cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2hxs7g
title: CCDC 2321566: Experimental Crystal Structure Determination

description: Related Article: Debotra Sarkar, Petra Vasko, Aisling F. Roper, Agamemnon E. Crumpton, Matthew M. D. Roy, Liam P. Griffin, Charlotte Bogle, Simon Aldridge|2024|J.Am.Chem.Soc.|146|11792|doi:10.1021/jacs.4c00376
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: 10.15468/dl.jfdpwy
title: Occurrence Download

description: A dataset containing 36632680 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((63.45703 4.63665,101.07422 7.43328,96.85547 38.05242,64.16016 38.60399,63.45703 4.63665))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 36632680 records from 2344 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0238141-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t3hgrj
title: Occurrence Download

description: A dataset containing 2372 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Crocodylus acutus (Cuvier, 1807)"
 ]
}

 The dataset includes 2372 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012285-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.spupex
title: Occurrence Download

description: A dataset containing 2176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is New Zealand",
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Orthodera novaezealandiae Colenso, 1882"
 ]
}

 The dataset includes 2176 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012286-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gdfzky
title: Occurrence Download

description: A dataset containing 92 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Dasypus novemcinctus Linnaeus, 1758"
 ]
}

 The dataset includes 92 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012287-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5xme2w
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material citation)",
 "DatasetKey is one of (Inventario de la biodiversidad de vertebrados terrestres de los Chimalapas, Oaxaca, Vertebrados terrestres del Corredor Biológico Sierra Madre del Sur, Chiapas, México, Fortalecimiento de las colecciones de ECOSUR. Primera fase (Herpetológica San cristóbal), Actualización y enriquecimiento de las bases de datos del proyecto de evaluación y análisis geográfico de la diversidad faunística de Chiapas, Anfibios y reptiles de las regiones Chimalapas (7) y bosque de coníferas y BMM de Veracruz (11), UCM Amphibian and Reptile Collection (Arctos))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "PublishingOrg is one of (ff90b050-c256-11db-b71b-b8a03c50a862, 4cadac10-3e7b-11d9-8439-b8a03c50a862)",
 "TaxonKey is Anolis pygmaeus Alvarez Del Toro & Smith, 1956",
 "Year 1950-2024"
 ]
}

 The dataset includes 37 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012290-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jdwpyh
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "GadmGid is CHN.19_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ligularia sibirica (L.) Cass."
 ]
}

 The dataset includes 21 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012288-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n6yvxt
title: Occurrence Download

description: A dataset containing 7332 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Crocodylus acutus (Cuvier, 1807)"
 ]
}

 The dataset includes 7332 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012289-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s6f24z
title: Occurrence Download

description: A dataset containing 5203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Crocodylus moreletii (Duméril & Bibron, 1851)"
 ]
}

 The dataset includes 5203 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012291-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11200973
title: GLObal river Width from Sentinel-2 (GLOW-S)

description: Introduction:

The GLOW-S dataset is a global river width dataset generated from Sentinel-2 imagery.                                                                                                                                                         

Dataset Features:



Temporal Coverage: Data spans from 2017 to 2022.

Spatial Resolution: Cross section level.


Postprocessing: When multiple observations for the same cross-section and date exist, the dataset averages these widths to enhance usability.

Column names for river width data:



crossSxnID (cross section identifier): R<COMID>XS<Section ID>

date (image capture date): YYYY-MM-DD

width (river width captured in meters)


Column names for cross section shapefiles:



crossSxnID (cross section identifier): R<COMID>XS<Section ID>

lat (latitude in WGS84)

lon (longitude in WGS84)
DOI: 10.5281/zenodo.11200974
title: GLOW-S River Width dataset

description: Introduction:

The GLOW-S dataset provides a comprehensive record of global river widths, captured with high-resolution Sentinel-2 imagery. This dataset focuses on delivering an enhanced spatial and temporal analysis of river widths, suitable for diverse hydrological and environmental studies.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     

Data Collection and Processing:



Image Source: River width data were extracted from Sentinel-2 satellite imagery accessed via Google Earth Engine, utilizing higher resolution capabilities than previous datasets based on Landsat.

Extraction Methodology: Following validated methods from prior research (Feng et al., 2019; 2022), the dataset involves:



Identification of rivers observable in Sentinel-2 images.

Construction of cross-sections for width extraction.

Application of quality filters on images to ensure accuracy in width measurements.

Determination of water extent and subsequent river width calculation at each cross-section for images spanning 2017 to 2022.



Dataset Features:



Temporal Coverage: Data spans from 2017 to 2022, capturing seasonal and interannual variations in river width.

Spatial Resolution: Utilizes the superior spatial resolution of 10m from Sentinel-2 for detailed river width analysis.


Postprocessing: When multiple observations for the same cross-section and date exist, the dataset averages these widths to enhance usability.

Column names:



riverID (cross section identifier): R<COMID>XS<Section ID>

date (image capture date): YYYY-MM-DD

width (river width captured in meters)


Additional data: The shapefiles of cross sections are also attached alongside.
DOI: 10.5281/zenodo.12627519
title: GLObal river Width from Sentinel-2 (GLOW-S)

description: Introduction:

The GLOW-S dataset is a global river width dataset generated from Sentinel-2 imagery.                                                                                                                                                         

Dataset Features:



Temporal Coverage: Data spans from 2017 to 2022.

Spatial Resolution: Cross section level.


Postprocessing: When multiple observations for the same cross-section and date exist, the dataset averages these widths to enhance usability.

Column names for river width data:



crossSxnID (cross section identifier): R<COMID>XS<Section ID>

date (image capture date): YYYY-MM-DD

width (river width captured in meters)


Column names for cross section shapefiles:



crossSxnID (cross section identifier): R<COMID>XS<Section ID>

lat (latitude in WGS84)

lon (longitude in WGS84)
DOI: 10.5281/zenodo.11122293
title: Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021

description: This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format.

In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available.The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.
DOI: 10.5281/zenodo.11122294
title: Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021

description: Ground-level PM2.5 data, derived from satellites using machine learning, play a crucial role in health and climate assessments. However, persistent uncertainties stem from the lack of spatially comprehensive observations. To mitigate this issue, we introduce a novel testbed utilizing numerical simulations to assess PM2.5 estimation across the entire spatial domain. This testbed mimics the conventional machine-learning paradigm by training models with grid data from ground monitor sites and evaluating predictive accuracy across other locations. As a result, it facilitates a comprehensive evaluation of various machine-learning methodologies in estimating PM2.5 concentrations throughout the spatial domain. Moreover, the testbed offers an efficient strategy to optimize model structure or training samples, thereby enhancing the performance of satellite-retrieval models. This optimization can involve integrating spatiotemporal features, particularly with CNN-based deep-learning approaches such as the ResNet model as presented here. Additionally, we provide and proposed machine learning method as well as the corresponding estimated daily PM2.5 concentrations spanning the past nine years (2013-2021) over China with 27km by 27km spatial resolution.
DOI: 10.5281/zenodo.12636976
title: Numerical model-informed testbed for surface PM2.5 concentration over China and its estimates during 2013-2021

description: This is an updated version of https://doi.org/10.5281/zenodo.11122294, the data provided in NetCDF format.

In addition to the long-term PM2.5 dataset created from Li et al (2024), which can be used for health assessments and studying air pollution influences, here we also provide testbed data crucial for evaluating machine learning-based retrieval methods, especially in scenarios where no ground-truth data is available.The testbed dataset includes all inputs and outputs following the physical model simulation, which naturally correlates with physical laws such as emissions, diffusion, advection, and deposition, representing typical conditions that any prediction method should meet. This data can be used to evaluate and compare methods using the same dataset, allowing for continuous improvement. Besides traditional cross-validation, the proposed testbed validation is highly recommended to examine a method’s predictive ability. We will continue updating the testbed data for other pollutants and with different resolutions and regions in future studies.
DOI: 10.5439/2372861
title: Baseline status of BOM instruments at Kennaook Cape Grim

description: When utilizing these data from Kennaook Cape Grim please acknowledge the Australian Bureau of Meteorology and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) for their long-term and continued support of the Kennaook Cape Grim Baseline Air Pollution Monitoring Station. In addition, please contact the Lead Scientists responsible for the collection of these data sets (stuart.baly@bom.gov.au, melita.keywood@csiro.au, ruhi.humphries@csiro.au and Erin.dunne@csiro.au) to discuss collaboration and authorship opportunities.
DOI: 10.15468/dl.m9btus
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "OccurrenceStatus is Present",
 "TaxonKey is Lama guanicoe (Müller, 1776)"
 ]
}

 The dataset includes 25 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012292-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.er97nc
title: Occurrence Download

description: A dataset containing 5593 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Crocodylus moreletii (Duméril & Bibron, 1851)"
 ]
}

 The dataset includes 5593 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012293-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9t8h2r
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.19_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Platanthera hologlottis Maxim."
 ]
}

 The dataset includes 16 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012294-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ang5n7
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "GadmGid is CHN.19_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Potentilla verticillaris Stephan ex Willd."
 ]
}

 The dataset includes 38 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012295-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8pg57r
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Crocodylus cataphractus Cuvier, 1824"
 ]
}

 The dataset includes 21 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012296-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yf6ru3
title: Occurrence Download

description: A dataset containing 298 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material citation, Material sample)",
 "DatasetKey is one of (KUBI Herpetology Collection, Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (vertebrados), UCM Amphibian and Reptile Collection (Arctos), Inventario herpetofaunístico del valle semiárido de Tehuacán-Cuicatlán (continuación), LACM Vertebrate Collection, Museum of Comparative Zoology, Harvard University, Inventario herpetofaunístico del valle semiárido de Tehuacán-Cuicatlán, Herpetofauna de la reserva de la biósfera Valle de Tehuacán-Cuicatlán (etapa final), INSDC Sequences, Estudio herpetofaunístico de la Sierra Mazateca, Oaxaca, México)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "PublishingOrg is one of (ff90b050-c256-11db-b71b-b8a03c50a862, b554c320-0560-11d8-b851-b8a03c50a862, 4cadac10-3e7b-11d9-8439-b8a03c50a862, 493fe050-055d-11d8-b84f-b8a03c50a862, b4640710-8e03-11d8-b956-b8a03c50a862, ada9d123-ddb4-467d-8891-806ea8d94230)",
 "TaxonKey is Anolis quercorum Fitch, 1978",
 "Year 1950-2024"
 ]
}

 The dataset includes 298 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012297-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2rgujj
title: Occurrence Download

description: A dataset containing 43918 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Triatoma Laporte, 1832"
 ]
}

 The dataset includes 43918 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012298-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ms3qc6
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Epinephelus coioides (Hamilton, 1822)"
 ]
}

 The dataset includes 19 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012299-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7v3nu6
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Artemidora disistaria Walker, 1862"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012300-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25582/data.2023-07.2837392/2387012
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2837475/2387013
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2837478/2387014
title: Data for EMSL Project 51287 from July 2023
DOI: 10.25582/data.2023-07.2837461/2387015
title: Data for EMSL Project 51287 from July 2023
DOI: 10.15468/dl.vuev2n
title: Occurrence Download

description: A dataset containing 3322531 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Quillaja saponaria Molina, Lithraea caustica Hook. & Arn., Vachellia caven (Molina) Seigler & Ebinger, Jubaea chilensis (Molina) Baill., Peumus boldus Molina, Kageneckia oblonga Ruiz & Pav., Cryptocarya alba (Molina) Looser, Cestrum parqui (Lam.) L'Hér., Maytenus boaria Molina, Citronella mucronata (Ruiz & Pav.) D.Don, Eriosyce curvispina (Bertero ex Colla) Katt., Escallonia pulverulenta (Ruiz & Pav.) Pers., Baccharis salicifolia (Ruiz & Pav.) Pers., Schinus polygama (Cav.) Cabrera, Ephedra chilensis C.Presl, Acacia dealbata Link, Plantago major L., Eriosyce subgibbosa (Haw.) Katt., Silybum marianum (L.) Gaertn., Dodonaea viscosa (L.) Jacq., Kageneckia angustifolia D.Don, Azara petiolaris (D.Don) I.M.Johnst., Leucostele chiloensis (Colla) Schlumpb., Portulaca oleracea L., Drimys winteri J.R.Forst. & G.Forst., Persea lingue (Ruiz & Pav.) Nees, Luma chequen (Molina) A.Gray, Pouteria valparadisaea (Molina), Zea mays L., Beilschmiedia miersii (Gay) Kosterm., Adesmia microphylla Hook. & Arn., Lupinus microcarpus Sims, Physalis peruviana L., Salix humboldtiana Willd., Hordeum murinum L., Poa annua L., Avena fatua L., Myrceugenia obtusa (DC.) O.Berg, Medicago sativa L., Robinia pseudoacacia L., Austrocedrus chilensis (D.Don) Pic.Serm. & Bizzarri, Araucaria araucana (Molina) K.Koch, Austrocactus spiniflorus (Phil.) F.Ritter, Nothofagus glauca (Phil.) Krasser, Blepharocalyx cruckshanksii (Hook. & Arn.) Nied., Araucaria angustifolia (Bertol.) Kuntze)"
 ]
}

 The dataset includes 3322531 records from 6168 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012265-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nrqovb
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Bacteria from Penguin Guano, Antarctica"
 ]
}

 The dataset includes 70 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031102-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gkccr2
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Rhinella rostrata (Noble, 1920)"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027894-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.