cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34740/kaggle/dsv/14684979
title: World Weather Repository ( Daily Updating )
DOI: 10.15468/dl.m79e3k
title: Occurrence Download

description: A dataset containing 926 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eurema andersoni"
 ]
}

 The dataset includes 926 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003137-260129131611470/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.28247063
title: <b>Geographic-dependent Parameter Optimization based on A-4DEnVar: Simulation with an </b><b>Idealized 2-D </b><b>Coupled Model</b>

description: Coupled climate models integrate multiple components including atmospheric, oceanic, and land submodels, while the uncertainty of model parameters from different parameterization schemes or empirically derived inevitably introduces systematic biases. Coupled parameter optimization (CPO) can reduce the systematic biasesin coupled model and enhance its capability for weather forecast and climate prediction. However, the implementation of CPO involves in dealing with strong nonlinear processes inherent in a coupled model. The analytical four-dimensional ensemble variational (A-4DEnVar) data assimilation methodretains the nonlinear processing capability of the four-dimensional variational (4D-Var) data assimilation methodbut gets rid of the dependence on the adjoint model. In this study, a novel dynamic independent point (DIP) scheme combined with a sample-space variable replacement algorithm, which enhances the convexity of the cost function, reduces computational dimensionality, and further expands the parameter subspace, is introduced to A-4DEnVar. Based on the improved A-4DEnVar, a series of geographic-dependent CPO experiments with an idealizedatmosphere-ocean-land coupled modelare carried out. The results show that,despite the strong nonlinear influence from the coupled model, A-4DEnVar can still accurately capture the geographical characteristics of model parameters, and exhibit high-quality performance in geographic-dependent optimization of cross-component parameters. This provides a new perspective when a coupled general circulation model is used for climate estimation and prediction.Additionally, the DIP scheme presents significant advantages compared to the static independent point scheme, especially with fewer independent points. In the case of only 90 independent points, satisfactory geographic-dependent CPO can be achieved.
DOI: 10.6084/m9.figshare.28247063.v4
title: <b>Geographic-dependent Parameter Optimization based on A-4DEnVar: Simulation with an </b><b>Idealized 2-D </b><b>Coupled Model</b>

description: Coupled climate models integrate multiple components including atmospheric, oceanic, and land submodels, while the uncertainty of model parameters from different parameterization schemes or empirically derived inevitably introduces systematic biases. Coupled parameter optimization (CPO) can reduce the systematic biasesin coupled model and enhance its capability for weather forecast and climate prediction. However, the implementation of CPO involves in dealing with strong nonlinear processes inherent in a coupled model. The analytical four-dimensional ensemble variational (A-4DEnVar) data assimilation methodretains the nonlinear processing capability of the four-dimensional variational (4D-Var) data assimilation methodbut gets rid of the dependence on the adjoint model. In this study, a novel dynamic independent point (DIP) scheme combined with a sample-space variable replacement algorithm, which enhances the convexity of the cost function, reduces computational dimensionality, and further expands the parameter subspace, is introduced to A-4DEnVar. Based on the improved A-4DEnVar, a series of geographic-dependent CPO experiments with an idealizedatmosphere-ocean-land coupled modelare carried out. The results show that,despite the strong nonlinear influence from the coupled model, A-4DEnVar can still accurately capture the geographical characteristics of model parameters, and exhibit high-quality performance in geographic-dependent optimization of cross-component parameters. This provides a new perspective when a coupled general circulation model is used for climate estimation and prediction.Additionally, the DIP scheme presents significant advantages compared to the static independent point scheme, especially with fewer independent points. In the case of only 90 independent points, satisfactory geographic-dependent CPO can be achieved.
DOI: 10.6084/m9.figshare.28247063.v3
title: <b>Geographic-dependent Parameter Optimization based on A-4DEnVar: Simulation with an </b><b>Idealized 2-D </b><b>Coupled Model</b>

description: Coupled climate models integrate multiple components including atmospheric, oceanic, and land submodels, while the uncertainty of model parameters from different parameterization schemes or empirically derived inevitably introduces systematic biases. Coupled parameter optimization (CPO) can reduce the systematic biasesin coupled model and enhance its capability for weather forecast and climate prediction. However, the implementation of CPO involves in dealing with strong nonlinear processes inherent in a coupled model. The analytical four-dimensional ensemble variational (A-4DEnVar) data assimilation methodretains the nonlinear processing capability of the four-dimensional variational (4D-Var) data assimilation methodbut gets rid of the dependence on the adjoint model. In this study, a novel dynamic independent point (DIP) scheme combined with a sample-space variable replacement algorithm, which enhances the convexity of the cost function, reduces computational dimensionality, and further expands the parameter subspace, is introduced to A-4DEnVar. Based on the improved A-4DEnVar, a series of geographic-dependent CPO experiments with an idealizedatmosphere-ocean-land coupled modelare carried out. The results show that,despite the strong nonlinear influence from the coupled model, A-4DEnVar can still accurately capture the geographical characteristics of model parameters, and exhibit high-quality performance in geographic-dependent optimization of cross-component parameters. This provides a new perspective when a coupled general circulation model is used for climate estimation and prediction.Additionally, the DIP scheme presents significant advantages compared to the static independent point scheme, especially with fewer independent points. In the case of only 90 independent points, satisfactory geographic-dependent CPO can be achieved.
DOI: 10.5281/zenodo.18439251
title: Glottography dataset derived from Haynie and Gavin 2019 "Modern language range mapping for the study of language diversity"

description: Cite the source of the dataset as:




Haynie, Hannah J. & Michael C. Gavin. 2019. Modern language range mapping for the study of language diversity. doi: 10.31235/osf.io/9fu7g.
DOI: 10.15468/dl.bwc6z8
title: Occurrence Download

description: A dataset containing 751 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Damnacanthus indicus indicus"
 ]
}

 The dataset includes 751 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003138-260129131611470/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.29603396
title: Deprecated Item

description: Artifact For ConUT.
DOI: 10.6084/m9.figshare.29603396.v5
title: Deprecated Item

description: Artifact For ConUT.
DOI: 10.6084/m9.figshare.29603396.v4
title: ConUT_DataSet

description: Artifact For ConUT.
DOI: 10.15468/dl.wezywd
title: Occurrence Download

description: A dataset containing 1225 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Polytremis lubricans"
 ]
}

 The dataset includes 1225 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003140-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uf6x7v
title: Occurrence Download

description: A dataset containing 352 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bia alienata",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 352 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003141-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cphfbp
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cyananthus formosus"
 ]
}

 The dataset includes 79 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003142-260129131611470/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.31219876
title: <b>An Aspect-Based Sentiment Analysis Dataset of Skincare Product Reviews from Bangladeshi E-Commerce Platforms</b>

description: This dataset presents an aspect-level sentiment annotated corpus of skincare product reviews collected from major Bangladeshi e-commerce platforms (Shajgoj, Ogerio, and Ohsogo). It comprises 2,003 aspect-level annotated instances, developed to support Aspect-Based Sentiment Analysis (ABSA) in the skincare domain. The dataset captures fine-grained consumer opinions by associating specific product aspects with corresponding sentiment polarity.<br><br>The data is structured into the following fields:<br><br>Product Name: Name of the skincare product<br>Product Category: Category of the product (e.g., Sunscreen)<br>Rating: User-provided rating on a 1–5 scale<br>Review Date: Date when the review was posted<br>Review Text: Customer-written review content<br>Aspect-Sentiment Annotation: Aspect category paired with sentiment polarity in JSON format<br><br>The dataset covers seven aspect categories—Suitability, Effectiveness, Quality, Texture, Sensitivity, Satisfaction, and Others—with sentiment labels classified as Positive or Negative. Reviews containing multiple aspects are split into separate aspect-level entries. The corpus was curated from real user reviews and manually annotated to ensure consistency and labeling accuracy.<br><b>Applications:</b><br>Aspect-Based Sentiment Analysis<br>Opinion Mining<br>Text Classification<br>Consumer Behavior Analysis<br>Explainable AI in E-commerce<br><b>Steps to reproduce</b>Download the dataset from Mendeley Data.<br>Load the dataset using spreadsheet software or data analysis tools such as Python or R.<br>Extract the review text and aspect–sentiment annotations.<br>Apply standard text preprocessing and use the data for sentiment analysis or aspect-based sentiment analysis tasks.
DOI: 10.6084/m9.figshare.31219876.v1
title: <b>An Aspect-Based Sentiment Analysis Dataset of Skincare Product Reviews from Bangladeshi E-Commerce Platforms</b>

description: This dataset presents an aspect-level sentiment annotated corpus of skincare product reviews collected from major Bangladeshi e-commerce platforms (Shajgoj, Ogerio, and Ohsogo). It comprises 2,003 aspect-level annotated instances, developed to support Aspect-Based Sentiment Analysis (ABSA) in the skincare domain. The dataset captures fine-grained consumer opinions by associating specific product aspects with corresponding sentiment polarity.<br><br>The data is structured into the following fields:<br><br>Product Name: Name of the skincare product<br>Product Category: Category of the product (e.g., Sunscreen)<br>Rating: User-provided rating on a 1–5 scale<br>Review Date: Date when the review was posted<br>Review Text: Customer-written review content<br>Aspect-Sentiment Annotation: Aspect category paired with sentiment polarity in JSON format<br><br>The dataset covers seven aspect categories—Suitability, Effectiveness, Quality, Texture, Sensitivity, Satisfaction, and Others—with sentiment labels classified as Positive or Negative. Reviews containing multiple aspects are split into separate aspect-level entries. The corpus was curated from real user reviews and manually annotated to ensure consistency and labeling accuracy.<br><b>Applications:</b><br>Aspect-Based Sentiment Analysis<br>Opinion Mining<br>Text Classification<br>Consumer Behavior Analysis<br>Explainable AI in E-commerce<br><b>Steps to reproduce</b>Download the dataset from Mendeley Data.<br>Load the dataset using spreadsheet software or data analysis tools such as Python or R.<br>Extract the review text and aspect–sentiment annotations.<br>Apply standard text preprocessing and use the data for sentiment analysis or aspect-based sentiment analysis tasks.
DOI: 10.15468/dl.aanc9w
title: Occurrence Download

description: A dataset containing 452891 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Empetrum nigrum"
 ]
}

 The dataset includes 452891 records from 720 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003116-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.772q4k
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bocquillonia arborea",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 19 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003144-260129131611470/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.30158038
title: LeafOCR-Line

description: Line segmentation is a crucial step in the manuscript digitization pipeline, as segmented lines are directly used for text recognition. A typical technology-assisted workflow involves manuscript collection, image preprocessing for noise removal and contrast enhancement, followed by segmentation of textual objects such as lines, words, or characters, and finally recognition using machine learning or deep learning methods. However, historical palm leaf manuscripts exhibit varying levels of deterioration—minimal, moderate, and severe—making character-level segmentation impractical, especially for moderately to highly deteriorated documents. Therefore, line-wise recognition is more effective and reliable for such manuscripts. This, in turn, requires accurate line segmentation and well-annotated datasets, which are currently limited in both size and diversity. LeafOCR-Line is a dataset designed to improve line segmentation for palm leaf manuscript digitization and OCR. It contains 1,710 Malayalam palm leaf images collected from 20 literary works, along with corresponding ground-truth masks. Images and masks follow the naming format <i>manuscriptname_pagenumber</i> with .jpg and .png extensions, respectively, and are organized into train, validation, and test folders under separate image and mask directories. The dataset includes 405 highly deteriorated, 911 moderately deteriorated, and 394 less deteriorated images. The training set has 1,198 images, validation 255, and test 257, distributed proportionally across deterioration levels.
DOI: 10.6084/m9.figshare.30158038.v1
title: LeafOCR-Line

description: Line segmentation is a crucial step in the manuscript digitization pipeline, as segmented lines are directly used for text recognition. A typical technology-assisted workflow involves manuscript collection, image preprocessing for noise removal and contrast enhancement, followed by segmentation of textual objects such as lines, words, or characters, and finally recognition using machine learning or deep learning methods. However, historical palm leaf manuscripts exhibit varying levels of deterioration—minimal, moderate, and severe—making character-level segmentation impractical, especially for moderately to highly deteriorated documents. Therefore, line-wise recognition is more effective and reliable for such manuscripts. This, in turn, requires accurate line segmentation and well-annotated datasets, which are currently limited in both size and diversity. LeafOCR-Line is a dataset designed to improve line segmentation for palm leaf manuscript digitization and OCR. It contains 1,710 Malayalam palm leaf images collected from 20 literary works, along with corresponding ground-truth masks. Images and masks follow the naming format <i>manuscriptname_pagenumber</i> with .jpg and .png extensions, respectively, and are organized into train, validation, and test folders under separate image and mask directories. The dataset includes 405 highly deteriorated, 911 moderately deteriorated, and 394 less deteriorated images. The training set has 1,198 images, validation 255, and test 257, distributed proportionally across deterioration levels.
DOI: 10.15468/dl.k6qb7x
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Borneodendron aenigmaticum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003146-260129131611470/datasets/export for details.
DOI: 10.5281/zenodo.18438037
title: SuperDARN data in netCDF format (2008-Oct)

description: 2008-Oct SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6579266
title: SuperDARN data in netCDF format (2008-Oct)

description: 2008-Oct SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.9hnbtz
title: Occurrence Download

description: A dataset containing 109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Brasiliocroton mamoninha",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 109 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003147-260129131611470/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.31220089
title: 无题作品

description: <b>Supporting data for ecosystem services assessment in the Great Bend of the Yellow River region</b>
DOI: 10.6084/m9.figshare.31220089.v1
title: 无题作品

description: <b>Supporting data for ecosystem services assessment in the Great Bend of the Yellow River region</b>
DOI: 10.5281/zenodo.18258349
title: Global modelling and performance analysis of a water-fueled radio-frequency gridded ion thruster (data)

description: This Zenodo project contains all the data used or obtained from the paper:

X. Wang and T. Lafleur, “Global modelling and performance analysis of a water-fueled radio-frequency gridded ion thruster,” Journal of Applied Physics (2026). https://doi.org/10.1063/5.0312768

The reaction rate coefficient data can be found in:



H2O_Rate_Coefficients.xlsx

H2_Rate_Coefficients.xlsx

O2_Rate_Coefficients.xlsx

OH_Rate_Coefficient.xlsx

H_Rate_Coefficients.xlsx

O_Rate_Coefficients.xlsx


The thermal conductivity of xenon can be found in:



Xenon thermal conductivity.dat


The corresponding figures for the uploaded data are listed below:



Figure 2a, 2b - RF power based densities.xlsx

Figure 2c - RF power based neutral temperature.xlsx 

Figure 2d - RF power based electron temperature.xlsx

Figure 3a - RF power based neutral composition ratio.xlsx

Figure 3b - RF power based ion composition ratio.xlsx

Figure 4a, 4b- Mass based densities.xlsx

Figure 4c - Mass based neutral temperature.xlsx

Figure 4d - Mass based electron temperature.xlsx

Figure 5a - Mass based neutral composition ratio.xlsx

Figure 5b - Mass based ion composition ratio.xlsx

Figure 6a - RF power based thrust.xlsx 

Figure 6b - Mass based thrust.xlsx 

Figure 7a, 7b - RF power based efficiencies.xlsx

Figure 8 - Mass based efficiencies.xlsx

Figure 9a - RF power based Isp.xlsx

Figure 9b - Mass based Isp.xlsx

Figure 11a (simulated ion beam current)- RF power based ion beam current.xlsx

Figure 11b (simulated ion beam current)- Mass based ion beam current 1.14W.xlsx, Mass based ion beam current 1.72W.xlsx, Mass based ion beam current 2.55W.xlsx, Mass based ion beam current 5.06W.xlsx, Mass based ion beam current 6.60W.xlsx


Note: For the thrust, the uploaded data are in units of newtons (N) rather than millinewtons (mN), and the species composition ratios are given as fractions rather than percentages. In the paper, the thrust values were converted to mN, and the species composition ratios were converted to percentages.