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This dataset will contain all the data collected from observation and tracking location of the users participating in the experiments using the CROSSCULT Pilot 1app. It will include information about the users’ interaction with the NG collection information. The dataset will comprise anonymised information about the users who will participate in the NG visits, using the CROSSCULT app. Additionally, for users who give their permission, information can be automatically extracted/retrieved from their devices location. User agreement must be obtained before tracking and processing data about the user. The dataset generated from the observation will provide information on user behaviour. The exact make-up of the fields included in this dataset will be determined as part of the work carried out within CROSSCULT.

Nálezy z archeologického výzkumu v Březnici (okr. Tábor) v letech 2005-2009 a 2019. Výzkum provedl O. Chvojka (Archeologický ústav FF JU v Českých Budějovicích). Data zahrnují údaje o keramice a dalších nálezech použitých k depoziční analýze sídlištních objektů mladší doby bronzové, zejména tzv. žlabů. Výsledky analýzy jsou publikovány v Chvojka et al. 2021. Popis databáze je obsažen v přiloženém PDF souboru. Podpořeno Grantovou agenturou ČR (18-10747S). Finds from the archaeological excavations in Březnice (Tábor district, South Bohemia, Czech Republic) in 2005-2009 and 2019. The fieldwork was directed by O. Chvojka (Institute of Archaeology, South Bohemian University in České Budějovice). Data concern the pottery fragments and other finds (daub, loom weights) used for the analysis of deposition processes in the Late Bronze Age settlement features. Based on this material, a model of house biography and the concept of closing rituals were formulated (see Chvojka et al. 2021). These models suggest an interpretation for the so-called trenches, specific sunken features filled with an unusually rich content of secondary-burnt pottery and other finds. Details of the database are given in the attached PDF file. Supported by the Czech Sceince Foundation (18-10747S). Chvojka, O. – Kuna, M. – Menšík, P. et al. 2021: Rituály ukončení a obnovy. Sídliště mladší doby bronzové v Březnici u Bechyně – Rituals of termination and renewal. The Late Bronze Age settlement in Březnice near Bechyně. České Budějovice – Praha – Plzeň. ISBN 978-80-7394-899-3; ISBN 978-80-7581-039-7; ISBN 978-80-261-1083-5.

The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs). Dataset Information In support of the global effort to achieve the Sustainable Development Goals (SDGs), OSDG is realising a series of SDG-labelled text datasets. The OSDG Community Dataset (OSDG-CD) is the direct result of the work of more than 1,000 volunteers from over 110 countries who have contributed to our understanding of SDGs via the OSDG Community Platform (OSDG-CP). The dataset contains tens of thousands of text excerpts (henceforth: texts) which were validated by the Community volunteers with respect to SDGs. The data can be used to derive insights into the nature of SDGs using either ontology-based or machine learning approaches. 📘 The file contains 40,067 text excerpts and a total of 277,524 assigned labels. Source Data The dataset consists of paragraph-length text excerpts derived from publicly available documents, including reports, policy documents and publication abstracts. A significant number of documents (more than 3,000) originate from UN-related sources such as SDG-Pathfinder and SDG Library. These sources often contain documents that already have SDG labels associated with them. Each text is comprised of 3 to 6 sentences and is about 90 words on average. Methodology All the texts are evaluated by volunteers on the OSDG-CP. The platform is an ambitious attempt to bring together researchers, subject-matter experts and SDG advocates from all around the world to create a large and accurate source of textual information on the SDGs. The Community volunteers use the platform to participate in labelling exercises where they validate each text's relevance to SDGs based on their background knowledge. In each exercise, the volunteer is shown a text together with an SDG label associated with it – this usually comes from the source – and asked to either accept or reject the suggested label. There are 3 types of exercises: All volunteers start with the mandatory introductory exercise that consists of 10 pre-selected texts. Each volunteer must complete this exercise before they can access 2 other exercise types. Upon completion, the volunteer reviews the exercise by comparing their answers with the answers of the rest of the Community using aggregated statistics we provide, i.e., the share of those who accepted and rejected the suggested SDG label for each of the 10 texts. This helps the volunteer to get a feel for the platform. SDG-specific exercises where the volunteer validates texts with respect to a single SDG, e.g., SDG 1 No Poverty. All SDGs exercise where the volunteer validates a random sequence of texts where each text can have any SDG as its associated label. After finishing the introductory exercise, the volunteer is free to select either SDG-specific or All SDGs exercises. Each exercise, regardless of its type, consists of 100 texts. Once the exercise is finished, the volunteer can either label more texts or exit the platform. Of course, the volunteer can finish the exercise early. All progress is saved and recorded still. To ensure quality, each text is validated by up to 9 different volunteers and all texts included in the public release of the data have been validated by at least 3 different volunteers. It is worth keeping in mind that all exercises present the volunteers with a binary decision problem, i.e., either accept or reject a suggested label. The volunteers are never asked to select one or more SDGs that a certain text might relate to. The rationale behind this set-up is that asking a volunteer to select from 17 SDGs is extremely inefficient. Currently, all texts are validated against only one associated SDG label. Column Description doi - Digital Object Identifier of the original document text_id - unique text identifier text - text excerpt from the document sdg - the SDG the text is validated against labels_negative - the number of volunteers who rejected the suggested SDG label labels_positive - the number of volunteers who accepted the suggested SDG label agreement - agreement score based on the formula \(agreement = \frac{|labels_{positive} - labels_{negative}|}{labels_{positive} + labels_{negative}}\) Further Information To learn more about the project, please visit the OSDG website and the official GitHub page. Do not hesitate to share with us your outputs, be it a research paper, a machine learning model, a blog post, or just an interesting observation. All queries can be directed to community@osdg.ai. This CSV file uses UTF-8 character encoding. For easy access on MS Excel, open the file using Data → From Text/CSV. Please split CSV data into different columns by using a TAB delimiter.

Motivo desenhado a partir de ortofoto gerado no Agisoft, com o modelo 3D de levantamento fotogramétrico, no âmbito do Project RARAA- Repositório de Arte Rupestre de Acesso Aberto - COA/OVD/0097/2019 Escudo tipo "Caetra" a ser utilizado pelo guerreiro em combate com o cavaleiro - Sítio da Vermelhosa, Rocha 3, Côa Valley, Portugal

Gastrophryne carolinensis calling, 08 July 2003.

UK Biobank custom height association statistics on ~700k genotyped SNPsThe zip file contains six files: (1) ukb_cal_v2_height_allancestry_10pcs_assoc_linear.tsv (2) ukb_cal_v2_height_allancestry_nopcs_assoc_linear.tsv (3) ukb_cal_v2_height_britishancestry_10pcs_assoc_linear.tsv (4) ukb_cal_v2_height_britishancestry_nopcs_assoc_linear.tsv (5) ukb_cal_v2_height_sibs_perm_qfam.tsv (6) ukb_cal_v2_height_wbsibs_perm_qfam.tsv (1) - (4) are height GWAS estimates on all samples / white British samples using 10 PCs as covariates or no PCs as covariates. Sex was included as covariate in all analyses. (3) is equivalent to the UK Biobank height GWAS from the Neale lab. The remaining small differences can be explained by genotype differences in the UK Biobank imputed data and genotyped data. (5) and (6) are family based estimates from 20166 sibling pairs of any ancestry (5) and 17358 sibling pairs where both siblings are of white British ancestry (6) in the UK Biobank. Pairs of samples with IBS0 > 0.0018 and Kinship coefficient > 0.185 were identified as sibling pairs. For the analyses in Sohail, Maier et al., only the subset of ~300,000 SNPs with SDS scores was used. For a description of the columns in files (1)-(4) please see the PLINK documentation for the ‘--linear’ command. Column “A2” has been added and denotes the non-effect allele. For a description of the columns in files (5) and (6) please see the PLINK documentation for the ‘--qfam’ command. Column “A2” has been added and denotes the non-effect allele. “EMP1” and “NP” refer to permutation p-value and number of permutations, respectively. Please note: These data are derived from the UK Biobank Resource under Application Number 18597.sohail_maier_2018.zip Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure. More generally, our results imply that typical constructions of polygenic scores are sensitive to population structure and that population-level differences should be interpreted with caution.

DgRv-3 is a Marpole phase village site (1700-1400 BP) within the Dionisio Point Provincial Park on Galiano Island, BC (Grier 2001). The site consists of five plank house depressions that form a terraced landscape on a northern inlet coast of the island. Three terraces, containing 4 of the 5 house depressions (Houses 2-5), are separated by elevations of 2 to 3 meters, while the lowest and most remote house (House 1) is situated just above a gravel beach to the northeast. Shell middens form the built-up ridges surround the house perimeters. During this project, Patrick Dolan and Colin Grier conducted magnetic gradiometery over Houses 1, and Houses 3 through 5. House 2 was not surveyed due to magnetic interference from metal and the extensive excavations. An additional small grid (Grid 1) was surveyed along the high midden deposit ridge to the east. Electromagnetic surveys were also run on small areas of Houses 1, 3 and 5. This upload contains the processed photogrammetric data for Unit 25. The .zip file contains the raw files for each output as well as an Index.txt file listing all files included in the .zip file. SPARC is currently supported by NSF Award #1822110.

Spanish Clinical Word Embeddings in FastText These embeddings have been generated from the largest corpus ever made from Spanish Clinical resources till the date. Citation @misc{temu2021spanish, title={Spanish Biomedical and Clinical Language Embeddings}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Casimiro Pio Carrino and Ona De Gibert and Aitor Gonzalez-Agirre and Marta Villegas}, year={2021}, eprint={2102.12843}, archivePrefix={arXiv}, primaryClass={cs.CL} } Copyright Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).

The 2022 dataset contains 537,262 cleaned and streamlined Latin inscriptions from the Epigraphic Database Clauss Slaby (EDCS, http://www.manfredclauss.de/), aggregated on 2022/09/12, created for the purpose of a quantitative study of epigraphic trends by the Social Dynamics in the Ancient Mediterranean Project (SDAM, http://sdam.au.dk). The dataset contains 27 attributes with original and streamlined data. Compared to the 2021 dataset, there are 36,726 more inscriptions and 2 fewer attributes containing redundant legacy data, thus the entire dataset is approximately the same size but some of the attributes are streamlined (465.5 MB in 2022 compared to 451.5 MB MB in 2021.): some of the attribute names have changed for better consistency, e.g. `Material` > `material`, `Latitude` > `latitude`; some attributes are no longer available due to the improvements of the LatEpig tool, e.g. `start_yr`, `notes_dating`, `inscription_stripped_final`; and some new attributes were added due to the improvements of the cleaning process, e.g. `clean_text_conservative`. For a complete overview, see the `Metadata` section. EDCS 2022 dataset metadata (https://github.com/sdam-au/EDCS_ETL/blob/master/EDCS_2022_dataset_metadata_SDAM.csv) with descriptions for all attributes. The full lifecycle of the transformation process, including programmatical access, modifications, and streamlining of the original dataset is documented by a sequence of Python and R scripts (https://github.com/sdam-au/EDCS_ETL). The dataset is stored as JSON file, ensuring compatibility both with Python and R. The scripts used to generate the dataset and their metadata are available via GitHub: https://github.com/sdam-au/EDCS_ETL.

Titel: Recueil chronologique de tous les placards, édits, décrets, réglemens, ordonnances, instructions et traités, concernans les titres & marques d'honneur ou de noblesse [...] depuis l'année 1431 jusqu'au mois de mai 1785, recueillis la plupart des editions originales [...] qui se trouvent dans une [...] collection [...] que possède l'imprimeur de ce recueil (Volume 2) Publisher: Jacq. Fr. Willerval Place: Douay. Year: 1730. Used version: The copy we used for the transcriptions is held at Ghent University. Link digitised version of the book: https://books.google.nl/books?id=H0FiAAAAcAAJ&dq=Recueil+des+%C3%A9dits,+d%C3%A9clarations,+arrests+et+reglemens+qui+sont+propres+et+particuliers+aux+provinces+du+ressort+du+parlement+de+Flandres&hl=nl&source=gbs_navlinks_s (Main) Language: French. Province: Flanders. Font: Roman. Model used: French_18thC_Print (public model in Transkribus) Version of Transkribus used: v.1.9.1. Other info: Abbyy FineReader v.11 has been used. Link model: For more information on the HTR-model used, please visit: https://lab.kb.nl/dataset/entangled-histories-ordinances-low-countries. Transcription conventions: The abbreviations have been written out into full words. The hyphens at the end of a line have been kept (when there). If you are in need of the original scans of the documents, please contact dataservices@kb.nl. This transcription is part of the dataset created with the ‘Entangled Histories’-project. PI: dr. C.A. Romein; Scientific Programmer: S.F. Veldhoen, MSc; Project Manager: drs. M. de Gruijter.