cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27993905.v1
title: ESC17 monitor files

description: DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.6084/m9.figshare.27993917.v1
title: ESC18 monitor files

description: DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.6084/m9.figshare.27993920
title: ESC19 monitor files

description: DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.6084/m9.figshare.27993920.v1
title: ESC19 monitor files

description: DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.15468/dl.tb3v4h
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Belize",
 "DatasetKey is one of (Plant Resources Center, Tropicos MO Specimen Data, Wisconsin State Herbarium, Vascular Plants, Catálogo electrónico de especímenes depositados en el Herbario de la Universidad de Texas en Austin, Fase IV (Tipos), NMNH Extant Specimen Records (USNM, US), University of Michigan Herbarium, The New York Botanical Garden Herbarium (NY))",
 "OccurrenceStatus is Present",
 "TaxonKey is Clusia suborbicularis Lundell"
 ]
}

 The dataset includes 10 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063844-250525065834625/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.27993884.v1
title: ESC13 monitor files

description: DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.6084/m9.figshare.27993875
title: ESC12 monitor files

description: Monitors for behavior experiment ESC12DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.6084/m9.figshare.27993875.v1
title: ESC12 monitor files

description: Monitors for behavior experiment ESC12DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.6084/m9.figshare.27993884
title: ESC13 monitor files

description: DAM system monitor files for Drosophila activity/rest recordings
DOI: 10.26188/24873330
title: <b>Automated transcoloration? Skin tone classification algorithms and the prominence of non-epidermic features in the Mexican racial schema</b>

description: The scale of anti-racist research employing independent measures for skin tone (i.e., classification algorithms and colorimeters) is increasingly growing. However, recent studies reveal that non-epidermic traits (i.e., hair, lips, gender) contribute to the <i>transcoloration</i> –whitening or darkening– of perceived skin tone, signaling that their importance in the racialization process has been underestimated. The magnitude of this effect of non-epidermic features is critical, and the appropriateness of technology-based anti-racist research hinges on it. To what extent are independent measures of skin tone a good approximation to the study of racial discrimination in Mexico? Examining a novel dataset (n=3,000), I compare two different skin color variables (one coded by humans and other by an automated algorithm), finding little agreement about exact estimates but substantial —and statistically significant— agreement on the general direction of the variables. These findings corroborate that non-epidermic features slightly alter people’s perception of skin tone, without causing a major digression from independent skin color assessments. The results validate and encourage research employing these technological methods, which tend to be more accessible and feasible than their alternatives.
DOI: 10.26188/24873330.v1
title: <b>Automated transcoloration? Skin tone classification algorithms and the prominence of non-epidermic features in the Mexican racial schema</b>

description: The scale of anti-racist research employing independent measures for skin tone (i.e., classification algorithms and colorimeters) is increasingly growing. However, recent studies reveal that non-epidermic traits (i.e., hair, lips, gender) contribute to the <i>transcoloration</i> –whitening or darkening– of perceived skin tone, signaling that their importance in the racialization process has been underestimated. The magnitude of this effect of non-epidermic features is critical, and the appropriateness of technology-based anti-racist research hinges on it. To what extent are independent measures of skin tone a good approximation to the study of racial discrimination in Mexico? Examining a novel dataset (n=3,000), I compare two different skin color variables (one coded by humans and other by an automated algorithm), finding little agreement about exact estimates but substantial —and statistically significant— agreement on the general direction of the variables. These findings corroborate that non-epidermic features slightly alter people’s perception of skin tone, without causing a major digression from independent skin color assessments. The results validate and encourage research employing these technological methods, which tend to be more accessible and feasible than their alternatives.
DOI: 10.26188/25538416
title: Diaspora humanitarians_Survey Data

description: Research ethics approval was obtained from Monash University (ID: 31459).The questionnaire was designed to gather information on <i>how</i> and <i>why</i> the diasporas engaged in humanitarian response. We analyse the crisis response actions, the sources of information communities relied on, the perceptions of their achievements, the challenges they faced, their engagement with other stakeholders, and the strategies they think could enhance diaspora responses in the future.Facilitating access to a wide range of members of diaspora communities was a central aim of the project. With the intention to make the research as accessible as possible, survey instruments were created in five different languages (English, Arabic, Nepalese, Burmese, and Dari). We used Qualtrics as the main platform to collect and manage data, however, surveys were completed in both online and paper formats, as preferred by each respondent.Due to the co-design process, each survey looked slightly different to the rest. The data specific to particular crises were used in the creation of research reports for each country. However, all surveys shared a core set of questions.
DOI: 10.26188/25538416.v1
title: Diaspora humanitarians_Survey Data

description: Research ethics approval was obtained from Monash University (ID: 31459).The questionnaire was designed to gather information on <i>how</i> and <i>why</i> the diasporas engaged in humanitarian response. We analyse the crisis response actions, the sources of information communities relied on, the perceptions of their achievements, the challenges they faced, their engagement with other stakeholders, and the strategies they think could enhance diaspora responses in the future.Facilitating access to a wide range of members of diaspora communities was a central aim of the project. With the intention to make the research as accessible as possible, survey instruments were created in five different languages (English, Arabic, Nepalese, Burmese, and Dari). We used Qualtrics as the main platform to collect and manage data, however, surveys were completed in both online and paper formats, as preferred by each respondent.Due to the co-design process, each survey looked slightly different to the rest. The data specific to particular crises were used in the creation of research reports for each country. However, all surveys shared a core set of questions.
DOI: 10.26188/28148234
title: <b>Quality or complexion? Experience and skin tone as determinants of electoral success in Mexico</b>

description: Does skin color significantly influence electoral outcomes in Mexico? Departing from traditional theoretical determinants of electoral success (i.e., partisanship and candidate’s quality), recent research suggests that skin color discrimination –pervasive in virtually all spheres of life in Mexico– translates over into electoral politics, hindering the probabilities dark-skinned candidates have of winning. Building on these trailblazing studies, this article examines the results of the 2024 Mexican General Election to replicate the analysis with completely transparent methods, fully available data and incorporating most, if not all, relevant control variables (including machine- and human-coded skin tone measures, three different experience variables, along with sex, age, education, party and state). Our results suggest that qualit<i>y, </i>rather than complexion<i>, </i>is the best predictor for electoral success. We conclude that, rather than disproving its existence, our findings reveal that electoral contests are not the best site to test the political salience of skin tone.
DOI: 10.26188/28148234.v1
title: <b>Quality or complexion? Experience and skin tone as determinants of electoral success in Mexico</b>

description: Does skin color significantly influence electoral outcomes in Mexico? Departing from traditional theoretical determinants of electoral success (i.e., partisanship and candidate’s quality), recent research suggests that skin color discrimination –pervasive in virtually all spheres of life in Mexico– translates over into electoral politics, hindering the probabilities dark-skinned candidates have of winning. Building on these trailblazing studies, this article examines the results of the 2024 Mexican General Election to replicate the analysis with completely transparent methods, fully available data and incorporating most, if not all, relevant control variables (including machine- and human-coded skin tone measures, three different experience variables, along with sex, age, education, party and state). Our results suggest that qualit<i>y, </i>rather than complexion<i>, </i>is the best predictor for electoral success. We conclude that, rather than disproving its existence, our findings reveal that electoral contests are not the best site to test the political salience of skin tone.
DOI: 10.15468/dl.y7nef8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amphora C.G.Ehrenberg ex F.T.Kützing, 1844",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.628 -23.023,35.6007 -22.9702,35.6007 -23.165,35.628 -23.023))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063845-250525065834625/datasets/export for details.
DOI: 10.15468/dl.kqu6sp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amphora C.G.Ehrenberg ex F.T.Kützing, 1844",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.6306 -23.844,35.6007 -23.8125,35.6007 -24.0157,35.6306 -23.844))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063846-250525065834625/datasets/export for details.
DOI: 10.15468/dl.zvjubf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlorella saccharophila (W.Krüger) W.Mig., 1907",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063848-250525065834625/datasets/export for details.
DOI: 10.15468/dl.x4smdw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlorella saccharophila (W.Krüger) W.Mig., 1907",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063849-250525065834625/datasets/export for details.
DOI: 10.15468/dl.fzd8kv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlorella saccharophila (W.Krüger) W.Mig., 1907",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1332 -20.158,25.5395 -23.2724,25.5395 -24.0715,30.5701 -24.0715,30.5701 -20.3186,29.6496 -19.6123,28.947 -19.4003,28.1821 -19.4609,27.6072 -19.7214,27.1332 -20.158))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063850-250525065834625/datasets/export for details.
DOI: 10.15468/dl.uyq57w
title: Occurrence Download

description: A dataset containing 18284 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hedychium gardnerianum Sheph. ex Ker Gawl."
 ]
}

 The dataset includes 18284 records from 125 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063851-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15151/esrf-es-2114579857
title: Development of novel small molecules to target cellular pathways

description: Small molecules represent powerful tools to dissect physiological and pathological states of signaling pathways, yet require characterization through X-ray crystallography. This proposal encompasses the investigation of new classes of small molecules that target difficult proteins and non-typical binding sites. This includes the development of supramolecular compounds or cyclic peptides to address “non-druggable" proteins. The main collaborative research fields are tackling Ras protein signaling with structure-guided ligand design, developing inhibitors of the Ras-binding protein PDEd, screening of a novel sp3-enriched fragment library or targeting genetic regulators. Cellular signaling will also be addressed through the structural analysis of deubiquitinating enzymes or regulators of the kinetochore. Finally, we plan to crystallize membrane proteins including GPCRs in order to understand their biological function, characterize their ligand specificity and develop novel regulators.
DOI: 10.15468/dl.ftyd3w
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlorella saccharophila (W.Krüger) W.Mig., 1907",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.0214 -34.0501,25.5395 -34.1098,25.5395 -32.9007,20.509 -32.9007,20.509 -34.5676,20.524 -34.555,20.8859 -34.5419,20.9606 -34.4398,21.2995 -34.5131,21.5296 -34.4338,21.8696 -34.4439,22.0323 -34.2835,22.221 -34.2428,22.2566 -34.1304,22.5993 -34.0741,22.9652 -34.1674,23.4069 -34.1858,23.4955 -34.0792,23.6056 -34.0563,24.8471 -34.2877,24.9447 -34.2397,25.0214 -34.0501))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063852-250525065834625/datasets/export for details.
DOI: 10.15468/dl.yk2akc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlorella saccharophila (W.Krüger) W.Mig., 1907",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((32.064 -28.9416,32.4847 -28.5786,30.5701 -28.5786,30.5701 -30.7329,30.6252 -30.6659,31.4179 -29.4257,31.8178 -29.0299,32.064 -28.9416))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063854-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15717490
title: Multidisciplinary Higher Education Teaching and Learning
title: Barriers, Trends and Future Scenarios (2015–2025)

description: What is this dataset? Who is it for?

This open dataset provides a comprehensive thematic analysis of how multidisciplinary teaching and learning in higher education has evolved over the past decade (2015–2025). It is designed for:





Educators developing multidisciplinary programs




Higher education researchers




Curriculum designers and institutional leaders




Faculty developers and policy makers




Researchers exploring the future role of AI in education



The dataset offers insights into how international scholarly discourse, institutional practices, and pedagogical innovations have shifted over this period — and how AI-driven technologies may shape the next phase of development.
description: Why this decade?

The global transition to large-scale virtual teaching and learning during 2019–2021 marked a methodological shock for higher education. This dataset treats this period not as a crisis focus, but as a structural turning point: an opportunity to observe how teaching, learning, and institutional practices adapted — and how multidisciplinary education evolved in response.
description: What’s in the dataset?





Full coding frameworks for inductive and deductive thematic analysis




Thematic matrices tracking trends across 2015–2025




Analyses of barriers, pedagogical innovations, and student motivation




Visualizations (radar charts, heatmaps) of structural change




A research article draft summarizing key findings




Open, editable formats for reuse and extension
description: How can it be used?





To inform the design of multidisciplinary curricula




To support faculty development initiatives




To guide institutional innovation and transformation




To compare the evolving role of digital and AI-based tools




To build future scenarios for multidisciplinary higher education
description: Call for community contributionsThis dataset is shared openly to encourage collaborative development. Educators, researchers, and academic communities are invited to expand and update this dataset — particularly by contributing AI-related perspectives and data. AI-driven technologies may represent a new transformative moment in multidisciplinary higher education — comparable in scale to the digital shifts of 2019–2021. Contributions are welcome to help explore this evolving landscape.

Please cite this dataset using the provided DOI.
DOI: 10.5281/zenodo.15717491
title: Multidisciplinary Higher Education Teaching and Learning
title: Barriers, Trends and Future Scenarios (2015–2025)

description: What is this dataset? Who is it for?

This open dataset provides a comprehensive thematic analysis of how multidisciplinary teaching and learning in higher education has evolved over the past decade (2015–2025). It is designed for:





Educators developing multidisciplinary programs




Higher education researchers




Curriculum designers and institutional leaders




Faculty developers and policy makers




Researchers exploring the future role of AI in education



The dataset offers insights into how international scholarly discourse, institutional practices, and pedagogical innovations have shifted over this period — and how AI-driven technologies may shape the next phase of development.
description: Why this decade?

The global transition to large-scale virtual teaching and learning during 2019–2021 marked a methodological shock for higher education. This dataset treats this period not as a crisis focus, but as a structural turning point: an opportunity to observe how teaching, learning, and institutional practices adapted — and how multidisciplinary education evolved in response.
description: What’s in the dataset?





Full coding frameworks for inductive and deductive thematic analysis




Thematic matrices tracking trends across 2015–2025




Analyses of barriers, pedagogical innovations, and student motivation




Visualizations (radar charts, heatmaps) of structural change




A research article draft summarizing key findings




Open, editable formats for reuse and extension
description: How can it be used?





To inform the design of multidisciplinary curricula




To support faculty development initiatives




To guide institutional innovation and transformation




To compare the evolving role of digital and AI-based tools




To build future scenarios for multidisciplinary higher education
description: Call for community contributionsThis dataset is shared openly to encourage collaborative development. Educators, researchers, and academic communities are invited to expand and update this dataset — particularly by contributing AI-related perspectives and data. AI-driven technologies may represent a new transformative moment in multidisciplinary higher education — comparable in scale to the digital shifts of 2019–2021. Contributions are welcome to help explore this evolving landscape.

Please cite this dataset using the provided DOI.
DOI: 10.6084/m9.figshare.29378570
title: Verkko 2.2.1 assemblies used in https://genome.cshlp.org/content/early/2025/06/11/gr.280383.124.abstract

description: verkko2.2.1 runs used for the evaluation in https://genome.cshlp.org/content/early/2025/06/11/gr.280383.124.abstract paper
DOI: 10.6084/m9.figshare.29378570.v1
title: Verkko 2.2.1 assemblies used in https://genome.cshlp.org/content/early/2025/06/11/gr.280383.124.abstract

description: verkko2.2.1 runs used for the evaluation in https://genome.cshlp.org/content/early/2025/06/11/gr.280383.124.abstract paper
DOI: 10.15468/dl.cp5k25
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chlorella saccharophila (W.Krüger) W.Mig., 1907",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.5808 -24.13,35.5909 -24.0715,30.5701 -24.0715,30.5701 -28.5786,32.4847 -28.5786,32.55 -28.5222,33.0346 -26.8668,32.9741 -26.7838,33.0415 -26.059,32.9304 -26.0011,32.7994 -26.1281,32.6834 -26.0015,32.813 -25.8728,32.8513 -25.7166,33.3048 -25.3731,35.1489 -24.6699,35.5808 -24.13))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063855-250525065834625/datasets/export for details.