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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.

: Data type: molecular data

Whether man is predisposed to lethal violence, ranging from homicide to warfare, and how that may have impacted human evolution, are among the most controversial topics of debate on human evolution. Although recent studies on the evolution of warfare have been based on various archaeological and ethnographic data, they have reported mixed results: it is unclear whether or not warfare among prehistoric hunter–gatherers was common enough to be a component of human nature and a selective pressure for the evolution of human behaviour. This paper reports the mortality attributable to violence, and the spatio-temporal pattern of violence thus shown among ancient hunter–gatherers using skeletal evidence in prehistoric Japan (the Jomon period: 13 000 cal BC–800 cal BC). Our results suggest that the mortality due to violence was low and spatio-temporally highly restricted in the Jomon period, which implies that violence including warfare in prehistoric Japan was not common. ESM for Violence in Japanese prehistoryDefinition of and sources of data for injured individuals in the Jomon period, and detailed information of all sites where skeletal remains have been recovered.supplement_corrected_final.docx

Parsed AMR files of the FIN10K dataset used in FLAG experiments.

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

Figure 3 Venn diagram of economic value type for cultivars.

This is a small hand-annotated partial treebank of Modern Tibetan, primarily in CoNLL-U format. Some texts were POS-tagged by machine, and then dependency relations between verbs and their arguments were added by hand. Other texts include only dependency relations and relevant POS-tags. A number of the texts have English translations which have been manually aligned to the Tibetan text. This work was created as part of the AHRC-funded project Lexicography in Motion (PI Ulrich Pagel, 2017-2021). Funded by the UK's Arts and Humanities Research Council (grant code: AH/P004644/1)

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.

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.