cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29243078.v1
title: The spatiotemporal evolution and spatial effect of digitalization on urban land intensive use in the Yangtze River Economic Belt

description: The data used in this article has been uploaded here. Due to copyright restrictions and compliance with database terms of use agreements, the original data cannot be shared publicly. Therefore, some data has been replaced with processed versions or code substitutions.
DOI: 10.5281/zenodo.15598739
title: A try code
DOI: 10.5281/zenodo.15600387
title: A try code
DOI: 10.15468/dl.x7ha59
title: Occurrence Download

description: A dataset containing 336369 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'FI'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2000 AND occurrence."year" &lt;= 2025)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 336369 records were derived from 512 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021862-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/rvlbkl
title: Vessel line-transect surveys of Arctic marine mammals in the Chuckchi Sea, Arctic Marine Biodiversity Observing Network (AMBON) research cruise on the vessel Norseman II from 2017-08-05 to - 2017-08-25

description: This dataset contains marine mammal observation data conducted during shipboard transects in the Chukchi Sea during the 5 August - 25 August 2017 Arctic Marine Biodiversity Observing Network (AMBON) research cruise. The dataset is two comma-separated values (csv) files exported from the marine bird and mammal observation software WinCruz from the entire survey. One data file contains the dates,times, latitude, longitude, species and best-high-low estimates of numbers for cetacean (whales) and pinniped (seals, seal lions, and walrus) sightings. The second data file contains the track of the ship (as geographic coordinates) and the Beaufort sea state at two minute intervals as a measurement of effort. This dataset was transformed from the native format into a table structure using Darwin Core Archive (DwC-A, occurrence core) term names as column names.
DOI: 10.5281/zenodo.15574702
title: Victim detection P

description: 📦 Segmentation Dataset PASCAL

This repository contains a dataset labeled with Roboflow in PASCAL VOC format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: XML PASCAL VOC-Number of classes: 10-Number of picture: 2276-Annotation source: Roboflow-Annotation type: Masks for segmentation tasks

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── README.dataset.txt └── README.roboflow.txt
DOI: 10.5281/zenodo.15574703
title: Victim detection P

description: 📦 Segmentation Dataset PASCAL

This repository contains a dataset labeled with Roboflow in PASCAL VOC format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: XML PASCAL VOC-Number of classes: 10-Number of picture: 2276-Annotation source: Roboflow-Annotation type: Masks for segmentation tasks

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── README.dataset.txt └── README.roboflow.txt
DOI: 10.15468/dl.fmu56v
title: Occurrence Download

description: A dataset containing 1534 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dioscorea acanthogene Rusby, Dioscorea alainii Raz, Dioscorea amazonum Mart. ex Griseb.)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1534 records from 123 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018315-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/fxjpbr
title: Vessel line-transect surveys of Arctic cetaceans in the Chuckchi Sea, Arctic Marine Biodiversity Observing Network (AMBON) research cruise on the vessel Norseman II, 2015-08-09 - 2015-09-03

description: This dataset contains cetacean observation data conducted during shipboard transects in the Chukchi Sea during the 9 August - 3 September 2015 Arctic Marine Biodiversity Observing Network (AMBON) research cruise. The dataset is two comma-separated values (csv) files describing the dates, times, latitude, longitude, species and best-high-low estimates of numbers for each cetacean (whales). This dataset was exported from the marine bird and mammal observation software WinCruz from the entire survey, and later transformed from the WinCruz format into Darwin Core formatted tables (DwC, occurrence core), with DwC term names as column names.
DOI: 10.5281/zenodo.15586836
title: X-ray Phase Contrast Tomography Human Olfactory Bulb Glomeruli Segmentation Dataset
DOI: 10.5281/zenodo.15586837
title: X-ray Phase Contrast Tomography Human Olfactory Bulb Glomeruli Segmentation Dataset
DOI: 10.5281/zenodo.15471135
title: 2011 - London - L1 #2, L2

description: University of Reading Grimmond Urban Micromet Group

City: London

Year: 2011

Sites: 

Data Levels: L1, L2
DOI: 10.5281/zenodo.15471136
title: 2011 - London - L1 #2, L2

description: University of Reading Grimmond Urban Micromet Group

City: London

Year: 2011

Sites: 

Data Levels: L1, L2
DOI: 10.15468/dl.tvpmr7
title: Occurrence Download

description: A dataset listing the 28932 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-9.22401 54.82811,-10.58909 54.68441,-10.68147 52.63166,-11.11255 52.05689,-11.03044 51.12289,-9.32665 50.74313,-7.13021 51.55397,-6.21673 51.56423,-5.4059 52.65219,-5.34431 53.75041,-4.97482 54.4894,-5.96507 55.34208,-6.76524 55.58932,-6.78124 56.07002,-9.13164 55.64921,-9.22401 54.82811))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 28932 records were derived from 823 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018313-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.paqus3
title: Occurrence Download

description: A dataset containing 13251 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-139.61323 48.53416,-164.92363 29.17914,-151.77215 -36.8264,-59.46364 -23.67492,-130.43201 62.92634,-161.20151 63.91891,-139.61323 48.53416))",
 "TaxonKey is Orcinus orca (Linnaeus, 1758)"
 ]
}

 The dataset includes 13251 records from 124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018319-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.az4gq4
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Registro de hongos endófitos aislados de Lippia alba en El Playón, Santander"
 ]
}

 The dataset includes 37 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018318-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sup3xe
title: Occurrence Download

description: A dataset containing 359551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Anser fabalis (Latham, 1787))",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 359551 records from 230 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018310-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15188702
title: Soil type detection

description: 📦 Segmentation Dataset 

This repository contains a dataset labeled with Roboflow in YOLOv8 segmentation format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: YOLOv8 Segmentation-Number of classes: 10-Number of picture: 3100-Annotation source: Roboflow-Annotation type: Masks for segmentation tasks

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── data.yaml├── README.dataset.txt └── README.roboflow.txt
DOI: 10.5281/zenodo.15188703
title: Obstacle and soil type detection

description: This dataset contains images and annotations in YOLOv8 format corresponding to obstacle detection and different terrain types.

It includes the training, validation, and test sets, as well as `data.yaml` and `README` files.
DOI: 10.5281/zenodo.15575017
title: Soil type detection

description: 📦 Segmentation Dataset 

This repository contains a dataset labeled with Roboflow in YOLOv8 segmentation format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: YOLOv8 Segmentation-Number of classes: 10-Number of picture: 3100-Annotation source: Roboflow-Annotation type: Masks for segmentation tasks

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── data.yaml├── README.dataset.txt └── README.roboflow.txt
DOI: 10.5281/zenodo.15586669
title: corosims precomputed data

description: corosims precomputed data.
DOI: 10.5281/zenodo.15586670
title: corosims precomputed data

description: corosims precomputed data.
DOI: 10.5281/zenodo.15575037
title: Soil type detection P

description: 📦 Segmentation Dataset PASCAL

This repository contains a dataset labeled with Roboflow in PASCAL VOC format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: XML PASCAL VOC-Number of classes: 10-Number of picture: 3100-Annotation source: Roboflow-Annotation type: Pascal VOC standard

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── README.dataset.txt └── README.roboflow.txt
DOI: 10.5281/zenodo.15575038
title: Soil type detection P

description: 📦 Segmentation Dataset PASCAL

This repository contains a dataset labeled with Roboflow in PASCAL VOC format, designed for detecting and segmenting different types of terrain surfaces and obstacle negotiability.

📄 Format

-Export format: XML PASCAL VOC-Number of classes: 10-Number of picture: 3100-Annotation source: Roboflow-Annotation type: Pascal VOC standard

🏷️ Available Classes

| ID | Label       | Description                                |----|-------------|--------------------------------------------|| 0  | `concrete`   | Hard surface, pavement or concrete         | 1  | `dirtroad`   | Dirt road or path                          | 2  | `grass`       | Grass or grassy terrain                    | 3  | `gravel`      | Gravel surface or loose rocky path         | 4  | `NS`           | Not traversable (Non-Surmountable)         | 5  | `S50`         | 50% Surmountable slope                     | 6  | `S75`         | 75% Surmountable slope                     | 7  | `S100`       | Fully Surmountable (100%)                  | 8  | `wood`      | Wooden surface

These classes are meant to support the training of terrain perception models, especially for legged robots operating in unstructured environments.

📁 Dataset Structure

dataset/├── train/ # Training images and labels├── valid/ # Validation images and labels├── test/ # Test images and labels├── README.dataset.txt └── README.roboflow.txt
DOI: 10.15468/dl.qc3r9h
title: Occurrence Download

description: A dataset containing 21213 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Achyranthes aspera L., Asplenium longissimum Blume, Asplenium macrophyllum Sw.)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 21213 records from 610 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018321-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p3m2aw
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AEH8683"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025626-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.krf9vj
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ricinus communis",
 "Country is Jordan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 42 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025178-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/f5mh7fzbs3
title: Zinc isotopes trace sediment recycling in the Lesser Antilles

description: To identify subducted materials and their transfer mechanisms into the mantle wedge, this study presents Zn isotope compositions for 22 arc lava samples from seven Lesser Antilles volcanoes and 50 marine sediment samples from DSDP Sites 543 and 144, combined with major-trace elements and radiogenic isotopes. Sediments exhibit significant Zn isotopic variations (δ66Zn = −0.15‰ to 0.50‰), primarily controlled by mineralogy. Clay-rich sediments from Site 543 are enriched in light Zn isotopes, suggesting an association with weathering intensity. In contrast, carbonate-rich sediments from Site 144 are isotopically heavy, with their biogenic carbonates likely recording deep seawater Zn isotopic compositions. Arc lavas display significant Zn isotopic variations (0.17‰ to 0.39‰) relative to MORB, reflecting recycled material contributions to mantle wedge. Integrated major-trace elements and Sr-Nd-Zn isotopes collectively support that recycled materials (sediments, AOC, and possibly serpentinites) were incorporated into the mantle wedge as mélange. This study highlights Zn isotopes as an effective tool for tracing sediment recycling and physical transfer mechanisms in subduction zones.
DOI: 10.17632/f5mh7fzbs3.1
title: Zinc isotopes trace sediment recycling in the Lesser Antilles

description: To identify subducted materials and their transfer mechanisms into the mantle wedge, this study presents Zn isotope compositions for 22 arc lava samples from seven Lesser Antilles volcanoes and 50 marine sediment samples from DSDP Sites 543 and 144, combined with major-trace elements and radiogenic isotopes. Sediments exhibit significant Zn isotopic variations (δ66Zn = −0.15‰ to 0.50‰), primarily controlled by mineralogy. Clay-rich sediments from Site 543 are enriched in light Zn isotopes, suggesting an association with weathering intensity. In contrast, carbonate-rich sediments from Site 144 are isotopically heavy, with their biogenic carbonates likely recording deep seawater Zn isotopic compositions. Arc lavas display significant Zn isotopic variations (0.17‰ to 0.39‰) relative to MORB, reflecting recycled material contributions to mantle wedge. Integrated major-trace elements and Sr-Nd-Zn isotopes collectively support that recycled materials (sediments, AOC, and possibly serpentinites) were incorporated into the mantle wedge as mélange. This study highlights Zn isotopes as an effective tool for tracing sediment recycling and physical transfer mechanisms in subduction zones.