cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.gth5ez
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bathytroctes microlepis Günther, 1878",
 "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 ((44.9648 10.8496,35.9648 10.8496,35.9648 1.8496,44.9648 1.8496,44.9648 10.8496))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010659-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15182302
title: Composition of East Siberian forests
title: Raster fractional cover maps of key vegetation types

description: The dataset contains tiled raster vegetation data for Southeastern Siberia. The fractional cover (from 0 to 100%) of each of the 6 classes is stored as image bands, split into 66 tiles. Data is stored in the Web Mercator projection (EPSG:3857). Tiling grid is also available as KML.

The images are named according to the vegetation types: bar – bare ground (rocks, bare soil, sealed surfaces), con – evergreen coniferous trees (Pinus sibirica, Pinus sylvestris, Abies sibirica, Picea obovata), dec – deciduous broadleaf trees (Betula spp., Populus spp., Alnus spp.), grs – non-tree vegetation (herbaceous, shrubby, mosses and lichens), lar – larch (Larix spp.), pum – dwarf pine (Pinus pumila). The information of the creation and validation of the dataset will be available in the upcoming article.
DOI: 10.5281/zenodo.15182301
title: Composition of East Siberian forests
title: Raster fractional cover maps of key vegetation types

description: The dataset contains tiled raster vegetation data for Southeastern Siberia. The fractional cover (from 0 to 100%) of each of the 6 classes is stored as image bands, split into 66 tiles. Data is stored in the Web Mercator projection (EPSG:3857). Tiling grid is also available as KML.

The images are named according to the vegetation types: bar – bare ground (rocks, bare soil, sealed surfaces), con – evergreen coniferous trees (Pinus sibirica, Pinus sylvestris, Abies sibirica, Picea obovata), dec – deciduous broadleaf trees (Betula spp., Populus spp., Alnus spp.), grs – non-tree vegetation (herbaceous, shrubby, mosses and lichens), lar – larch (Larix spp.), pum – dwarf pine (Pinus pumila). The information of the creation and validation of the dataset will be available in the upcoming article.
DOI: 10.6084/m9.figshare.29082530
title: <b>Supplementary Table S1: </b>One-tube master mix composition for simplex LAMP assays.

description: <b>Supplementary Table S1</b>. One-tube master mix composition for simplex LAMP assays.Volumes of each reagent used in the master mix for the LAMP assays performed using the Portable LAMP Case and Pocket LAMP devices.
DOI: 10.6084/m9.figshare.29099291
title: Evaluation of ankle proprioception and instability among individuals with and without flexible flatfoot- a comparative study

description: This study compares the differences in ankle proprioception and ankle instability among young adults aged 18 to 30 with and without flatfoot. 153 students participated voluntarily and were screened using the navicular drop test and selected based on inclusion and exclusion criterias. 75 subjects were determined as having flexible flatfoot and 78 without flexible flatfoot. Ankle proprioception was assessed using joint position sense test by a digital inclinometer, while ankle instability was assessed using anterior drawer test for the laxity of ATFL.This dataset contains clinical measurements collected as a part of this study.<b>Sheet 1</b>: Combined data of all participants with corresponding assessment values.<b>Sheet 2 (with)</b>: Data specific to individuals identified with flatfoot.<b>Sheet 3(</b><b>without)</b>: Data specific to individuals without flatfoot.<b>Variables Included</b>:<b>Participant Id</b>: Unique ID for each participant.<b>Age</b>: Age in years.<b>Gender</b>: Participant’s gender.<b>BMI</b>: Body Mass Index (kg/m²).<b>Navicular Drop Test (NDT)</b>: Measured in mm for both right and left feet.<b>Joint Position Error (JPE)</b>: Error in degrees for both sides.<b>Anterior Drawer Test (ADT)</b>: Measurement of ankle joint laxity in cm for both right and left ankles.<b>Ethical Approval: </b>The ICMR National Ethical Guidelines for biomedical and health research involving human participants 2017 is followed. The study was approved by the ethical committee of Yenepoya ethics committee 2 on 14/05/2024 (protocol number- YEC2/2024/111).
DOI: 10.15468/dl.r4cm2p
title: Occurrence Download

description: A dataset containing 1333 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-5.89249 34.78121,-3.60941 34.53575,6.83311 34.64167,13.2066 35.44778,14.83955 35.8583,16.78354 38.86793,16.00961 40.54444,12.75913 43.19487,8.73471 45.06663,3.93637 44.05967,-1.01676 41.99853,-5.89249 34.78121))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Callinectes sapidus"
 ]
}

 The dataset includes 1333 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007060-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dd.jtwdjr
title: GBIF_test1

description: A small example 1
DOI: 10.5281/zenodo.17242954
title: Monitoring des Agrarumweltsystems Schweiz MAUS

description: This dataset contains the results of the monitoring of the Swiss Agri-Environmental System (MAUS).
DOI: 10.5281/zenodo.17242953
title: Monitoring des Agrarumweltsystems Schweiz MAUS

description: This dataset contains the results of the monitoring of the Swiss Agri-Environmental System (MAUS).
DOI: 10.15468/dd.3axqee
title: GBIF_test1

description: A small example 1
DOI: 10.34740/kaggle/dsv/13859758
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.34740/kaggle/dsv/13805053
title: Static Gestures of Indian Sign Language (ISL)
DOI: 10.15468/dl.nm6v8m
title: Occurrence Download

description: A dataset containing 357181 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhinolophus"
 ]
}

 The dataset includes 357181 records from 2286 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007052-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13990765
title: A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1

description: This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.

The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.

This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)
DOI: 10.5281/zenodo.13990766
title: A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1

description: This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.

The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.

This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)
DOI: 10.5281/zenodo.14032829
title: A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6

description: This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.

The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.

This Zenodo repository provides data on following land-use classes: cropland used for production of potatoes (CL-POTA); sweet potatoes and yams (CL-SWPY); and rest of crops (CL-REST)
DOI: 10.5281/zenodo.15275542
title: M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets

description: This dataset consists of tweets, sentiment labels and their respective images.

Some of the tweets were pulled unsuccessfully due to EU restrictions. These tweets have a custom error message and are the same throughout the dataset.

 Kindly check the tweet text and either filter them out or rehydrate those respective tweets.
DOI: 10.5281/zenodo.15275541
title: M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets

description: This dataset consists of tweets, sentiment labels and their respective images.

Some of the tweets were pulled unsuccessfully due to EU restrictions. These tweets have a custom error message and are the same throughout the dataset.

 Kindly check the tweet text and either filter them out or rehydrate those respective tweets.
DOI: 10.5281/zenodo.15261613
title: The supporting datasets for the 2024JE008802RR submission "Electron structures in Titan's induced magnetosphere and low-frequency wave activity"

description: The provided datasets are used in a paper submitted to JGR: Planets titled "Electron structures in Titan's induced magnetosphere and low-frequency wave activity". We investigate the T36 flyby by using the measurements from both particle and field instruments. In particular, the Cassini Plasma Spectrometer (CAPS) ion  (IMS) and electron (ELS) spectrometers, the Radio and Plasma Wave Science (RPWS) Langmuir probe (LP) and upper-hybrid resonance (Fuh), and the magnetometer (MAG). We store data in .mat-files as structures. 

To produce figures, the authors used data from the Planetary Data System: Planetary Plasma Interactions (https://pds-ppi.igpp.ucla.edu/). The internal structure of each file corresponds to the latest PDS format at the date of submission. The corresponding datasets are: 



data_els_mom.mat - CAPS ELS derived moments from the distribution function, including the electron temperature and density;

data_els.mat - CAPS ELS spectrogram data, including the counts per second for 8 anodes and the field-of-view;

data_mag.mat - MAG high-resolution magnetic field data, including the three components of the magnetic field;

data_rpws.mat - RPWS the spacecraft potential, electron density and temperature; the electron density is calculated using three methods (LP sweeps, "20 Hz" and upper-hybrid resonance);

data_sng.mat - CAPS IMS single data (SNG), including counts per second for each anode and the field-of-view;

data_eph.mat - ephemeris data for the T36 flyby.


To reproduce figures, one has to use: 



Figure 1 - dataset (6);

Figure 2 - all datasets;

Figure 3 - dataset (2);

Figure 4 - datasets (2,3);

Figure 5 - dataset (3);

Figure 6 - dataset (3);

Figure 7 - dataset (2,3);

Figure A1-3 - dataset (3);


The archived raw datasets can also be found at the following DOIs: Gurnett (RPWS, 2020, doi: 10.17189/1518302), Dougherty (MAG, 2023, doi: 10.1007/s11214-004-1432-2), Waite and Wilson (CAPS, 2022, doi: 10.17189/CM56-W882).
DOI: 10.5281/zenodo.15261614
title: The supporting datasets for the 2024JE008802RR submission "Electron structures in Titan's induced magnetosphere and low-frequency wave activity"

description: The provided datasets are used in a paper submitted to JGR: Planets titled "Electron structures in Titan's induced magnetosphere and low-frequency wave activity". We investigate the T36 flyby by using the measurements from both particle and field instruments. In particular, the Cassini Plasma Spectrometer (CAPS) ion  (IMS) and electron (ELS) spectrometers, the Radio and Plasma Wave Science (RPWS) Langmuir probe (LP) and upper-hybrid resonance (Fuh), and the magnetometer (MAG). We store data in .mat-files as structures. 

To produce figures, the authors used data from the Planetary Data System: Planetary Plasma Interactions (https://pds-ppi.igpp.ucla.edu/). The internal structure of each file corresponds to the latest PDS format at the date of submission. The corresponding datasets are: 



data_els_mom.mat - CAPS ELS derived moments from the distribution function, including the electron temperature and density;

data_els.mat - CAPS ELS spectrogram data, including the counts per second for 8 anodes and the field-of-view;

data_mag.mat - MAG high-resolution magnetic field data, including the three components of the magnetic field;

data_rpws.mat - RPWS the spacecraft potential, electron density and temperature; the electron density is calculated using three methods (LP sweeps, "20 Hz" and upper-hybrid resonance);

data_sng.mat - CAPS IMS single data (SNG), including counts per second for each anode and the field-of-view;

data_eph.mat - ephemeris data for the T36 flyby.


To reproduce figures, one has to use: 



Figure 1 - dataset (6);

Figure 2 - all datasets;

Figure 3 - dataset (2);

Figure 4 - datasets (2,3);

Figure 5 - dataset (3);

Figure 6 - dataset (3);

Figure 7 - dataset (2,3);

Figure A1-3 - dataset (3);


The archived raw datasets can also be found at the following DOIs: Gurnett (RPWS, 2020, doi: 10.17189/1518302), Dougherty (MAG, 2023, doi: 10.1007/s11214-004-1432-2), Waite and Wilson (CAPS, 2022, doi: 10.17189/CM56-W882).
DOI: 10.15468/dl.gpbkhf
title: Occurrence Download

description: A dataset containing 632 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Rhododendron siderophyllum Franch."
 ]
}

 The dataset includes 632 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001259-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5h41k04w
title: MassIVE MSV000097729 - GNPS GCMS Dataset Creation for Deconvolution
title: Antibacterial activity of crude extracts of two endophytic fungal isolates
DOI: 10.15468/dl.tbavbq
title: Occurrence Download

description: A dataset containing 6496476 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Aix sponsa"
 ]
}

 The dataset includes 6496476 records from 465 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004848-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4s892z
title: Occurrence Download

description: A dataset containing 267 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Teratoscincus roborowskii"
 ]
}

 The dataset includes 267 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004865-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.30497
title: Single-cell transcriptome sequencing data on PCV2d
title: 有关PCV2d的单细胞转录组测序数据

description: The single-cell transcriptome sequencing data of PCV2d mainly includes the changes in several major cell types and related genes in the lung tissue of mice in the normal group, virus infected group, vaccine injected group after virus infection, and adjuvant injected group after virus infection. 
description: The single-cell transcriptome sequencing data of PCV2d mainly includes the changes in several major cell types and related genes in the lung tissue of mice in the normal group, virus infected group, vaccine injected group after virus infection, and adjuvant injected group after virus infection.
DOI: 10.57760/sciencedb.32066
title: B-Integral Measurement on High-Power Laser Facility based on Off-Axis Aberration
title: B-Integral Measurement on High-Power Laser Facility based on Off-Axis Aberration

description: We propose a spatially resolved B-integral measurement method for high-power laser drivers, based on coma aberration off-axis aberration characterization. Theoretical analysis confirms the feasibility and high precision of this approach, where intensity modulation in a coma-shaped form is intentionally introduced into the laser system, imprinting the nonlinear phase modulation with a corresponding aberration profile. The B-integral is then extracted by measuring the coma component of the output beam via a Shack-Hartmann (SH) sensor. Experimental results demonstrate a 5.8% deviation between the measured and simulated B-integral values for coma aberration, significantly outperforming the 67.4% error observed with defocus-based measurements. This method requires no modifications to the laser setup, offers single-shot measurement capability, and achieves high accuracy and excellent repeatability. By directly quantifying wavefront phase distortions, it provides a practical solution for nonlinear phase modulation diagnostics in high-power laser systems.
description: We propose a spatially resolved B-integral measurement method for high-power laser drivers, based on coma aberration off-axis aberration characterization. Theoretical analysis confirms the feasibility and high precision of this approach, where intensity modulation in a coma-shaped form is intentionally introduced into the laser system, imprinting the nonlinear phase modulation with a corresponding aberration profile. The B-integral is then extracted by measuring the coma component of the output beam via a Shack-Hartmann (SH) sensor. Experimental results demonstrate a 5.8% deviation between the measured and simulated B-integral values for coma aberration, significantly outperforming the 67.4% error observed with defocus-based measurements. This method requires no modifications to the laser setup, offers single-shot measurement capability, and achieves high accuracy and excellent repeatability. By directly quantifying wavefront phase distortions, it provides a practical solution for nonlinear phase modulation diagnostics in high-power laser systems.
DOI: 10.57760/sciencedb.30197
title: Nanosecond damage of dispersive mirrors
title: 色散镜纳秒损伤

description: Recorded the nanosecond damage data of the three designed and manufactured dispersion mirrors, including design model, damage experiment, damage morphology, and damage analysis data
description: Recorded the nanosecond damage data of the three designed and manufactured dispersion mirrors, including design model, damage experiment, damage morphology, and damage analysis data
DOI: 10.57760/sciencedb.32021
title: EAST Framework and Simulation Model KPIs
title: EAST Framework and Simulation Model KPIs

description: This submission has two excel sheets. KPIs: The results of the six relevant KPIs for the five simulation models.EAST Networks: The nodes and links used to construct the three networks of the EAST Framework (task, social, and knowledge)
description: This submission has two excel sheets. KPIs: The results of the six relevant KPIs for the five simulation models.EAST Networks: The nodes and links used to construct the three networks of the EAST Framework (task, social, and knowledge)
DOI: 10.57760/sciencedb.32054
title: Thesis DE and TR dataset
title: Thesis DE and TR dataset

description: Dataset of Academic Representations on the Role of Family in Education in doctoral thesis
description: Dataset of Academic Representations on the Role of Family in Education in doctoral thesis
DOI: 10.15468/dl.s8r77m
title: Occurrence Download

description: A dataset containing 2007 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Ambystoma velasci"
 ]
}

 The dataset includes 2007 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004866-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wuk3s6
title: Occurrence Download

description: A dataset containing 30867 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Europe, Oceania, Asia, Africa)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mimosa pigra"
 ]
}

 The dataset includes 30867 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004867-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.