cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c50863j1f
title: MassIVE MSV000097103 - Ulnaria_parasites_temperature_cell_profiling
title: Comparison of metabolic profiling from algal cell extracts using UHPLC-HRMS (Orbitrap Q Exactive). Benthic diatom Ulnaria were healthy or infected with either chytrid or oomycete parasites, and subjected to temperature increase. Data from 20231125_xx was analyzed using C18 column, data from 20251125_xx_zh was analyzed using Zic-Hilic column; both dataset consists of cell profiling with polarity switch (MS1) and ddMS acquisition on the QC pool sample in positive and negative polarity (indicated by neg_ID or pos_ID in the title name). QC pool blank was also run with the polarity switch to acquire MS1 data. Data from 20241222_xx was analyzed using RP18 column, in negative polarity and using ddMS acquisition for all samples, including blank and QC pool sample. In supplementary files are the study and result files from Compound Discoverer, the metadata table with normalization factor, sample code and condition, and the resulting peak lists after peak deconvolution that were used for annotation (.xls) and for statistical analysis in MetaboAnalyst (.csv)
DOI: 10.15468/dl.tgynu2
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trissolcus mitsukurii (Ashmead, 1904)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 47 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024877-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cdg26x
title: Occurrence Download

description: A dataset containing 5411 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-150.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Linepithema humile (Mayr, 1868)"
 ]
}

 The dataset includes 5411 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024876-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/x98c-3e14
title:  A Comprehensive Study on Energy Storage Technology for Microgrid

description: The current paper examines and highlights the numerous energy storage system (ESS) technologies used in microgrids, as well as their architectures, configurations, performances, benefits, and drawbacks, also by providing a tangible outline for prospective efficient and sustainable ESS. As a result, there is also a comparison of the various technologies. As a consequence, there is also a comparison of the different technologies. Finally, some of the present ESS concerns and difficulties are explored.
DOI: 10.15468/dl.akgt9k
title: Occurrence Download

description: A dataset containing 391 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Acropora anthocercis (Brook, 1893)"
 ]
}

 The dataset includes 391 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024879-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14823961
title: BisCrop: Greenhouse Tomato Growth and Irrigation Dataset from Biskra, Algeria
DOI: 10.5281/zenodo.14823962
title: BisCrop: Greenhouse Tomato Growth and Irrigation Dataset from Biskra, Algeria
DOI: 10.15468/dl.7nuur6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetId is one of (https://doi.org/10.15468/lwu4fj)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024881-250127130748423/datasets/export for details.
DOI: 10.5281/zenodo.14794707
title: Data regarding the manuscript: Plasmonic Superchiral Lattice Resonances in the Mid-Infrared
DOI: 10.5281/zenodo.14794708
title: Data regarding the manuscript: Plasmonic Superchiral Lattice Resonances in the Mid-Infrared
DOI: 10.15468/dl.4m4avq
title: Occurrence Download

description: A dataset containing 4791 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((50.03777 26.01855,49.9715 25.95976,50.11793 25.64552,50.37552 25.35694,50.47812 25.43496,50.30604 25.6327,50.15534 25.8967,50.11365 26.02282,50.03777 26.01855))"
 ]
}

 The dataset includes 4791 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024882-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14855518
title: Anime Style Transfer Dataset

description: Description:





The Anime Style Transfer Dataset is ideal for training AI models to perform style transformation between real human faces and anime-style illustrations. It contains two main folders:



Training Set: 820 pairs of real faces and their anime-style representations.

Test Set: 93 similar pairs for validation.


Applications: This dataset supports various AI research areas, including style transfer, GANs, and creative projects in animation and gaming industries.

 







Download Dataset

 









Enhanced Features:



Advanced Augmentation: To diversify the dataset, techniques such as flipping, rotation, or color changes can be applied.

Metadata Enrichment: Including annotations for facial features, pose, and lighting conditions can help develop more nuanced models capable of capturing specific anime styles.


Use Cases:



Digital Content Creation: Automating the conversion of real-life photos into anime avatars for media or gaming.

Artistic Tools: Enabling new tools for anime artists by simplifying style transition between real faces and animated ones.

AI-driven Filters and Augmented Reality: Expanding its use in creative social media filters and personalized avatars for various platforms.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14855519
title: Anime Style Transfer Dataset

description: Description:





The Anime Style Transfer Dataset is ideal for training AI models to perform style transformation between real human faces and anime-style illustrations. It contains two main folders:



Training Set: 820 pairs of real faces and their anime-style representations.

Test Set: 93 similar pairs for validation.


Applications: This dataset supports various AI research areas, including style transfer, GANs, and creative projects in animation and gaming industries.

 







Download Dataset

 









Enhanced Features:



Advanced Augmentation: To diversify the dataset, techniques such as flipping, rotation, or color changes can be applied.

Metadata Enrichment: Including annotations for facial features, pose, and lighting conditions can help develop more nuanced models capable of capturing specific anime styles.


Use Cases:



Digital Content Creation: Automating the conversion of real-life photos into anime avatars for media or gaming.

Artistic Tools: Enabling new tools for anime artists by simplifying style transition between real faces and animated ones.

AI-driven Filters and Augmented Reality: Expanding its use in creative social media filters and personalized avatars for various platforms.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.14855488
title: Occupational Health Literacy Data for Healthcare Workers in Henan Province, 2023
DOI: 10.5281/zenodo.14855489
title: Occupational Health Literacy Data for Healthcare Workers in Henan Province, 2023
DOI: 10.5281/zenodo.14852196
title: IAGOS Adjusted Water Vapor Climatologies

description: Content

The files contain adjusted mean water vapor values based on the IAGOS ICH measurements. The data are provided to a 5x5° grid for different levels of potential temperatureand on pressure. The dataset contains the adjusted water vapor and an error estimate, which is based on the measurement uncertainity as well as the uncertainty of the adjusmtent methdology.

 

Data Coverage

Mean values are derived from measurements in the period 1996 to 2022

 

Data Creation

The adjustment methodology is decribed in detail in the following publication, Section 4: 

Konjari, P. and Rolf, C. and Hegglin, M. I. and Rohs, S. and Li, Y. and Zahn, A. and Bönisch, H. and Krämer, M. and Petzold, A., Water Vapor climatologies in the extra-tropical Upper Troposphere and Lower Stratosphere derived from a Synthesis of Passenger and  Research Aircraft Measurements, EGUsphere,  doi: 10.5194/egusphere-2024-2360

 

Application



The adjusted water vapor climatologies can be used to further study the spatial and seasonal variability over water vapor in the UTLS. 

For comparison of the adjusted IAGOS data with other datasets, the data coverage of the adjusted data should be considered. For this purpose, for every bin, the amount of data for every single month of the respective years is provided in the variable 'num_measurements' (dimension: longitude x latitude x theta x nyear x month, with nyear=0 for 1996).


 

Caveats:



Because a sufficient amount of data in the order of several thousand measurement points is needed to derive the adjusted mean values, only multi-annual means are provided, and no trends can be derived

Uncertainties of the adjustment methodology: The uncertainty of the water vapor data can exceed 10 % and reach up to 20 % for dry stratospheric air masses (<10 ppmv). These uncertainties, provided within the variable 'H2O_gas_err', should always be considered in the analyses.

Due to  temporal inhomogenety in the amount of data per year and bin (latitude x longitude x Theta), the temporal coverage differs from bin to bin. Hence, differences between bins might also occur because of this reason.
DOI: 10.5281/zenodo.14852197
title: IAGOS Adjusted Water Vapor Climatologies

description: Content

The files contain adjusted mean water vapor values based on the IAGOS ICH measurements. The data are provided to a 5x5° grid for different levels of potential temperatureand on pressure. The dataset contains the adjusted water vapor and an error estimate, which is based on the measurement uncertainity as well as the uncertainty of the adjusmtent methdology.

 

Data Coverage

Mean values are derived from measurements in the period 1996 to 2022

 

Data Creation

The adjustment methodology is decribed in detail in the following publication, Section 4: 

Konjari, P. and Rolf, C. and Hegglin, M. I. and Rohs, S. and Li, Y. and Zahn, A. and Bönisch, H. and Krämer, M. and Petzold, A., Water Vapor climatologies in the extra-tropical Upper Troposphere and Lower Stratosphere derived from a Synthesis of Passenger and  Research Aircraft Measurements, EGUsphere,  doi: 10.5194/egusphere-2024-2360

 

Application



The adjusted water vapor climatologies can be used to further study the spatial and seasonal variability over water vapor in the UTLS. 

For comparison of the adjusted IAGOS data with other datasets, the data coverage of the adjusted data should be considered. For this purpose, for every bin, the amount of data for every single month of the respective years is provided in the variable 'num_measurements' (dimension: longitude x latitude x theta x nyear x month, with nyear=0 for 1996).


 

Caveats:



Because a sufficient amount of data in the order of several thousand measurement points is needed to derive the adjusted mean values, only multi-annual means are provided, and no trends can be derived

Uncertainties of the adjustment methodology: The uncertainty of the water vapor data can exceed 10 % and reach up to 20 % for dry stratospheric air masses (<10 ppmv). These uncertainties, provided within the variable 'H2O_gas_err', should always be considered in the analyses.

Due to  temporal inhomogenety in the amount of data per year and bin (latitude x longitude x Theta), the temporal coverage differs from bin to bin. Hence, differences between bins might also occur because of this reason.
DOI: 10.15151/esrf-es-1997567622
title: CryoEM BAG for Ile-de-France

description: We are a group of structural biologists based in Paris that would like to benefit from high-end data collection shifts at ESRF. Our research spans various life science fields: bacterial transporters (Biou, Broutin) and machineries (Bellinzoni, Gubellini, Mechaly, Petrella), nucleic acid research (Leulliot, Sauguet, Tisné), eukaryotic channels and receptors (Prevost, Venien-Bryan, Wolff), virus biology and vaccine development (Barba-Spaeth, Guardado Calvo). Our group is already active as a community and the local proximity allows us to meet regularly. We have good access to screening microscopes but would need data collection on 300kV systems to reach cutting-edge resolution.
DOI: 10.15468/dl.uewaxs
title: Occurrence Download

description: A dataset containing 1933 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oryx leucoryx (Pallas, 1777)"
 ]
}

 The dataset includes 1933 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024883-250127130748423/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.28106717
title: E-POSE: A Large Scale Event Camera Dataset for Object Pose Estimation

description: E-POSE: A Large Scale Event Camera Dataset for Object Pose Estimation
DOI: 10.4121/bbb3cf31-043a-40a4-9bbc-42c599874db3
title: Data processing code of the HU method to assess wave dissipation capacity of coastal and estuarine forested wetlands

description: <strong>Data processing code of <em>HU</em> method, which applies Ursell number as an index for the assessment of</strong> wave dissipation capacity by coastal and estuarine forested wetlands<strong>. </strong>The data processing procedure can be done with Python or MATLAB scripts, with a readme file. Before using the scripts, the users need to prepare measured data of significant wave height, water depth and peak wave period from field measurements or lab experiments. The distance between each station should also be prepared. An instruction of setting up field measuring transects and <strong>mechanism of the <em>HU</em> method</strong> can be find in the deposited file 'practice workflow.docx'. The Python codes are supported by Zhitong Jiang from School of Ecology, Environment and Resources, Guangdong University of Technology.
DOI: 10.4121/bbb3cf31-043a-40a4-9bbc-42c599874db3.v1
title: Data processing code of the HU method to assess wave dissipation capacity of coastal and estuarine forested wetlands

description: <strong>Data processing code of <em>HU</em> method, which applies Ursell number as an index for the assessment of</strong> wave dissipation capacity by coastal and estuarine forested wetlands<strong>. </strong>The data processing procedure can be done with a few simple MATLAB scripts. Before using the scripts, the users need to prepare measured data of significant wave height, water depth and peak wave period from field measurements or lab experiments. The distance between each station should also be prepared. An instruction of setting up field measuring transects and m<strong>echanism of the <em>HU</em> method</strong> can be find in the deposited file 'practice workflow.docx'.
DOI: 10.4121/bbb3cf31-043a-40a4-9bbc-42c599874db3.v2
title: Data processing code of the HU method to assess wave dissipation capacity of coastal and estuarine forested wetlands

description: <strong>Data processing code of <em>HU</em> method, which applies Ursell number as an index for the assessment of</strong> wave dissipation capacity by coastal and estuarine forested wetlands<strong>. </strong>The data processing procedure can be done with a few simple MATLAB scripts. Before using the scripts, the users need to prepare measured data of significant wave height, water depth and peak wave period from field measurements or lab experiments. The distance between each station should also be prepared. An instruction of setting up field measuring transects and m<strong>echanism of the <em>HU</em> method</strong> can be find in the deposited file 'practice workflow.docx'.
DOI: 10.4121/bbb3cf31-043a-40a4-9bbc-42c599874db3.v3
title: Data processing code of the HU method to assess wave dissipation capacity of coastal and estuarine forested wetlands

description: <strong>Data processing code of <em>HU</em> method, which applies Ursell number as an index for the assessment of</strong> wave dissipation capacity by coastal and estuarine forested wetlands<strong>. </strong>The data processing procedure can be done with Python or MATLAB scripts, with a readme file. Before using the scripts, the users need to prepare measured data of significant wave height, water depth and peak wave period from field measurements or lab experiments. The distance between each station should also be prepared. An instruction of setting up field measuring transects and <strong>mechanism of the <em>HU</em> method</strong> can be find in the deposited file 'practice workflow.docx'.
DOI: 10.4121/bbb3cf31-043a-40a4-9bbc-42c599874db3.v4
title: Data processing code of the HU method to assess wave dissipation capacity of coastal and estuarine forested wetlands

description: <strong>Data processing code of <em>HU</em> method, which applies Ursell number as an index for the assessment of</strong> wave dissipation capacity by coastal and estuarine forested wetlands<strong>. </strong>The data processing procedure can be done with Python or MATLAB scripts, with a readme file. Before using the scripts, the users need to prepare measured data of significant wave height, water depth and peak wave period from field measurements or lab experiments. The distance between each station should also be prepared. An instruction of setting up field measuring transects and <strong>mechanism of the <em>HU</em> method</strong> can be find in the deposited file 'practice workflow.docx'.
DOI: 10.4121/bbb3cf31-043a-40a4-9bbc-42c599874db3.v5
title: Data processing code of the HU method to assess wave dissipation capacity of coastal and estuarine forested wetlands

description: <strong>Data processing code of <em>HU</em> method, which applies Ursell number as an index for the assessment of</strong> wave dissipation capacity by coastal and estuarine forested wetlands<strong>. </strong>The data processing procedure can be done with Python or MATLAB scripts, with a readme file. Before using the scripts, the users need to prepare measured data of significant wave height, water depth and peak wave period from field measurements or lab experiments. The distance between each station should also be prepared. An instruction of setting up field measuring transects and <strong>mechanism of the <em>HU</em> method</strong> can be find in the deposited file 'practice workflow.docx'. The Python codes are supported by Zhitong Jiang from School of Ecology, Environment and Resources, Guangdong University of Technology.
DOI: 10.15468/dl.re3qjx
title: Occurrence Download

description: A dataset containing 946 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brillantaisia lamium (Nees) Benth.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 946 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024886-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hh87ky
title: Occurrence Download

description: A dataset containing 29245 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Cyclamen hederifolium Aiton"
 ]
}

 The dataset includes 29245 records from 291 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024887-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zah7k5
title: Occurrence Download

description: A dataset containing 235 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gastrolobium oxylobioides Benth."
 ]
}

 The dataset includes 235 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024888-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j38n5c
title: Occurrence Download

description: A dataset containing 2802 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Diceros bicornis (Linnaeus, 1758)"
 ]
}

 The dataset includes 2802 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024889-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.frmtz8
title: Occurrence Download

description: A dataset containing 340474 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-18.72641 27.40508,9.45334 27.40508,9.45334 48.23056,-18.72641 48.23056,-18.72641 27.40508))",
 "HasGeospatialIssue is false",
 "TaxonKey is Trifolium pratense L."
 ]
}

 The dataset includes 340474 records from 2268 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024873-250127130748423/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.26621002
title: Additional file 3 of Optimization of long-lasting insecticidal bed nets for resistance management: a modelling study and user-friendly app

description: Additional file 3: Experimental data (extracted from the literature) inputted into the model that was used to fit the default values for the chemical, physical, mortality and utilization properties of bed nets.
DOI: 10.6084/m9.figshare.26621002.v1
title: Additional file 3 of Optimization of long-lasting insecticidal bed nets for resistance management: a modelling study and user-friendly app

description: Additional file 3: Experimental data (extracted from the literature) inputted into the model that was used to fit the default values for the chemical, physical, mortality and utilization properties of bed nets.
DOI: 10.6084/m9.figshare.26621005
title: Additional file 4 of Optimization of long-lasting insecticidal bed nets for resistance management: a modelling study and user-friendly app

description: Additional file 4: R script for using the codebase of the app to run massive simulations to extract data from the model underlying the bed net optimization app.
DOI: 10.6084/m9.figshare.26621005.v1
title: Additional file 4 of Optimization of long-lasting insecticidal bed nets for resistance management: a modelling study and user-friendly app

description: Additional file 4: R script for using the codebase of the app to run massive simulations to extract data from the model underlying the bed net optimization app.
DOI: 10.6084/m9.figshare.26621008
title: Additional file 5 of Optimization of long-lasting insecticidal bed nets for resistance management: a modelling study and user-friendly app

description: Additional file 5: R script for plotting the figures in the manuscript using the simulation data in Additional file 2.