cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.x5qwsy
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alouatta discolor",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 44 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024041-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/dans-znb-gdah
title: PAN-00121211 - terra sigillata cup type Drag. 27

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00121211&lt;/p&gt;
description: Modified: 2025-05-06T12:07:09+02:00
DOI: 10.15468/dl.3jbaah
title: Occurrence Download

description: A dataset containing 11014 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to 24.085278",
 "DecimalLatitude is less than or equal to 29.085278",
 "DecimalLongitude is greater than or equal to 50.0",
 "DecimalLongitude is less than or equal to 55.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 11014 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024042-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25505/fiz.icsd.cc2p6c7j
title: CSD 2478767: Experimental Crystal Structure Determination

description: Related Article: Debattam Sarkar, Shima Shahabfar, Yukun Liu, Premakumar Yanda, Thomas S. Ie, Hengdi Zhao, Pronoy Nandi, Kodimana Ramakrishnan Pradeep, Chandra Shekhar, Claudia Felser, Vinayak P. Dravid, Christopher M. Wolverton, Mercouri G. Kanatzidis|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c16757
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: 10.25505/fiz.icsd.cc2p6c8k
title: CSD 2478768: Experimental Crystal Structure Determination

description: Related Article: Debattam Sarkar, Shima Shahabfar, Yukun Liu, Premakumar Yanda, Thomas S. Ie, Hengdi Zhao, Pronoy Nandi, Kodimana Ramakrishnan Pradeep, Chandra Shekhar, Claudia Felser, Vinayak P. Dravid, Christopher M. Wolverton, Mercouri G. Kanatzidis|2025|J.Am.Chem.Soc.|||doi:10.1021/jacs.5c16757
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: 10.25505/fiz.icsd.cc2pxkdl
title: CSD 2500100: Experimental Crystal Structure Determination

description: Related Article: Matt Boswell, Mingyu Xu, Saban M. Hus, Antonio M. dos Santos, Weiwei Xie|2025|Chem.Mater.|||doi:10.1021/acs.chemmater.5c02470
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: 10.25505/fiz.icsd.cc2pxkfm
title: CSD 2500101: Experimental Crystal Structure Determination

description: Related Article: Matt Boswell, Mingyu Xu, Saban M. Hus, Antonio M. dos Santos, Weiwei Xie|2025|Chem.Mater.|||doi:10.1021/acs.chemmater.5c02470
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: 10.58119/ulg/7deaq2
title: CaTiO3 Second-Principles Interatomic Potential

description: Second-principles interatomic potential for the perovskite CaTiO3.
DOI: 10.15468/dl.fvcwfu
title: Occurrence Download

description: A dataset containing 1472 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Japan",
 "TaxonKey is Trochodendron aralioides"
 ]
}

 The dataset includes 1472 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024043-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.db9yzt
title: Occurrence Download

description: A dataset containing 52240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rhaphiolepis bibas, Rhaphiolepis malipoensis, Rhaphiolepis obovata, Rhaphiolepis prinoides, Rhaphiolepis salwinensis)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 52240 records from 430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024031-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/d56a6215ce394ddd8dff6bea5dbb0780
title: ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Terra, level 3 collated (L3C) global product (2000-2021), version 4.00

description: This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Terra (Terra). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.

Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Terra equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.

The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.

The daily dataset starts from 24th February 2000 and ends 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.

In Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.

The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.

The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.
DOI: 10.6084/m9.figshare.28207895
title: Global Household Size and Energy Use Intensity Under Shared Socioeconomic Pathways from 1960 to 2100

description: <b>PUBLICATION:</b> Rodrigues, E., Tinseth, J., Pajek, L., & Fernandes, M. S. (2026). Forecasting global building internal heat gains to 2100: an applied XGBoost model driven by shared socioeconomic pathways. <i>Energy and Buildings</i>, <i>350</i>, 116642. https://doi.org/10.1016/j.enbuild.2025.116642<b>NOTE:</b> Dataset has been updated after using Time Series Blocked Cross Validation XGBoost hyperparameter tuning.This dataset includes synthetic household sizes and end-use energy intensities for countries around the world, spanning various Shared Socioeconomic Pathways (SSP) from 1960 to 2100. Historical data up to 2021 was utilized to calibrate an XGBoost algorithm, which allows for interpolation and extrapolation of data in one-year intervals up to 2021 and in five-year intervals between 2025 and 2100. Five SSPs were predicted: SSP1, SSP2, SSP3, SSP4, and SSP5.Household sizes were predicted for each country, covering the national average, urban areas, and rural regions. Harmonized household sizes from the CORESIDENCE project (https://www.nature.com/articles/s41597-024-02964-3) were used to train an XGBoost model with customized objective function and evaluation metrics to prevent rural and urban household sizes from being both greater or smaller than the national average.Each country's end-use energy intensity for cooking, lights, appliances, and water heating was predicted using an XGBoost model. The free version of the IEA's Energy End-Uses and Efficiency Indicators (https://www.iea.org/data-and-statistics/data-product/energy-efficiency-indicators-highlights) was used.The model's predictors were obtained from the World Bank Database (https://data.worldbank.org), and future projections for each SSP of the same predictors were obtained from the Wittgenstein Centre Human Capital Data Explorer (https://dataexplorer.wittgensteincentre.org/wcde-v3/).
DOI: 10.6084/m9.figshare.28207895.v1
title: Global Household Size and Energy Use Intensity Under Shared Socioeconomic Pathways from 1960 to 2100

description: This dataset includes synthetic household sizes and end-use energy intensities for countries around the world, spanning various Shared Socioeconomic Pathways (SSP) from 1960 to 2100. Historical data up to 2021 was utilized to calibrate an XGBoost algorithm, which allows for interpolation and extrapolation of data in one-year intervals up to 2021 and in five-year intervals between 2025 and 2100. Five SSPs were predicted: SSP1, SSP2, SSP3, SSP4, and SSP5.Household sizes were predicted for each country, covering the national average, urban areas, and rural regions. Harmonized household sizes from the CORESIDENCE project (https://www.nature.com/articles/s41597-024-02964-3) were used to train an XGBoost model with customized objective function and evaluation metrics to prevent rural and urban household sizes from being both greater or smaller than the national average.Each country's end-use energy intensity for cooking, lights, appliances, and water heating was predicted using an XGBoost model. The free version of the IEA's Energy End-Uses and Efficiency Indicators (https://www.iea.org/data-and-statistics/data-product/energy-efficiency-indicators-highlights) was used.The model's predictors were obtained from the World Bank Database (https://data.worldbank.org), and future projections for each SSP of the same predictors were obtained from the Wittgenstein Centre Human Capital Data Explorer (https://dataexplorer.wittgensteincentre.org/wcde-v3/).
DOI: 10.6084/m9.figshare.28207895.v2
title: Global Household Size and Energy Use Intensity Under Shared Socioeconomic Pathways from 1960 to 2100

description: <b>PUBLICATION:</b> Rodrigues, E., Tinseth, J., Pajek, L., & Fernandes, M. S. (2026). Forecasting global building internal heat gains to 2100: an applied XGBoost model driven by shared socioeconomic pathways. <i>Energy and Buildings</i>, <i>350</i>, 116642. https://doi.org/10.1016/j.enbuild.2025.116642<b>NOTE:</b> Dataset has been updated after using Time Series Blocked Cross Validation XGBoost hyperparameter tuning.This dataset includes synthetic household sizes and end-use energy intensities for countries around the world, spanning various Shared Socioeconomic Pathways (SSP) from 1960 to 2100. Historical data up to 2021 was utilized to calibrate an XGBoost algorithm, which allows for interpolation and extrapolation of data in one-year intervals up to 2021 and in five-year intervals between 2025 and 2100. Five SSPs were predicted: SSP1, SSP2, SSP3, SSP4, and SSP5.Household sizes were predicted for each country, covering the national average, urban areas, and rural regions. Harmonized household sizes from the CORESIDENCE project (https://www.nature.com/articles/s41597-024-02964-3) were used to train an XGBoost model with customized objective function and evaluation metrics to prevent rural and urban household sizes from being both greater or smaller than the national average.Each country's end-use energy intensity for cooking, lights, appliances, and water heating was predicted using an XGBoost model. The free version of the IEA's Energy End-Uses and Efficiency Indicators (https://www.iea.org/data-and-statistics/data-product/energy-efficiency-indicators-highlights) was used.The model's predictors were obtained from the World Bank Database (https://data.worldbank.org), and future projections for each SSP of the same predictors were obtained from the Wittgenstein Centre Human Capital Data Explorer (https://dataexplorer.wittgensteincentre.org/wcde-v3/).
DOI: 10.5281/zenodo.17798988
title: ACEM 10321 - raw data
DOI: 10.5281/zenodo.17798989
title: ACEM 10321 - raw data
DOI: 10.17026/ar/v6inpp
title: PAN-00006526 - Roman conical spindle whorl (metal)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00006526&lt;/p&gt;
description: Modified: 2025-08-01T18:15:44+02:00
DOI: 10.5285/a92c495827ef4c81a901f878adb6ef70
title: ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from MODIS (Moderate resolution Infra-red Spectroradiometer) on Aqua, level 3 collated (L3C) global product (2002-2021), version 4.00

description: This dataset contains daily-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System – Aqua (Aqua). Satellite land surface temperatures are skin temperatures which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.

Daytime and night-time temperatures are provided in separate files corresponding to the daytime and night-time Aqua equator crossing times which are 13:30 and 01:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.

The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. MODIS achieves full Earth coverage nearly twice per day so the daily files have small gaps primarily close to the equator where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.

Dataset coverage starts on 4th July 2002 and ends on 31st December 2021. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.

In Version 4.00 the time series has been extended to 2021. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.

The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using a generalised split window retrieval algorithm and data were processed in the UoL processing chain.
The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.
DOI: 10.15468/dl.u8qbe9
title: Occurrence Download

description: A dataset containing 1039 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alouatta guariba",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 1039 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024044-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/4a40c6fe12cc4c0786608065da06d287
title: ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land surface temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3A, level 3 collated (L3C) global product (2016-2023), version 4.00

description: This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.

Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel-3A equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.

The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRA achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.

Dataset coverage starts on 1st May 2016 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.

In Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.

The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.

The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

An extended version of this dataset is also provided through the EOCIS project.
DOI: 10.5285/49b4836314db4fec8d05d14b85a8614d
title: ESA Land Surface Temperature Climate Change Initiative (LST_cci): Daily land Surface Temperature from SLSTR (Sea and Land Surface Temperature Radiometer) on Sentinel 3B, level 3 collated (L3C) global product (2018-2023), version 4.00

description: This dataset contains land surface temperatures (LSTs) and their uncertainty estimates from the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.

Daytime and night-time temperatures are provided in separate files corresponding to the morning and evening Sentinel 3B equator crossing times which are 10:00 and 22:00 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.

The dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. SLSTRB achieves full Earth coverage in 1 day so the daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.

Dataset coverage starts on 17th November 2018 and ends on 31st December 2023. There are minor interruptions (1-10 days) during satellite/instrument maintenance periods or instrument anomalies.

In Version 4.00 the temporal coverage is extended to 31st December 2023. The emissivities used in the retrieval come from the Combined ASTER and MODIS Emissivity over Land (CAMEL) Version 2 database; in Version 4.00 a climatology is used since there are temporal instabilities in the CAMEL time series.

The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.

The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

An extended version of this dataset is also provided through the EOCIS project.
DOI: 10.5281/zenodo.10869371
title: List of bKOM/HOM/DAM Radio Sources crossings during Juno's 77 first perijoves

description: This document shows a dataset of bKOM/HOM/DAM sources detected during the 77 first Juno perijoves. These sources are identified from Juno/Waves Burst mode measurements with the criterion fce<f<fce+1% from Louis (2019).

 

This new version extends the catalogue up to PJ77 (2025-10-17) and completes the previous version with kilometric sources identified in Collet et al. 2025 (doi.org/10.1029%2F2024GL114447) and Collet et al. 2026 (tbp)