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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.

Figure 4 - Impressions from a poster session organized during the 1st European Conference on Echinoderms, held 1979 in Brussels. (A) Jan J. S. Broertjes, Haruo Kanatani, Peter A. Voogt. (B) Guy Coppois, Chantal De Ridder, Edith Bricourt, Lucie Jangoux, Robert Fenaux, Lucienne Fenaux. (C) Mrs. Monteiro Marques, Vasco M. A. Monteiro Marques, Gustave Cherbonnier, Lucie Jangoux, Solveig Sjögren. (D) Alfred Goldschmid (background), Tim Bowmer, Claude Massin, Jean-Pierre Féral, John Costelloe, Brendan O'Connor. (E) Richard M. Pagett, Iain C. Wilkie, Alan M. Raymond, Roland H. Emson. (F) Raimundo Pidal, Ricard Martínez, Enrico Tortonese, F. Jensenius Madsen, Bent Hansen.

Figure 5 - Conference logos from selected national, regional, and international echinoderm meetings held from 1978 to 2013. (A) 3rd International Echinoderm Conference, Sydney, Australia, 1978. (B) 5th Florida Echinoderm Festival, Tampa, Florida, USA, 1999. (C) 1. Arbeitstreffen deutschsprachiger Echinodermenforscher, Greifswald, Germany, 2001. (D) International Conference on Sea-Urchin Fisheries and Aquaculture, Puerto Varas, Chile, 2003. (E) 3. Arbeitstreffen deutschsprachiger Echinodermenforscher, Ingelfingen, Germany, 2004. (F) ACIAR-SPC Asia-Pacific Tropical Sea Cucumber Aquaculture Symposium, Nouméa, New Caledonia, 2011. (G) 2° Congreso Latinoamericano de Equinodermos, São Sebastião, Brazil, 2013. (H) 5. Arbeitstreffen deutschsprachiger Echinodermenforscher, Stuttgart, Germany, 2013.

Figures 25-34 - Diversity of Nepticulidae, all on same scale. 25 Enteucha acetosae, male, Austria 26 Stigmella mespilicola, male, Switzerland, holotype 27 Roscidotoga callicomae, female paratype, Australia, NSW 28 Menurella libera, male holotype, Australia, NSW 29 Pectinivalva caenodora, male holotype, Australia, NSW 30 Glaucolepis lituanica , male, Austria 31 Bohemannia auriciliella, male, The Netherlands 32 Trifurcula iberica, male paratype, Spain 33 Fomoria weaveri, female, Sweden 34 Parafomoria helianthemella, female, Czech Republic. Scale 1 mm. Watercolours by Roland Johansson, 25, 26, 31 and 33 published earlier by Johansson et al. (1990), 28 and 29 by Hoare et al. (1997). The left wings of 32 and 34 are digitally mirrored images of the right wings. These figures may be reproduced given that their author Roland Johansson and the present publication are credited.

This data set contains relative humidity measurements obtained with sensors installed in the apse vault of the Cathedral of Valencia in Spain. The interest of these sensors is to monitor the conservation conditions of Renaissance frescoes. Included files are: Cathedral_of_Valencia_RH_2008.csv : Relative humidity measurements for the year 2008 Cathedral_of_Valencia_RH_2010.csv : Relative humidity measurements for the year 2010

Figure 3 Venn diagram of economic value type for cultivars.

Information layer "road names" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format

Slice created from parent data set: Full Virginia Girls' Reformatory Admissions Database 1910-1938 Data set of 2,370 individual female reformatory inmates admitted to the Virginia Home and Industrial School for Girls at Bon Air and the Industrial Home School for Colored Girls at Peake’s Turnout between 1910 and 1938. Created out of the unpublished and archived admissions books of these institutions. Due to Virginia’s 75-year privacy restriction, I stopped collecting at December 1938 for each institution. Data from the Home at Bon Air runs 1910 to 1938; from the Home at Peake’s Turnout from 1915 to 1938. Each reformatory kept separate books, which were archived into separate collections. There was enough similarity between the two books to transcribe the data into one large data set. Reformatory administrators hand wrote basic administrative information about each girl into bound books. For incoming delinquent girls, they recorded: student number, name, birthdate or age at admittance, date of admittance, and committing jurisdiction (by county or city jurisdiction.) The books also recorded the individual’s parole history, including first parole (and up to her third parole on an individual’s performance) and any return dates; administrators recorded the destination of the first parole, but this was inconsistently recorded. The books note when and to whom inmates were married, usually after their official dismissal. Because transferring an inmate officially removed them from the responsibility of the reformatory, administrators recorded transfer information, including where they went and when. Lastly, the books recorded the official dismissal date and reason. Bon Air’s books were more consistent with recording dismissals and neither institution used consistent definitions of “transfer” versus “dismissal.” I manually transcribed these books verbatim into a database. From this core data, I added categories of information to aid my analysis. These include: race, gender, and reformatory; calculations of either age or birthdate (Peake’s recorded birthdates, Bon Air only ages); parole year taken from the first parole date; parole type determined by me based on the parole destination, if recorded. To help me analyze transfer and dismissal information, I determined the “type” and “category” of each transfer or dismissal and added new categories. These allowed me to “rollup” the varieties of recorded data into fewer descriptive types and categories. Because administrators recorded only the institution name or location when girls were transferred and dismissed elsewhere, this allowed me to organize this info into 18 “types”: asylum, colony, court, death, department of public welfare, escape, family, honorable discharge, illegal commitment, maternity, orphanage, other, penal, private, reorganization (only used for Bon Air in 1914), sanitorium/hospital, venereal disease, and wages. These 18 “types” were then further distilled into 9 “categories” to capture the broadest possible categorization of the reasons why girls were transferred or dismissed: administrative, death, escape, mental, penal, physical, private, unknown, and work. I have removed the names of the individual inmates upon publication. Researchers interested in using this data in their own work can contact me at erin@erinbush.org to request the versions that include full name fields. The Data Dictionary is also available upon request. The contents of these data sets, as government records, I believe fall under fair use.

The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by over 1,400 OSDG Community Platform (OSDG-CP) citizen scientists from over 140 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 1,400 volunteers from over 130 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 42,065 (+376) text excerpts and a total of 301,314 (+3,689) assigned labels. To learn more about the project, please visit the OSDG website and the official GitHub page. Explore a detailed overview of the OSDG methodology in our recent paper "OSDG 2.0: a multilingual tool for classifying text data by UN Sustainable Development Goals (SDGs)". 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 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.

We analyse new genomic data (0.05–2.95x) from 14 ancient individuals from Portugal distributed from the Middle Neolithic (4200–3500 BC) to the Middle Bronze Age (1740–1430 BC) and impute genomewide diploid genotypes in these together with published ancient Eurasians. While discontinuity is evident in the transition to agriculture across the region, sensitive haplotype-based analyses suggest a significant degree of local hunter-gatherer contribution to later Iberian Neolithic populations. A more subtle genetic influx is also apparent in the Bronze Age, detectable from analyses including haplotype sharing with both ancient and modern genomes, D-statistics and Y-chromosome lineages. However, the limited nature of this introgression contrasts with the major Steppe migration turnovers within third Millennium northern Europe and echoes the survival of non-Indo-European language in Iberia. Changes in genomic estimates of individual height across Europe are also associated with these major cultural transitions, and ancestral components continue to correlate with modern differences in stature. Index for VCF fileIndex for VCF filepost_imputation_Martiniano_et_al_2017_public.vcf.gz.tbiVCF file containing imputed genotype data belonging to 67 newly sequenced and publicly available ancient samples.VCF file containing imputed genotype data belonging to 67 newly sequenced and publicly available ancient samples which we analysed in Martiniano et al. (2017).post_imputation_Martiniano_et_al_2017_public.vcf.gzREADME_Martiniano_et_al_2017Description of the methods used for genotype imputation.