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This dataset contains audio files and transcripts in Italian and related to manufacturing. We collected the scripts during the Horizon Europe RIA COALA (GA 957296, project reference website) from industrial use cases and hired a service provider to generate the related audio files (BIBA - Bremer Institut für Produktion und Logistik GmbH ordered the service). The service provider checked the audio files for quality. The service provider recruited crowd workers, and gathered their audio records, informed consent (privacy) and agreement that their records become public domain (Creative Commons 0; https://creativecommons.org/share-your-work/public-domain/cc0/). The service provider declared to follow a Crowd Code of Ethics and a Fair Pay policy. The metadata file contains the following information: file_name: name of the audio file script: script the speaker had to speak scriptId: the numeric identifier of the script participantId: the numeric identifier of the participant (speaker) gender: the gender as indicated by the participant (MALE or FEMALE) age: the age in years as indicated by the participant age_range: the age range in years (18-30, 31-45, 46+) country: the birth country indicated by the participant current_country: the country of residence indicated by the participant primary_language: the language indicated as primary by the participant ever_worked_factory: answer to the question: "Have you ever worked in a factory, manufacturing setting?" (Yes/No) years_worked_factory: answer to the question: "If yes, for how many years?" (1-10, 10+) background_noise_type: background noise in the audio as indicated by the participant (mild, humming/technical, no noise) gdpr_and_ipr_consent: answer to the privacy notice and the ipr transfer to CC-0 (Yes) date_signed: date when the participant signed the consent form (US format, MM.DD.YYYY) Feedback should be directed to info@coala-h2020.eu.

This dataset contains audio files and transcripts in Dutch and related to manufacturing. We collected the scripts during the Horizon Europe RIA COALA (GA 957296, project reference website) from industrial use cases and hired a service provider to generate the related audio files (BIBA - Bremer Institut für Produktion und Logistik GmbH ordered the service). The service provider checked the audio files for quality. The service provider recruited crowd workers, and gathered their audio records, informed consent (privacy) and agreement that their records become public domain (Creative Commons 0; https://creativecommons.org/share-your-work/public-domain/cc0/). The service provider declared to follow a Crowd Code of Ethics and a Fair Pay policy. The metadata file contains the following information: file_name: name of the audio file script: script the speaker had to speak scriptId: the numeric identifier of the script participantId: the numeric identifier of the participant (speaker) gender: the gender as indicated by the participant (MALE or FEMALE) age: the age in years as indicated by the participant age_range: the age range in years (18-30, 31-45, 46+) country: the birth country indicated by the participant current_country: the country of residence indicated by the participant primary_language: the language indicated as primary by the participant ever_worked_factory: answer to the question: "Have you ever worked in a factory, manufacturing setting?" (Yes/No) years_worked_factory: answer to the question: "If yes, for how many years?" (1-10, 10+) background_noise_type: background noise in the audio as indicated by the participant (mild, humming/technical, no noise) gdpr_and_ipr_consent: answer to the privacy notice and the ipr transfer to CC-0 (Yes) date_signed: date when the participant signed the consent form (US format, MM.DD.YYYY) Feedback should be directed to info@coala-h2020.eu.

203 Israeli and Palestinian students participated in a quasi-experimental pre-test post-test design and played the games PeaceMaker and Fact Finders in a different order (Peace Maker first, Fact Finders second or Fact Finders first, Peace Maker second) or played one of the two games (either PeaceMaker or Fact Finders). Their viewpoints and attitudes towards both conflicts (Israel-Palestine conflict and Cyprus conflict) were measured before and after playing each game. The EU funded the development of the game Fact Finders as part of the Horizon2020 project RePAST (2018-2021). This study used the game Fact Finders (which is a game for social impact that is provided free of charge in 8 different languages) in an effort to examine attitude changes for the conflict described in the game and for conflicts other than the one described in the game.

This dataset contains audio files and transcripts in English and related to manufacturing. We collected the scripts during the Horizon Europe RIA COALA (GA 957296, project reference website) from industrial use cases and hired a service provider to generate the related audio files (BIBA - Bremer Institut für Produktion und Logistik GmbH ordered the service). The service provider checked the audio files for quality. The service provider recruited crowd workers, and gathered their audio records, informed consent (privacy) and agreement that their records become public domain (Creative Commons 0; https://creativecommons.org/share-your-work/public-domain/cc0/). The service provider declared to follow a Crowd Code of Ethics and a Fair Pay policy. The metadata file contains the following information: file_name: name of the audio file script: script the speaker had to speak scriptId: the numeric identifier of the script participantId: the numeric identifier of the participant (speaker) gender: the gender as indicated by the participant (MALE or FEMALE) age: the age in years as indicated by the participant age_range: the age range in years (18-30, 31-45, 46+) country: the birth country indicated by the participant current_country: the country of residence indicated by the participant primary_language: the language indicated as primary by the participant ever_worked_factory: answer to the question: "Have you ever worked in a factory, manufacturing setting?" (Yes/No) years_worked_factory: answer to the question: "If yes, for how many years?" (1-10, 10+) background_noise_type: background noise in the audio as indicated by the participant (mild, humming/technical, no noise) gdpr_and_ipr_consent: answer to the privacy notice and the ipr transfer to CC-0 (Yes) date_signed: date when the participant signed the consent form (US format, MM.DD.YYYY) Feedback should be directed to info@coala-h2020.eu.

This dataset contains a proof-of-concept GIS database of over 29,000 individual historical road polyline segments as a shapefile dataset, covering over 11,000 km2 in the western Finland from the city of Turku to northern parts of the province of Satakunta. These polylines capture the regional layout of the overland transport infrastructure of late nineteenth and early twentieth century Finland. {"references": ["Oksanen, Eljas, Saarenp\u00e4\u00e4, Ida. and Lahtinen, Anu. 2023. The HISCOM Project. Exploring Methodologies for Large-scale Digitisation of Historical Roadways. SKAS 2/2022, 8-14"]}

Art&Emotion experiment description The Art & Emotions dataset was collected in the scope of EU funded research project SPICE (https://cordis.europa.eu/project/id/870811) with the goal of investigating the relationship between art and emotions and collecting written data (User Generated Content) in the domain of arts in all the languages of the SPICE project (fi, en, es, he, it). The data was collected through a set of Google Forms (one for each language) and it was used in the project (along the other datasets collected by museums in the different project use cases) in order to train and test Emotion Detection Models within the project. The experiment consists of 12 artworks, chosen from a group of artworks provided by the GAM Museum of Turin (https://www.gamtorino.it/) one of the project partners. Each artwork is presented in a different section of the form; for each of the artworks, the user is asked to answer 5 open questions: 1. What do you see in this picture? Write what strikes you most in this image. 2. What does this artwork make you think about? Write the thoughts and memories that the picture evokes. 3. How does this painting make you feel? Write the feelings and emotions that the picture evokes in you 4. What title would you give to this artwork? 5. Now choose one or more emoji to associate with your feelings looking at this artwork. You can also select "other" and insert other emojis by copying them from this link: https://emojipedia.org/ For each of the artworks, the user can decide whether to skip to the next artwork, if he does not like the one in front of him or go back to the previous artworks and modify the answers. It is not mandatory to fill all the questions for a given artwork. The question about emotions is left open so as not to force the person to choose emotions from a list of tags which are the tags of a model (e.g. Plutchik), but leaving him free to express the different shades of emotions that can be felt. Before getting to the heart of the experiment, with the artworks sections, the user is asked to leave some personal information (anonymously), to help us getting an idea of the type of users who participated in the experiment. The questions are: Age (open) Gender (male, female, prefer not to say, other (open)) How would you define your relationship with art? My job is related to the art world I am passionate about the art I am a little interested in art I am not interested in art 4. Do you like going to museums or art exhibitions? I like to visit museums frequently I go occasionally to museums or art exhibitions I rarely visit museums or art exhibitions --------------------- Dataset structure: FI.csv: form data (personal data + open questions) in Finnish (UTF-8) EN.csv: form data (personal data + open questions) in English (UTF-8) ES.csv: form data (personal data + open questions) in Spanish (UTF-8) HE.csv: form data (personal data + open questions) in Hebrew (UTF-8) IT.csv: form data (personal data + open questions) in Italian (UTF-8) artworks.csv: the list of artworks including title, author, picture name (the pictures can be found in pictures.zip) and the mapping between the columns in the form data and the questions about that artwork pictures.zip: the jpeg of the artworks The dataset was created within the scope of EU funded research project SPICE (https://cordis.europa.eu/project/id/870811)

Art&Emotion experiment description The Art & Emotions dataset was collected in the scope of EU funded research project SPICE (https://cordis.europa.eu/project/id/870811) with the goal of investigating the relationship between art and emotions and collecting written data (User Generated Content) in the domain of arts in all the languages of the SPICE project (fi, en, es, he, it). The data was collected through a set of Google Forms (one for each language) and it was used in the project (along the other datasets collected by museums in the different project use cases) in order to train and test Emotion Detection Models within the project. The experiment consists of 12 artworks, chosen from a group of artworks provided by the GAM Museum of Turin (https://www.gamtorino.it/) one of the project partners. Each artwork is presented in a different section of the form; for each of the artworks, the user is asked to answer 5 open questions: 1. What do you see in this picture? Write what strikes you most in this image. 2. What does this artwork make you think about? Write the thoughts and memories that the picture evokes. 3. How does this painting make you feel? Write the feelings and emotions that the picture evokes in you 4. What title would you give to this artwork? 5. Now choose one or more emoji to associate with your feelings looking at this artwork. You can also select "other" and insert other emojis by copying them from this link: https://emojipedia.org/ For each of the artworks, the user can decide whether to skip to the next artwork, if he does not like the one in front of him or go back to the previous artworks and modify the answers. It is not mandatory to fill all the questions for a given artwork. The question about emotions is left open so as not to force the person to choose emotions from a list of tags which are the tags of a model (e.g. Plutchik), but leaving him free to express the different shades of emotions that can be felt. Before getting to the heart of the experiment, with the artworks sections, the user is asked to leave some personal information (anonymously), to help us getting an idea of the type of users who participated in the experiment. The questions are: Age (open) Gender (male, female, prefer not to say, other (open)) How would you define your relationship with art? My job is related to the art world I am passionate about the art I am a little interested in art I am not interested in art 4. Do you like going to museums or art exhibitions? I like to visit museums frequently I go occasionally to museums or art exhibitions I rarely visit museums or art exhibitions --------------------- Dataset structure: FI.csv: form data (personal data + open questions) in Finnish (UTF-8) EN.csv: form data (personal data + open questions) in English (UTF-8) ES.csv: form data (personal data + open questions) in Spanish (UTF-8) HE.csv: form data (personal data + open questions) in Hebrew (UTF-8) IT.csv: form data (personal data + open questions) in Italian (UTF-8) artworks.csv: the list of artworks including title, author, picture name (the pictures can be found in pictures.zip) and the mapping between the columns in the form data and the questions about that artwork pictures.zip: the jpeg of the artworks

The GitHub repository hosting the XML documents for the open access critical edition of the 885222-GreekSchools ERC project.