cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14824975
title: Ischemic core detection threshold of computed tomography perfusion (CTP) in acute stroke

description: Abstract



Purpose: This study aimed to determine the accuracy of detecting ischemic core volume using computed tomography perfusion (CTP) in patients with suspected acute ischemic stroke compared to diffusion-weighted magnetic resonance imaging (DW-MRI) as the reference standard.

Methods: This retrospective monocentric study included patients who underwent CTP and DW-MRI for suspected acute ischemic stroke. The ischemic core size was measured at DW-MRI. The detectability threshold volume was defined as the lowest volume detected by each method. Clinical data on revascularization therapy, along with the clinical decision that influenced the choice, were collected. Volumes of the ischemic cores were compared using the Mann-Whitney U test.

Results: Of 83 patients who underwent CTP, 52 patients (median age 73 years, IQR 63-80, 36 men) also had DW-MRI and were included, with a total of 70 ischemic cores. Regarding ischemic cores, only 18/70 (26%) were detected by both CTP and DW-MRI, while 52/70 (74%) were detected only by DW-MRI. The median volume of the 52 ischemic cores undetected on CTP (0.6 mL, IQR 0.2-1.3 mL) was significantly lower (p < 0.001) than that of the 18 ischemic cores detected on CTP (14.2 mL, IQR 7.0-18.4 mL). The smallest ischemic core detected on CTP had a volume of 5.0 mL. Among the 20 patients with undetected ischemic core on CTP, only 10% (2/20) received thrombolysis treatment.

Conclusions: CTP maps failed in detecting ischemic cores smaller than 5 mL. DW-MRI remains essential for suspected small ischemic brain lesions to guide a correct treatment decision-making.
DOI: 10.5281/zenodo.14824976
title: Ischemic core detection threshold of computed tomography perfusion (CTP) in acute stroke

description: Abstract



Purpose: This study aimed to determine the accuracy of detecting ischemic core volume using computed tomography perfusion (CTP) in patients with suspected acute ischemic stroke compared to diffusion-weighted magnetic resonance imaging (DW-MRI) as the reference standard.

Methods: This retrospective monocentric study included patients who underwent CTP and DW-MRI for suspected acute ischemic stroke. The ischemic core size was measured at DW-MRI. The detectability threshold volume was defined as the lowest volume detected by each method. Clinical data on revascularization therapy, along with the clinical decision that influenced the choice, were collected. Volumes of the ischemic cores were compared using the Mann-Whitney U test.

Results: Of 83 patients who underwent CTP, 52 patients (median age 73 years, IQR 63-80, 36 men) also had DW-MRI and were included, with a total of 70 ischemic cores. Regarding ischemic cores, only 18/70 (26%) were detected by both CTP and DW-MRI, while 52/70 (74%) were detected only by DW-MRI. The median volume of the 52 ischemic cores undetected on CTP (0.6 mL, IQR 0.2-1.3 mL) was significantly lower (p < 0.001) than that of the 18 ischemic cores detected on CTP (14.2 mL, IQR 7.0-18.4 mL). The smallest ischemic core detected on CTP had a volume of 5.0 mL. Among the 20 patients with undetected ischemic core on CTP, only 10% (2/20) received thrombolysis treatment.

Conclusions: CTP maps failed in detecting ischemic cores smaller than 5 mL. DW-MRI remains essential for suspected small ischemic brain lesions to guide a correct treatment decision-making.
DOI: 10.15468/dl.ksykfn
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Casimiroa tetrameria Millsp.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018023-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6rt638
title: Occurrence Download

description: A dataset containing 413509 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Magnoliopsida",
 "Country is Gabon",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 413509 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018012-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n8vemk
title: Occurrence Download

description: A dataset listing the 1618 species recorded in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-97.08882 31.94804,-97.18651 31.87651,-97.25234 31.73886,-97.16124 31.59471,-97.03609 31.55604,-96.89436 31.63807,-96.80926 31.69907,-96.81252 31.82185,-96.84177 31.90197,-96.94898 31.96865,-97.08882 31.94804))"
 ]
}

 The dataset's 1618 records were derived from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018007-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14824897
title: Collocated brightness temperature measurements at 89 GHz from GMI and EarthCARE CPR noise measurements

description: This dataset contains collocated measurements from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and noise floor estimates from the EarthCARE Cloud Profiling Radar (CPR). The dataset facilitates the study of the relationship between brightness temperature (TB) and CPR receiver noise, with a focus on clear-sky and cloudy scenes.

Each file in the dataset contains the following variables:

Variables



Scene Classification & Spatial Matching- clear_sky_flag: Binary flag indicating cloud presence (1 = clear sky, 0 = cloudy), based on the C-FMR product.- dist_tb_km: Distance (in km) between the EarthCARE footprint and the nearest GMI footprint.- dist_tb_s: Time difference (in seconds) between EarthCARE and GMI observations.

Surface Properties- emmis_89h: Surface emissivity at 89 GHz for horizontal polarization (valid only over water bodies).- emmis_89v: Surface emissivity at 89 GHz for vertical polarization.- land_flag: Surface type indicator (0 = land, 1 = ocean).

Geolocation & Time Information- latitude: Latitude of the observation.- longitude: Longitude of the observation.- time: Timestamp of the observation.

CPR Receiver Noise Estimates- noise_dbm: CPR receiver noise in dBm, estimated from the L1 CPR-NOM-1B product.- noisel2_dbm: CPR receiver noise in dBm, estimated from the L2 CPR-FMR-2B product.

Environmental Variables- SST: Sea surface temperature (K).

Measured & Simulated Brightness Temperatures (TBs)-tb_89_v: GMI measured TB at 89 GHz, vertical polarization.-tb_89_h: GMI measured TB at 89 GHz, horizontal polarization.-tbs_89_v: Simulated TB at 89 GHz, vertical polarization, assuming clear-sky conditions. Simulation used a pure emission-absorption radiative transfer model based on the ECMWF-AUX product.-tbs_89_h: Simulated TB at 89 GHz, horizontal polarization, assuming clear-sky conditions, based on the same model.-tbs_89_v_edd: Simulated TB at 89 GHz, vertical polarization, assuming clear-sky conditions. Simulation used the Eddington radiative transfer model based on the ECMWF-AUX product.-tbs_89_h_edd: Simulated TB at 89 GHz, horizontal polarization, assuming clear-sky conditions, based on the Eddington radiative transfer model.
DOI: 10.5281/zenodo.14824898
title: Collocated brightness temperature measurements at 89 GHz from GMI and EarthCARE CPR noise measurements

description: This dataset contains collocated measurements from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and noise floor estimates from the EarthCARE Cloud Profiling Radar (CPR). The dataset facilitates the study of the relationship between brightness temperature (TB) and CPR receiver noise, with a focus on clear-sky and cloudy scenes.

Each file in the dataset contains the following variables:

Variables



Scene Classification & Spatial Matching- clear_sky_flag: Binary flag indicating cloud presence (1 = clear sky, 0 = cloudy), based on the C-FMR product.- dist_tb_km: Distance (in km) between the EarthCARE footprint and the nearest GMI footprint.- dist_tb_s: Time difference (in seconds) between EarthCARE and GMI observations.

Surface Properties- emmis_89h: Surface emissivity at 89 GHz for horizontal polarization (valid only over water bodies).- emmis_89v: Surface emissivity at 89 GHz for vertical polarization.- land_flag: Surface type indicator (0 = land, 1 = ocean).

Geolocation & Time Information- latitude: Latitude of the observation.- longitude: Longitude of the observation.- time: Timestamp of the observation.

CPR Receiver Noise Estimates- noise_dbm: CPR receiver noise in dBm, estimated from the L1 CPR-NOM-1B product.- noisel2_dbm: CPR receiver noise in dBm, estimated from the L2 CPR-FMR-2B product.

Environmental Variables- SST: Sea surface temperature (K).

Measured & Simulated Brightness Temperatures (TBs)-tb_89_v: GMI measured TB at 89 GHz, vertical polarization.-tb_89_h: GMI measured TB at 89 GHz, horizontal polarization.-tbs_89_v: Simulated TB at 89 GHz, vertical polarization, assuming clear-sky conditions. Simulation used a pure emission-absorption radiative transfer model based on the ECMWF-AUX product.-tbs_89_h: Simulated TB at 89 GHz, horizontal polarization, assuming clear-sky conditions, based on the same model.-tbs_89_v_edd: Simulated TB at 89 GHz, vertical polarization, assuming clear-sky conditions. Simulation used the Eddington radiative transfer model based on the ECMWF-AUX product.-tbs_89_h_edd: Simulated TB at 89 GHz, horizontal polarization, assuming clear-sky conditions, based on the Eddington radiative transfer model.
DOI: 10.15468/dl.rjv2d2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Casimiroa watsonii Engl.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018024-250127130748423/datasets/export for details.
DOI: 10.15468/dl.dgdyra
title: Occurrence Download

description: A dataset containing 187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Decatropis bicolor (Zucc.) Radlk.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 187 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018025-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14812760
title: Sequential Cascaded Multi-Stage RFR Machine Learning Models for Predicting Electrical and Thermal Behavior in High-Capacity Lithium-Ion Batteries

description: Figure A1 and the accompanying code present the results of systematically training, validating, and testing a cascaded multi-stage machine-learning framework on the battery dataset. The framework consists of three sequential models, each trained to predict key battery parameters based on discharge conditions.





Stage 1: Predicting Temperature Rise



Inputs: Discharge time, ambient temperature, and C-rate

Output: Predicted temperature rise





Stage 2: Predicting Depth of Discharge (DoD)



Inputs: Discharge time, ambient temperature, C-rate, and the predicted temperature rise from Stage 1

Output: Predicted Depth of Discharge (DoD)





Stage 3: Predicting Heat Generation



Inputs: Discharge time, ambient temperature, C-rate, predicted temperature rise (from Stage 1), and predicted DoD (from Stage 2)

Output: Predicted heat generation




During evaluation, user-defined values for discharge time, ambient temperature, and C-rate are supplied as inputs to initiate the prediction process. The model sequentially computes temperature rise, DoD, and heat generation, using predicted outputs from earlier stages as inputs for subsequent predictions.

The model performance is assessed using the R-squared metric:



R² ≈ 1: Strong agreement with experimental data

R² = 0: Performance equivalent to a constant model

R² < 0: Worse performance than a naive model


After training and cross-validation, the models are tested on the complete dataset, generating predictive plots for temperature rise, DoD, and heat generation. These visualizations demonstrate the accuracy of the models across different operating conditions, validating their ability to predict battery behavior effectively.
DOI: 10.5281/zenodo.14812761
title: Sequential Cascaded Multi-Stage RFR Machine Learning Models for Predicting Electrical and Thermal Behavior in High-Capacity Lithium-Ion Batteries

description: Figure A1 and the accompanying code present the results of systematically training, validating, and testing a cascaded multi-stage machine-learning framework on the battery dataset. The framework consists of three sequential models, each trained to predict key battery parameters based on discharge conditions.





Stage 1: Predicting Temperature Rise



Inputs: Discharge time, ambient temperature, and C-rate

Output: Predicted temperature rise





Stage 2: Predicting Depth of Discharge (DoD)



Inputs: Discharge time, ambient temperature, C-rate, and the predicted temperature rise from Stage 1

Output: Predicted Depth of Discharge (DoD)





Stage 3: Predicting Heat Generation



Inputs: Discharge time, ambient temperature, C-rate, predicted temperature rise (from Stage 1), and predicted DoD (from Stage 2)

Output: Predicted heat generation




During evaluation, user-defined values for discharge time, ambient temperature, and C-rate are supplied as inputs to initiate the prediction process. The model sequentially computes temperature rise, DoD, and heat generation, using predicted outputs from earlier stages as inputs for subsequent predictions.

The model performance is assessed using the R-squared metric:



R² ≈ 1: Strong agreement with experimental data

R² = 0: Performance equivalent to a constant model

R² < 0: Worse performance than a naive model


After training and cross-validation, the models are tested on the complete dataset, generating predictive plots for temperature rise, DoD, and heat generation. These visualizations demonstrate the accuracy of the models across different operating conditions, validating their ability to predict battery behavior effectively.
DOI: 10.5281/zenodo.14825072
title: Sequential Cascaded Multi-Stage RFR Machine Learning Models for Predicting Electrical and Thermal Behavior in High-Capacity Lithium-Ion Batteries

description: Figure A1 and the accompanying code present the results of systematically training, validating, and testing a cascaded multi-stage machine-learning framework on the battery dataset. The framework consists of three sequential models, each trained to predict key battery parameters based on discharge conditions.





Stage 1: Predicting Temperature Rise



Inputs: Discharge time, ambient temperature, and C-rate

Output: Predicted temperature rise





Stage 2: Predicting Depth of Discharge (DoD)



Inputs: Discharge time, ambient temperature, C-rate, and the predicted temperature rise from Stage 1

Output: Predicted Depth of Discharge (DoD)





Stage 3: Predicting Heat Generation



Inputs: Discharge time, ambient temperature, C-rate, predicted temperature rise (from Stage 1), and predicted DoD (from Stage 2)

Output: Predicted heat generation




During evaluation, user-defined values for discharge time, ambient temperature, and C-rate are supplied as inputs to initiate the prediction process. The model sequentially computes temperature rise, DoD, and heat generation, using predicted outputs from earlier stages as inputs for subsequent predictions.

The model performance is assessed using the R-squared metric:



R² ≈ 1: Strong agreement with experimental data

R² = 0: Performance equivalent to a constant model

R² < 0: Worse performance than a naive model


After training and cross-validation, the models are tested on the complete dataset, generating predictive plots for temperature rise, DoD, and heat generation. These visualizations demonstrate the accuracy of the models across different operating conditions, validating their ability to predict battery behavior effectively.
DOI: 10.17028/rd.lboro.26096761
title: DrivAer model

description: DrivAer Dataset for NWTF
DOI: 10.15468/dl.vm4t8y
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Decatropis coulteri Hook.fil.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018026-250127130748423/datasets/export for details.
DOI: 10.15468/dl.ycr3hf
title: Occurrence Download

description: A dataset containing 140770 records in GBIF matching the query:
SELECT
 "year",
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude,
 COALESCE(coordinateUncertaintyInMeters, 1000)) AS mgrsCode,
 speciesKey,
 species,
 family, 
 COUNT(*) AS n,
 MIN(COALESCE(coordinateUncertaintyInMeters, 1000)) AS minCoordinateUncertaintyInMeters, 
 IF(ISNULL(family), NULL, SUM(COUNT(*)) OVER (PARTITION BY family)) AS familyCount
 FROM
 occurrence
 WHERE
 occurrenceStatus = 'PRESENT'
 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 countryCode = 'BE'
 AND "year" &gt;= 2007
 AND "year" &lt;= 2022
 AND speciesKey IS NOT NULL
 AND decimalLatitude IS NOT NULL
 AND decimalLongitude IS NOT NULL
 AND class = 'Aves'
 AND collectionCode = 'ABV'
 GROUP BY
 "year",
 mgrsCode,
 speciesKey,
 family,
 species
 ORDER BY
 "year" ASC,
 mgrsCode ASC,
 speciesKey ASC

 The dataset's 140770 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017999-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17028/rd.lboro.27268284.v3
title: Wingtip Vortex

description: For the NWTF database
DOI: 10.15468/dl.bygur6
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Poland",
 "GadmGid is POL.15_1",
 "OccurrenceStatus is Present",
 "StateProvince is Kalisz",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 44 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018027-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14613618
title: Dataset for lability and recalcitrance of sedimentary organic carbon in Svalbard fjords

description: (https://github.com/user-attachments/files/18341594/Svalbard-Fjord-RPO-Svalbard.zip)
DOI: 10.15468/dl.nhcp4t
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Decatropis paucijuga (Donn.Sm.) Loes.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018028-250127130748423/datasets/export for details.
DOI: 10.15468/dl.ftcm3x
title: Occurrence Download

description: A dataset containing 471344 records in GBIF matching the query:
SELECT identificationVerificationStatus, COUNT(*) as occ_count
FROM occurrence
GROUP BY identificationVerificationStatus
ORDER BY occ_count DESC;

 The dataset's 471344 records were derived from 70800 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018016-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.whcykv
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Decazyx esparzae F.Chiang)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018029-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.46yrjj
title: Occurrence Download

description: A dataset containing 1053 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Poland",
 "GadmGid is POL.15.8_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 1990-2025"
 ]
}

 The dataset includes 1053 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018030-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rgbe69
title: Occurrence Download

description: A dataset containing 118056 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "CoordinateUncertaintyInMeters 0.0-5000.0",
 "DatasetKey is one of (Vascular plants occurrences in Dokdo islands, Korea, based on herbarium collections and legacy botanical literature, iNaturalist Research-grade Observations, Flora of the Korean Peninsula, Vascular plants of the Amur River Basin, Russia: specimen based occurrence dataset of 100 selected species, The vegetation data from the Sogwangri, forest genetic resource reserve, Moscow University Herbarium (MW), T.B. Lee Herbarium Vascular Plant Collection, TB Lee Herbarium Vascular Plant Collection Phase II, Local floras of Russia: records from literature)",
 "Geometry MULTIPOLYGON(((127.06356 50.39592,122.72538 51.05869,115.61891 34.10353,129.5412 31.28055,140.35938 47.4402,127.06356 50.39592)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 118056 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001856-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.