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We introduce the new comprehensive Open Review–Based dataset (ORB); it includes a curated list of more than 10,000 scientific papers with their more than 31,000 reviews and final decisions. We gather this information from two sources: the OpenReview.net and SciPost.org websites

This dataset collects the bibliographic entries used in the first phases of the research. They have been compiled by several members of the project through a shared tool such as the Zotero bibliographic manager./ Este conjunto de datos recoge las entradas bibliográficas empleadas en las primeras fases de la investigación. Han sido recogidas por diversos miembros del proyecto a través de una herramienta compartida como es el gestor bibliográfico Zotero. [ES] Las bibliografías generadas por la investigación del proyecto EuQu recogen fuentes primarias y secundarias en torno a la presencia del texto coránico en Europa y mundo mediterráneo. [EN] Bibliographies generated by the EuQu research project include primary and secondary sources on the presence of the Qur'anic text in Europe and the Mediterranean world. Este conjunto de datos está sujeto a una licencia CC BY 4.0 Peer reviewed

Description The dataset contains 130 155 articles sourced from the websites of three Swiss francophone newspapers: Arc Info, La Cote, and Le Nouvelliste, spanning the time period from 01/01/2015 to 30/06/2022. The dataset, compiled from the temporary data feeds provided by the press agency, consists of the articles in their entirety including the title, headline, and content, along with metadata for each article. The collected articles are primarily in French language and are categorically sorted by topics and region, everything encoded in the JSON format. Reference If you use this dataset, please cite the following publication: Victor Bros and Daniel Gatica-Perez, The Suisse Romande Local News Dataset, Idiap Technical Report, 2023. 

The SynSemClass synonym verb lexicon version 5.0 is a multilingual resource that enriches previous editions of this event-type ontology with a new language, Spanish. The existing languages, English, Czech and German, are further substantially extended by a larger number of classes. SSC 5.0 data also contain lists (in a separate removed_cms.zip file) with originally (pre-)proposed but later rejected class members. All languages are organized into classes and have links to other lexical sources. In addition to the existing links, links to Spanish sources have been added. The Spanish entries are linked to ADESSE (http://adesse.uvigo.es/), Spanish SenSem (http://grial.edu.es/sensem/lexico?idioma=en), Spanish WordNet (https://adimen.si.ehu.es/cgi-bin/wei/public/wei.consult.perl), AnCora (https://clic.ub.edu/corpus/en/ancoraverb_es), and Spanish FrameNet (http://sfn.spanishfn.org/SFNreports.php). The English entries are linked to EngVallex (http://hdl.handle.net/11858/00-097C-0000-0023-4337-2), CzEngVallex (http://hdl.handle.net/11234/1-1512), FrameNet (https://framenet.icsi.berkeley.edu/) VerbNet (https://uvi.colorado.edu/ and http://verbs.colorado.edu/verbnet/index.html), PropBank (http://propbank.github.io/), Ontonotes (http://clear.colorado.edu/compsem/index.php?page=lexicalresources&sub=ontonotes), and English Wordnet (https://wordnet.princeton.edu/). Czech entries are linked to PDT-Vallex (http://hdl.handle.net/11858/00-097C-0000-0023-4338-F), Vallex (http://hdl.handle.net/11234/1-3524), and CzEngVallex (http://hdl.handle.net/11234/1-1512). The German entries are linked to Woxikon (https://synonyme.woxikon.de), E-VALBU (https://grammis.ids-mannheim.de/verbvalenz), and GUP (http://alanakbik.github.io/multilingual.html and https://github.com/UniversalDependencies/UD_German-GSD).

The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality. We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023. In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization. Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person with the following information: A description of the use case Affiliation of the researchers involved How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d

The Object Detection for Olfactory References (ODOR) Dataset Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception. How to use To download the dataset images, run the `download_imgs.py` script in the subfolder. The images will be downloaded to the `imgs` folder. The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py). The mapping between the `images` array of the `annotations.json` and the `metadata.csv` file can be accomplished via the `file_name` attribute of the elements of the `images` array and the unique `File Name` column of the `metadata.csv` file, respectively.

Emotions in Literature (Multilingual) With automatic translation from English into Dutch, French, and Italian. Literature sentences from Project Gutenberg. 38 emotion labels (+neutral examples). Semi-Supervised dataset. Automatic translations using Google Translate. More information: The Original Dataset - English only. Article: Detecting Fine-Grained Emotions in Literature Code for training and evaluation available on Github. Please cite: @Article{app13137502, AUTHOR = {Rei, Luis and Mladenić, Dunja}, TITLE = {Detecting Fine-Grained Emotions in Literature}, JOURNAL = {Applied Sciences}, VOLUME = {13}, YEAR = {2023}, NUMBER = {13}, ARTICLE-NUMBER = {7502}, URL = {https://www.mdpi.com/2076-3417/13/13/7502}, ISSN = {2076-3417}, DOI = {10.3390/app13137502} } {"references": ["Rei, Luis, and Dunja Mladeni\u0107. 2023. \"Detecting Fine-Grained Emotions in Literature\" Applied Sciences 13, no. 13: 7502. https://doi.org/10.3390/app13137502"]}

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.