cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17808431
title: AI Media & Online Authenticity 2025 (Dataset)

description: This dataset is part of the Human Clarity Institute’s AI–Human Experience Data Series, examining how adults perceive authenticity in the age of AI-generated media. It measures how people judge whether online content is real, their confidence in detecting synthetic media, and the emotional and behavioural effects of uncertainty created by AI-mediated environments.

The dataset includes:• validated 1–7 Likert-scale items• measures of online authenticity judgement• confidence indicators for detecting AI-generated media• emotional and behavioural responses to uncertainty• strategies and cues used to verify online information• trust, caution, and perceived realism of AI media• open-text reflections on moments of uncertainty• demographic variables across multiple countries

Data were collected via Prolific in 2025 from adults across English-speaking countries. All responses were fully cleaned, anonymised, and verified according to the Human Clarity Institute’s open-data publication protocol.

This dataset contributes to understanding how AI-generated media influences digital trust, authenticity perception, and human confidence in verification. It provides foundational data for tracking how synthetic media shapes behaviour, certainty, and trust in the AI era.
DOI: 10.15468/dl.e2aact
title: Occurrence Download

description: A dataset containing 7713 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Country is one of (Ghana, Côte d’Ivoire)",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 1960-2025"
 ]
}

 The dataset includes 7713 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026486-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17744555
title: AI Media & Online Authenticity 2025 (Dataset)

description: This dataset is part of the Human Clarity Institute’s AI–Human Experience Data Series, examining how adults perceive authenticity in the age of AI-generated media. It measures how people judge whether online content is real, their confidence in detecting synthetic media, and the emotional and behavioural effects of uncertainty created by AI-mediated environments.

The dataset includes:• validated 1–7 Likert-scale items• measures of online authenticity judgement• confidence indicators for detecting AI-generated media• emotional and behavioural responses to uncertainty• strategies and cues used to verify online information• trust, caution, and perceived realism of AI media• open-text reflections on moments of uncertainty• demographic variables across multiple countries

Data were collected via Prolific in 2025 from adults across English-speaking countries. All responses were fully cleaned, anonymised, and verified according to the Human Clarity Institute’s open-data publication protocol.

This dataset contributes to understanding how AI-generated media influences digital trust, authenticity perception, and human confidence in verification. It provides foundational data for tracking how synthetic media shapes behaviour, certainty, and trust in the AI era.
DOI: 10.15468/dl.936xru
title: Occurrence Download

description: A dataset containing 436 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinus",
 "GadmGid is one of (USA.6_1, USA.17_1, USA.27_1, USA.28_1, USA.32_1, USA.35_1, USA.37_1, USA.42_1, USA.44_1, USA.51_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2014",
 "Month is greater than or equal to January",
 "Month is less than or equal to December"
 ]
}

 The dataset includes 436 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026487-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17744453
title: AI Media & Online Authenticity 2025 (Dataset)

description: This dataset is part of the Human Clarity Institute’s AI–Human Experience Data Series, examining how adults perceive authenticity in the age of AI-generated media. It measures how people judge whether online content is real, their confidence in detecting synthetic media, and the emotional and behavioural effects of uncertainty created by AI-mediated environments.

The dataset includes:• validated 1–7 Likert-scale items• measures of online authenticity judgement• confidence indicators for detecting AI-generated media• emotional and behavioural responses to uncertainty• strategies and cues used to verify online information• trust, caution, and perceived realism of AI media• open-text reflections on moments of uncertainty• demographic variables across multiple countries

Data were collected via Prolific in 2025 from adults across English-speaking countries. All responses were fully cleaned, anonymised, and verified according to the Human Clarity Institute’s open-data publication protocol.

This dataset contributes to understanding how AI-generated media influences digital trust, authenticity perception, and human confidence in verification. It provides foundational data for tracking how synthetic media shapes behaviour, certainty, and trust in the AI era.
DOI: 10.15468/dl.b6cz97
title: Occurrence Download

description: A dataset containing 726 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Bombus bohemicus"
 ]
}

 The dataset includes 726 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026488-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2a5mjg
title: Occurrence Download

description: A dataset containing 205821 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((-76.91998 38.96112,-76.91249 38.98015,-76.90208 38.99505,-76.92254 38.99874,-76.93002 39.00158,-76.94281 39.00073,-76.95066 38.99448,-76.95595 38.98682,-76.95394 38.97717,-76.95029 38.97177,-76.9492 38.96638,-76.95157 38.96467,-76.93988 38.96382,-76.92783 38.96268,-76.91998 38.96112))",
 "HasGeospatialIssue is false",
 "Year 2015-2025"
 ]
}

 The dataset includes 205821 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026462-251120083545085/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.28934993
title: 2019-2022 10-m maize and soybean maps over the United States

description: High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022, available at the website of the Global Land Analysis and Discovery (GLAD) team at the University of Maryland (https://glad.umd.edu/dataset/mapping-crops-10-m-resolution-united-states). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95% with standard errors of less than 1%. User’s accuracies (UAs) and producer’s accuracies (PAs) for maize were higher than 91% and 84% across the years, and UAs and PAs for soybean were greater than 88% and 82%, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the 30-m mixed pixels reduction at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1% to 7%, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels reduction across all counties were 8% and 9%, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales.
DOI: 10.6084/m9.figshare.28934993.v1
title: 2019-2022 10-m maize and soybean maps over the United States

description: High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for operational crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022. We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95% with standard errors of less than 1%. User’s accuracies (UAs) and producer’s accuracies (PAs) for maize were higher than 91% and 84% across the years, and UAs and PAs for soybean were greater than 88% and 82%, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the amount of 30-m mixed pixels that can be reduced at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1% to 10%, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels across all counties were 14% and 16%, respectively, illustrating the substantial benefits of 10-m maps over 30-m maps. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales.
DOI: 10.6084/m9.figshare.28934993.v2
title: 2019-2022 10-m maize and soybean maps over the United States

description: High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022, available at the website of the Global Land Analysis and Discovery (GLAD) team at the University of Maryland (https://glad.umd.edu/dataset/mapping-crops-10-m-resolution-united-states). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95% with standard errors of less than 1%. User’s accuracies (UAs) and producer’s accuracies (PAs) for maize were higher than 91% and 84% across the years, and UAs and PAs for soybean were greater than 88% and 82%, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the 30-m mixed pixels reduction at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1% to 7%, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels reduction across all counties were 8% and 9%, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales.
DOI: 10.15468/dl.zdzsx2
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Balladyna",
 "HasCoordinate is true"
 ]
}

 The dataset includes 37 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026489-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.62ysxp
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pistia stratiotes",
 "GadmGid is one of (USA.6_1, USA.17_1, USA.27_1, USA.28_1, USA.32_1, USA.35_1, USA.37_1, USA.42_1, USA.44_1, USA.51_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2014",
 "Month is greater than or equal to January",
 "Month is less than or equal to December"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026490-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tyytda
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Endoxylina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 33 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026491-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25422/azu.data.30142003
title: Data for "Measuring the integration of dataset contributors into the publication team: Metrics for assessing team integration in genomics and biomedicine and implications for citation impact and scientific capacity"

description: We describe the DLI Ratio and variations and apply it in two case studies in the biomedical sciences and genomics. We examine the metadata of scientific datasets and their associated publications deposited to GenBank over 29 years (1992–2021). Findings indicate that using these measures, we can newly estimate whether teams with highly integrated dataset contributors are strongly correlated with higher citation impact.<br><br><br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
DOI: 10.25422/azu.data.30142003.v1
title: Data for "Measuring the integration of dataset contributors into the publication team: Metrics for assessing team integration in genomics and biomedicine and implications for citation impact and scientific capacity"

description: We describe the DLI Ratio and variations and apply it in two case studies in the biomedical sciences and genomics. We examine the metadata of scientific datasets and their associated publications deposited to GenBank over 29 years (1992–2021). Findings indicate that using these measures, we can newly estimate whether teams with highly integrated dataset contributors are strongly correlated with higher citation impact.<br><br><br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
DOI: 10.15468/dl.drq8qa
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypostomus plecostomus",
 "GadmGid is one of (USA.6_1, USA.17_1, USA.27_1, USA.28_1, USA.32_1, USA.35_1, USA.37_1, USA.42_1, USA.44_1, USA.51_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2014",
 "Month is greater than or equal to January",
 "Month is less than or equal to December"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026492-251120083545085/datasets/export for details.
DOI: 10.5517/ccdc.csd.cc2n3pr7
title: CCDC 2446419: Experimental Crystal Structure Determination

description: Related Article: M. Wanat, E. Zaorska, M. Malinska|2025|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|81|517|doi:10.1107/S2052520625008625
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2n3ps8
title: CCDC 2446420: Experimental Crystal Structure Determination

description: Related Article: M. Wanat, E. Zaorska, M. Malinska|2025|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|81|517|doi:10.1107/S2052520625008625
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2n3pvb
title: CCDC 2446422: Experimental Crystal Structure Determination

description: Related Article: M. Wanat, E. Zaorska, M. Malinska|2025|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|81|517|doi:10.1107/S2052520625008625
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2n3pxd
title: CCDC 2446424: Experimental Crystal Structure Determination

description: Related Article: M. Wanat, E. Zaorska, M. Malinska|2025|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|81|517|doi:10.1107/S2052520625008625
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2pqpfk
title: CSD 2494459: Experimental Crystal Structure Determination

description: Related Article: N.F. Hasmuni, D. Urushihara, R. Hayashi, R. Sato, K. Fukuda, M.Z. Mohd Yusop, T. Asaka|2025|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|81|565|doi:10.1107/S2052520625008820
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2n3pwc
title: CCDC 2446423: Experimental Crystal Structure Determination

description: Related Article: M. Wanat, E. Zaorska, M. Malinska|2025|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|81|517|doi:10.1107/S2052520625008625
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h1v8p
title: CCDC 2295682: Experimental Crystal Structure Determination

description: Related Article: T. Prangé, G. Nam Phan, W. Shepard, M. Ponchet, L. Lapeyre, M. Mehiri|2025|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|81|502|doi:10.1107/S2052520625008583
description: Related Article: Thierry Prange|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.