cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.xv9fgy
title: Occurrence Download

description: A dataset containing 40286 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Lepidium campestre (L.) W.T.Aiton)"
 ]
}

 The dataset includes 40286 records from 1012 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011466-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.55h4ym
title: Occurrence Download

description: A dataset containing 177 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Lepidium chalepensis L.)"
 ]
}

 The dataset includes 177 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011468-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wc7bjk
title: Occurrence Download

description: A dataset containing 13217 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Peltigera rufescens (Weiss) Humb."
 ]
}

 The dataset includes 13217 records from 248 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011467-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17495447
title: CHM_Tmin: A New High-Resolution Minimum Temperature Dataset for Mainland China

description: 1.  Dataset information

Dataset name: CHM_Tmin

Summary: CHM_Tmin, an innovative and comprehensive long-term daily Minimum temperature (Tmin) dataset with a spatial resolution of 0.1° and data collected from 1961 to 2024 in mainland China. 

Content of the data set:

(1)  Metadata for CHM_Tmin.docx: This document provides detailed information about the dataset.

(2)  CHM_Tmin_01_daily_1961_2024.zip: Compressed file containing gridded daily minimum temperature (Tmin) fields at 0.1° spatial resolution for the period 1961–2024 (NetCDF format; “01” = 0.1°, “daily” = interpolated daily data, “1961_2024” = years).

To facilitate downloading, we have split the data into nc data every ten years.

CHM_Tmin_1961_1969.nc 

CHM_Tmin_1970_1979.nc

CHM_Tmin_1980_1989.nc

CHM_Tmin_1990_1999.nc

CHM_Tmin_2000_2009.nc

CHM_Tmin_2010_2019.nc

CHM_Tmin_2020_2024.nc

2.  Brief calculation introduction:

The dataset utilizes high-density meteorological station data and follows a structured framework based on the China Hydro-Meteorology dataset (CHM; https://zenodo.org/communities/chm/records?q=&l=list&p=1&s=10). To ensure data integrity, rigorous quality control was applied and missing values were removed to facilitate reliable interpolation.

Key meteorological variables—Tmax, Tmin, Tmean—were interpolated to a 0.1° spatial resolution using angular distance weighting (ADW). This method incorporates both angular and distance weighting, improving robustness against outliers. We interpolated the basic meteorological variables (Tmax, Tmin, Tmean; see Fig. 1), and in the interpolation process adopted ADW interpolation, which considers angle weight in addition to the distance weight function, making it more robust to outliers. We interpolated meteorological elements to 0.1° spatial resolution, which is consistent with CHM_Drought (https://zenodo.org/records/14634774) and CHM_PRE (https://zenodo.org/records/15735374). The ADW approach employed a modified Shepard’s algorithm, which integrates the correlation decay distance (CDD). CDD defines the distance at which inter-station correlation falls below 1/e, roughly equivalent to a 0.05 significance level in large samples (Tmax≈272, Tmeans≈99, Tmins≈136). Using CDD to constrain the number of stations per grid cell enhances interpolation accuracy.

3.  Data advantage characteristics:

In addition, compared with other datasets, the three temperature data (Tmax, Tmin, Tmean) in this dataset all take into account the influence of altitude. For every 100 meters increase in altitude, the temperature drops by approximately 0.6 degrees Celsius. 


4.  Details of the variables in the file

Each NetCDF file contains the following four variables:

(1) lat: Latitude dimension, measured in degrees (°).

(2) lon: Longitude dimension, measured in degrees (°).

(3) time: Time dimension, measured in days since January 1, 1961.

(4) Tmin: Daily minimum temperature (time, lat, lon).

5.  Authors and contacts

Qi Zhang (qizhang@qhnu.edu.cn)

Chiyuan Miao (miaocy@bnu.edu.cn)

Jinlong Hu (Hujl98@mail.bnu.edu.cn)
DOI: 10.5281/zenodo.17495448
title: CHM_Tmin: A New High-Resolution Minimum Temperature Dataset for Mainland China

description: 1.  Dataset information

Dataset name: CHM_Tmin

Summary: CHM_Tmin, an innovative and comprehensive long-term daily Minimum temperature (Tmin) dataset with a spatial resolution of 0.1° and data collected from 1961 to 2024 in mainland China. 

Content of the data set:

(1)  Metadata for CHM_Tmin.docx: This document provides detailed information about the dataset.

(2)  CHM_Tmin_01_daily_1961_2024.zip: Compressed file containing gridded daily minimum temperature (Tmin) fields at 0.1° spatial resolution for the period 1961–2024 (NetCDF format; “01” = 0.1°, “daily” = interpolated daily data, “1961_2024” = years).

To facilitate downloading, we have split the data into nc data every ten years.

CHM_Tmin_1961_1969.nc 

CHM_Tmin_1970_1979.nc

CHM_Tmin_1980_1989.nc

CHM_Tmin_1990_1999.nc

CHM_Tmin_2000_2009.nc

CHM_Tmin_2010_2019.nc

CHM_Tmin_2020_2024.nc

2.  Brief calculation introduction:

The dataset utilizes high-density meteorological station data and follows a structured framework based on the China Hydro-Meteorology dataset (CHM; https://zenodo.org/communities/chm/records?q=&l=list&p=1&s=10). To ensure data integrity, rigorous quality control was applied and missing values were removed to facilitate reliable interpolation.

Key meteorological variables—Tmax, Tmin, Tmean—were interpolated to a 0.1° spatial resolution using angular distance weighting (ADW). This method incorporates both angular and distance weighting, improving robustness against outliers. We interpolated the basic meteorological variables (Tmax, Tmin, Tmean; see Fig. 1), and in the interpolation process adopted ADW interpolation, which considers angle weight in addition to the distance weight function, making it more robust to outliers. We interpolated meteorological elements to 0.1° spatial resolution, which is consistent with CHM_Drought (https://zenodo.org/records/14634774) and CHM_PRE (https://zenodo.org/records/15735374). The ADW approach employed a modified Shepard’s algorithm, which integrates the correlation decay distance (CDD). CDD defines the distance at which inter-station correlation falls below 1/e, roughly equivalent to a 0.05 significance level in large samples (Tmax≈272, Tmeans≈99, Tmins≈136). Using CDD to constrain the number of stations per grid cell enhances interpolation accuracy.

3.  Data advantage characteristics:

In addition, compared with other datasets, the three temperature data (Tmax, Tmin, Tmean) in this dataset all take into account the influence of altitude. For every 100 meters increase in altitude, the temperature drops by approximately 0.6 degrees Celsius. 


4.  Details of the variables in the file

Each NetCDF file contains the following four variables:

(1) lat: Latitude dimension, measured in degrees (°).

(2) lon: Longitude dimension, measured in degrees (°).

(3) time: Time dimension, measured in days since January 1, 1961.

(4) Tmin: Daily minimum temperature (time, lat, lon).

5.  Authors and contacts

Qi Zhang (qizhang@qhnu.edu.cn)

Chiyuan Miao (miaocy@bnu.edu.cn)

Jinlong Hu (Hujl98@mail.bnu.edu.cn)
DOI: 10.2908/ilc_chmd07
title: Enfants (de moins de 16 ans) ne pouvant pas consommer des fruits et légumes frais une fois par jour pour des raisons financières, selon la situation de risque de pauvreté ou d’exclusion sociale
title: Kinder (unter 16 Jahren), die aus finanziellen Gründen nicht einmal täglich frisches Obst und Gemüse verzehren können, nach Armuts- oder sozialer Ausgrenzungsgefährdung
title: Children (aged less than 16) unable to have fresh fruits and vegetables once a day due to financial reasons by risk of poverty or social exclusion

description: Anteil der Kinder (unter 16 Jahren), die aufgrund eines erzwungenen mangelnden Zugangs zu einer täglichen Versorgung mit frischem Obst und Gemüse betroffen sind, nach Armuts- oder sozialer Ausgrenzungsgefährdung.
description: Share of children (aged less than 16 years) who suffer from an enforced lack of access to fresh fruits and vegetables once a day by at risk of poverty or social exclusion situation.
description: Part des enfants (de moins de 16 ans) souffrant d’un manque d’accès imposé à la consommation quotidienne de fruits et légumes frais, selon la situation de risque de pauvreté ou d’exclusion sociale.
DOI: 10.2908/ilc_chmd08
title: Enfants (de moins de 16 ans) ne pouvant pas consommer un repas avec de la viande, du poulet, du poisson (ou un équivalent végétarien) au moins une fois par jour pour des raisons financières, selon la situation de risque de pauvreté ou d’exclusion sociale
title: Kinder (unter 16 Jahren), die aus finanziellen Gründen nicht mindestens einmal täglich eine Mahlzeit mit Fleisch, Geflügel, Fisch (oder einer vegetarischen Alternative) verzehren können, nach Armuts- oder sozialer Ausgrenzungsgefährdung
title: Children (aged less than 16) unable to have a meal with meat, chicken, fish (or vegetarian equivalent) at least once a day due to financial reasons by risk of poverty or social exclusion

description: Anteil der Kinder (unter 16 Jahren), die aufgrund eines erzwungenen mangelnden Zugangs zu einer täglichen Mahlzeit mit Fleisch, Geflügel, Fisch (oder einer vegetarischen Alternative) betroffen sind, nach Armuts- oder sozialer Ausgrenzungsgefährdung
description: Share of children (aged less than 16 years) who suffer from an enforced lack of access to a meal with meat, chicken, fish (or vegetarian equivalent) at least once a day by at risk of poverty or social exclusion situation.
description: Part des enfants (de moins de 16 ans) souffrant d’un manque d’accès imposé à un repas comprenant de la viande, du poulet, du poisson (ou un équivalent végétarien) au moins une fois par jour, selon la situation de risque de pauvreté ou d’exclusion sociale.
DOI: 10.2908/ilc_chmd05
title: Enfants (de moins de 16 ans) ne pouvant pas participer aux sorties scolaires et aux activités scolaires pour des raisons financières, selon la situation de risque de pauvreté ou d’exclusion sociale
title: Kinder (unter 16 Jahren), die aus finanziellen Gründen nicht an Schulfahrten und Schulveranstaltungen teilnehmen können, nach Armuts- oder sozialer Ausgrenzungsgefährdung
title: Children (aged less than 16) unable to participate in school trips and school events due to financial reasons by risk of poverty or social exclusion

description: Anteil der Kinder (unter 16 Jahren), die aufgrund erzwungener mangelnder Teilnahme an Schulfahrten und Schulveranstaltungen betroffen sind, nach Armuts- oder sozialer Ausgrenzungsgefährdung.
description: Share of children (aged less than 16 years) who suffer from an enforced lack of access to school trips and school events by at risk of poverty or social exclusion situation. 
description: Part des enfants (de moins de 16 ans) souffrant d’un manque d’accès imposé aux sorties scolaires et aux activités scolaires, selon la situation de risque de pauvreté ou d’exclusion sociale.
DOI: 10.2908/ilc_chmd06
title: Enfants (de moins de 16 ans) ne pouvant pas participer régulièrement à des activités de loisirs pour des raisons financières, selon la situation de risque de pauvreté ou d’exclusion sociale
title: Kinder (unter 16 Jahren), die aus finanziellen Gründen nicht regelmäßig an Freizeitaktivitäten teilnehmen können, nach Armuts- oder sozialer Ausgrenzungsgefährdung
title: Children (aged less than 16) unable to participate in regular leisure activities due to financial reasons by risk of poverty or social exclusion

description: Anteil der Kinder (unter 16 Jahren), die aufgrund eines erzwungenen mangelnden Zugangs zu regelmäßigen Freizeitaktivitäten betroffen sind, nach Armuts- oder sozialer Ausgrenzungsgefährdung.
description: Share of children (aged less than 16 years) who suffer from an enforced lack of access to regular leisure activities by at risk of poverty or social exclusion situation.
description: Part des enfants (de moins de 16 ans) souffrant d’un manque d’accès imposé aux activités de loisirs régulières, selon la situation de risque de pauvreté ou d’exclusion sociale.
DOI: 10.15468/dl.7kdnbd
title: Occurrence Download

description: A dataset containing 685844 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Pakistan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2000-2025"
 ]
}

 The dataset includes 685844 records from 236 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011579-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wdevvp
title: Occurrence Download

description: A dataset containing 50092 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Vascular Plant Herbarium: Herbarium Generale",
 "Country is Sweden"
 ]
}

 The dataset includes 50092 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011590-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wsx68q
title: Occurrence Download

description: A dataset containing 7244750 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrence Data of Vascular Plants collected or compiled for the Flora of Bavaria"
 ]
}

 The dataset includes 7244750 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011586-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17720471
title: Data for "Acute Inhibition of NMDA Receptor Function by Bisphenol A and Bisphenol F in Adult Brain"
title: Morris_Water_Maze_BPA_during_reversal_only

description: This dataset contains behavioral results from rats tested in the Morris Water Maze, focusing on the reversal learning phase. Animals received either saline (1 mg/kg) or Bisphenol A (BPA; 400 µg/ml/kg) 50 minutes before the reversal day. Data are split across two sheets:



Sheet 1: Average latency (time to reach the platform) across training days, computed from 8 swims per day.

Sheet 2: Probe-trial parameters quantifying spatial memory performance after reversal learning.


Variables include treatment group, sex (0 = males, 1 = females), latencies, distance from the former platform area, time spent in the target zone, frequency of target crossings, and latency to first crossing.
DOI: 10.5281/zenodo.17720472
title: Data for "Acute Inhibition of NMDA Receptor Function by Bisphenol A and Bisphenol F in Adult Brain"
title: Morris_Water_Maze_BPA_during_reversal_only

description: This dataset contains behavioral results from rats tested in the Morris Water Maze, focusing on the reversal learning phase. Animals received either saline (1 mg/kg) or Bisphenol A (BPA; 400 µg/ml/kg) 50 minutes before the reversal day. Data are split across two sheets:



Sheet 1: Average latency (time to reach the platform) across training days, computed from 8 swims per day.

Sheet 2: Probe-trial parameters quantifying spatial memory performance after reversal learning.


Variables include treatment group, sex (0 = males, 1 = females), latencies, distance from the former platform area, time spent in the target zone, frequency of target crossings, and latency to first crossing.
DOI: 10.5281/zenodo.17709072
title: HisDiff: Multi-Scale Histology-Conditioned Diffusion Models for Spatial Transcriptomics Inference
DOI: 10.5281/zenodo.17709073
title: HisDiff: Multi-Scale Histology-Conditioned Diffusion Models for Spatial Transcriptomics Inference
DOI: 10.15468/dl.5k44cf
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((12.31116 46.04722,12.37988 46.04722,12.37988 46.07470,12.31116 46.07470,12.31116 46.04722))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Strix uralensis"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011596-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17489937
title: CHM_Tmax: A New High-Resolution Maximum Temperature Dataset for Mainland China

description: 1.  Dataset information

Dataset name: CHM_Tmax

Summary: CHM_Tmax, an innovative and comprehensive long-term daily Maximum temperature (Tmax) dataset with a spatial resolution of 0.1° and data collected from 1961 to 2024 in mainland China. 

Content of the data set:

(1)  Metadata for CHM_Tmax.docx: This document provides detailed information about the dataset.

(2)  CHM_Tmax_01_daily_1961_2024.zip: Compressed file containing gridded daily maximum temperature (Tmax) fields at 0.1° spatial resolution for the period 1961–2024 (NetCDF format; “01” = 0.1°, “daily” = interpolated daily data, “1961_2024” = years).

To facilitate downloading, we have split the data into nc data every ten years.

CHM_Tmax_1961_1969.nc

CHM_Tmax_1970_1979.nc

CHM_Tmax_1980_1989.nc

CHM_Tmax_1990_1999.nc

CHM_Tmax_2000_2009.nc

CHM_Tmax_2010_2019.nc

CHM_Tmax_2020_2024.nc

2.  Brief calculation introduction:

The dataset utilizes high-density meteorological station data and follows a structured framework based on the China Hydro-Meteorology dataset (CHM; https://zenodo.org/communities/chm/records?q=&l=list&p=1&s=10). To ensure data integrity, rigorous quality control was applied and missing values were removed to facilitate reliable interpolation.

Key meteorological variables—Tmax, Tmin, Tmean—were interpolated to a 0.1° spatial resolution using angular distance weighting (ADW). This method incorporates both angular and distance weighting, improving robustness against outliers. We interpolated the basic meteorological variables (Tmax, Tmin, Tmean; see Fig. 1), and in the interpolation process adopted ADW interpolation, which considers angle weight in addition to the distance weight function, making it more robust to outliers. We interpolated meteorological elements to 0.1° spatial resolution, which is consistent with CHM_Drought (https://zenodo.org/records/14634774) and CHM_PRE (https://zenodo.org/records/15735374). The ADW approach employed a modified Shepard’s algorithm, which integrates the correlation decay distance (CDD). CDD defines the distance at which inter-station correlation falls below 1/e, roughly equivalent to a 0.05 significance level in large samples (Tmax≈272, Tmeans≈99, Tmins≈136). Using CDD to constrain the number of stations per grid cell enhances interpolation accuracy.

3.  Data advantage characteristics:

In addition, compared with other datasets, the three temperature data (Tmax, Tmin, Tmean) in this dataset all take into account the influence of altitude. For every 100 meters increase in altitude, the temperature drops by approximately 0.6 degrees Celsius. 


4.  Details of the variables in the file

Each NetCDF file contains the following four variables:

(1) lat: Latitude dimension, measured in degrees (°).

(2) lon: Longitude dimension, measured in degrees (°).

(3) time: Time dimension, measured in days since January 1, 1961.

(4) Tmax: Daily Maximum temperature (time, lat, lon).

5.  Authors and contacts

Qi Zhang (qizhang@qhnu.edu.cn)

Chiyuan Miao (miaocy@bnu.edu.cn)

Jinlong Hu (Hujl98@mail.bnu.edu.cn)
DOI: 10.5281/zenodo.17489938
title: CHM_Tmax: A New High-Resolution Maximum Temperature Dataset for Mainland China

description: 1.  Dataset information

Dataset name: CHM_Tmax

Summary: CHM_Tmax, an innovative and comprehensive long-term daily Maximum temperature (Tmax) dataset with a spatial resolution of 0.1° and data collected from 1961 to 2024 in mainland China. 

Content of the data set:

(1)  Metadata for CHM_Tmax.docx: This document provides detailed information about the dataset.

(2)  CHM_Tmax_01_daily_1961_2024.zip: Compressed file containing gridded daily maximum temperature (Tmax) fields at 0.1° spatial resolution for the period 1961–2024 (NetCDF format; “01” = 0.1°, “daily” = interpolated daily data, “1961_2024” = years).

To facilitate downloading, we have split the data into nc data every ten years.

CHM_Tmax_1961_1969.nc

CHM_Tmax_1970_1979.nc

CHM_Tmax_1980_1989.nc

CHM_Tmax_1990_1999.nc

CHM_Tmax_2000_2009.nc

CHM_Tmax_2010_2019.nc

CHM_Tmax_2020_2024.nc

2.  Brief calculation introduction:

The dataset utilizes high-density meteorological station data and follows a structured framework based on the China Hydro-Meteorology dataset (CHM; https://zenodo.org/communities/chm/records?q=&l=list&p=1&s=10). To ensure data integrity, rigorous quality control was applied and missing values were removed to facilitate reliable interpolation.

Key meteorological variables—Tmax, Tmin, Tmean—were interpolated to a 0.1° spatial resolution using angular distance weighting (ADW). This method incorporates both angular and distance weighting, improving robustness against outliers. We interpolated the basic meteorological variables (Tmax, Tmin, Tmean; see Fig. 1), and in the interpolation process adopted ADW interpolation, which considers angle weight in addition to the distance weight function, making it more robust to outliers. We interpolated meteorological elements to 0.1° spatial resolution, which is consistent with CHM_Drought (https://zenodo.org/records/14634774) and CHM_PRE (https://zenodo.org/records/15735374). The ADW approach employed a modified Shepard’s algorithm, which integrates the correlation decay distance (CDD). CDD defines the distance at which inter-station correlation falls below 1/e, roughly equivalent to a 0.05 significance level in large samples (Tmax≈272, Tmeans≈99, Tmins≈136). Using CDD to constrain the number of stations per grid cell enhances interpolation accuracy.

3.  Data advantage characteristics:

In addition, compared with other datasets, the three temperature data (Tmax, Tmin, Tmean) in this dataset all take into account the influence of altitude. For every 100 meters increase in altitude, the temperature drops by approximately 0.6 degrees Celsius. 


4.  Details of the variables in the file

Each NetCDF file contains the following four variables:

(1) lat: Latitude dimension, measured in degrees (°).

(2) lon: Longitude dimension, measured in degrees (°).

(3) time: Time dimension, measured in days since January 1, 1961.

(4) Tmax: Daily Maximum temperature (time, lat, lon).

5.  Authors and contacts

Qi Zhang (qizhang@qhnu.edu.cn)

Chiyuan Miao (miaocy@bnu.edu.cn)

Jinlong Hu (Hujl98@mail.bnu.edu.cn)