cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2j2rad
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Kajikia audax (Philippi, 1887)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027844-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hnzp9g
title: Occurrence Download

description: A dataset containing 130 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudopleuronectes herzensteini (Jordan & Snyder, 1901)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 130 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027846-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10607782
title: Datasets for Insights into prismatic loop formation in irradiated Fe-Cr alloys from hypothesis-driven active learning and causal analysis

description: Datasets for irradiated Fe-Cr alloys are collected from the experimental reports on dislocation loop type and dislocation density. We have constructed a data set from experimental literature containing 182 data points. To address such challenges to predict dislocation density, we have implemented a three-step ML approach as listed in the following:











impute dataset to fill in the missing data to construct a predictive model using the RF regression algorithm. 




generate functionalized features and evaluate feature importance using the predictive model




use the physics-based important functionalized features as hypotheses (physics-augmented GP models) in a hypothesis-driven active learning scheme to learn and predict dislocation density for all alloys.
DOI: 10.5281/zenodo.10607783
title: Datasets for Insights into prismatic loop formation in irradiated Fe-Cr alloys from hypothesis-driven active learning and causal analysis

description: Datasets for irradiated Fe-Cr alloys are collected from the experimental reports on dislocation loop type and dislocation density. We have constructed a data set from experimental literature containing 182 data points. To address such challenges to predict dislocation density, we have implemented a three-step ML approach as listed in the following:











impute dataset to fill in the missing data to construct a predictive model using the RF regression algorithm. 




generate functionalized features and evaluate feature importance using the predictive model




use the physics-based important functionalized features as hypotheses (physics-augmented GP models) in a hypothesis-driven active learning scheme to learn and predict dislocation density for all alloys.
DOI: 10.15468/dl.23w6ah
title: Occurrence Download

description: A dataset containing 140709 records in GBIF matching the query:
SELECT PRINTF('%04d-%02d', "year", "month") AS yearMonth, decimalLatitude AS lat, decimalLongitude AS lon, family, familyKey, genus, genusKey, species, speciesKey, iucnRedListCategory, basisOfRecord FROM occurrence WHERE iucnRedListCategory IN ('NT','VU','EN','CR','EW','EX','LC') AND occurrenceStatus = 'PRESENT' AND countryCode = 'ES' AND classKey = 359 AND hasCoordinate = TRUE AND (coordinateUncertaintyInMeters &lt;= 1000 OR coordinateUncertaintyInMeters IS NULL) AND speciesKey IS NOT NULL AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID') AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH') AND "month" IS NOT NULL AND basisOfRecord IN ('HUMAN_OBSERVATION','MACHINE_OBSERVATION','MATERIAL_CITATION','OBSERVATION','OCCURRENCE') GROUP BY yearMonth, lat, lon, familyKey, family, genusKey, genus, speciesKey, species, iucnRedListCategory, basisOfRecord

 The dataset's 140709 records were derived from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027837-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.um8u2a
title: Occurrence Download

description: A dataset containing 319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudopleuronectes yokohamae (Günther, 1877)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 319 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027847-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26317678.v1
title: Dataset and scripts to perform phylogenetic reconstruction and climate modelling on the evolution and spatial distribution of melanism in vipers

description: In this work we investigate the evolutionary significance of melanism in Viperidae, under an ecological perspective by accounting for the thermal advantages provided by melanism to thermoregulation efficiency, thus testing for the effect of climate on the spatial distribution of this phenotypic trait.
DOI: 10.15468/dl.h6bbde
title: Occurrence Download

description: A dataset containing 631 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scomber japonicus Houttuyn, 1782",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 631 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027848-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12752335
title: 2011-2012 SANAE SuperDARN Radar data

description: The data from the South African National Antarctic Expedition (SANAE) Super Dual Auroral Radar Network (Su-perDARN) High Frequency (HF) radar located in Antarctica between 2011 and 2012.
DOI: 10.5281/zenodo.12752336
title: 2011-2012 SANAE SuperDARN Radar data

description: The data from the South African National Antarctic Expedition (SANAE) Super Dual Auroral Radar Network (Su-perDARN) High Frequency (HF) radar located in Antarctica between 2011 and 2012.
DOI: 10.15468/dl.2urpqa
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scomber colias Gmelin, 1789",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027849-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.7515603
title: NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model

description: Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. 

An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023 

Dataset: A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution. "NH-SWE_dataset_MAP.png" shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. 

Files: The dataset is provided in two different formats:



Individual .csv files for each station in the NH-SWE dataset at "NH_SWE_dataset_vector_files.zip"

Full-dataset .csv matrices with dates as rows and NH-SWE stations as columns at "NH_SWE_dataset_matrix_files.zip"


Metadata: "NH_SWE_METADATA.csv" Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the "README_fileformats.txt" file. 

ΔSNOW model parameter regionalisation: The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in "DeltaSNOW_parameter_regionalisation.zip". The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the "README_regionalisation.txt" file. 

Data use: Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See "README_datausage.txt"

Version history:v1: Initial upload. The ΔSNOW model regionalisation was missing.v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.

Reported errors:The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY). The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations. Japan country code is "JA" in the metadata, although the official country code should be JP.
DOI: 10.5281/zenodo.7565252
title: NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model

description: Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. 

An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript published in the journal Earth System Science Data: https://essd.copernicus.org/articles/15/2577/2023/essd-15-2577-2023 

Dataset: A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution. "NH-SWE_dataset_MAP.png" shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. 

Files: The dataset is provided in two different formats:



Individual .csv files for each station in the NH-SWE dataset at "NH_SWE_dataset_vector_files.zip"

Full-dataset .csv matrices with dates as rows and NH-SWE stations as columns at "NH_SWE_dataset_matrix_files.zip"


Metadata: "NH_SWE_METADATA.csv" Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the "README_fileformats.txt" file. 

ΔSNOW model parameter regionalisation: The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in "DeltaSNOW_parameter_regionalisation.zip". The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the "README_regionalisation.txt" file. 

Data use: Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See "README_datausage.txt"

Version history:v1: Initial upload. The ΔSNOW model regionalisation was missing.v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code.

Reported errors:The dataset accidentally contains one station from the Southern Hemisphere (NH-SWE ID 500001), located in Antarctica (Country code AY). The longitude of a few stations exceeds +180 decimal degrees. To obtain the correct value within the [-180,180] decimal degree longitude bounds, the value exceeding +180 needs to be added to -180 degrees (e.g. +181.0 degrees is actually -179.0 degrees).Swedish stations have two different country codes, SE for the ECA&D stations, and SW for the GHCNd stations. Japan country code is "JA" in the metadata, although the official country code should be JP.
DOI: 10.5281/zenodo.12740202
title: Transcriptional profiling of murine embrionic fibroblasts infected with high capacity MCMV vector.

description: To gain a detailed knowledge about the virus cycle of murine cytomegalovirus (MCMV) and its high capacity vector in cross-species settings, we analyzed the early and late viral and host transcriptome upon infection of human cells. ARPE-19, A549, and 911 cells (5.0×105 cells/well) were infected in a 24-well format 4 h post-seeding with MCMV-wt or Q4-LRBAs-GLuc, a high capacity replication competent vector based on MCMV, at an MOI of 3. As control, we used infection of mouse embryonic fibroblast, which is the natural host of the wild type MCMV, treated similarly. Harvesting occurred at 8 hpi and 31 hpi through centrifugation at 1.000 g for 5 min. Cell pellets were washed with PBS, re-suspended in 350 µL of RLT buffer, and processed using the RNeasy Mini kit as per the manufacturer’s instructions in independent triplicates. At least 1.500 ng of RNA in a 30 µL volume was isolated and sent for Illumina next-generation sequencing, resulting in paired-end sequencing with a read length of 2×100 bp and a depth of 20 million reads. This submission contains the reads, we obtained analyzing the infections of MEFs. The data sheet for the samples and the reference genomes (.gb), which we used in the analysis published in Riedl et al. Vaccines 2024 can be found in the REFERENCES_MEF.zip folder. Please, find the control reads for uninfected cells in a separate upload entitled: Mock infected controls for transcriptional profiling of infections with high capacity MCMV vector (DOI 10.5281/zenodo.1250410).
DOI: 10.5281/zenodo.12740203
title: Transcriptional profiling of murine embrionic fibroblasts infected with high capacity MCMV vector.

description: To gain a detailed knowledge about the virus cycle of murine cytomegalovirus (MCMV) and its high capacity vector in cross-species settings, we analyzed the early and late viral and host transcriptome upon infection of human cells. ARPE-19, A549, and 911 cells (5.0×105 cells/well) were infected in a 24-well format 4 h post-seeding with MCMV-wt or Q4-LRBAs-GLuc, a high capacity replication competent vector based on MCMV, at an MOI of 3. As control, we used infection of mouse embryonic fibroblast, which is the natural host of the wild type MCMV, treated similarly. Harvesting occurred at 8 hpi and 31 hpi through centrifugation at 1.000 g for 5 min. Cell pellets were washed with PBS, re-suspended in 350 µL of RLT buffer, and processed using the RNeasy Mini kit as per the manufacturer’s instructions in independent triplicates. At least 1.500 ng of RNA in a 30 µL volume was isolated and sent for Illumina next-generation sequencing, resulting in paired-end sequencing with a read length of 2×100 bp and a depth of 20 million reads. This submission contains the reads, we obtained analyzing the infections of MEFs. The data sheet for the samples and the reference genomes (.gb), which we used in the analysis published in Riedl et al. Vaccines 2024 can be found in the REFERENCES_MEF.zip folder. Please, find the control reads for uninfected cells in a separate upload entitled: Mock infected controls for transcriptional profiling of infections with high capacity MCMV vector (DOI 10.5281/zenodo.1250410).
DOI: 10.15468/dl.d8x8ae
title: Occurrence Download

description: A dataset containing 643 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pagrus major (Temminck & Schlegel, 1843)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 643 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027851-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5523/bris.34e2l8q4im5jc2ls5hru6s7a8l
title: AMU Wildlife Survey USA

description: Raw data from a survey conducted in the USA on antimicrobial and probiotic use in wildlife rehabiltiation centers.
DOI: 10.15468/dl.95yxkn
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Octopus sinensis d'Orbigny, 1834",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 20 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027853-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4prueg
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Octopus vulgaris Cuvier, 1797",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 38 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027854-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cma29r
title: Occurrence Download

description: A dataset containing 852 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gadus macrocephalus Tilesius, 1810",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 852 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027856-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8281216
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2001)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8281215
title: LGHAP v2:  Global daily 1-km gap-free AOD grids (2001)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8313612
title: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2020)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.
DOI: 10.5281/zenodo.8313613
title: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2020)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.