cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28888034
title: Corner-Case Dataset for Autonomous Vehicle Testing Based on Naturalistic Driving Data

description: This is a dataset constructed from multi-source naturalistic driving data, forming a standardized corner-case database specifically designed for autonomous vehicle testing. It features diverse interaction patterns, a unified data format, and strong applicability.The <i>corner case-openX</i> directory contains standardized corner case data. In addition to the extracted corner-case data, a substantial volume of accident scenario videos exhibiting pronounced marginality characteristics—stored as tachograph recordings—are shared in the <i>corner case-videos</i> directory.
DOI: 10.5281/zenodo.17939399
title: Annual number of hours with a dangerous heat index: A global dataset based on hourly CMIP6-based ISIMIP3b climate model data

description: This dataset provides the annual number of hours with dangerous heat index (above 103°F, 39.4°C) as NetCDF files and compiled maps as PNG files.

The calculation is based on hourly CMIP6-based ISIMIP3b climate model data (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) across the historical, ssp126, ssp370, and ssp585 experiments.

Historical data is provided from 1981-2010 in 5-year slices. Scenario data is provided from 2015-2100 in 5-year slices.

The Teddy-Toov (v1.3) is used for the temporal disaggregation of daily ISIMIP3b climate data to hourly data (Zabel and Poschlod 2023).
DOI: 10.5281/zenodo.17939400
title: Annual number of hours with a dangerous heat index: A global dataset based on hourly CMIP6-based ISIMIP3b climate model data

description: This dataset provides the annual number of hours with dangerous heat index (above 103°F, 39.4°C) as NetCDF files and compiled maps as PNG files.

The calculation is based on hourly CMIP6-based ISIMIP3b climate model data (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) across the historical, ssp126, ssp370, and ssp585 experiments.

Historical data is provided from 1981-2010 in 5-year slices. Scenario data is provided from 2015-2100 in 5-year slices.

The Teddy-Toov (v1.3) is used for the temporal disaggregation of daily ISIMIP3b climate data to hourly data (Zabel and Poschlod 2023).
DOI: 10.34740/kaggle/dsv/14793407
title: Public Opinion on Climate Change (Updated Daily)
DOI: 10.15468/dl.gczubh
title: Occurrence Download

description: A dataset containing 616 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dubautia menziesii",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 616 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005002-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qgwzx4
title: Occurrence Download

description: A dataset containing 937 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erythrina velutina",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 937 records from 113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005004-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ndkkw9
title: Occurrence Download

description: A dataset containing 356 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Acacia auriculiformis"
 ]
}

 The dataset includes 356 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005001-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.chn39h
title: Occurrence Download

description: A dataset containing 33847839 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Nycticorax mauritianus, Alopochen mauritiana, Rallicula mayri, Larus melanocephalus, Larus canus, Zapornia nigra, Anas fulvigula, Charadrius montanus, Cairina moschata, Biziura lobata, Cygnus olor, Nannopterum brasilianum, Hypotaenidia insignis, Porphyrio kukwiedei, Megacrex inepta, Scolopax rosenbergii, Poliocephalus rufopectus, Aythya novaeseelandiae, Coenocorypha aucklandica, Himantornis haematopus, Gallinago nobilis, Porphyrio mantelli, Jacana spinosa, Vanellus vanellus, Anas acuta, Chauna chavaria, Spatula clypeata, Micropygia schomburgkii, Larus atlanticus, Anhinga melanogaster, Charadrius veredus, Glareola maldivarum, Neochen jubata, Anas superciliosa, Pluvialis fulva, Larus pacificus, Gavia pacifica, Egretta sacra, Neocrex erythrops, Mycteria leucocephala, Urile perspicillatus, Larus ichthyaetus, Tadorna variegata, Calidris melanotos, Urile pelagicus, Pelecanus thagus, Hydrophasianus chirurgus, Scolopax bukidnonensis, Recurvirostra avosetta, Egretta picata, Hoploxypterus cayanus, Haematopus longirostris, Podilymbus podiceps, Pelecanus rufescens, Malacorhynchus membranaceus, Anser brachyrhynchus, Botaurus pinnatus, Gallinago stenura, Charadrius melodus, Theristicus caerulescens, Pardirallus sanguinolentus, Dendrocygna eytoni, Phoenicoparrus jamesi, Plegadis ridgwayi, Charadrius alticola, Gallinago andina, Spatula puna, Ardea purpurea, Calidris maritima, Porphyrio porphyrio, Microcarbo pygmaeus, Radjah radjah, Calidris canutus, Phalaropus fulicarius, Aphanapteryx bonasia, Spatula platalea, Laterallus leucopyrrhus, Anas erythrorhyncha, Chroicocephalus novaehollandiae scopulinus, Mergus serrator, Charadrius ruficapillus, Sarothrura rufa, Netta rufina, Egretta rufescens, Urile urile, Fulica rufifrons, Fulica armillata, Aythya americana, Erythrogonys cinctus, Fulica cristata, Poikilocarbo gaimardi, Rallina fasciata, Pseudibis papillosa, Recurvirostra novaehollandiae, Rallina tricolor, Podiceps grisegena, Phalaropus lobatus, Calidris ruficollis, Gavia stellata, Vanellus indicus)",
 {
 "or" : [
 "Year is greater than or equal to 2000",
 "Year is less than or equal to 2025"
 ]
 }
 ]
}

 The dataset includes 33847839 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004965-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18543287
title: A combination of Time-Variable Gravity Field Solutions from Multi-Satellite Datasets (1993-2024) via Least-Squares Collocation

description: This dataset provides monthly, gapless, filter-free gravity field solutions from January 1993 to December 2024. The solutions are derived by merging multiple satellite missions (GRACE, SLR, and LEO satellites) using constrained Least-Squares Collocation (LSC), without relying on hydrological or climate models, and achieve higher accuracy than existing gravity field reconstruction methods (e.g., machine learning, EOF, and VCE). Evaluations of sea-level budget closure and water balance at global and basin scales demonstrate substantial improvements. The results agree well with IMBIE estimates of ice-sheet mass change in Antarctica and Greenland, supporting the dataset’s suitability for monitoring Earth’s water and ice systems.
description: Time-variable gravity field solutions from GRACE and GRACE-FO have been successfully applied in hydrological and geophysical studies; however, inter- and intra-mission gaps and limited record length constrain their broader utility. Current approaches involve hydrometeorological-forced machine-learning reconstructions and satellite-tracking-observation combinations; however, the former is constrained by the accuracy and completeness of data inputs, while the latter requires additional filtering due to limited spectral sensitivity, resulting in filtering-dependent solutions. Both approaches neglect covariance information of observation noise and signal, precluding optimal solutions. To address these limitations, this study develops gapless monthly solutions up to degree/order 60 spanning January 1993 to December 2024 using constrained Least-Squares Collocation (LSC), which integrates combination and denoising processes of gravity field solutions without explicit filtering. LSC-based Combined Solutions (LSC-CS) integrates trends, annual and semi-annual variations, and non-seasonal signals from multi-satellite observations (GRACE/-FO, Low Earth Orbit satellites, and Satellite Laser Ranging) without external hydrometeorological inputs, while incorporating covariance matrices of observation errors and combined signals to optimally balance error reduction and signal preservation. Evaluation results indicate that LSC-CS significantly eliminates striping noise and high-degree coefficient noise while effectively preserving low-degree gravity signals (e.g., C20 and C30) and achieving high signal-to-noise ratios. Comparison with three reconstructed products (IGG-SLR-DORIS, RESDCAE, BNML) shows that LSC-CS achieves the lowest sea level budget misclosures, with reductions of 40%, 2.9%, and 49%, respectively. Across 52 major basins, LSC-CS has the smallest water balance errors, with reductions of 4.6%, 2.6%, and 1.5%, respectively. For Antarctic and Greenland ice sheet mass changes, LSC-CS closely match IMBIE estimates, with trend consistency improvements of 46.8% and 32.7% over IGG-SLR-DORIS and 48.6% and 67.4% over RESDCAE, respectively.
DOI: 10.5281/zenodo.18543286
title: A combination of Time-Variable Gravity Field Solutions from Multi-Satellite Datasets (1993-2024) via Least-Squares Collocation

description: This dataset provides monthly, gapless, filter-free gravity field solutions from January 1993 to December 2024. The solutions are derived by merging multiple satellite missions (GRACE, SLR, and LEO satellites) using constrained Least-Squares Collocation (LSC), without relying on hydrological or climate models, and achieve higher accuracy than existing gravity field reconstruction methods (e.g., machine learning, EOF, and VCE). Evaluations of sea-level budget closure and water balance at global and basin scales demonstrate substantial improvements. The results agree well with IMBIE estimates of ice-sheet mass change in Antarctica and Greenland, supporting the dataset’s suitability for monitoring Earth’s water and ice systems.
description: Time-variable gravity field solutions from GRACE and GRACE-FO have been successfully applied in hydrological and geophysical studies; however, inter- and intra-mission gaps and limited record length constrain their broader utility. Current approaches involve hydrometeorological-forced machine-learning reconstructions and satellite-tracking-observation combinations; however, the former is constrained by the accuracy and completeness of data inputs, while the latter requires additional filtering due to limited spectral sensitivity, resulting in filtering-dependent solutions. Both approaches neglect covariance information of observation noise and signal, precluding optimal solutions. To address these limitations, this study develops gapless monthly solutions up to degree/order 60 spanning January 1993 to December 2024 using constrained Least-Squares Collocation (LSC), which integrates combination and denoising processes of gravity field solutions without explicit filtering. LSC-based Combined Solutions (LSC-CS) integrates trends, annual and semi-annual variations, and non-seasonal signals from multi-satellite observations (GRACE/-FO, Low Earth Orbit satellites, and Satellite Laser Ranging) without external hydrometeorological inputs, while incorporating covariance matrices of observation errors and combined signals to optimally balance error reduction and signal preservation. Evaluation results indicate that LSC-CS significantly eliminates striping noise and high-degree coefficient noise while effectively preserving low-degree gravity signals (e.g., C20 and C30) and achieving high signal-to-noise ratios. Comparison with three reconstructed products (IGG-SLR-DORIS, RESDCAE, BNML) shows that LSC-CS achieves the lowest sea level budget misclosures, with reductions of 40%, 2.9%, and 49%, respectively. Across 52 major basins, LSC-CS has the smallest water balance errors, with reductions of 4.6%, 2.6%, and 1.5%, respectively. For Antarctic and Greenland ice sheet mass changes, LSC-CS closely match IMBIE estimates, with trend consistency improvements of 46.8% and 32.7% over IGG-SLR-DORIS and 48.6% and 67.4% over RESDCAE, respectively.
DOI: 10.15468/dl.rzbaem
title: Occurrence Download

description: A dataset containing 391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lysimachia remyi",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 391 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005005-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8hekjk
title: Occurrence Download

description: A dataset containing 2234 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cenostigma pyramidale",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 2234 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005006-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sqm4tc
title: Occurrence Download

description: A dataset containing 96354 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Pelophylax lessonae"
 ]
}

 The dataset includes 96354 records from 692 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004973-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18588536
title: GRACE/GRACE-FO Total Water Storage Mean Field Trend

description: More than 20 yr of measurement data of the gravity missions GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow-On) allow detailed investigations of long-term trends in continental terrestrial water storage (TWS). However, the spatial resolution of conventional GRACE/GRACE-FO data products is limited to a few hundred kilometres which restrains from investigating hydrological trends at smaller spatial scales. In this study GRACE and GRACE-FO data have been used to calculate TWS trends with maximized spatial resolution. Conventionally, GRACE/GRACE-FO is presented as a series of either unconstrained gravity fields post-processed with spatial low pass filters or constrained inversions commonly known as Mascon products. This paper demonstrates that both approaches to suppress spatially correlated noise are mathematically equivalent. Moreover, we demonstrate that readily inverting all available sensor data from GRACE/GRACE-FO for a single TWS trend map, together with annual variations and a mean gravity field, provides additional spatial detail not accessible from the standard products. The variable trade-off between spatial and temporal resolution as a unique feature of satellite gravimetry allows for gravity products that are tailored towards specific geophysical applications. We show additional signal content in terms of long-term water storage trends for four dedicated examples (Lake Victoria, Northwest India, Bugachany Reservoir and High Plains Aquifer) for which external information from other remote sensing instruments corroborates the enhanced spatial resolution of the new mean-field trend product.
DOI: 10.5281/zenodo.18588535
title: GRACE/GRACE-FO Total Water Storage Mean Field Trend

description: More than 20 yr of measurement data of the gravity missions GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow-On) allow detailed investigations of long-term trends in continental terrestrial water storage (TWS). However, the spatial resolution of conventional GRACE/GRACE-FO data products is limited to a few hundred kilometres which restrains from investigating hydrological trends at smaller spatial scales. In this study GRACE and GRACE-FO data have been used to calculate TWS trends with maximized spatial resolution. Conventionally, GRACE/GRACE-FO is presented as a series of either unconstrained gravity fields post-processed with spatial low pass filters or constrained inversions commonly known as Mascon products. This paper demonstrates that both approaches to suppress spatially correlated noise are mathematically equivalent. Moreover, we demonstrate that readily inverting all available sensor data from GRACE/GRACE-FO for a single TWS trend map, together with annual variations and a mean gravity field, provides additional spatial detail not accessible from the standard products. The variable trade-off between spatial and temporal resolution as a unique feature of satellite gravimetry allows for gravity products that are tailored towards specific geophysical applications. We show additional signal content in terms of long-term water storage trends for four dedicated examples (Lake Victoria, Northwest India, Bugachany Reservoir and High Plains Aquifer) for which external information from other remote sensing instruments corroborates the enhanced spatial resolution of the new mean-field trend product.
DOI: 10.15468/dl.2ewksr
title: Occurrence Download

description: A dataset containing 1137 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eragrostis plana"
 ]
}

 The dataset includes 1137 records from 112 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005007-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tfj94s
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Schiedea diffusa",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 16 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005008-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.esh3av
title: Occurrence Download

description: A dataset containing 693 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hymenaea martiana",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 }
 ]
}

 The dataset includes 693 records from 91 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005009-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6d83n
title: Occurrence Download

description: A dataset containing 218942 records in GBIF matching the query:

 SELECT gbifid, datasetkey, specieskey, kingdom, phylum, class, "order", family, genus, species, scientificname, decimallatitude, decimallongitude, eventdate, institutioncode, "year" AS obs_year, basisofrecord, coordinateuncertaintyinmeters, issue
 FROM occurrence
 WHERE
 decimallatitude IS NOT NULL
 AND decimallongitude 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 "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND coordinateuncertaintyinmeters IS NOT NULL
 AND coordinateuncertaintyinmeters &gt; 0
 AND coordinateuncertaintyinmeters &lt;= 200.0
 AND decimallatitude &gt;= 56.135291543055025
 AND decimallatitude &lt;= 61.2993700571118
 AND decimallongitude &gt;= 64.759591695172
 AND decimallongitude &lt;= 79.10424340471731
 AND GBIF_Within('POLYGON ((79.104243 56.135292, 79.104243 61.299370, 64.759592 61.299370, 64.759592 56.135292, 79.104243 56.135292))', decimallatitude, decimallongitude) = TRUE
 

 The dataset's 218942 records were derived from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004994-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.58032/agh/wcdzmu
title: Python script for predicting glass forming ability (GFA) - Physics-informed machine learning of glass-forming ability - integrating thermodynamic limits and quantum signatures - research data

description: Accelerated discovery of oxide glasses is hindered by the combinatorial complexity of multicomponent systems and the inability of standard "black-box" Machine Learning (ML) models to respect thermodynamic phase boundaries. In this work, a Physics-Informed Indirect Framework is introduced, shifting the paradigm from direct regression of stability indices to the prediction of fundamental characteristic temperatures (Tg, Tx, Tliq). This approach is empowered by a novel hierarchical feature engineering architecture that fuses statistical entropy metrics with quantum signatures (orbital s/p/d/f occupancies), enabling the distinction between network formers and modifiers at a sub-atomic level. Validated on the complex Na2O - B2O3 - SiO2 system, the immiscibility gap was autonomously identified by the model as a mathematical singularity in the derived Hruby parameter (KH) - a feat unachievable by direct models, which smoothed over this "forbidden zone." Furthermore, the non-linear Mixed Alkali Effect (MAE) was successfully captured, and "stability hallucinations" in data-sparse regions were eliminated by the indirect framework through the use of the liquidus temperature as a physical safety fuse. Finally, it was confirmed via SHAP analysis that predictive decisions are driven by enthalpy and orbital topology rather than spurious correlations. This methodology bridges the gap between data science and glass physics, offering a robust tool for navigating the stability landscape of disordered materials.