cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7806078
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.5281/zenodo.7882498
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.5281/zenodo.7882694
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.5281/zenodo.7993511
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.5281/zenodo.8103664
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.5281/zenodo.8202339
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.5281/zenodo.8319652
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.5281/zenodo.8396369
title: ARTigo: Social Image Tagging (Raw Data)

description: ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers <em>Games With a Purpose</em>: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices.
description: {"references": ["Schneider, Stefanie; Kristen, Maximilian; Vollmer, Ricarda (2023): Re: ARTigo. Neuentwurf eines Social-Tagging-Frameworks aus funktionalen Programmbausteinen. In: DHd 2023. Open Humanities, Open Culture. Konferenzabstracts, 173\u2013178.", "Bry, Fran\u00e7ois; Schefels, Clemens; Schemainda, Corina (2018): Eine qualitative Analyse der ARTigo-Annotationen. In: Kuroczy\u0144ski, Piotr; Bell, Peter; Dieckmann, Lisa (Eds.): Computing Art Reader. Einf\u00fchrung in die digitale Kunstgeschichte, Heidelberg, 97\u2013114.", "Schneider, Stefanie; Kohle, Hubertus (2017): The Computer as Filter Machine. A Clustering Approach to Categorize Artworks Based on a Social Tagging Network. In: Artl@s 6.3: 81\u201389.", "Wieser, Christoph; Bry, Fran\u00e7ois; B\u00e9rard, Alexandre; Lagrange, Richard (2013): ARTigo. Building an Artwork Search Engine With Games and Higher-Order Latent Semantic Analysis. In: Proceedings of Disco 2013, Workshop on Human Computation and Machine Learning in Games at HComp, Palm Springs, CA, USA."]}
DOI: 10.6084/m9.figshare.23499498.v4
title: <b>Replication Data for: Observations of internal tides in the East China Sea around the diurnal critical latitude</b>

description: The data is used to diagnost the spatiotemporal variation of internal tides in ECS shelf.
DOI: 10.6084/m9.figshare.22573969.v1
title: Supplementary Material for: Integrative post-GWAS analyses relevant to psychiatric disorders: Imputing transcriptome and proteome signals

description: Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing 'omic' information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using i) GWAS summary statistics and ii) reference transcriptomic/proteomic/genomic data sets. TWAS and PWAS are suitable as analysis tools for i) primary association scan and ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes in PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization (MR) tools that use GWAS statistics for both trait and reference data sets, e.g., SMR. We base our recommendation on i) being able to use the same tool for both TWAS and PWAS, ii) not requiring the pre-computed weights (and thus easier to update for larger reference data sets) and iii) most larger transcriptome reference data sets are publicly available and easy to transform into a compatible format for SMR analysis.
DOI: 10.6084/m9.figshare.16565148.v1
title: MvTrinity.fasta

description: De novo Transcriptome Assembly file (.fasta)<br>Total RNA was extracted from multiple early developmental stages of Mytilisepta virgata.Preparation of a paired-end library using TruSeq Stranded mRNA Sample Preparation Kit and sequencing on a HiSeq2000 platform (Illumina) were performed by the Beijing Genomics Institute. Short reads were deposited in the DNA Data Bank of Japan Sequence Read Archive (DRA011111). Filtering of low-quality reads was conducted using the IlluQC_PRLL.pl in NGSQCToolkit (v2.3) with default parameter settings for filtering. De novo assembly was performed using Trinity with default settings (version: trinityrnaseq_r20140717)
DOI: 10.6084/m9.figshare.22691275.v2
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.6011129.v2
title: Wave data during two severe storms in a Dutch Wadden Sea marsh

description: This dataset (netCDF file) includes wave data (e.g. significant wave height, wave period, etc.) during two winter storms (Jan.11, 2015, and Jan. 13, 2017) observed in a Dutch Wadden Sea marsh using wave sensors deployed at multiple locations in the marsh or in the bare mudflat.
DOI: 10.60893/figshare.pof.27261150.v1
title: Supplementary information

description: Complete experimental details are presented
DOI: 10.15468/dl.h8hp7z
title: Occurrence Download

description: A dataset containing 246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Ruellia humilis Nutt.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 246 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013517-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.793839.v3
title: Supplemental Item 2 - all raw data and traits.

description: Supplemental Item 2. All raw data and traits (workings given as comments in the excel file) used in the morphometric analysis and figures.