dataesr/scientific-paragraphs-categorization huggingface dataset:
csv") 4 Using and adapting trees from glottolog. com Glottolog. com (Hammarström et al. 2021 ) contains many useful resources for quantitative typology and the package glottoTrees  (Round 2021a ) has been written to help linguists make the most of these resources, including by modifying them as they desire. This section covers the glottolog data itself and the functionality of glottoTrees. We introduce glottolog's genealogical data in Section 4. 1, discussing how to locate metadata about languages and families of interest, and how to view glottolog's linguistic family trees. Since genealogicallysensitive averages and proportions require languages to be represented within a single tree, we then discuss how glottolog's individual trees can be combined in Section 4. 2. Since typological studies will often examine language varieties at a level of granularity that differs from glottolog's own, in Section 4.
Glottolog's genealogical data Glottolog provides metadata about the world's language varieties, their division into language families and the hierarchical subgrouping of languages inside those families. Naturally, there are many points of contention in linguistics about what the world's stock of languages and dialects actually is, how it groups into families, and how the families themselves are subgrouped. Glottolog provides one set of answers, and structures them in a way which provides typologists with a basis for carrying out changes to suit their own hypotheses. In later sections we will see how this can be done. In this section we describe glottolog's own global linguistic metadata. At time of writing, the current version of glottolog is v4. 4. The glottoTrees package contains a copy of the v4.
4 metadata covering language names, language identification codes, family names, geographical groupings, and family trees. The original metadata files that contain this information are currently available at https://glottolog. org/meta/downloads, where a file named tree_glottolog_newick. txt 3 contains glottolog's trees, and languages_and_dialects_geo. csv provides geographical metadata. Language metadata can be accessed using the glottoTrees function get_glottolog_languages(). This function returns a dataframe of close to twenty-six thousand rows. To view it in full, we suggest saving it to a CSV file and opening it in spreadsheet software such as Excel: language_metadata <-get_glottolog_languages() write. csv(language_metadata, "language_metadata. csv") Here are the first ten rows: Listed here are glottolog's languages, dialects, subgroups and families.
These entities are identified by a name, an ISO-639-3 code if available (format: three letters) and a glottolog-specific glottocode (format: four letters followed by four digits 4 ). Also described is the entity's relationship to a glottolog tree: the representation of its name in the tree (which may differ slightly from the name used elsewhere by glottolog 5  ), its position (as tip or node), and the tree's number and name. By default, the metadata functions in glottoTrees, such as get_glottolog_languages(), will return information about the most recent version of glottolog which the package contains. Glottolog v4. 4 divides the world's languages into 420 families, including 138 isolates, and it provides a tree for each. Together, the 420 trees contain 8,209 internal nodes and 17,008 tips, many of which represent varieties that would typically be considered dialects.
Geographically, glottolog assigns each language variety to one of six macroareas: Africa, Australia, Eurasia, Papunesia, South America or North America. The glottoTrees metadata includes a column main_macroarea. This is the one macroarea which contains more of the family's language varieties than any other. We will see how this information can be useful in Section 4. 2. Glottolog's 420 family trees are stored in a multiPhylo object named glottolog_trees_v4. 4. For example, here is glottolog's representation of the Great Andamanese family, which is tree 340 within the object glottolog_trees_v4. 4. For readability, we plot this tree horizontally: tree_GA <-glottolog_trees_v4. 4[[340] ] plot(tree_GA, x. lim = c(-0. 3, 14)) If you know the name of one or more families and would like to now the number of their trees, use which_tree(): which_tree("GreatAndamanese") ## GreatAndamanese ## 340 which_tree(c("Turkic", "Tupian", "Tuu")) ## Turkic Tupian Tuu ## 217 32 76 Both get_glottolog_trees() and which_tree() allow the usage of a glottolog_version argument, to refer to older versions of glottolog.
For instance, here are the tree numbers of the same families in version 4. 1: which_tree("GreatAndamanese", glottolog_version = "4. 1") ## GreatAndamanese ## 6 which_tree(c("Turkic", "Tupian", "Tuu"), glottolog_version = "4. 1") ## Turkic Tupian Tuu ## 66 297 80 In glottolog's trees, the tip labels are rather long, consisting of a name followed by a glottocode in angled brackets, an ISO code in angled brackets (if one exists) and possibly the string "-l-". Node labels (not shown in the tree above) have the same structure. The glottoTrees function abridge_labels() will shorten labels to just the glottocode, for example: The function abridge_labels() will issue a warning if there are tip or node labels in which it is unable to identify a glottocode. We will see an example of this shortly below.
How to combine trees As discussed in the main paper, the comparison of languages across language families unavoidably carries a commitment to a genealogical hypothesis, even if that hypotheses is, tacitly, that all families are equally (un)related. Given that making such hypotheses is unavoidable, it will be most beneficial for progress in the field to make them explicit. To enable typologists to explore genealogical hypotheses and to make those hypotheses explicit, glottoTrees provides tools for combining multiple glottolog trees into one. To begin with a small example, here we combine five glottolog families to represent the hypothesised Arnhem group in northern Australia  (Green 2003). First we create a multiPhylo object containing the five glottolog language families (Gunwinyguan, Mangarrayi-Maran, Maningrida, and the isolates Kungarakany and Gaagudju): arnhem_family_names <c("Gunwinyguan", "Mangarrayi-Maran", "Maningrida", "Kungarakany", "Gaagudju") multiPhylo_arnhem <-get_glottolog_trees(arnhem_family_names) The glottoTrees function assemble_rake() enables the trees in a multiPhylo object to be assembled into a single tree with a rake structure at its root.
Grouping of macroareas is achieved by setting the macro_groups argument to a list whose items are the desired groups of macroareas. Each group will then appear as one of the highest-level nodes of the tree, and all of its families below it. For instance, to keep all of glottolog's macroareas separate, but to combine North and South America into a single group, the following code would be used. First we define a list, which we've called my_list, within which any groupings containing more than one macroarea are represented as a vector, using the c() function: my_list <-list("Africa", "Australia", "Eurasia", "Papunesia", c("South America", "North America")) We then use that list as the macro_groups argument of assemble_supertree(): my_supertree <-assemble_supertree(macro_groups = my_list) Taking a second example, to create a supertree containing only the families whose main_macroarea is either Africa or Eurasia, and to place Africa and Eurasia under separate, highest-level nodes, we would use: my_list <-list("Africa", "Eurasia") my_supertree <-assemble_supertree(macro_groups = my_list).
How to modify trees There are several reasons why typologists may wish to use a tree that departs from the glottolog trees. Most commonly, a typological study will cover a set of languages that differs from the set of tips in any single glottolog tree, either through the exclusion of some of the lects that glottolog represents as tips or through the distinction of additional lects. A third case that can arise is when glottolog places one or more dialects at the tree's tips and more a general, language node above them. The typologist may have data that applies to the language (an internal node) rather than the dialects (the tips), yet the calculation of genealogically-sensitive averages and proportions requires one's typological variables to be related to the tips of trees, not to internal nodes.
In these cases and many others, the typologist may wish to alter the glottolog tree to suit the purposes of the research. The glottoTrees package supplies a set of functions to aid in performing each of these tree manipulations. In the section we introduce them and illustrate their use. In the following examples, we will make use glottolog's representation of the Great Andamanese family, whose labels we shorten to just the glottocodes using abridge_labels():.
How to remove tips and convert nodes to tips As mentioned earlier, many of the tips in glottolog's trees correspond to dialects, with languages represented as nodes above the dialectal tips. One usage case we foresee is that a typologist will wish to study a set of language varieties, some of which correspond to glottolog's tips and some of which correspond to nodes. The glottoTrees function keep_as_tips() takes an argument label which can contain both tip labels and node labels. Any tips will be kept, and any nodes will be converted into tips, with all of the structure below them being removed. Be mindful when using keep_as_tips() that it is not possible to both convert a node into a tip and also retain the structure below it, such as tips that it dominates. Here we keep the same tips as in the tree above, while also converting the node boca1235 into a tip: 7  tree_GAe <-keep_as_tip(tree_GA_abr, label = c("akar1243", "akak1251", "akak1252", "apuc1241", "okoj1239", "boca1235")) plot_glotto(tree_GAe) One workflow that we envision for keep_as_tip() is that the typologist has prepared a CSV file, one of whose columns is named tip and contains the glottocodes of all the language varieties in the study.
Putting it together: A worked example In this section we provide a real worked example of the use of the methods described above. Yin (2020) examined violations of the sonority sequencing principle in 496 languages, and calculated the genealogically-sensitive proportions of languages in which various violations occurred. The language sample consisted of 496 languages in the CLICS2 database  (Anderson et al. 2018 ) and the AusPhon-Lexicon database  (Round 2017). The language sample was not balanced in the traditional sense, and phylogenetic methods were used to help produce a principled interpretation of the data. Yin's raw data consisted of a table of languages' names and glottocodes and indications of whether or not the languages had consonant clusters in word-initial onsets or word-final codas that contained sonority reversals, coded as 1 for yes and 0 for no.
This dataset is provided with the phyloWeights package as a dataframe named yin_2020_data whose columns are name, tip, has_onset_violation and has_coda_violation. The first ten rows are shown here: head(yin_2020_data, n = 10) ## name tip has_onset_violation has_coda_violation ## 1 Abkhaz abkh1244 1 1 ## 2 Abui abui1241 0 0 ## 3 Achagua acha1250 0 1 ## 4 Adang adan1251 0 1 ## 5 Adnyamathanha adny1235 0 0 ## 6 Adyghe adyg1241 1 1 ## 7 Hokkaidoainu ainu1240 0 0 ## 8 Alawa alaw1244 1 0 ## 9 Standardalbanian alba1267 1 1 ## 10 Aleut aleu1260 1 1.
Preparing a tree The tree for Yin's study was constructed from a glottolog supertree, using glottolog version 4. 2. Yin's supertree made use of glottolog's macroareas. Since the language sample covered relatively few families in the Americas, a single group was used for South America and North America. Additionally, the only African language available in the sample was Arabic, so Africa and Eurasia were grouped together: yin_macro <-list(c("South America", "North America"), c("Africa", "Eurasia"), "Papunesia", "Australia") supertree <-assemble_supertree(macro_groups = yin_macro, glottolog_version = "4. 2") supertree_abr <-abridge_labels(supertree) ## Warning in abridge_labels(supertree): Labels without glottocodes were detected and left ## unchanged for: 0 tip(s); 5 node(s): World, SouthAmerica-NorthAmerica, Africa-Eurasia, ## Papunesia, Australia Five tips were cloned, in cases where Yin had data for two varieties corresponding to just one tip in the glottolog supertree: supertree_a <-clone_tip(supertree_abr, subgroup = TRUE, label = c("ayab1239", "basu1242", "biri1256", "ikar1243", "peri1265")) supertree_b <-apply_duplicate_suffixes(supertree_a) Eight tips were added, in case where for sister lects (A,B), glottolog placed A as a node above B.
In such cases, in new tip A was placed below the existing glottolog node A: supertree_c <-supertree_b nodes_to_add_as_tips <-c("alor1249", "gami1243", "guri1247", "mand1415", "sins1241", "wang1291", "warl1254", "yand1253") # Loop through these nodes, and use add_tip() to add the new tip: for (node_i in nodes_to_add_as_tips) { supertree_c <-add_tip(supertree_c, label = node_i, parent_label = node_i) } From this supertree, the 496 languages in Yin's dataset were kept. The internal node mada1298 was collapsed, as were all non-branching internal nodes: supertree_d <-keep_as_tip(supertree_c, label = yin_2020_data$tip) supertree_e <-collapse_node(supertree_d, label = "mada1298") supertree_f <-collapse_node(supertree_e, label = nonbranching_nodes(supertree_e)) Finally, branch lengths were assigned.
Using these methods in typological research We hope that typologists find the arguments in our main paper compelling at the conceptual level and the methods in this supplementary document convenient at the practical level. If you are interested in trying out the methods described here, please check for updates and additional information at their web pages, https://github. com/erichround/glottoTrees and https://github. com/erichro und/phyloWeights. Through the use of glottoTrees and phyloWeights, we hope that in the future, linguistic trees and the code used to produce them can be published together with typological studies. This will enable subsequent researchers to replicate the study's findings, and just as importantly, to modify its assumptions by modifying the trees, and thereby to test further hypotheses inspired by the initial research.
StephenMorey. 1999. The Yorta Yorta (Bangerang)language of the Murray Goulburn including Yabula Yabula (Pacific Linguistics Series C 154). Canberra: Pacific Linguistics. 286 pp. John Giacon & Amanda Lissarrague. 2003. Gamilaraay, Yuwaalaraay & Yuwaalayaay dictionary. Alice Springs, NT, Australia: IAD Press. 344 pp. Data files are archived at https://doi. org/10. 5281/zenodo. 5602216, in the following directory structure: • data -laminals_binary. csv a file in CSV format of languages and the presence/absence of the laminal contrast. -laminals_continuous. csv a file in CSV format of languages and continuous phonotactic variable data. -PN_100_tree_sample. trees a file in Newick format containing the Pama-Nyungan tree sample. • results -D_results. csv a file in CSV format of D test results (cf Section 6.
Data availability statement Data and results files are available on Zenodo at https://doi. org/10. 5281/zenodo. 5602216. Documentation and code for performing the analysis is available in Supplementary Materials Section S2. The R packages glottoTrees (Round  2021a)  and phyloWeights (Round 2021b) referred to in Supplementary Materials Section S1 are available at https://github. com/erichround/glottoTrees and https://github. com/erichround/phyloWeights. ## [1] "jeru1239" "sout2683" The function collapse_node() can also be used to alter a subgrouping hypothesis, and specifically, to remove a layer of subgrouping, converting a nested structure ((A,B),C) into a flat structure (A,B,C). For instance, here we remove the okol1242 node of the original glottolog Great Andamanese tree, converting its two daughter languages into sisters of okoj1239: tree_GAh <-collapse_node(tree_GA_abr, label= "okol1242") plot_glotto(tree_GAh) okoj1239 akak1253 apuc1241 akak1252 akab1248 akac1240 mixe1288 akak1251 akab1249 akar1243 grea1241 midd1323 nort3276 nort2678 boca1235 jeru1239 akaj1239 sout2683 4.
METHODS The global research methodology followed in this work is illustrated in Figure  1. Specimens 1 and 2 belong to the medium sized caste of the Messor barbarus species (more details in Section 2. 1). Specimen 1 was used to build a 3D model from micro-computed tomography (Section 2. 2). 3D models of body segments were used to extract joint geometrical parameters and to create a multibody model (Sections 2. 3 and 2. 4). Specimen 2 was used to acquire experimental kinematic data and to extract marker trajectories (Section 2. 5). Experimental kinematic data was used to scale the multibody model and to run an inverse kinematics simulation (Section 2. 6). To evaluate the impact of the propagation of model parameter uncertainties on joint angles, two Monte Carlo (MC) simulations were conducted (Section 2.
Micro-Computed Tomography Following the procedure used by  Peeters et al. (2020) , specimen 1 was stored in 90% ethanol, then stained in a 2 M iodine solution for a minimum of 24 h and transferred into micro tubes filled with 99% ethanol. It was then transferred to Okinawa Institute of Science and Technology Graduate University (OIST, Japan) to be scanned using micro-Computed Tomography (µ-CT). This was performed using a Zeiss Xradia 510 Versa 3D X-ray microscope operated by the Zeiss Scout-and-Scan Control System software (version 11. 1). A vertical stitching enabled a 3 times scanning along a head-trunk-gaster axis each with a resolution of 933 × 1013 × 988 pixels (providing a voxel of 5. 7 µm). These scans were compiled to increase the resolution of the whole ant body to 3159 × 1013 × 988 pixels.
Extraction of joint geometrical parameters Defining the types of joints was done from both literature and morphometric data  (Liu et al. , 2019). From the 3D models of the body segments, joint geometrical parameters were estimated from the articular surfaces of the exoskeleton using a CAD software (3D EXPERIENCE, Dassault Systèmes, France). For ball-and-socket joints, the center of a sphere fitted to the articular surface was considered as the center of rotation of the joint (see Figure  2 (b, f )). For hinge joints, the rotation axis was defined as the line passing through the center of two spheres fitted to the condyles of the joint (see Figure  2 (c, d, e )). The procedure to determine joint geometrical parameter was also used in insect biomechanical modeling by  Blanke et al. (2017). Because of low perceived motion, and to facilitate the convergence of the inverse kinematics algorithm, the internal rotation of the metatarsus of each leg was not considered (it was assumed as a blocked degree of freedom (DOF)).
Abduction: positive rotation about the x-axis. Adduction: negative rotation about the x-axis. Internal rotation: positive rotation about the y-axis. External rotation: negative rotation about the y-axis. Flexion: negative rotation about the z-axis. Extension: positive rotation about the z-axis. The model was composed of 39 segments and 65 DOF. Segments were considered as rigid bodies and joints were considered without clearance. Half of the kinematic chain of this model is represented in Figure  3. 47 virtual markers were placed on the model according to the tracked anatomical landmarks (see Figure  4 ). The model was created using the software tool NSM Builder (version 2. 1)  (Valente et al. , 2017)   1  and 2. Figure  3. Kinematic chain representing half of the ant locomotor system. Anatomy: th (thorax), pet (petiole), abd (abdomen), cox (coxa), tro (trochanter), fe (femur), ti (tibia), mt (metatarsus), ta (tarsus).
Kinematic data acquisition and treatment Kinematic data of a free walking ant (mean speed over the length of the calibrated walkway: 3. 4 mm s -1 ) was acquired using high-speed synchronized video cameras (AI GO-5000M-PMCL). The experimental setup was composed of a wide walkway where the ant walked through, 5 cameras (1 of the top and 2 for each side or the walkway) and 3 infra-red spots (see Figure  5 ). The shutter time was 1/3333 s, the acquisition time was set to 10 s with a sampling frequency of 300 Hz. The infrared spots were added to compensate this short shutter time. The resolution of the camera sensor was 2560 × 2048 pixels. Using Hiris software of R&D Vision (version 5. 2. 0), the active sensor window was adjusted to the ant size in a 2000 × 418 pixels rectangular area. The average field of vision of the cameras was 15.
8 x 4. 9 x 7. 8 mm that gives a spatial resolution of 0. 0096 mm/pixel. Obtained raw videos are available from the project repository. Video recordings were processed afterwards with the Vicon Peakmotus (version 10) software tool. Segment extremities were tracked semi-automatically during a gait cycle using a pattern matching technique. The gait cycle was defined when the left middle leg leaves the ground and lifts, and it ends when that same leg leaves the ground again. Kinematic data was filtered with 4th order Butterworth low-pass filters with a cut-off frequency of 5 Hz. It was then resampled from 300 to 100 Hz to decrease computation time. Spatial coordinates of the anatomical landmarks (those represented in Figure  4 ) were exported on a c3d format file. This file is available from the project repository.
Model scaling and inverse kinematics analysis Spatial coordinates of the anatomical landmarks were used to scale the multibody model and to run inverse kinematics simulations. A scaling procedure was carried out to fit the model (originally created from the morphology of specimen 1) to the morphology of specimen 2. This was performed using the open source software tool OpenSim (version 4. 0)  (Seth et al. , 2018). Using the scaled model, inverse kinematics simulations were also performed in OpenSim. Joint angles as well as root mean square errors (RMSE) were obtained from these simulations.
Besides, the model could allow biologists to study function/structure relationships of Messor barbarus. The whole set of experimental and simulated kinematic data is also available from the project repository. In spite of the differences in morphology of the studied species, the obtained joint angles were in the same order of magnitude as those reported in the literature about ant kinematics (see Table  6 ). The difference between angle range of left and right legs comes from the fact that the ant did not walk perfectly straight. Obtained kinematic data are valuable for roboticians to implement bio-inspired gaits in robots (see  Ouyang et al. (2021)  for example). A possible error source in the conducted kinematic simulation could be linked to the use of two different specimens for acquiring experimental data (one for the geometrical 3D model and one for the experimental kinematic data).
ACKNOWLEDGEMENTS The authors would like to thank  Pr. Evan Okonomo  and  Dr. Francisco Hita Garcia  ( Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology, Japan ) who performed the scans that enabled to determine segment and joint geometries. They would also like to acknowledge  Moran Le Gleau 's contribution to the treatment of the µ-CT under the supervision of  Adam Khalife , as well as  Tanguy Puluhen  for his help during the development of the multibody model. Thanks also to the  CNRS GDR 2088 Biomim  who encouraged this work.