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  • Research objective: determine whether DNA loop-extruding SMC complexes can bypass DNA-bound roadblocksType of research: Single-molecule in vitro reconstitution of DNA loop extrusion using purified proteinsMethod of data collection: HiLO microscopyType of data: Images (.tif). Due to the large size of the raw data, pointers to the data location are included in this dataset

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jaroslaw Szumega; Lamine Bougueroua; Blerina Gkotse; Pierre Jouvelot; +1 Authors

    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

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Blin, Inès; Stork, Lise; Spillner, Laura; Santagiustina, Carlo Romano Marcello Alessandro;

    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

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: ZENODO; Sygma
    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: ZENODO; Sygma
      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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    Authors: Zinnen, Mathias; Madhu, Prathmesh; Kosti, Ronak; Bell, Peter; +2 Authors

    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.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Nicolaidou, Iolie; Kampf, Ronit;

    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.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Lozić, Edisa; Štular, Benjamin;

    This is supplemental material to the article "Fluent but Not Factual: A Comparative Analysis of ChatGPT and Other AI Chatbots' Proficiency and Originality in Scientific Writing for Humanities" (https://doi.org/10.3390/fi15100336), which was published as a Pre-print titled "ChatGPT v Bard v Bing v Claude 2 v Aria v human-expert. How good are AI chatbots at scientific writing?" (https://doi.org/10.48550/arXiv.2309.08636). It includes 3 appendices of tagged data. See the article for more details.

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    ZENODO
    Collection . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Collection . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Stefan Wellsandt; Mina Foosherian; Samuel Kernan Freire; Evangelos Niforatos; +1 Authors

    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.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: Datacite; Sygma
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: Datacite; Sygma
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    Authors: Oksanen, Eljas; Saarenpää, Ida; Lahtinen, Anu;

    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"]}

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Kozowyk, Paul; Baron, Liliana; Langejans, Geeske;

    This data set contains gas chromatography mass spectrometry (GC-MS) data of eight experimentally reproduced Palaeolithic birch bark tars. The intentional production of birch bark tar by European Neanderthals plays an important role in discussions about the technological and behavioural complexity of Pleistocene hominins. However, research is hampered because it is currently unknown how Neanderthals were producing birch tar. Organic biomarkers, identified with Gas Chromatograph-Mass Spectrometry (GC-MS), provide a promising way to identify possible production processes. Analysis of this data is presented and discussed in a research article written by Kozowyk et al. in Scientific Reports 2023. The attached GC-MS data provides additional information on the chemical composition of experimental birch bark tars. The dataset is made up of 2 types of files: Raw GC-MS data in .zip files for each experiment (containing .acaml, .dx, .mfx, .bin, .rx, .pmx, .amx) which can be used for further analysis and post-processing, and a summary (.xlsx) file documenting the key biomarkers and peak retention times used in the publication and information about the tar samples. The summary GC-MS .xlsx file contains peaks automatically assigned by the software from the NIST database and may contain errors. Relevant peaks used in the manuscript have been manually checked and renamed where appropriate. Finally, all data used and produced for PCA is available as .csv files.

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Alessio Bosca;

    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)

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • Research objective: determine whether DNA loop-extruding SMC complexes can bypass DNA-bound roadblocksType of research: Single-molecule in vitro reconstitution of DNA loop extrusion using purified proteinsMethod of data collection: HiLO microscopyType of data: Images (.tif). Due to the large size of the raw data, pointers to the data location are included in this dataset

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    Authors: Jaroslaw Szumega; Lamine Bougueroua; Blerina Gkotse; Pierre Jouvelot; +1 Authors

    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

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Blin, Inès; Stork, Lise; Spillner, Laura; Santagiustina, Carlo Romano Marcello Alessandro;

    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

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: ZENODO; Sygma
    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: ZENODO; Sygma
      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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    Authors: Zinnen, Mathias; Madhu, Prathmesh; Kosti, Ronak; Bell, Peter; +2 Authors

    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.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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