cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17897/s37e-cm02
title: GlacioBasis Zackenberg - Near surface climate - Wind speed, ZAC_U

description: Wind speed Near surface climate:Near surface climate GlacioBasis Zackenberg:Since 2008, the GlacioBasis programme has been monitoring the glacier surface mass balance at the A.P. Olsen Ice Cap and its response to climate forcing. Greenland’s glaciers and Ice Caps are losing mass at a rate more than double of the Ice Sheet when adjusted for size, accounting for 14-20 % of the total sea level rise contribution from Greenland. As the glaciological component of a core WMO GCW CryoNet site and a contributor to the GTN-G through WGMS, the GlacioBasis datasets complement the ongoing monitoring based at Zackenberg, enable process-based understanding of physical processes driven by glacier melt water run-off in the downstream terrestrial and marine ecosystems, and can inform regional and global assessments under AMAP and IPCC. GlacioBasis operates the year-round automatic monitoring of A.P. Olsen Ice Cap near-surface weather at three stations, a network of ablation stakes covering the entire altitude range, and geophysical survey campaigns on the ground (radar snow depth) and airborne (surface elevation from lidar, ice thickness from radar). GlacioBasis data collection is adaptively updated to support research in processes emerging as key sources of nonlinearity in the cryosphere, including melt water retention and albedo feedbacks. As a representative Northeast Greenland Ice Cap, A.P. Olsen observations delivered by GlacioBasis are available to the national and international remote sensing and climate modelling communities for calibration, validation and upscaling purposes.
DOI: 10.17897/87hs-jn44
title: GlacioBasis Zackenberg - Snow cover - Bulk snow density

description: Point observations of snow density. Snow cover:Snow cover GlacioBasis Zackenberg:Since 2008, the GlacioBasis programme has been monitoring the glacier surface mass balance at the A.P. Olsen Ice Cap and its response to climate forcing. Greenland’s glaciers and Ice Caps are losing mass at a rate more than double of the Ice Sheet when adjusted for size, accounting for 14-20 % of the total sea level rise contribution from Greenland. As the glaciological component of a core WMO GCW CryoNet site and a contributor to the GTN-G through WGMS, the GlacioBasis datasets complement the ongoing monitoring based at Zackenberg, enable process-based understanding of physical processes driven by glacier melt water run-off in the downstream terrestrial and marine ecosystems, and can inform regional and global assessments under AMAP and IPCC. GlacioBasis operates the year-round automatic monitoring of A.P. Olsen Ice Cap near-surface weather at three stations, a network of ablation stakes covering the entire altitude range, and geophysical survey campaigns on the ground (radar snow depth) and airborne (surface elevation from lidar, ice thickness from radar). GlacioBasis data collection is adaptively updated to support research in processes emerging as key sources of nonlinearity in the cryosphere, including melt water retention and albedo feedbacks. As a representative Northeast Greenland Ice Cap, A.P. Olsen observations delivered by GlacioBasis are available to the national and international remote sensing and climate modelling communities for calibration, validation and upscaling purposes.
DOI: 10.17897/nvhk-0s25
title: GlacioBasis Zackenberg - Snow cover - Snow depth at ZAC_A

description: Snow depth from sonic ranger. Snow cover:Snow cover GlacioBasis Zackenberg:Since 2008, the GlacioBasis programme has been monitoring the glacier surface mass balance at the A.P. Olsen Ice Cap and its response to climate forcing. Greenland’s glaciers and Ice Caps are losing mass at a rate more than double of the Ice Sheet when adjusted for size, accounting for 14-20 % of the total sea level rise contribution from Greenland. As the glaciological component of a core WMO GCW CryoNet site and a contributor to the GTN-G through WGMS, the GlacioBasis datasets complement the ongoing monitoring based at Zackenberg, enable process-based understanding of physical processes driven by glacier melt water run-off in the downstream terrestrial and marine ecosystems, and can inform regional and global assessments under AMAP and IPCC. GlacioBasis operates the year-round automatic monitoring of A.P. Olsen Ice Cap near-surface weather at three stations, a network of ablation stakes covering the entire altitude range, and geophysical survey campaigns on the ground (radar snow depth) and airborne (surface elevation from lidar, ice thickness from radar). GlacioBasis data collection is adaptively updated to support research in processes emerging as key sources of nonlinearity in the cryosphere, including melt water retention and albedo feedbacks. As a representative Northeast Greenland Ice Cap, A.P. Olsen observations delivered by GlacioBasis are available to the national and international remote sensing and climate modelling communities for calibration, validation and upscaling purposes.
DOI: 10.17897/mf0d-6r07
title: GlacioBasis Zackenberg - Snow cover - Snow depth at ZAC_L

description: Snow depth from sonic ranger. Snow cover:Snow cover GlacioBasis Zackenberg:Since 2008, the GlacioBasis programme has been monitoring the glacier surface mass balance at the A.P. Olsen Ice Cap and its response to climate forcing. Greenland’s glaciers and Ice Caps are losing mass at a rate more than double of the Ice Sheet when adjusted for size, accounting for 14-20 % of the total sea level rise contribution from Greenland. As the glaciological component of a core WMO GCW CryoNet site and a contributor to the GTN-G through WGMS, the GlacioBasis datasets complement the ongoing monitoring based at Zackenberg, enable process-based understanding of physical processes driven by glacier melt water run-off in the downstream terrestrial and marine ecosystems, and can inform regional and global assessments under AMAP and IPCC. GlacioBasis operates the year-round automatic monitoring of A.P. Olsen Ice Cap near-surface weather at three stations, a network of ablation stakes covering the entire altitude range, and geophysical survey campaigns on the ground (radar snow depth) and airborne (surface elevation from lidar, ice thickness from radar). GlacioBasis data collection is adaptively updated to support research in processes emerging as key sources of nonlinearity in the cryosphere, including melt water retention and albedo feedbacks. As a representative Northeast Greenland Ice Cap, A.P. Olsen observations delivered by GlacioBasis are available to the national and international remote sensing and climate modelling communities for calibration, validation and upscaling purposes.
DOI: 10.17897/fgg7-5605
title: GlacioBasis Zackenberg - Snow cover - Snow depth at ZAC_U

description: Snow depth from sonic ranger. Snow cover:Snow cover GlacioBasis Zackenberg:Since 2008, the GlacioBasis programme has been monitoring the glacier surface mass balance at the A.P. Olsen Ice Cap and its response to climate forcing. Greenland’s glaciers and Ice Caps are losing mass at a rate more than double of the Ice Sheet when adjusted for size, accounting for 14-20 % of the total sea level rise contribution from Greenland. As the glaciological component of a core WMO GCW CryoNet site and a contributor to the GTN-G through WGMS, the GlacioBasis datasets complement the ongoing monitoring based at Zackenberg, enable process-based understanding of physical processes driven by glacier melt water run-off in the downstream terrestrial and marine ecosystems, and can inform regional and global assessments under AMAP and IPCC. GlacioBasis operates the year-round automatic monitoring of A.P. Olsen Ice Cap near-surface weather at three stations, a network of ablation stakes covering the entire altitude range, and geophysical survey campaigns on the ground (radar snow depth) and airborne (surface elevation from lidar, ice thickness from radar). GlacioBasis data collection is adaptively updated to support research in processes emerging as key sources of nonlinearity in the cryosphere, including melt water retention and albedo feedbacks. As a representative Northeast Greenland Ice Cap, A.P. Olsen observations delivered by GlacioBasis are available to the national and international remote sensing and climate modelling communities for calibration, validation and upscaling purposes.
DOI: 10.17897/ehk8-bc66
title: GlacioBasis Zackenberg - Snow cover - Snow line altitude

description: Snow line altitude from Sentinel-2, manually outlined from best images in August. Only covering the part of the ice cap within the hydrological catchment of Zackenberg River. The elevation is based on a single DEM (GRLDEM from klimadatastyrelsen.dk) and thus does not take into account the annual change in elevation. Snow cover:Snow cover GlacioBasis Zackenberg:Since 2008, the GlacioBasis programme has been monitoring the glacier surface mass balance at the A.P. Olsen Ice Cap and its response to climate forcing. Greenland’s glaciers and Ice Caps are losing mass at a rate more than double of the Ice Sheet when adjusted for size, accounting for 14-20 % of the total sea level rise contribution from Greenland. As the glaciological component of a core WMO GCW CryoNet site and a contributor to the GTN-G through WGMS, the GlacioBasis datasets complement the ongoing monitoring based at Zackenberg, enable process-based understanding of physical processes driven by glacier melt water run-off in the downstream terrestrial and marine ecosystems, and can inform regional and global assessments under AMAP and IPCC. GlacioBasis operates the year-round automatic monitoring of A.P. Olsen Ice Cap near-surface weather at three stations, a network of ablation stakes covering the entire altitude range, and geophysical survey campaigns on the ground (radar snow depth) and airborne (surface elevation from lidar, ice thickness from radar). GlacioBasis data collection is adaptively updated to support research in processes emerging as key sources of nonlinearity in the cryosphere, including melt water retention and albedo feedbacks. As a representative Northeast Greenland Ice Cap, A.P. Olsen observations delivered by GlacioBasis are available to the national and international remote sensing and climate modelling communities for calibration, validation and upscaling purposes.
DOI: 10.15468/dl.8853hk
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.80937299999999 24.512155, 88.732198 24.506578, 88.656943 24.489986, 88.58547900000001 24.462791, 88.519581 24.425669, 88.46087799999999 24.379543, 88.41082 24.325558, 88.370636 24.265051, 88.341307 24.19952, 88.323538 24.130586, 88.317752 24.059948, 88.32407000000001 23.989349, 88.34231699999999 23.920525, 88.372028 23.855166, 88.41245600000001 23.794876, 88.462598 23.74113, 88.521216 23.695244, 88.586871 23.658339, 88.657954 23.631316, 88.73272900000001 23.614834, 88.80937299999999 23.609295, 88.886016 23.614834, 88.960792 23.631316, 89.031875 23.658339, 89.09753000000001 23.695244, 89.156148 23.74113, 89.20629 23.794876, 89.246718 23.855166, 89.276428 23.920525, 89.294676 23.989349, 89.300994 24.059948, 89.295207 24.130586, 89.277439 24.19952, 89.248109 24.265051, 89.207925 24.325558, 89.15786799999999 24.379543, 89.099165 24.425669, 89.033266 24.462791, 88.961803 24.489986, 88.886548 24.506578, 88.80937299999999 24.512155))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081471-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17542520
title: Towards Sustainable Proton Exchange Membranes: Materials and Challenges for Water Electrolysis

description: Dataset description:

The list of research articles used for the review paper, entitled "Towards Sustainable Proton Exchange Membranes: Materials and Challenges for Water Electrolysis."

Funding:

This research was funded by MSMT, OP JTF “REFRESH” project, reg. no. CZ.10.03.01/00/22_0030/0000048, the NPO project reg. no. TN7023812D “Innovative solutions for sustainable energy” within the National Center for Energy II, reg. no. TN02000025 and MSMT, OP JAK project “Advanced Materials for Energy and Environmental technologies”, reg. No. CZ.02.01.01/00/23_021/0008592.
DOI: 10.5281/zenodo.17965206
title: Sensor Data monitoring Allergen and Particle Concentration

description: The PollenSense 2025 dataset contains high-resolution, near-real-time measurements of airborne particle concentrations collected by PollenSense devices across four European countries. The devices operate automatically by drawing in air, capturing images of particles collected on a rotating transparent tape, and using a commercial algorithm to detect and classify the particles. These include pollen, fungal spores, and other organic or inorganic particulates.

Key characteristics:



Geographical coverage: Georgia, Greece, United Kingdom, Finland

Time period: February to October 2025 (varies by country)

Measurement Unit: Particle concentration per volume of air.

Sampling Frequency: Sampling time for Pollensense (plnsns) is 5 min
DOI: 10.6084/m9.figshare.31023511
title: <b>Temperature shapes the similarity of life-history strategy between native and non-native freshwater fishes</b>

description: Freshwater ecosystems are highly vulnerable to biological invasions, with increasing numbers of non-native fish species invoking severe ecological impacts. Life-history strategies reflect how fishes adapt to specific environments and play a critical role throughout the invasion process, leading to their application to understand large-scale patterns and drivers of freshwater fish invasions. Here, we compiled ecological traits for 14,679 freshwater fish species across 3,118 river basins worldwide to classify life-history strategies of native and non-native fishes and attempt to explain large-scale distributional patterns in diverse environments.
DOI: 10.6084/m9.figshare.31023511.v1
title: <b>Temperature shapes the similarity of life-history strategy between native and non-native freshwater fishes</b>

description: Freshwater ecosystems are highly vulnerable to biological invasions, with increasing numbers of non-native fish species invoking severe ecological impacts. Life-history strategies reflect how fishes adapt to specific environments and play a critical role throughout the invasion process, leading to their application to understand large-scale patterns and drivers of freshwater fish invasions. Here, we compiled ecological traits for 14,679 freshwater fish species across 3,118 river basins worldwide to classify life-history strategies of native and non-native fishes and attempt to explain large-scale distributional patterns in diverse environments.
DOI: 10.15468/dl.fxu73p
title: Occurrence Download

description: A dataset containing 126241 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (September, August)",
 "OccurrenceStatus is Present",
 "TaxonKey is Charadriidae",
 "Year 2002-2025"
 ]
}

 The dataset includes 126241 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081406-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/yklamn
title: Archeologisch Bureau-en booronderzoek Lampe’s Veentie te Norg, gemeente Noordenveld (DR)

description: &lt;p&gt;Uit het bureauonderzoek blijkt dat het onderzoeksgebied waarschijnlijk op grondmorenewelvingen ligt met loopodzolbodems. In de directe omgeving zijn archeologische resten vanaf het neolithicum bekend. Het dorp Norg, waar het onderzoeksgebied ligt, is in de vroege middeleeuwen ontstaan. Bebouwing binnen het onderzoeksgebied vindt plaats vanaf de jaren ’60 van de vorige eeuw. De speeltuin binnen het plangebied is eveneens in de jaren ’60 aangelegd waarbij waarschijnlijk vervuilde grond is opgebracht. 
&lt;/p&gt;&lt;p&gt;
Uit het booronderzoek blijkt dat in het dekzand slechts nog een gedeeltelijk podzolprofiel aanwezig is. Hierbij is er in één boring een B-horizont en in twee een BC-horizont aangetroffen. Daarnaast is binnen drie boringen (veraard) veen aangetroffen. Wegens de geringe dikte van het veenpakket (maximaal 0,8 m) kan worden vastgesteld dat er geen sprake is van een pingoruïne. In vier boringen is een esdek aangetroffen. In alle boringen bestaat het bovenste pakket uit opgebrachte grond met glas, puin en baksteen.
&lt;/p&gt;&lt;p&gt;

De enige boring met een B-horizont is bemonsterd en doorzocht op archeologische indicatoren. Deze zijn niet aangetroffen.&lt;/p&gt;
DOI: 10.15468/dl.uf2wte
title: Occurrence Download

description: A dataset listing the 80 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-104.3504 23.50037,-104.3504 23.41705,-104.2866 23.41705,-104.2504 23.41705,-104.2504 23.50038,-104.3504 23.50037))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset's 80 records were derived from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081445-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.brqkz5
title: Occurrence Download

description: A dataset containing 110926 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "DatasetKey is EOD – eBird Observation Dataset",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (July, June)",
 "OccurrenceStatus is Present",
 "TaxonKey is Charadriidae",
 "Year 2002-2025"
 ]
}

 The dataset includes 110926 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081407-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dbnv3h
title: Occurrence Download

description: A dataset containing 21133 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Gleditsia triacanthos"
 ]
}

 The dataset includes 21133 records from 348 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081472-251120083545085/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.31027897
title: Ear Emoload Dataset

description: <b>Ear-EmoLoad</b> is an ear-EEG dataset for studying <b>emotion and cognitive load</b> in naturalistic listening and learning scenarios. The dataset is collected using a <b>custom ear-worn EEG system embedded in in-ear headphones</b>, enabling unobtrusive neural sensing during everyday tasks.<br>Participants engaged in <b>audio-visual learning and listening activities</b> designed to elicit variations in <b>emotional valence</b>and <b>cognitive load</b>. The dataset supports joint investigation of affective responses and mental workload in an e-learning–like context.<br>Ear-EmoLoad primarily includes <b>multi-channel ear-EEG recordings</b> with synchronized task markers and annotations. Each data segment is labeled with <b>emotion-related and cognitive load information</b>, along with subject-level metadata. Preprocessing steps are applied to ensure signal quality suitable for ear-EEG analysis.<br>This dataset is intended for research in <b>affective computing</b>, <b>cognitive load assessment</b>, <b>wearable brain–computer interfaces</b>, and <b>EEG representation learning</b>.<br>All data were collected with informed consent and are anonymized for research use only.
DOI: 10.6084/m9.figshare.31027897.v1
title: Ear Emoload Dataset

description: <b>Ear-EmoLoad</b> is an ear-EEG dataset for studying <b>emotion and cognitive load</b> in naturalistic listening and learning scenarios. The dataset is collected using a <b>custom ear-worn EEG system embedded in in-ear headphones</b>, enabling unobtrusive neural sensing during everyday tasks.<br>Participants engaged in <b>audio-visual learning and listening activities</b> designed to elicit variations in <b>emotional valence</b>and <b>cognitive load</b>. The dataset supports joint investigation of affective responses and mental workload in an e-learning–like context.<br>Ear-EmoLoad primarily includes <b>multi-channel ear-EEG recordings</b> with synchronized task markers and annotations. Each data segment is labeled with <b>emotion-related and cognitive load information</b>, along with subject-level metadata. Preprocessing steps are applied to ensure signal quality suitable for ear-EEG analysis.<br>This dataset is intended for research in <b>affective computing</b>, <b>cognitive load assessment</b>, <b>wearable brain–computer interfaces</b>, and <b>EEG representation learning</b>.<br>All data were collected with informed consent and are anonymized for research use only.
DOI: 10.15468/dl.hdprqn
title: Occurrence Download

description: A dataset containing 137178606 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 137178606 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081430-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dtwuu6
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "InstitutionKey is a4e8fc5e-fb6c-4c08-b9f1-ef8724870e89",
 "TaxonKey is Hemiptera"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081474-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j8snnd
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "StartDayOfYear 28-33",
 "TaxonKey is Rhopalodiales"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081477-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18185662
title: PhononBench

description: PhononBench is a phonon-based benchmark for large-scale dynamical stability evaluation of AI-generated crystals, featuring 100k+ structures, DFT-level MatterSim phonon calculations, and open-source high-throughput workflows.
DOI: 10.5281/zenodo.18185661
title: PhononBench

description: PhononBench is a phonon-based benchmark for large-scale dynamical stability evaluation of AI-generated crystals, featuring 100k+ structures, DFT-level MatterSim phonon calculations, and open-source high-throughput workflows.