dataesr/scientific-paragraphs-categorization huggingface dataset:
In many cases, scholars helped us by sharing fieldwork data (such as yanglalo 47 , derived from  48  , and bodtkhobwa 49 , derived from  50  ), or providing the unpublished data underlying a previous publication (e. g. satterthwaitetb 51 , derived from  52  ). In other cases, we profited from the digitization efforts of large documentation projects such as STEDT  53  (the source of the suntb 54 dataset, originally derived from  55  ), and Northeuralex 56,57. In the second step, we identify all relevant sources used to create a specific dataset and store them in BibTeX format, the standard for bibliographic entries required by CLDF (2). We do this on a per-entry level, guaranteeing that for each data point it will always be possible to identify the original source; the pylexibank library will dutifully list all rows missing bibliographic references, treating them as incomplete entries.
Given the large amount of bibliographic entries from language resources provided by aggregators like Glottolog  38  , this step is usually straightforward, although it may require more effort when the original dataset does not properly reference its sources. The third and fourth steps comprise linking language varieties and concepts used in a dataset to the Glottolog (3) and the Concepticon catalogs (4), respectively. Both such references are curated on publicly accessible GitHub repositories, allowing researchers easy access to the entire catalog, and enabling them to request changes and  additions. In both cases, on-line interfaces are available for open consultation. While these linking tasks require some linguistic expertise, such as for distinguishing the language varieties involved in a study, both projects provide libraries and tools for semi-automatic mapping that facilitate and speed up the tasks.
In a fifth step, we use the pylexibank API to clean and standardize lexical entries, and remove systematic errors (5). This API allows users to convert data in raw format -when bibliographic references, links to Glottolog, and mappings to Concepticon are provided -to proper CLDF datasets. Given that linguistic datasets are often inconsistent regarding lexical form rendering, the programming interface is used to automatically clean the entries by (a) splitting multiple synonyms from their original value into unique forms each, (b) deleting brackets, comments, and other parts of the entry which do not reflect the original word form, but authors' and compilers' comments, (c) making a list of entries to ignore or correct, in case the automatic routine does not capture all idiosyncrasies, and (d) using explicit mapping procedures for converting from orthographies to phonological transcriptions.
The resulting CLDF dataset contains both the original and unchanged textual information, labeled Value, and its processed version, labeled Form, explicitly informing what is taken from the original source and what results from our manipulations, always allowing to compare the original and curated state of the data. Even when the original is clearly erroneous, for example due to misspellings, the Value is left unchanged and we only correct the information in the Form. As a final step, CLDF datasets are publicly released (6). The datasets live as individual Git repositories on GitHub that can be anonymously accessed and cloned. A dataset package contains all the code and data resources required to recreate the CLDF data locally, as well as interfaces for easily installing and accessing the data in any Python environment.
Packages can be frozen and released on platforms like Zenodo, supplying them with persistent identifiers and archiving for reuse and data provenance. The datasets for CLICS3, for example, are aggregated within the CLICS Zenodo community (https://zenodo. org/communities/clics/, accessed on November 15, 2019). Besides the transparency in line with the best practices for open access and reproducible research, the improvements to the CLICS project show the efficiency of this workflow and of the underlying initiative. The first version  18  was based on only four datasets publicly available at the time of its development. The project was well received and reviewed, particularly due to the release of its aggregated data in an open and reusable format, but as a cross-linguistic project it suffered from several shortcomings in terms of data coverage, being heavily biased towards European and South-East Asian languages.
The second version of CLICS 34 combined 15 different datasets already in CLDF format, making data reuse much easier, while also increasing quality and coverage of the data. The new version doubles the number of datasets without particular needs for changes in CLICS itself. The project is fully integrated with Lexibank and with the CLDF libraries, and, as a result, when a new dataset is published, it can be installed to any local CLICS setup which, if instructed to rebuild its database, will incorporate the new information in all future analyses. Likewise, it is easy to restrict experiments by loading only a selected subset of the installed datasets. The rationale behind this workflow is shared by similar projects in related fields (e. g. computational linguistics), where data and code are to be strictly separated, allowing researchers to test different approaches and experimental setups with little effort.
Colexification analysis with CLICS. CLICS is distributed as a standard Python package comprising the pyclics programming library and the clics command-line utility. Both the library and the utility require a CLICS-specific lexical database; the recommended way of creating one is through the load function: calling clics load from the command-line prompt will create a local SQLite database for the package and populate it with data from the installed Lexibank datasets. While this allows researchers with specific needs to select and manually install the datasets they intend, for most use cases we recommend using the curated list of datasets distributed along with the project and found in the clicsthree/datasets. txt file. The list follows the structure of standard requirements. txt files and the entire set can be installed with the standard pip utility.
The installation of the CLICS tools is the first step in the workflow for conducting colexification analyses. The following points describe the additional steps, and the entire workflow is illustrated in the diagram of Fig. 4. First, we assemble a set of CLDF datasets into a CLICS database. Once the database has been generated, a colexification graph can be computed. As already described when introducing CLICS  18  and CLICS2  34  , a colexification graph is an undirected graph in which nodes represent comparable concepts and edges express the colexification weight between the concepts they link: for example, wood and tree, two concepts that as already mentioned colexify in many languages, will have a high edge weight, while water and dog, two concepts without a single instance of lexical identity in our data, will have an edge weight of zero.
Second, we normalize all forms in the database. Normalized forms are forms reduced to more basic and comparable versions by additional operations of string processing, removing information such as morpheme boundaries or diacritics, eventually converting the forms from their Unicode characters to the closest ASCII approximation by the unidecode library  59. Third, colexifications are then computed by taking the combination of all comparable concepts found in the data and, for each language variety, comparing for equality the cleaned forms that express both concepts (the comparison might involve over two words, as it is common for sources to list synonyms). Information on the colexification for each concept pair is collected both in terms of languages and language families, given that patterns found across different language families are more likely to be a polysemy stemming from human cognition than patterns because of vertical transmission or random resemblance.
Cases of horizontal transmission ("borrowings") might confound the clustering algorithms to be applied in the next stage, but our experience has shown that colexifications are actually a useful tool for identifying candidates of horizontal transmission and areal features. Once the number of matches has been collected, edge weights are adjusted according to user-specified parameters, for which we provide sensible defaults. The output of running CLICS3 with default parameters, reporting the most common colexifications and their counts for the number of language families, languages, and words, is shown in Table  1. Finally, the graph data generated by the colexification computation, along with the statistics on the score of each colexification and the number of families, languages, and words involved, can be used in different quantitative analyses, e.
After clustering, we can obtain additional summary statistics with the clics graph-stats command: for standard CLICS3 with default parameters (and the seed 42 to fix the randomness of the random walk approach) and clustering with the recommended and default infomap algorithm, the process results in 1647 nodes, 2960 edges, 92 components, and 249 communities. The data generated by following the workflow outlined in 4 can be used in multiple different ways (see Section Usage-notes), e. g. for preparing a web-based representation of the computed data using the CLLD  65  toolkit.
Data Records CLICS3 is distributed with 30 different datasets, as detailed in Table  2 , of which half were added for this new release. Most datasets were originally collected for purpose of language documentation and historical linguistics, such as bodtkhobwa  49  (derived from  50  ), while a few were generated from existing lexical collections, such as logos  66  (derived from  18  ), or from previous linguistic studies, as in the case of wold 67 (derived from  68  ). We selected datasets for inclusion either due to interest for historical linguistics, to maximize the coverage of CLICS2 in terms of linguistic families and areas, or because of on-going collaborations with the authors of the studies.
Technical Validation To investigate to which degree our enhanced workflows would improve the efficiency of data creation and curation within the CLDF framework, we conducted two tests. First, we tested the workflow ourselves by actively searching for new datasets which could be added to our framework, noting improvements that could be made for third-party usage and public release. Second, we organized two student tasks with the goal of adding new datasets to CLICS, both involving the delegation of parts of the workflow to students of Linguistics. In the following paragraphs, we will quickly discuss our experiences with these tasks, besides presenting some detailed information on the notable differences between CLICS2 and the improved CLICS3 resulting from both tests.
Workflow validation. In order to validate the claims of improved reproducibility and the general validity of the workflow for preparing, adding, and analyzing new datasets, we conducted two student tasks in which participants at graduate and undergraduate level were asked to contribute to CLICS3 by using the tools we developed. The first student task was carried out as part of a seminar for doctoral students on Semantics in Contact, taught by M. Koptjevskaja-Tamm (MKT) as part of a summer school of the Societas Linguistica Europaea (August 2018, University of Tartu). The second task was carried out as part of an M. A. level course on Methods in Linguistic Typology, taught by V. Gast (VG) as a regular seminar at the Friedrich Schiller University (Jena) in the winter semester of 2018/2019. MKT's group was first introduced to CLICS2, to the website accompanying the CLICS project, and to the general ideas behind a colexification database.
This helped to shape a better understanding of what is curated in the context of CLICS. In a second step, we provided a task description tailored for the students, which was presented by MKT. In a shortened format, it comprised (1) general requirements for CLICS datasets (as described in previous sections), (2) steps for digitizing and preparing data tables (raw input processing), (  3  Table  2. Datasets included in CLICS3, along with individual counts for glosses ("Glosses"), concepts mapped to Concepticon ("Concepts"), language varieties ("Varieties"), language varieties mapped to Glottolog ("Glottocodes"), and language families ("Families"); new datasets included for the CLICS3 release are also indicated. Each dataset was published as an independent work on Zenodo, as per the respective citations.
linking (aided by semi-automatic mapping), (4) Glottolog linking (identifying languages with Glottocodes), (  5 ) providing bibliographic information with BibTeX, (6) providing provenance information and verbal descriptions of the data. The students were split into five groups of two people, and each group was tasked with carrying out one of the six tasks for a specific dataset we provided. The students were not given strict deadlines, but we informed them that they would be listed as contributors to the next update of the CLICS2 database if they provided the data up to two months after we introduced the task to them. While the students were working on their respective tasks, we provided additional help by answering specific questions, such as regarding the detailed mapping of certain concepts to Concepticon, via email.
All student groups finished their tasks successfully, with only minor corrections and email interactions from our side. The processed data provided by the students lead to the inclusion of five new datasets to CLICS3: castrosui  43  , a collection of Sui dialects of the Tai-Kadai family spoken in Southern China derived from  44  , halenepal  69  , a large collection of languages from Nepal derived from  70  , marrisonnaga 71 , a collection of Naga languages (a branch of the Sino-Tibetan family) derived from  72  , yanglalo 47 , a dataset of regional varieties of Lalo (a Loloish language cluster spoken in Yunnan, part of the Sino-Tibetan family) derived from  48  , and mitterhoferbena  73  , a collection of Bena dialects spoken in Tanzania derived from  74. A similar approach was taken by VG and his group of students, with special emphasis being placed on the difficulties and advantages of a process for collaborative and distributed data preparation.
They received instruction material similar to that of MKT's group, but more nuanced towards the dataset they were asked to work with, namely diacl 45 , a collection of linguistic data from 26 large language families all over the world derived from  46. Pre-processed data was provided by us and special attention was paid to the process of concept mapping. In summary, the outcome of the workflow proposed was positive for both groups, and the data produced by the students and their supervisors helped us immensely with extending CLICS3. Some students pointed us to problems in our software pipeline, such as missing documentation on dependencies in our installation instructions. They also indicated difficulties during the process of concept mapping, such as problems arising from insufficient concept definitions for linking elicitation glosses to concept sets.
CLICS3 validation. The technical validation of CLICS3 is based on functions for deconstructing forms and consequences of this for mapping and finding colexifications. If we compare the data status of CLICS2 with the amount of data available with the release of CLICS3, we can see a substantial increase in data, both regarding the number of languages being covered by CLICS3, and the total number of concepts now included. When looking at the detailed comparisons in Fig. 6 , however, we can see that the additions of data occurred in different regions of the world. While we note a major increase of data points in Papunesia, a point of importance for better coverage of "hot spots"  75  , and a moderate increase in Eurasia, the data is unchanged in Africa, North America, and Australia, and has only slightly increased in South America.
As can be easily seen from Fig. 7 , Africa and North America are still only sparsely covered in CLICS3. Future work should try to target these regions specifically. While this shows, beyond doubt, that our data aggregation strategy based on transparent workflows that create FAIR data was, by and large, successful, it is important to note that the average mutual coverage, which is defined as the average number of concepts for which two languages share a translation  34, 76  , is rather low. This, however, is not surprising, given that the original datasets were collected for different purposes. While low or skewed coverage of concepts is not a problem for CLICS, which is still mostly used as a tool for the manual inspection of colexifications, it should be made very clear that quantitative approaches dealing with CLICS2 and CLICS3 need to control explicitly for missing data.
Usage Notes The CLICS pipeline produces several artifacts that can serve as an entry point for researchers: a locally browsable interface, well-suited for exploratory research, a SQLite database containing all data points, languoids and additional information, and colexification clusters in the Graph Modelling Language (GML  77  ). The SQLite database can easily be processed with programming languages like R and Python, while the GML representation of CLICS colexification graphs is fully compatible with tools for advanced network analyses, e. g. Cytoscape  78. Researchers have the choice between different clustering algorithms (currently supported and implemented: highly connected subgraphs  79  , infomap or map equation  64  , Louvain modularity 80 , hierarchical clustering  81  , label propagation  82  , and connected component clustering  83  ) and can easily plug-in and experiment with different clustering techniques using a custom package (https://github.
Acknowledgements This research would not have been possible without the generous support by many institutes and funding agencies. TT, MSW, NES, YL, and JML were funded by the the  ERC Starting Grant 715618 Computer-Assisted Language Comparison  (http://calc. digling. org). SJG was supported by the  Australian Research Council's Discovery Projects funding scheme  (project number  DE 120101954 ) and the  ARC Center of Excellence  for the  Dynamics of Language grant  ( CE140100041 ). MKT was supported by the  Riksbankens Jubileums Fond  (Grant  SAB17-0588:1 ). TB was funded by the  Swiss National Science Foundation ,  P2BEP1_181779 , " Reconstruction of Proto-Western Kho-Bwa ". We are also very grateful for the help and data provided by many researchers, among them:  Cathryn Yang fordata on Lolo languages 47  ,  Andy Castro  fordata on Sui dialects 43 ,  Michael Cysouw  for help with the digitization of 41,97, 101  ,  Claire Bowern  fordata on Pama-Nyungan 91 ,  Gerd Carling  fordata from the  Diachronic Atlas of Comparative Linguistics  project  45  ,  Doug Cooper  fordata on Miao and Yao languages 93 , and the  STEDT  project for providing digital versions of the data underlying 70,72, 105.
) Concepticon Dataset Source Glosses Concepticon Varieties Glottocodes Families New 1 abrahammonpa 84 85 304 304 30 16 2 Yes 2 allenbai 86 87 499 499 9 9 1 3 bantubvd 88 89 420 415 10 10 1 4 beidasinitic 41 42 736 735 18 18 1 5 bodtkhobwa 49 50 553 536 8 8 1 Yes 6 bowernpny 90 91 338 338 175 172 1 7 castrosui 43 44 510 508 16 3 1 Yes 8 chenhmongmien 92 93 793 793 22 20 1 Yes 9 diacl 45 46 537 537 371 351 25 Yes 10 halenepal 69 70 699 662 13 13 2 Yes 11 hantganbangime 94 95 299 299 22 22 5 Yes 12 hubercolumbian 96 97 346 345 69 65 16 13 ids 98 99 1310 1308 320 275 60 14 kraftchadic 100 101 433 428 66 59 2 15 lexirumah 102 103 604 602 357 231 12 Yes 16 logos 66 18 707 707 5 5 1 Yes 17 marrisonnaga 71 72 580 572 40 39 1 Yes 18 mitterhoferbena 73 74 342 335 13 13 1 Yes 19 naganorgyalrongic 104 105 969 877 10 8 1 Yes 20 northeuralex 56 57 952 951 107 107 21 21 robinsonap 106 107 391 391 13 13 1 22 satterthwaitetb 51 52 418 418 18 18 1 23 sohartmannchin 108 109 279 279 8 7 1 Yes 24 suntb 54 55 929 929 49 49 1 25 tls 110 111 1140 811 126 107 1 26 transnewguineaorg 112 113 904 865 1004 760 106 Yes 27 tryonsolomon 114 115 317 314 111 96 5 28 wold 67 68 1459 1458 41 41 24 29 yanglalo 47 48 875 869 7 7 1 Yes 30 zgraggenmadang 116 117 311 310 98 98 1 TOTAL 2906 3156 2271 200.
We propose here an original methodological study looking at departures from bilateral symmetry in different anatomical areas, the endocast and the humeri, in relation with biological and behavioral influences. We will investigate the anatomical asymmetries of the endocast, specifically the petalias because they are known to be related to manual lateralization, as well as the internal parameters of pairs of humeri. In order to do this, we will analyze CT data for an archaeological sample that preserves associated skulls and pairs of humeri. We will, therefore, test if we can identify potential variation and correlations in directional asymmetries for the brain endocasts and humeri which are known indicators of brain/manual lateralization. A second, and larger, objective will be to explore patterns of variations in fluctuating asymmetry for each of the anatomical traits analyzed, in order to discuss possible variation in the respective biological responses of the brain and arms to lateralized behaviors.
In order to analyze the association between the asymmetries of the endocast and arms, the skulls and pairs of humeri belonging to 32 adult individuals were scanned using computed tomography (CT). All material was scanned in the same cranio-caudal position with a standard protocol (0. 625 × 0. 625 × 0. 625 mm 3 cubic voxels), using 32-slice CT (Siemens SOMATOM Sensation). Next, for each skull, a reconstruction of the endocast was prepared using the endomaker R package  [50, 51].
We then searched for possible aberrant values in our database of relative measurements. Grubbs' test statistic identified two significant outlier data within our database of size-corrected measurements (7. 78, p = 0. 035 for the vertical component of the occipital petalia in one individual and 11. 41, p = 0. 012 for Imax/Imin in another). Those data were excluded from subsequent analyses of asymmetry, explaining why N is only 31 for these two parameters and 32 for all the others (Table  2 ). We tested the sample for putative sex-related variation and found that the means for males and females do not differ (Hotelling's T-squared tests) for any of the analyzed traits. As a result, all the following analyses are done on the complete pooled sample. Several parameters were considered to describe the variation for each of the analyzed traits (Table  2 ) and the distribution for each trait has been explored and is illustrated (Figures  2  and 3 ).
Acknowledgments: The authors thank the reviewers for their helpful comments. We thank  Anna Wrzesi  ńska from the  Museum of the First Piasts at Lednica , for giving the possibility to use skeletal material from  Ostrów Lednicki. They also thank  Dariusz Kurkiewicz  for technical support in scanning the osteological material and  Antonio Profico  for help in improving the methodology of 3D endocast reconstruction. Anna Maria Kubicka  thanks the  Polish National Agency for Academic Exchange (NAWA)  for the scholarship at the  Musée de l'Homme , which allowed to conduct this research.