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Anonymized responses to the ARIADNEplus questionnaire to gather information for the aggregation of metadata about archaelogical resources to be included in the ARIADNEplus Knowledge Base and portal (https://portal.ariadne-infrastructure.eu/). The csv includes only the plain responses as provided by 31 archaelogical content providers until 18 October 2021. The excel file includes also two additional sheets where the responses about the formats and the aggregation update schedule have been normalised. The responses are discussed in deliverable D12.4 "Final report on data integration" currently under preparation.

MALDI-TOF-MS spectra of extracted collagen from modern reference and archaeological bone samples to develop markers for Zooarchaeology by Mass Spectrometry (ZooMS) to distinguish between Equus species. For each sample digestions were done in both trypsin and chymotrypsin separately. Information about the species of the samples can be found in 'sample metadata.csv' file. Information on the extraction and digestion protocol can be found in the associated manuscript. The sequence data contains alignments of the proteins COL1A1 and COL1A2 for available Equus collagen protein sequences. More information on these files can be found in the corresponding manuscript to this dataset.

SILKNOW Multimodal Cultural Heritage Dataset. Includes text descriptions, images, labels, and predictions made by individual modality classifiers. The data resulted from an export of the SILKNOW Knowledge Graph. See: https://zenodo.org/record/5743090 Repository with code using this dataset available at: https://github.com/silknow/multimodal_cultural_heritage

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. Work within the SSHOC project aimed to reformat the data to create a more FAIR data-set, 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. This is a draft data-set and further work is planned to debug and improve its semantic structure.This deposit contains the CIDOC-CRM mapped data formatted in XML and an example model diagram representing some of the key relationships covered in the data-set. Live access to this data, with documentation and worked examples, 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, In 2021/21 as part of the SSHOC Project the raw data stored within the system was mapped to the CIDOC CRM using a custom set of Python scripts (https://doi.org/10.5281/zenodo.6461654). 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. This live data presentation can been found Here. This deposit contains the CIDOC-CRM mapped data formatted in XML and an example model diagram representing some of the key relationships covered in the data-set. Live access to this data, with documentation and worked examples, can be found at: https://rdf.ng-london.org.uk/sshoc

HIPE-2022 datasets used for the HIPE 2022 shared task on named entity recognition and classification (NERC) and entity linking (EL) in multilingual historical documents. HIPE-2022 datasets are based on six primary datasets assembled and prepared for the shared task. Primary datasets are composed of historical newspapers and classic commentaries covering ca. 200 years, feature several languages and different entity tag sets and annotation schemes. They originate from several European cultural heritage projects, from HIPE organizers’ previous research project, and from the previous HIPE-2020 campaign. Some are already published, others are released for the first time for HIPE-2022. The HIPE-2022 shared task assembles and prepares these primary datasets in HIPE-2022 release(s), which correspond to a single package composed of neatly structured and homogeneously formatted files. Primary datasets undergo the following preparation steps: conversion to the HIPE format (with correction of data inconsistencies and metadata consolidation); rearrangement or composition of train and dev splits. Please also refer to: HIPE-2022 shared task website: https://hipe-eval.github.io/HIPE-2022/ HIPE-2022 data repository: https://github.com/hipe-eval/HIPE-2022-data Here is an overview of the primary datasets: Dataset alias Readme Document type Languages Suitable for Project hipe2020 link historical newspapers de, fr, en NERC-Coarse, NERC-Fine, EL CLEF-HIPE-2020 newseye link historical newspapers de, fi, fr, sv NERC-Coarse, NERC-Fine, EL NewsEye sonar link historical newspapers de NERC-Coarse, EL SoNAR letemps link historical newspapers fr NERC-Coarse, NERC-Fine LeTemps topres19th link historical newspapers en NERC-Coarse, EL Living with Machines ajmc link classical commentaries de, fr, en NERC-Coarse, NERC-Fine, EL AjMC The HIPE-2022 team expresses her greatest appreciation to the partnering projects, namely AJMC, impresso, HIPE-2020, Living with Machines, NewsEye, and SoNAR, for contributing their NE-annotated datasets (and hiding a part thereof for the time of the evaluation campaign). New releases are planned. Check the HIPE-2022 website for updates.

These datasets have been generated as part of rurAllure project (funded by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no 101004887). Main goal of rurAllure is the promotion of rural museums and heritage sites in the vicinity of European pilgrimage routes: https://rurallure.eu/project/about/ Dataset will be updated regularly.

Since 2012, the WageIndicator Foundation has maintained a Collective Agreements Database, where the texts of 1600 collective agreements (CBAs) from 61 countries and in 27 languages have been uploaded, coded and annotated. This database is a unique example at global level: collective agreements are documents containing conditions of employment that result from negotiations between independent unions and employers, and their content is often surrounded by an atmosphere of secrecy. Under the SSHOC project and with the support of the CLARIN Research Infrastructure, the agreements have been manually and automatically annotated on several levels: for each agreement, the team answers a series of questions and selects the appropriate piece of text (clause) for each. One of the results of the collective agreements' annotation process is the dataset which is available here and includes all the clauses selected for each variable (WageIndicator_CBADatabase_Selected_Clauses). The full collective agreements' texts are stored in another dataset, also available here (WageIndicator_CBADatabase_Full_Texts_211019). A codebook is also included (210125-wageindicator-cba-codebook.pdf). {"references": ["Cite the full texts dataset: WageIndicator Collective Agreements Database - Full Texts Dataset . Amsterdam, WageIndicator Foundation, October 2021.", "Cite the selected clauses dataset: WageIndicator Collective Agreements Database - Selected Clauses Dataset . Amsterdam, WageIndicator Foundation, October 2021.", "Cite the codebook: Ceccon, D., Medas, G. (2021). Codebook WageIndicator Collective Agreements Database \u2013 Version 4 \u2013 January 2021. WageIndicator Foundation, Amsterdam."]}

This dataset contains temperature measurements in Celsius degrees collected at the church of Saint Thomas and Saint Philip Neri in Valencia (Spain) by multiple wireless sensors. The data were collected from August 2017 to March 2019 and aligned at the same time point using linear interpolation.

This documentation is a supplement of D3.5 Report on citation enabled SSH catalogues and SSH citation exploitation