cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12551087.v1
title: Demographics of E. californica and exotic grass species in the San Joaquin Desert: a field survey and greenhouse experiment

description: Shrubs facilitate the abundance and productive of annual plants in desert ecosystems.However, these shrub microhabitats favour plant species with competitive life histories. Ephedra californica is a dominant shrub in the San Joaquin desert that has been identified as a facilitator. Here, we explore the factors that limit the Ephedra californica recruitment into the San Joaquin desert including substrate, water availability, and herbivory. We also explore the role of the invasive grass Bromus madritensis on limiting establishment of E. californica. Vegetation surveys were conducted in the field during 2013 and collected seed was then used to conduct two greenhouse trials. The first explore germination and establishment techniques for E. california and the optimal substrate. The second examined E. californica establishment in present of the invasive B. madritensis responding to different water levels and herbivory. These results can have implications for land managers in the San Joaquin Valley to maintain native shrub biodiversity in the region.<br>The study available with further details of methods and results can be found in Applied Vegetation Science
DOI: 10.15468/dl.qxmtc5
title: Occurrence Download

description: A dataset containing 1320 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.decimallatitude &gt;= -13.47925
 AND occurrence.decimallatitude &lt;= -13.29746
 AND (occurrence.decimallongitude &gt;= -39.1169 AND occurrence.decimallongitude &lt;= -38.89659)
 AND GBIF_WITHIN('POLYGON ((-38.96839 -13.30321, -39.11493 -13.29746, -39.1169 -13.47065, -38.92905 -13.47925, -38.89659 -13.47734, -38.92905 -13.30321, -38.96839 -13.30321))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 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 1320 records were derived from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018437-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.37km52
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Bangladesh",
 "GbifRegion is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Podocarpus L'Hér. ex Pers."
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018446-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15587380
title: Supplementary Materials - Adaptive robotic gripper design with endoskeletons and bending sensor.

description: Supplementary Materials - Adaptive robotic gripper design with endoskeletons and bending sensor. Contains Data S1 - Dataset for described blocking experiment ; Data S2 for describe Gripping data ; and Movie S2 demonstrating the gripper's testing and general gripping abilities.
DOI: 10.15468/dl.aykerh
title: Occurrence Download

description: A dataset containing 389734 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 (Larus glaucoides B.Meyer, 1822)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 389734 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018429-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eqt6bm
title: Occurrence Download

description: A dataset containing 43622 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Nocardioides Prauser, 1976"
 ]
}

 The dataset includes 43622 records from 475 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018445-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/c7py-8939
title: "Phase Modulation for Low and High Voltages Data for Double-Balanced Mixer"

description: "Measured phase change of a 2 GHz carrier when a 0.1 Hz sinewave (black dotted curve) was applied to the phase modulator for 2 differentmodulating signal amplitudes \u2013 0.6Vpp and 1.6Vpp."
DOI: 10.15468/dl.3v46rv
title: Occurrence Download

description: A dataset containing 12134 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Opuntia engelmannii Salm-Dyck ex Engelm."
 ]
}

 The dataset includes 12134 records from 211 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018447-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14038648
title: Brick Kiln Dataset for Pakistan's IGP Region Using AI

description: This dataset represents the first geospatial mapping of brick kiln sites in the IGP region of Pakistan, providing an invaluable resource for understanding the spatial distribution of these sites. Each data point captures a brick kiln's precise location, including coordinates, state, and other important information, standardized in Coordinate Reference System (CRS) EPSG:4326 (WGS 84). This dataset, to the best of our knowledge, is the first of its kind to consolidate and geolocate brick kiln operations across this region, where air pollution impacts from kiln emissions are a significant environmental and public health concern.

In addition to the primary geolocation data, the dataset also includes an initial, secondary estimation of emissions (PM10, PM2.5, NOx, and SOx) from these sites. This supplementary information supports preliminary risk assessments, emphasizing proximity-based exposure for populations and sensitive areas (e.g., schools, hospitals) within a 1 km radius of each kiln site. 

The dataset is made available in multiple formats to facilitate wide usage across spatial analysis platforms:



Geojson (.geojson)

Shapefile (.shp)

Comma-Separated Values (.csv)


Metadata:



Geographic Coverage: IGP - Pakistan

CRS: EPSG:4326 (WGS 84)


1. Dataset - Main Version



Columns:



id: Unique identifier for each brick kiln site.

lat: Latitude of the brick kiln in decimal degrees.

lon: Longitude of the brick kiln in decimal degrees.

state: Administrative state or region of the kiln.

type: Classification of brick kiln (FCBK or ZigZag).

schools1km: Number of schools within a 1 km radius.

hosp1km: Number of hospitals within a 1 km radius.

pop1km: Estimated population within a 1 km radius.




2. Dataset - Estimates Version



Columns:



Basic ID & Location: id, lat, lon, state, type (to allow for cross-referencing with the Main Version).

Production Data:



avg_bricks: Average number of kilns in operation per day.

dailyprod(kg): Estimated daily production of bricks in kilograms.

seasonprod(kg): Seasonal production in kilograms (Excluding the Monsoon and Smog Days)



Emission Estimates:



Daily Emissions:



pm2.5d(kg): Daily PM2.5 emissions in kilograms.

pm10d(kg): Daily PM10 emissions in kilograms.

noxd(kg): Daily NOx emissions in kilograms.

soxd(kg): Daily SOx emissions in kilograms.



Seasonal Emissions:



pm2.5s(kg): Seasonal PM2.5 emissions in kilograms.

pm10s(kg): Seasonal PM10 emissions in kilograms.

noxs(kg): Seasonal NOx emissions in kilograms.

soxs(kg): Seasonal SOx emissions in kilograms.








Funding Sources: This research and data collection were funded by Amazon Web Services (AWS) and Smith School of Enterprise and The Environment (University of Oxford).
DOI: 10.6084/m9.figshare.29228951
title: Supplemental Tables for Reinforcement and cline evolution over half a century in the Upland chorus frog (<i>Pseudacris feriarum</i>)

description: Supplemental tables with captions for the manuscript Reinforcement and cline evolution over half a century in the Upland chorus frog (<i>Pseudacris feriarum</i>).
DOI: 10.6084/m9.figshare.29228951.v1
title: Supplemental Tables for Reinforcement and cline evolution over half a century in the Upland chorus frog (<i>Pseudacris feriarum</i>)

description: Supplemental tables with captions for the manuscript Reinforcement and cline evolution over half a century in the Upland chorus frog (<i>Pseudacris feriarum</i>).
DOI: 10.6084/m9.figshare.29228945
title: Portico_Nelly

description: Es solo una prueba de como se utiliza
DOI: 10.6084/m9.figshare.29228945.v1
title: Portico_Nelly

description: Es solo una prueba de como se utiliza
DOI: 10.15468/dl.dhny8a
title: Occurrence Download

description: A dataset containing 43622 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Nocardioides Prauser, 1976"
 ]
}

 The dataset includes 43622 records from 475 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018450-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6tej3f
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xenodermichthys copei (Gill, 1884)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((51.9067 10.3601,51.6539 10.8496,44.9648 10.8496,44.9648 1.8496,45.9851 1.8496,46.3652 2.0796,48.3314 4.1285,49.4027 5.7662,49.6642 6.5145,51.2543 9.1551,51.377 9.8685,51.7452 10.0272,51.9067 10.3601))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010560-250525065834625/datasets/export for details.
DOI: 10.15468/dl.p2v33p
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Algunas especies de anfibios y reptiles contenidos en el Proyecto de Norma Oficial Mexicana PROY-NOM-059-ECOL-2000",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "PublishingOrg is ff90b050-c256-11db-b71b-b8a03c50a862",
 "TaxonKey is Eleutherodactylus maurus Hedges, 1989",
 "Year 1960-2025"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018565-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t4d5ve
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lasiurus villosissimus (E.Geoffroy, 1806)"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018566-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15586654
title: combination

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: 10835-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.15586655
title: combination

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: 10835-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.15586666
title: combination 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: 10835-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.15586667
title: combination 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: 10835-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.3e49zc
title: Occurrence Download

description: A dataset containing 36448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Streptomyces Waksman & Henrici, 1943"
 ]
}

 The dataset includes 36448 records from 425 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018567-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/s7d1ak
title: Replication Data for: The Prodigal Child Returns? Attitudes towards Return Migration in a Developing Economy

description: Data estimates suggest that up to half of all migrants return to the country of origin within 5 years of leaving. Return migration is known to be a boon for the local economy and a catalyst for political reform. However, these effects are conditional on successful reintegration, which is dependent on the preferences of nonmigrants. What causes negative attitudes towards return migration, given its significant potential economic benefits? I argue that nonmigrants are concerned about both the economic and political competition of returnees. Nonmigrants prefer to welcome back migrants who can bring financial capital and employment back home, but will oppose competitors on the job market when unemployment is high. Furthermore, nonmigrants are concerned about the potential role of return migrants as norm entrepreneurs. I test my hypotheses with a conjoint survey experiment conducted in Colombia, as well as an analysis of the 2016 peace referendum.
DOI: 10.15468/dl.bs9bkg
title: Occurrence Download

description: A dataset containing 143 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alebion carchariae Krøyer, 1863"
 ]
}

 The dataset includes 143 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018568-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15424389
title: X-ray diffraction patterns of SrWO4 and CdWO4 nanoparticles

description: X-ray diffraction patterns of SrWO4 and CdWO4 nanoparticles after coprecipitation at RT (SrWO4_1.dat and CdWO4_1.dat), synthesized with citric acid and left at RT (SrWO4_2.dat and CdWO4_2.dat), synthesized with citric acid and heated in the autoclave for 24 h at 160◦C (SrWO4_3.dat and CdWO4_3.dat).
DOI: 10.5281/zenodo.15424390
title: X-ray diffraction patterns of SrWO4 and CdWO4 nanoparticles

description: X-ray diffraction patterns of SrWO4 and CdWO4 nanoparticles after coprecipitation at RT (SrWO4_1.dat and CdWO4_1.dat), synthesized with citric acid and left at RT (SrWO4_2.dat and CdWO4_2.dat), synthesized with citric acid and heated in the autoclave for 24 h at 160◦C (SrWO4_3.dat and CdWO4_3.dat).
DOI: 10.6084/m9.figshare.29223002
title: <b>Unraveling Hydrological Dynamics</b><b> in the Qaidam Basin, Qinghai-Tibet Plateau</b>

description: Our work introduces an interdisciplinary framework that combines newly collected water isotopic data with previously published records. We utilize advanced spatial interpolation techniques, including Inverse Distance Weighting (IDW) and Ordinary Kriging, alongside the HYSPLIT model, to track moisture transport trajectories. These spatial analysis techniques allow us to visualize isotopic gradients across the basin and identify correlations with geographical factors, such as elevation and proximity to moisture sources. Additionally, the HYSPLIT model is employed to simulate moisture transport pathways and sources, utilizing backward trajectory analysis to trace the origins of moisture. This modeling approach, integrated with reanalysis data, provides valuable insights into the transport of water vapor and its impact on precipitation patterns. This, in turn, enhances our understanding of evaporation processes and moisture recycling in the Qaidam Basin. This study underscores the critical importance of region-specific water management strategies to address the challenges posed by climate change and water scarcity. By integrating isotopic data with moisture source analysis, our research provides essential insights for sustainable water resource management, while highlighting the need for continued monitoring to mitigate future hydrological challenges in arid regions like the Qaidam Basin.