first columns
 give the meaning and the language; the other columns give all the
 characters used to encode the word. We also include an excel file
 containing exactly the same data. Details of the coding system can be
 found in the supplementary materials of the associated paper. Cell values
 of "UNDEF" or "NA" indicate that the coding field was
 not applicable to the sign. For example, a sign that is produced in
 neutral signing space does not receive a code for body part. ## Results
 Contains the resulting samples of trees (EBNZ_1.nex, EBNZ_2.nex and
 Asia.nex), and the resulting consensus trees (EBNZ_1_annote.nex,
 EBNZ_2_annote.nex and Asia_annote.nex). We did not include the full output
 of the SMC as the files are too large; these files can be reproduced using
 the code below. ## Reproducing results from the paper R scripts are
 available at the root of the folder. They correspond to different
 analyses: * AsianSL.R corresponds to the code for the study of the Asian
 dataset * EuropeanSL.R corresponds to the code for the study of the
 European sign languages (with New Zealand sign language). * AllSL.R
 includes Asian, European and New Zealand sign languages. (This last
 analysis gives trees which should not be reused, as it is based upon the
 unwarranted assumptions that all sign languages belong to a single tree.)
 To reproduce the results of the paper, execute the relevant R script. We
 recommend using a large cluster as the running time is about a day with 40
 cores; the number of cores can be changed in the last lines of each
 script. In all cases, you should then execute the PostProcessing.R script,
 which post-processes the output and saves the trees in the Nexus format
 for interpretation in standard phylogenetic software. Users wishing to
 make slight modifications to the analyses will presumably be particularly
 interested in changing the following parameter values: * prior information
 on ages are set in the object "Contraintesages" * the set of
 characters used is given by object "qui" * the set of languages
 included is set by object "quelleslangues" ## Using the code on
 other data sets The script GenericTemplate.R gives a template which can be
 adapted to apply the method to other data. To use this script, re-users
 will need to fill out certain parameters, which are marked explicitly.
 This script goes through the whole process: formatting the dataset for the
 inference, setting of the parameters, launching of the SMC, description of
 the output. Here too, you should then execute the PostProcessing.R script.
 It plots all the parameters and produces the .nex files needed for
 subsequent phylogenetic analyses. The resulting .nex files can be fed to
 standard phylogenetic software. ## Thanks The implementation of the
 Dirichlet distribution comes from the gtools package.