cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.a5nzga
title: Occurrence Download

description: A dataset containing 1656 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-73.9879715000000004 40.8676176999999967, -73.9879715000000004 40.8695810099999974, -73.9879715000000004 40.8713261700000032, -73.9879715000000004 40.8721260299999969, -73.9879715000000004 40.8738711900000027, -73.9879715000000004 40.8748891999999984, -73.9861577100000005 40.8748891999999984, -73.9850695399999978 40.8748891999999984, -73.9834010099999944 40.8748891999999984, -73.9823853900000046 40.8748891999999984, -73.9808619500000049 40.8748891999999984, -73.9799186999999989 40.8748891999999984, -73.9799186999999989 40.8735803300000029, -73.9799186999999989 40.8721987500000026, -73.9799186999999989 40.8710353100000034, -73.9799186999999989 40.8701627299999970, -73.9799186999999989 40.8687811400000029, -73.9799186999999989 40.8676176999999967, -73.9812972200000019 40.8676176999999967, -73.9825304799999941 40.8676176999999967, -73.9837637400000006 40.8676176999999967, -73.9855048099999948 40.8676176999999967, -73.9879715000000004 40.8676176999999967))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 1656 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080287-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-77137
title: Redemption tidings, vol. 56, nos. 1-52, 3 Jan. - 25 Dec. 1980

description: [description english/roman)] Issued weekly. "The pentecostal voice of Assemblies of God" -- subtitle. Includes Global action, nos. 1-6 (in vol. 56, nos. 3, 11, 18, 29, 40 & 48).
DOI: 10.15468/dl.7525y2
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Pepsis thisbe Lucas, 1895"
 ]
}

 The dataset includes 23 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004750-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bkcpzb
title: Occurrence Download

description: A dataset containing 618685 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((89.68166 -1.68951,115.75531 -1.68951,115.1938 29.44839,88.94511 31.06284,89.68166 -1.68951))",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (NT, VU, EN, CR, EW, EX)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 618685 records from 453 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034239-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13777813
title: NONE

description: 1. OverviewThe Global_Extent_of_Human_Regulated_Water_Bodies.shp (GHRW) dataset provides a comprehensive global inventory of human-regulated water bodies (HRWs), identified using remote sensing and deep learning techniques. The dataset includes 1.1 million HRWs, ranging in size from 0.001 km², with a complete record for HRWs larger than 0.1 km².

2. Production MethodologyThe dataset was produced through a multi-step process involving the identification, classification, and validation of water bodies using the Joint Research Centre Global Surface Water (GSW) dataset, Sentinel-2 imagery, and a deep learning classification model (EfficientNet_V2_S). The GSW dataset was utilized to extract water bodies larger than 0.001 km², and Sentinel-2 cloud-free composite images were used to classify these water bodies into human-regulated or natural categories. The deep learning model was trained with manually labeled HRWs and natural water bodies, followed by iterative refinement using a human-in-the-loop approach. 

3. Attribute DescriptionsThe dataset consists of the following attributes:



area_km2: This attribute represents the area of each water body in square kilometers (km²). It was calculated from the GSW maximum extent layer and other data sources.

source: In cases where the developed product overlapped with datasets from CRD, GOODD, or GeoDAR, we prioritized the sources in the following order: CRD, followed by GeoDAR, and finally GOODD. Specifically, if an overlap occurred between the developed product and any of these datasets, we retained the source data from CRD as the highest priority. If CRD data was not available, we used data from GeoDAR, with GOODD being used as the last option.



'Hao et al. 2024': Data directly derived and classified using the methodologies described in Hao et al.

'GeoDAR v1.1': HRWs identified from the GeoDAR v1.1 dataset.

'GOODD': HRWs identified from the GOODD dataset.

'CRD': HRWs identified from the CRD dataset.



lon: Longitude coordinate of the centroid of the water body. 

lat: Latitude coordinate of the centroid of the water body.


Reference:



Hao, Z. (2024). Global_Extent_of_Human_Regulated_Water_Bodies (GHRW) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13777813

Song, C., Fan, C., Zhu, J., Wang, J., Sheng, Y., Liu, K., ... & Ke, L. (2022). A comprehensive geospatial database of nearly 100 000 reservoirs in China. Earth System Science Data, 14(9), 4017-4034.

Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., ... & Wada, Y. (2021). GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations. Earth System Science Data Discussions, 2021, 1-52.

Mulligan, M., van Soesbergen, A., & Sáenz, L. (2020). GOODD, a global dataset of more than 38,000 georeferenced dams. Scientific Data, 7(1), 31.
DOI: 10.5281/zenodo.13369411
title: NONE

description: 1. OverviewThe Global_Extent_of_Human_Regulated_Water_Bodies.shp (GHRW) dataset provides a comprehensive global inventory of human-regulated water bodies (HRWs), identified using remote sensing and deep learning techniques. The dataset includes 1.1 million HRWs, ranging in size from 0.001 km², with a complete record for HRWs larger than 0.1 km².

2. Production MethodologyThe dataset was produced through a multi-step process involving the identification, classification, and validation of water bodies using the Joint Research Centre Global Surface Water (GSW) dataset, Sentinel-2 imagery, and a deep learning classification model (EfficientNet_V2_S). The GSW dataset was utilized to extract water bodies larger than 0.001 km², and Sentinel-2 cloud-free composite images were used to classify these water bodies into human-regulated or natural categories. The deep learning model was trained with manually labeled HRWs and natural water bodies, followed by iterative refinement using a human-in-the-loop approach. 

3. Attribute DescriptionsThe dataset consists of the following attributes:



area_km2: This attribute represents the area of each water body in square kilometers (km²). It was calculated from the GSW maximum extent layer and other data sources.

source: In cases where the developed product overlapped with datasets from CRD, GOODD, or GeoDAR, we prioritized the sources in the following order: CRD, followed by GeoDAR, and finally GOODD. Specifically, if an overlap occurred between the developed product and any of these datasets, we retained the source data from CRD as the highest priority. If CRD data was not available, we used data from GeoDAR, with GOODD being used as the last option.



'Hao et al. 2024': Data directly derived and classified using the methodologies described in Hao et al.

'GeoDAR v1.1': HRWs identified from the GeoDAR v1.1 dataset.

'GOODD': HRWs identified from the GOODD dataset.

'CRD': HRWs identified from the CRD dataset.



lon: Longitude coordinate of the centroid of the water body. 

lat: Latitude coordinate of the centroid of the water body.


Reference:



Hao, Z. (2024). Global_Extent_of_Human_Regulated_Water_Bodies (GHRW) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13369411

Song, C., Fan, C., Zhu, J., Wang, J., Sheng, Y., Liu, K., ... & Ke, L. (2022). A comprehensive geospatial database of nearly 100 000 reservoirs in China. Earth System Science Data, 14(9), 4017-4034.

Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., ... & Wada, Y. (2021). GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations. Earth System Science Data Discussions, 2021, 1-52.

Mulligan, M., van Soesbergen, A., & Sáenz, L. (2020). GOODD, a global dataset of more than 38,000 georeferenced dams. Scientific Data, 7(1), 31.
DOI: 10.15468/dl.wn6wpe
title: Occurrence Download

description: A dataset containing 72029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "DatasetKey is Museum of Comparative Zoology, Harvard University"
 ]
}

 The dataset includes 72029 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031093-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13761486
title: Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A General Framework for Selective Inhibitor Design and Optimization in Structure-Based Drug Discovery

description: Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models. Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.This project provide the source dataset used to train and evaluate the overall model.
DOI: 10.15468/dl.brvy6x
title: Occurrence Download

description: A dataset containing 18262 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Occurrence evidence)",
 "Country is Mexico",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 18262 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031094-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tbah3e
title: Occurrence Download

description: A dataset containing 4539 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "DatasetKey is UCM Amphibian and Reptile Collection (Arctos)",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 4539 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031095-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7r4m3f
title: Occurrence Download

description: A dataset containing 18251 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "DatasetKey is UCM Amphibian and Reptile Collection (Arctos)",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 18251 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031096-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9n6f5q
title: Occurrence Download

description: A dataset containing 209 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "DatasetKey is UCM Amphibian and Reptile Collection (Arctos)",
 "TaxonKey is Testudines"
 ]
}

 The dataset includes 209 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031097-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13369410
title: NONE

description: 1. OverviewThe Global_Extent_of_Human_Regulated_Water_Bodies.shp (GHRW) dataset provides a comprehensive global inventory of human-regulated water bodies (HRWs), identified using remote sensing and deep learning techniques. The dataset includes 1.1 million HRWs, ranging in size from 0.001 km², with a complete record for HRWs larger than 0.1 km².

2. Production MethodologyThe dataset was produced through a multi-step process involving the identification, classification, and validation of water bodies using the Joint Research Centre Global Surface Water (GSW) dataset, Sentinel-2 imagery, and a deep learning classification model (EfficientNet_V2_S). The GSW dataset was utilized to extract water bodies larger than 0.001 km², and Sentinel-2 cloud-free composite images were used to classify these water bodies into human-regulated or natural categories. The deep learning model was trained with manually labeled HRWs and natural water bodies, followed by iterative refinement using a human-in-the-loop approach. 

3. Attribute DescriptionsThe dataset consists of the following attributes:



area_km2: This attribute represents the area of each water body in square kilometers (km²). It was calculated from the GSW maximum extent layer and other data sources.

source: In cases where the developed product overlapped with datasets from CRD, GOODD, or GeoDAR, we prioritized the sources in the following order: CRD, followed by GeoDAR, and finally GOODD. Specifically, if an overlap occurred between the developed product and any of these datasets, we retained the source data from CRD as the highest priority. If CRD data was not available, we used data from GeoDAR, with GOODD being used as the last option.



'Hao et al. 2024': Data directly derived and classified using the methodologies described in Hao et al.

'GeoDAR v1.1': HRWs identified from the GeoDAR v1.1 dataset.

'GOODD': HRWs identified from the GOODD dataset.

'CRD': HRWs identified from the CRD dataset.



lon: Longitude coordinate of the centroid of the water body. 

lat: Latitude coordinate of the centroid of the water body.


Reference:



Hao, Z. (2024). Global_Extent_of_Human_Regulated_Water_Bodies (GHRW) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13777813

Song, C., Fan, C., Zhu, J., Wang, J., Sheng, Y., Liu, K., ... & Ke, L. (2022). A comprehensive geospatial database of nearly 100 000 reservoirs in China. Earth System Science Data, 14(9), 4017-4034.

Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., ... & Wada, Y. (2021). GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations. Earth System Science Data Discussions, 2021, 1-52.

Mulligan, M., van Soesbergen, A., & Sáenz, L. (2020). GOODD, a global dataset of more than 38,000 georeferenced dams. Scientific Data, 7(1), 31.
DOI: 10.5281/zenodo.13836046
title: LBPM Simulations

description: This data upload is part of a broader study that explores a wide range of simulations conducted using the LBPM simulation software (available at https://github.com/OPM). The dataset consists of extensive GPU-based LBM (Lattice Boltzmann Method) simulations for determining relative permeability and pore-scale morphologcial properties, representing one of the largest open-access datasets of its kind. The data are structured in CSV files and R scripts are available for post analysis.These simulations, provide critical insights into the influence of wettability, multiscale system upscaling, and ganglion dynamics on multiphase flow behavior.

The dataset is not only valuable for traditional analysis but also serves as a foundation for emerging techniques such as deep learning-assisted time-stepping, phase morphological generation, and predictive modeling of relative permeability and/or phase morphological evolution. By offering this comprehensive dataset to the porous media research community, we aim to foster broad collaboration and support innovative future research directions.
DOI: 10.5281/zenodo.13836047
title: LBPM Simulations

description: This data upload is part of a broader study that explores a wide range of simulations conducted using the LBPM simulation software (available at https://github.com/OPM). The dataset consists of extensive GPU-based LBM (Lattice Boltzmann Method) simulations for determining relative permeability and pore-scale morphologcial properties, representing one of the largest open-access datasets of its kind. The data are structured in CSV files and R scripts are available for post analysis.These simulations, provide critical insights into the influence of wettability, multiscale system upscaling, and ganglion dynamics on multiphase flow behavior.

The dataset is not only valuable for traditional analysis but also serves as a foundation for emerging techniques such as deep learning-assisted time-stepping, phase morphological generation, and predictive modeling of relative permeability and/or phase morphological evolution. By offering this comprehensive dataset to the porous media research community, we aim to foster broad collaboration and support innovative future research directions.
DOI: 10.6084/m9.figshare.25523734
title: 2020 U.S. Congressional Elections Tweet Data

description: 2020 U.S. Congressional Elections Tweet Data
DOI: 10.6084/m9.figshare.25523734.v3
title: 2020 U.S. Congressional Elections Tweet Data

description: 2020 U.S. Congressional Elections Tweet Data
DOI: 10.5281/zenodo.13841142
title: Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization

description: Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models. Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.This project provide the source dataset used to train and evaluate the overall model.
DOI: 10.5281/zenodo.13761485
title: Coarse-Grained and Multi-Dimensional Data-Driven Molecular Generation: A Structure-Based Framework for Selective Inhibitor Design and Optimization

description: Many approaches not only fail to consider the intricate binding pocket interactions, leading to molecules with suboptimal properties and stability, but also struggle with designing selective inhibitors. To address this challenge, we have developed an innovative structure-based three-dimensional molecular generation framework named Coarse-grained and Multi-dimensional Data-driven molecular generation (CMD-GEN). This framework bridges three-dimensional ligand-protein complex data with two-dimensional drug-like molecule data by utilizing coarse-grained pharmacophore points sampled from diffusion models, thereby enriching the training data for generative models. Through a hierarchical architecture, it decomposes the generation of three-dimensional molecules within the pocket into sampling of coarse-grained pharmacophore points, generating of chemical structures, and alignment of conformations, avoiding the instability issues associated with inherent in deep generative model-based generation of molecular conformations.This project provide the source dataset used to train and evaluate the overall model.
DOI: 10.5519/qd.sjw5zpys
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (136 records)
DOI: 10.15468/dl.d79nt6
title: Occurrence Download

description: A dataset containing 10265 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Actualización del banco de datos florístico de la Península de Yucatán (BAFLOPY)"
 ]
}

 The dataset includes 10265 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031101-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9bh2an
title: Occurrence Download

description: A dataset containing 283583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Squamata",
 "Year 1970-2024"
 ]
}

 The dataset includes 283583 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031092-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jpgzum
title: Occurrence Download

description: A dataset containing 8734 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Protozoa"
 ]
}

 The dataset includes 8734 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031100-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xkxx5
title: Occurrence Download

description: A dataset containing 57955 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Linum lewisii Pursh, Populus tremuloides Michx., Camissonia pusilla P.H.Raven)"
 ]
}

 The dataset includes 57955 records from 393 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031102-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nsfrew
title: Occurrence Download

description: A dataset containing 81034 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "GadmGid is one of (COL.21.27_2)"
 ]
}

 The dataset includes 81034 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031103-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.