This is the initial release of the research compendium for the article by Tsirintoulaki, K., Matzig, D.N., Riede, F. (2022) "A 2D geometric morphometric assessment of chrono-cultural trends in osseous barbed points of the European Final Palaeolithic and Early Mesolithic" submitted to Open Archaeology. It contains all data and code, and will generate the results as found in the publication. The files hosted at https://github.com/yesdavid/Osseous_Barbed_Points_2D_GMM are the development versions and may have changed since the publication.
It-Sr-NER tool is a CLARIN compatible NER web service for parallel texts with case study on Italian and Serbian; it can be used for recognizing and classifying named entities in bilingual natural language texts. Input parallel texts should be TMX (Translation Memory eXchange) files, e.g. Sr-It. It-Sr-NER can recognize six NER classes: demonyms (DEMO), works of art (WORK), person names (PERS), places (LOC), events (EVENT) and organisations (ORG). The service can also be used for monolingual text NER annotation for available spaCy NER models. It-Sr-NER uses a CNN architecture within the spaCy tool and Named Entity linking with Wikidata using spaCyOpenTapioca. For further details: API usage is described in: http://ners.jerteh.rs/4api A Postman example is available at https://github.com/rankastankovic/It-Sr-NER/blob/main/static/Postman_call_ners-mono.PNG
Project: EC | Persia and Babylonia (682241), EC | Persia and Babylonia (682241)
This release contains two files with R scripts for analysing "A Dataset Documenting Representations of Machine Vision Technologies in Artworks, Games and Narratives". The first provides general import and reformatting of the data, and the second contains scripts for plotting figures showing geographical distribution of the works in dataset, and showing distribution in time.
SPARQL Endpoint query and data presentation examples for the SSHOC Heritage Science Datasets This code is used to create the National Gallery SSHOC Data Site, which presents data formatted as part of a task entitled '''Issues in providing Open Data in Heritage Science''' (T5.6) within the SSHOC Horizon-2020 project. This work, led by the National Gallery, has explored increasing the accessibility and interoperability of existing Heritage Science data sets and making them more FAIR. A live version of this software can be found at: https://rdf.ng-london.org.uk/sshoc
This is an interactive live modelling system which can automatically convert simple tab separated triples or JSON-LD into graphical models using the mermaid library. General updates and improvements have been carried out, with the addition of the Mermaid SubGraph functionality to aid the organisation of more complex models. This version includes several small improvements related to the work carried out in the SSHOC project, particularly in relation to ensuring that the results of SPARQL queries carried out via the SSHOC National Gallery Data presentation system can be automatically displayed as graphical relational models. Instructions: Several worked examples, including some simple instructions relating to how this system works are included in the interactive web site: dynamic modelling, with all of the related code presented in GitHub.
In 2018 the IPERION-CH Grounds Database was presented to examine how the data produced through the scientific examination of historic painting preparation or grounds samples, from multiple institutions could be combined in a flexible digital form. Exploring the presentation of interrelated high resolution images, text, complex metadata and procedural documentation. The original main user interface is live, though password protected at this time. The SSHOC work aimed to make this data more FAIR so in addition to mapping it to a standard ontology, to increase interoperability, it has also been made available in the form of open linkable data combined with a SPARQL end-point. A draft version of this live data presentation can been found Here. A live version of the mapped data created with this software can be found at: https://rdf.ng-london.org.uk/sshoc
In 2007 the Raphael Research Resource project began to examine how complex conservation, scientific and art historical research could be combined in a flexible digital form. Exploring the presentation of interrelated high resolution images and text, along with how the data could be stored in relation to an event driven ontology in the form of RDF triples. The original main user interface is still live and the data stored within the system is presented here in the form of open linkable data combined with a SPARQL end-point. An open version of this live data presentation, complete with a range of example queries can been found Here. A live version of the mapped data created with this software can be found at: https://rdf.ng-london.org.uk/sshoc
The archive NERD provides a collation of 11,081 radiocarbon dates from 1027 archaeological sites in the Near East from the Late Pleistocene until the Late Holocene (15 - 1.5 cal. kyr BP). These dates have been collected from existing online digital archives, and electronic and print original publications. This is an ongoing dataset that will be updated step by step with new published radiocarbon dates. A detailed description of the present dataset is available via the following data paper: Palmisano, A., Bevan, A., Lawrence, D., and Shennan, S., 2022. The NERD Dataset: Near East Radiocarbon Dates between 15,000 and 1,500 cal. yr. BP. Journal of Open Archaeology Data, 10(2), 1-9. List of versions: 5.0 25 March 2022 — 54 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md'). 4.0 8 December 2021 — 380 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md'). 3.0 7 April 2021 — 10 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md'). 2.0. 7 April 2021 — 31 new dates added (update of the files 'References.txt', 'nerd.csv', and 'Readme.md'). 1.0 6 April 2021 — First public release of the dataset on Zenodo
Publisher: Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)
Project: EC | SSHOC (823782)
En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip). Their main use should be in-domain translation of social surveys. Models are compatible with Tensor2tensor version 1.6.6. For details about the model training (data, model hyper-parameters), please contact the archive maintainer. Evaluation on MCSQ test set (BLEU): en->de: 67.5 (train: genuine in-domain MCSQ data only) de->en: 75.0 (train: additional in-domain backtranslated MCSQ data) (Evaluated using multeval: https://github.com/jhclark/multeval)