cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17026/ar/fmeuad
title: A-25.0045: Wijchen, Wijchen-west, zuidelijk deelgebied Tussen duin en Wijchens Maasje. Een proefsleuvenonderzoek te Wijchen-West, deelgebied Zuid 'de Vormers' (fase1)

description: &lt;p&gt;In opdracht van de gemeente Wijchen heeft BAAC een proefsleuvenonderzoek (IVO-p) uitgevoerd in het zuidelijke deel van plangebied Wijchen West ‘de Vormers’. Het onderzoeksgebied wordt in twee fasen onderzocht, vanwege een groenperceel met bomen die nog gekapt moeten worden. Deze rapportage gaat over fase 1, ‘de Vomers’, van het proefsleuvenonderzoek. Mogelijk wordt later dit jaar fase 2 uitgevoerd, wanneer de bomen op dat terrein zijn gerooid. Die fase concentreert zich direct ten zuiden van het onderhavig terrein en valt binnen hetzelfde plan- en onderzoeksgebied. Hier wordt een verspoeld rivierduin verwacht, waarop sporen en vondsten aangetroffen kunnen worden.&lt;br&gt;Op basis van het vooronderzoek is het onderhavig onderzoeksgebied beschreven als een komgebied van het Wijchens Maasje, maar er restten toch nog enkele landschappelijke vragen. Mogelijke terrasopduikingen, oeverpakketten en crevasseafzettingen kunnen in dit landschap aanwezig zijn, die mogelijk in vroegere tijden intensief gebruikt zijn. Bovendien is het plangebied ingeklemd tussen meerdere terreinen van hoge archeologische waarden.&lt;br&gt;Het is daarom aannemelijk dat ook in de lagere delen van het landschap archeologische resten aangetroffen kunnen worden.&lt;br&gt;Toch heeft dit proefsleuvenonderzoek slechts geresulteerd in één spoor en enkele vondsten. Dit spoor dateert weliswaar uit het einde van het midden-neolithicum, maar van een grotere vindplaats is geen sprake. Dit spoor (S11002) duidt op sporadisch gebruik van het lagere komgebied: een vuur van voorbijgangers uit de periode van de Vlaardingencultuur. Een grotere vindplaats mag dan ook in de directe omgeving verwacht worden. Hiervoor zijn het rivierduin in het zuiden en de oeverwallen in het noorden de beste kanshebbers. Ook zijn in het noorden van het onderzoeksgebied uitlopers van oeverpakketten en crevasse-afzettingen gevonden. Door deze waarnemingen blijven de terreinen rondom het huidige onderzoeksgebied onverminderd kansrijk op het aantreffen van archeologische resten.&lt;br&gt;Binnen het huidige onderzoeksgebied (specifiek fase 1 van dit proefsleuvenonderzoek) is op basis van de waardering weliswaar een behoudenswaardige vindplaats aanwezig, maar enkel dit spoor en de bijhorende vondsten maken geen onderdeel uit van een grotere complexe vindplaats. Het spoor moet eerder gezien worden als een spoor van activiteit in een landschap dat extensief gebruikt is door mensen in het neolithicum. De kuil is gecoupeerd, geadministreerd, bemonsterd en daarmee direct veiliggesteld. Op basis van de beperkte grootte en aard van deze vindplaats en het contemporaine landschap (komgebied) wordt gesteld dat geen vervolgonderzoek nodig is. Daarom is het advies van BAAC om het plangebied van Wijchen-West, deelgebied zuid (specifiek het terrein van ‘fase 1’) vrij te geven voor de voorgenomen werkzaamheden.&lt;/p&gt;
DOI: 10.17030/uclan.data.00000643
title: Life after prison: The Journey Back to the Deaf Community
DOI: 10.15468/dl.s7xeh4
title: Occurrence Download

description: A dataset containing 3129557825 species occurrences available in GBIF matching the query:
{
 "BasisOfRecord" : [
 "is Human Observation"
 ]
}

 The dataset includes 3129557825 records from 44332 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039009-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p9agxqsr
title: USGS Benchmark Glacier Project Comprehensive Data Collection

description: Mountain glaciers are closely coupled to climate processes, ecosystems, and regional water resources. To enhance physical understanding of these connections, the USGS maintains a collection of glacier mass balance and climate data across the western United States and Alaska. In some cases, records of glacier mass balance extend back to the mid-1940s. These data have been incorporated from various sources, primarily original USGS studies, but also including work from the University of Alaska, and the Juneau Icefield Research Program (JIRP). The core of this collection is composed of mass balance data from the USGS Benchmark Glaciers. These five glaciers are Lemon Creek Glacier, AK (1953 -Present), South Cascade Glacier, WA (1958 - Present), Gulkana and Wolverine glaciers, AK (1966 - Present), and Sperry Glacier, MT (2005 - Present). Datasets from each benchmark glacier are composed of, at a minimum, point mass balances, glacier hypsometry, daily temperature and precipitation, geodetic mass balances, and glacier-wide mass balances. Data from other glaciers within this collection may be less complete, continuous, or representative as data from the benchmark glaciers. In these cases, we urge users to carefully inspect the associated metadata of each specific data release for further details.
DOI: 10.15468/dl.cy4rdn
title: Occurrence Download

description: A dataset containing 27997 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spilopelia senegalensis (Linnaeus, 1766)",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 27997 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006152-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rqzq59
title: Occurrence Download

description: A dataset containing 66963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Mangifera L."
 ]
}

 The dataset includes 66963 records from 634 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006150-250515123054153/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.29095870
title: Additional file 1 of Review of radiolarian microfossils as a tool for reconstructing sea surface temperature of the past in the Northwest Pacific

description: Supplementary material 1
DOI: 10.6084/m9.figshare.29095870.v1
title: Additional file 1 of Review of radiolarian microfossils as a tool for reconstructing sea surface temperature of the past in the Northwest Pacific

description: Supplementary material 1
DOI: 10.5066/f7m043g7
title: Raw Ground Penetrating Radar Data on North American Glaciers

description: U.S. Geological Survey researchers conducted time-series ground-penetrating radar (GPR) surveys with a Sensors and Software 500-MHz Pulse Ekko Pro system. This data release contains ground-based (ski and snowmobile) as well as airborne common-offset profiles. All profiles are linked to coincident GPS observations. Additionally, common-midpoint data was collected at specific glacier locations. Coincident in-situ data may provide calibration information, and may be composed of any of the following: snow pits and/or snow-pit/snow-core combinations, probe profiles, and ablation stake data. This supplemental information provides estimates of snow properties which may be used to calibrate radar velocity.
DOI: 10.15468/dl.mmrsvq
title: Occurrence Download

description: A dataset containing 183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "TaxonKey is Collothecacea"
 ]
}

 The dataset includes 183 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006156-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dx66ue
title: Occurrence Download

description: A dataset containing 928644 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-03-23",
 "EventDate is less than or equal to 2025-04-22",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 928644 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006149-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2vq2j3
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (BOL.6.4.3_2, BOL.6.1.1_2, BOL.6.2.2_2, BOL.6.2.3_2)",
 "OccurrenceStatus is Present",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 24 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006157-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s7rvk5
title: Occurrence Download

description: A dataset containing 1891 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Manicaria Gaertn."
 ]
}

 The dataset includes 1891 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006158-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9y5dpz
title: Occurrence Download

description: A dataset containing 2397236 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Odonata"
 ]
}

 The dataset includes 2397236 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006154-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hghfxw
title: Occurrence Download

description: A dataset containing 168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acropora digitifera (Dana, 1846)"
 ]
}

 The dataset includes 168 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006160-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15460790
title: RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches

description: Fourteen million colonoscopies are performed annually just in the U.S. However, the videos from these colonoscopies are not saved due to storage constraints (each video from a high-definition colonoscope camera can be in tens of gigabytes). Instead, a few relevant individual frames are saved for documentation/reporting purposes and these are the frames on which most current colonoscopy AI models are trained on. While developing new unsupervised domain translation methods for colonoscopy (e.g. to translate between real optical and virtual/CT colonoscopy), it is thus typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, additional contiguous frames are added with a modified deep learning architecture to train a new model from scratch for temporal consistency. This transition to temporally-consistent deep learning models, however, requires significantly more computational and memory resources for training. In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy: haustral fold segmentation (indicative of missed surface) and realistic colonoscopy simulator video generation. We also release a first-of-its kind temporal dataset for colonoscopy for the above use cases. The datasets, accompanying code, and pretrained models will be made available on our Computational Endoscopy Platform GitHub (this https URL). The supplementary video is available at this https URL.
DOI: 10.5281/zenodo.15460791
title: RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches

description: Fourteen million colonoscopies are performed annually just in the U.S. However, the videos from these colonoscopies are not saved due to storage constraints (each video from a high-definition colonoscope camera can be in tens of gigabytes). Instead, a few relevant individual frames are saved for documentation/reporting purposes and these are the frames on which most current colonoscopy AI models are trained on. While developing new unsupervised domain translation methods for colonoscopy (e.g. to translate between real optical and virtual/CT colonoscopy), it is thus typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, additional contiguous frames are added with a modified deep learning architecture to train a new model from scratch for temporal consistency. This transition to temporally-consistent deep learning models, however, requires significantly more computational and memory resources for training. In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy: haustral fold segmentation (indicative of missed surface) and realistic colonoscopy simulator video generation. We also release a first-of-its kind temporal dataset for colonoscopy for the above use cases. The datasets, accompanying code, and pretrained models will be made available on our Computational Endoscopy Platform GitHub (this https URL). The supplementary video is available at this https URL.
DOI: 10.5281/zenodo.10035903
title: Landsat-based dataset for mapping annual center-pivot irrigated cropland in Brazil

description: Center-pivot irrigated cropland (CPIC) is a critical component of irrigation and plays an essential role in improving water use efficiency and increasing food production. To automatically extract the spatial distribution of CPIC in Brazil based on the remote sensing technology, we constructed a training dataset that supports the semantic segmentation models.
The dataset were built with the  Landsat 5 , 7 and 8  images as well as the CPIC maps from ANA reference data. We used the Landsat images in 2005, 2010 and 2015 to build the dataset.
The samples in train_images and train_masks were used to train and valid the Convolutional Neural Network models; 
The samples in valid_data were used to test the model's prediction accuracy.
Pixels with values 255 and 0 in the mask samples represent the CPIC and background categories.
For technical details that used to create the dataset, please refer to https://doi.org/10.1016/j.isprsjprs.2023.10.007.
DOI: 10.5281/zenodo.10035904
title: Landsat-based dataset for mapping annual center-pivot irrigated cropland in Brazil

description: Center-pivot irrigated cropland (CPIC) is a critical component of irrigation and plays an essential role in improving water use efficiency and increasing food production. To automatically extract the spatial distribution of CPIC in Brazil based on the remote sensing technology, we constructed a training dataset that supports the semantic segmentation models.
The dataset were built with the  Landsat 5 , 7 and 8  images as well as the CPIC maps from ANA reference data. We used the Landsat images in 2005, 2010 and 2015 to build the dataset.
The samples in train_images and train_masks were used to train and valid the Convolutional Neural Network models; 
The samples in valid_data were used to test the model's prediction accuracy.
Pixels with values 255 and 0 in the mask samples represent the CPIC and background categories.
For technical details that used to create the dataset, please refer to https://doi.org/10.1016/j.isprsjprs.2023.10.007.
DOI: 10.15468/dl.pqe4u3
title: Occurrence Download

description: A dataset containing 5224 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Occurrence data for native Asian tree species gathered during the APFORGIS project"
 ]
}

 The dataset includes 5224 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006163-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7rtmwk
title: Occurrence Download

description: A dataset containing 2399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vitex trifolia L."
 ]
}

 The dataset includes 2399 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006164-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8p4xb4
title: Occurrence Download

description: A dataset containing 1172571 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Spinus spinus (Linnaeus, 1758)",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 1172571 records from 513 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006155-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5qx48e
title: Occurrence Download

description: A dataset containing 168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acropora digitifera (Dana, 1846)"
 ]
}

 The dataset includes 168 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006166-250515123054153/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.29095823
title: Data for Yu et al., 2025 “Predicting Avian Roadkill Risks in Shanghai: Insights from Ensemble Modelling of Small, Opportunistic Data”

description: Data for Yu et al., 2025 "Predicting Avian Roadkill Risks in Shanghai: Insights from Ensemble Modelling of Small, Opportunistic Data". For the zipped files, "Env.zip" contained all pre-processed candidate explanatory variables listed in this study (original sources are listed in the publication); "Predictions.zip" contained the output raster files of the ensemble model (i.e. the predicted avian roadkill risks in Shanghai). Two .csv files are the pre-processed avian roadkill samples used to develop the ensemble model (i.e. "Preprocessed_avian_roadkill_occurrence_sample.csv") and independent validation sample data digitalised from the interactive map in a citizen science mobile app, known in translation as “Road-killed Animal Survey Records”, developed by the Lab of Animal Behavior & Conservation from Nanjing University.
DOI: 10.15468/dl.6bqxr4
title: Occurrence Download

description: A dataset containing 23814 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Stercorarius longicaudus Vieillot, 1819",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 23814 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006167-250515123054153/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.25127135
title: Foundation Models for Geospatial Reasoning: Assessing the Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations

description: AI foundation models have demonstrated some capabilities for the understanding of geospatial semantics. However, applying such pre-trained models directly to geospatial datasets remains challenging due to their limited ability to represent and reason with geographical entities, specifically vector-based geometries and natural language descriptions of complex spatial relations. To address these issues, we investigate the extent to which a well-known-text (WKT) representation of geometries and their spatial relations (e.g., topological predicates) are preserved during spatial reasoning when the geospatial vector data are passed to large language models (LLMs) including GPT-3.5-turbo, GPT-4, and DeepSeek-R1. Our workflow employs three distinct approaches to complete the spatial reasoning tasks for comparison, i.e., geometry embedding-based, prompt engineering-based, and everyday language-based evaluation. Our experiment results demonstrate that both the embedding-based and prompt engineering-based approaches to geospatial question-answering tasks with GPT models can achieve an accuracy of over 0.6 on average for the identification of topological spatial relations between two geometries. Among the evaluated models, GPT-4 with few-shot prompting achieved the highest performance with over 0.66 accuracy on topological spatial relation inference. Additionally, GPT-based reasoner is capable of properly comprehending inverse topological spatial relations and including an LLM-generated geometry can enhance the effectiveness for geographic entity retrieval. GPT-4 also exhibits the ability to translate certain vernacular descriptions about places into formal topological relationships, and adding the geometry-type or place-type context in prompts may improve inference accuracy, but it varies by instance. The performance of these spatial reasoning tasks unveils the strengths and limitations of the current LLMs in the processing and comprehension of geospatial vector data and offers valuable insights for the refinement of LLMs with geographical knowledge towards the development of geo-foundation models capable of geospatial reasoning.