cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/24dxycb9gm.1
title: Non-STEM_TextBook_Bahasa

description: The full corpus is curated across multiple STEM/Non-STEM disciplines and structured for use in LLM training, evaluation, and instruction tuning (SFT/RLHF). This sample represents the structure and quality of the larger dataset.

Dataset composition (full corpus):

Text corpus: 1.6B+ words of curated STEM and Non-STEM educational content across 22000+ texbooks in 7 languages(English, Hindi, Arabic, Bahasa, Tamil, Telegu, Kannada)
Question–Answer pairs: 6.5M+ high-quality Q&A pairs of STEM and Non-STEM in (English, Arabic, Hindi and Indic languages)
Video data: 100K+ hours of STEM Videos and 30K+ hours of UGC.
Audio data: 821K+ hours of Podcasts and Call Center data(Dual Channel)
Medical datasets: 30M+ files including clinical and diagnostic data like CT Scan, MRI, X-ray, Pathology, EHRs, USG Reports and Echo Reports.
This repository includes:

A small preview subset of the Non-STEM Bahasa Textbook data
Flat, viewer-friendly schema for inspection
Parquet files suitable for benchmarking and evaluation
Purpose of this dataset:

Dataset preview and validation
Model evaluation and experimentation
Schema and format inspection before full-scale access
warning: Note: This repository contains sample data only. Access to the complete dataset is available separately under appropriate licensing or partnership terms. Note: This is not the full dataset.

For full details, Please contact [Em: vipul.mishra@infobay.ai]
DOI: 10.17632/f39zt9g2c4
title: Deep Learning Model Training and Validation Data for Global Floating Algae Detection

description: This dataset contains the training and validation data used to develop and evaluate a ResUNet deep learning (DL) segmentation model for detecting floating algae from MODIS/Aqua imagery at the global scale. The model was trained using inputs including MODIS Rayleigh corrected reflectance (Rrc) in 7 spectral bands and the Alternative Floating Algae Index (AFAI), and is capable of identifying both microalgae (phytoplankton) scums and macroalgae (seaweed) mats. These include Trichodesmium, Noctiluca, Dinoflagellates, Cyanobacteria, Sargassum, and Ulva.
DOI: 10.17632/f39zt9g2c4.2
title: Deep Learning Model Training and Validation Data for Global Floating Algae Detection

description: This dataset contains the training and validation data used to develop and evaluate a ResUNet deep learning (DL) segmentation model for detecting floating algae from MODIS/Aqua imagery at the global scale. The model was trained using inputs including MODIS Rayleigh corrected reflectance (Rrc) in 7 spectral bands and the Alternative Floating Algae Index (AFAI), and is capable of identifying both microalgae (phytoplankton) scums and macroalgae (seaweed) mats. These include Trichodesmium, Noctiluca, Dinoflagellates, Cyanobacteria, Sargassum, and Ulva.
DOI: 10.15468/dl.s8cvp9
title: Occurrence Download

description: A dataset containing 728 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Betula maximowicziana",
 "BasisOfRecord is one of (Human Observation)"
 ]
}

 The dataset includes 728 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009472-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/5f8zz9fr2p
title: Cauliflower Plant Health Image Dataset for Smart Agriculture Applications

description: Dataset Description:

This dataset comprises RGB images of cauliflower plants collected under real field conditions for the purpose of plant health assessment and smart agriculture research. The images were acquired from a local agricultural field located in Penamaluru village, near Vijayawada, Andhra Pradesh, India. Data collection was carried out during daytime under natural lighting conditions using a high-resolution mobile phone camera, ensuring realistic environmental variability.

The dataset is organized into two distinct classes: Healthy and Damaged. The Healthy class contains images of visually healthy cauliflower plants without observable symptoms of disease or stress. The Damaged class includes images of plants exhibiting visible signs of pest infestation, disease, discoloration, or physiological stress. All images were manually reviewed and labeled to ensure accurate class representation.
DOI: 10.17632/5f8zz9fr2p.1
title: Cauliflower Plant Health Image Dataset for Smart Agriculture Applications

description: Dataset Description:

This dataset comprises RGB images of cauliflower plants collected under real field conditions for the purpose of plant health assessment and smart agriculture research. The images were acquired from a local agricultural field located in Penamaluru village, near Vijayawada, Andhra Pradesh, India. Data collection was carried out during daytime under natural lighting conditions using a high-resolution mobile phone camera, ensuring realistic environmental variability.

The dataset is organized into two distinct classes: Healthy and Damaged. The Healthy class contains images of visually healthy cauliflower plants without observable symptoms of disease or stress. The Damaged class includes images of plants exhibiting visible signs of pest infestation, disease, discoloration, or physiological stress. All images were manually reviewed and labeled to ensure accurate class representation.
DOI: 10.17632/yppvthc5v5
title: Biological regulation of organic and inorganic carbon in the tidal flats of China: new insights for blue carbon inventories

description: This study uses a station-based intertidal dataset that integrates climate, hydrographic conditions, sediment properties, sediment organic and inorganic carbon (OC and IC), and meiofaunal metrics (abundance and/or community composition). All variables are harmonized in a single table (rows = stations/samples; columns = variables) to support multivariate analyses of environmental controls on OC/IC patterns across tidal flats.
DOI: 10.17632/yppvthc5v5.1
title: Biological regulation of organic and inorganic carbon in the tidal flats of China: new insights for blue carbon inventories

description: This study uses a station-based intertidal dataset that integrates climate, hydrographic conditions, sediment properties, sediment organic and inorganic carbon (OC and IC), and meiofaunal metrics (abundance and/or community composition). All variables are harmonized in a single table (rows = stations/samples; columns = variables) to support multivariate analyses of environmental controls on OC/IC patterns across tidal flats.
DOI: 10.17632/vxw93xd7d6
title: Donepezil Hydrochloride Ameliorates Peripheral Inflammation in Vascular Dementia by Regulating Treg Homeostasis. Sai Qi

description: 研究假设：盐酸多奈哌齐可能通过调控外周Treg稳态抑制VD患者外周炎症进而改善认知功能。
数据显示内容：简易精神状态检查表（MMSE）、阿尔茨海默病评估量表-认知分量表（ADAS-cog）、日常生活活动量表（ADL）、临床医生面试和照料者补充的总体变化印象量表（CIBIC-plus）；流式细胞术（FCM）和酶联免疫吸附试验（ELISA）。
数据解读方式：采用SPSS27.0和GraphPad Prism10.0统计软件对数据进行分析，对所有数据进行正态性检验及方差齐性检验。正态分布或近似正态分布的计量资料以均数±标准差（Mean ± SD）表示，多组计量资料符合正态分布采用单因素方差分析（One-way ANOVA），方差齐者采用LSD检验，方差不齐者采用Dunnett’s T3检验；不符合正态分布的数据用中位数（P25，P75）表示，采用非参数检验中K个独立样本中克鲁斯卡尔-沃利斯检验（Kruskal-Wallis test）。所有以P＜0.05表示差异具有统计学意义。
DOI: 10.15468/dl.kxdnf4
title: Occurrence Download

description: A dataset containing 9598 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Abramis brama",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1975",
 "Year is less than or equal to 1979"
 ]
}

 The dataset includes 9598 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009473-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vxw93xd7d6.1
title: Donepezil Hydrochloride Ameliorates Peripheral Inflammation in Vascular Dementia by Regulating Treg Homeostasis. Sai Qi

description: 研究假设：盐酸多奈哌齐可能通过调控外周Treg稳态抑制VD患者外周炎症进而改善认知功能。
数据显示内容：简易精神状态检查表（MMSE）、阿尔茨海默病评估量表-认知分量表（ADAS-cog）、日常生活活动量表（ADL）、临床医生面试和照料者补充的总体变化印象量表（CIBIC-plus）；流式细胞术（FCM）和酶联免疫吸附试验（ELISA）。
数据解读方式：采用SPSS27.0和GraphPad Prism10.0统计软件对数据进行分析，对所有数据进行正态性检验及方差齐性检验。正态分布或近似正态分布的计量资料以均数±标准差（Mean ± SD）表示，多组计量资料符合正态分布采用单因素方差分析（One-way ANOVA），方差齐者采用LSD检验，方差不齐者采用Dunnett’s T3检验；不符合正态分布的数据用中位数（P25，P75）表示，采用非参数检验中K个独立样本中克鲁斯卡尔-沃利斯检验（Kruskal-Wallis test）。所有以P＜0.05表示差异具有统计学意义。
DOI: 10.17632/8nygjx2nbm
title: R2 JAN 2026 Data and Codes for the manuscript: "Does National Geopolitical Risk Shape Corporate Dividend Policy? Empirical Insights from Emerging Economies"

description: This replication package provides all raw publicly available data, detailed data sources, and coding for appending and creating new variables. Our research dataset can be easily created from the synthetic raw data that has all the variables used in the baseline analysis and the same number of observations. Synthetic data variable names, symbols, and mean, SD, min, and max values are the same as the research data reported and can conveniently regress the primary results of the above-mentioned research paper. The package is designed to facilitate transparency and enable verification of all statistical findings reported in this study. Furthermore, we are also willing to provide all detailed research data and further requirements to interested parties upon reasonable request via the corresponding author.
DOI: 10.17632/8nygjx2nbm.1
title: R2 JAN 2026 Data and Codes for the manuscript: "Does National Geopolitical Risk Shape Corporate Dividend Policy? Empirical Insights from Emerging Economies"

description: This replication package provides all raw publicly available data, detailed data sources, and coding for appending and creating new variables. Our research dataset can be easily created from the synthetic raw data that has all the variables used in the baseline analysis and the same number of observations. Synthetic data variable names, symbols, and mean, SD, min, and max values are the same as the research data reported and can conveniently regress the primary results of the above-mentioned research paper. The package is designed to facilitate transparency and enable verification of all statistical findings reported in this study. Furthermore, we are also willing to provide all detailed research data and further requirements to interested parties upon reasonable request via the corresponding author.
DOI: 10.5281/zenodo.17779499
title: Data package for the research article "Insights into OH/H2O Formation and Diurnal Variability in the Mairan Crater Region from M³ Spectroscopy"

description: This repository contains the datasets used for the analysis of the Mairan crater region and its surrounding areas, employing both high spatial and spectral resolution observations (target-mode) as well as the more commonly used lower-resolution datasets acquired by the Moon Mineralogy Mapper (global-mode). The repository is organized as follows:





Spectral indices (global): This folder contains the spectral indices used to generate Figures 3 and 4. The data are stored in .img files accompanied by their respective .hdr header files.




Unmixing (global): This folder contains the scalar coefficients derived from the fully constrained least-squares (FCLS) linear spectral unmixing used to generate Figure 5. The data are stored in .img files with the corresponding .hdr headers.




Clusters: This folder includes two subfolders containing the reflectance and continuum-removed spectra associated with Figure 7 and Figure 8. These data are stored in .txt files, where each column corresponds to a specific cluster.




Spectral indices (target): This folder contains the spectral indices computed for the target-mode data clusters. These data are stored in .csv files.




ESPAT (global): This folder contains the ESPAT values for each thermal-removal method used to generate Figure 12. The data are stored in .img files with the respective .hdr header files.
DOI: 10.17632/9wr4v4jyzt
title: Data for: Atomic Force Microscopy Captures Light-Induced Higher-Order Structural Dynamics in Photosystem II Supercomplexes

description: The dataset includes raw high-speed AFM images of PSII supercomplexes in grana membranes, as well as numerical data obtained from single-particle analysis of the AFM images.
DOI: 10.5281/zenodo.18247720
title: Baccharis psiadioides (Less.) Joch.Müll. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.5281/zenodo.18247721
title: Baccharis psiadioides (Less.) Joch.Müll. In: Marchesi E., Mailhos A. & Bonifacino J.M. (Eds.). Flora Uruguaya: Flora Vascular del Uruguay. ISSN 3121-2050, V. 1.0.

description: Citable snapshot. Includes PDF and CSV of collections. Date: January 13th, 2026 at 16:42
DOI: 10.17632/9wr4v4jyzt.1
title: Data for: Atomic Force Microscopy Captures Light-Induced Higher-Order Structural Dynamics in Photosystem II Supercomplexes

description: The dataset includes raw high-speed AFM images of PSII supercomplexes in grana membranes, as well as numerical data obtained from single-particle analysis of the AFM images.
DOI: 10.5281/zenodo.17779500
title: Data package for the research article "Insights into OH/H2O Formation and Diurnal Variability in the Mairan Crater Region from M³ Spectroscopy"

description: This repository contains the datasets used for the analysis of the Mairan crater region and its surrounding areas, employing both high spatial and spectral resolution observations (target-mode) as well as the more commonly used lower-resolution datasets acquired by the Moon Mineralogy Mapper (global-mode). The repository is organized as follows:





Spectral indices (global): This folder contains the spectral indices used to generate Figures 3 and 4. The data are stored in .img files accompanied by their respective .hdr header files.




Unmixing (global): This folder contains the scalar coefficients derived from the fully constrained least-squares (FCLS) linear spectral unmixing used to generate Figure 5. The data are stored in .img files with the corresponding .hdr headers.




Clusters: This folder includes two subfolders containing the reflectance and continuum-removed spectra associated with Figure 7 and Figure 8. These data are stored in .txt files, where each column corresponds to a specific cluster.




Spectral indices (target): This folder contains the spectral indices computed for the target-mode data clusters. These data are stored in .csv files.




ESPAT (global): This folder contains the ESPAT values for each thermal-removal method used to generate Figure 12. The data are stored in .img files with the respective .hdr header files.
DOI: 10.17632/n5fwdyzwt8
title: Dataset about Vietnamese civil servants (Original data)

description: This dataset was constructed to measure the impact of digital skills on the output of local government officials in Vietnam to meet research needs. We employed the snowball sampling technique to collect online survey responses from civil servants currently working in local government agencies across all six socio-economic regions of Vietnam in the post-amalgamation period. Within 20 days (from November 15, 2025, to December 5, 2025), a total of 458 responses were collected, but some were not eligible for analysis such as extreme answers (all answers were 1 or 5) and arbitrary answers (trap questions). The remains of 380 samples were used for further analysis with the rate of 82.9%.
DOI: 10.17632/n5fwdyzwt8.2
title: Dataset about Vietnamese civil servants (Original data)

description: This dataset was constructed to measure the impact of digital skills on the output of local government officials in Vietnam to meet research needs. We employed the snowball sampling technique to collect online survey responses from civil servants currently working in local government agencies across all six socio-economic regions of Vietnam in the post-amalgamation period. Within 20 days (from November 15, 2025, to December 5, 2025), a total of 458 responses were collected, but some were not eligible for analysis such as extreme answers (all answers were 1 or 5) and arbitrary answers (trap questions). The remains of 380 samples were used for further analysis with the rate of 82.9%.
DOI: 10.17632/x9nynx5dt8.1
title: Data and code supporting "Market Professional Performance under Insider Information Leakage"

description: This dataset contains all the necessary files to replicate the findings, tables, and figures presented in the manuscript “Market Professional Performance under Insider Information Leakage.”
1. Data File (dataset.dta): The final, cleaned panel dataset used for all analyses in the study.
Source: The raw data were originally obtained from the CSMAR (China Stock Market & Accounting Research Database) and RESSET databases. This file contains the processed and merged variables as described in the manuscript.

2. Analysis Script (empirical_analysis.do): A Stata *.do file that executes the main econometric analyses and generates the results reported in the manuscript (e.g., regression tables).

3. Visualization Scripts (generate_Figure_1.py): Python scripts used to generate all figures presented in the manuscript.
DOI: 10.17632/bwvn4zby8m
title: Dataset for: Multimodal Upper Limb Rehabilitation Assessment System integrating Mechanomyographic and Surface Electromyographic Signals

description: Purpose: Objective and accurate clinical muscle strength assessment is the foun-
dation for formulating personalized rehabilitation programs for stroke survivors.
However, traditional scales are mostly limited by the subjectivity of clinicians.
This study proposes a quantitative assessment framework integrating surface
electromyography (sEMG) and mechanomyography (MMG).
Methods: This study collected multimodal signals from the deltoid muscle.
High-dimensional features were extracted from the time domain, frequency
domain, and cepstral domain, with the innovative introduction of Mel Frequency
Cepstral Coefficients (MFCC) to capture fine mechanical vibrations in MMG sig-
nals. Five commonly used machine learning models were constructed based on
multimodal data and individual MMG data respectively, including Support Vec-
tor Machine (SVM), Random Forest (RF), Decision Tree, Logistic Regression,
and Gradient Boosting Tree. The model performance was evaluated using four
metrics: Accuracy, Precision, Recall, and F1 Score. Feature importance analy-
sis was conducted for both data approaches to elucidate the electro-mechanical
signal fusion mechanism。Results: Among the five evaluated machine learning models, the RF model
achieved the best performance with an F1 score of 0.9279, representing a 27.14%
improvement compared to the single-modal electromyography model. The F1
scores of the Logistic Regression, Decision Tree, SVM, and Gradient Boost-
ing Tree models were 0.8139, 0.8820, 0.9035, and 0.9148 respectively. Feature
importance analysis revealed that, through feature weight transformation, the
relationship between sEMG and MMG is not a simple linear mapping but exhibits
complex nonlinear dynamic characteristics.
Conclusion: The integration of sEMG and MMG signals effectively enhances
the accuracy of rehabilitation assessments. This non-invasive, high-precision
technology offers a novel rehabilitation assessment method for clinical settings,
presenting new feasibility for objective rehabilitation evaluation in both clinical
and remote rehabilitation environments
DOI: 10.17632/bwvn4zby8m.1
title: Dataset for: Multimodal Upper Limb Rehabilitation Assessment System integrating Mechanomyographic and Surface Electromyographic Signals

description: Purpose: Objective and accurate clinical muscle strength assessment is the foun-
dation for formulating personalized rehabilitation programs for stroke survivors.
However, traditional scales are mostly limited by the subjectivity of clinicians.
This study proposes a quantitative assessment framework integrating surface
electromyography (sEMG) and mechanomyography (MMG).
Methods: This study collected multimodal signals from the deltoid muscle.
High-dimensional features were extracted from the time domain, frequency
domain, and cepstral domain, with the innovative introduction of Mel Frequency
Cepstral Coefficients (MFCC) to capture fine mechanical vibrations in MMG sig-
nals. Five commonly used machine learning models were constructed based on
multimodal data and individual MMG data respectively, including Support Vec-
tor Machine (SVM), Random Forest (RF), Decision Tree, Logistic Regression,
and Gradient Boosting Tree. The model performance was evaluated using four
metrics: Accuracy, Precision, Recall, and F1 Score. Feature importance analy-
sis was conducted for both data approaches to elucidate the electro-mechanical
signal fusion mechanism。Results: Among the five evaluated machine learning models, the RF model
achieved the best performance with an F1 score of 0.9279, representing a 27.14%
improvement compared to the single-modal electromyography model. The F1
scores of the Logistic Regression, Decision Tree, SVM, and Gradient Boost-
ing Tree models were 0.8139, 0.8820, 0.9035, and 0.9148 respectively. Feature
importance analysis revealed that, through feature weight transformation, the
relationship between sEMG and MMG is not a simple linear mapping but exhibits
complex nonlinear dynamic characteristics.
Conclusion: The integration of sEMG and MMG signals effectively enhances
the accuracy of rehabilitation assessments. This non-invasive, high-precision
technology offers a novel rehabilitation assessment method for clinical settings,
presenting new feasibility for objective rehabilitation evaluation in both clinical
and remote rehabilitation environments
DOI: 10.15468/dl.u3pydk
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"equals","key":"GADM_LEVEL_2_GID","value":"IND.26.26_1","matchCase":false},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009474-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/69mvrnkfbt
title: De-identified survey data on Integrated Learning Systems (ILS) adoption in Belizean secondary schools (post-COVID-19)
DOI: 10.17632/69mvrnkfbt.1
title: De-identified survey data on Integrated Learning Systems (ILS) adoption in Belizean secondary schools (post-COVID-19)
DOI: 10.17632/smfhndcy95
title: Atomistic mechanisms of reactive oxygen species generation via O2 adsorption and activation on corundum surfaces: Implications for transition metal redox

description: Data associated with submitted manuscript "Atomistic mechanisms of reactive oxygen species generation via O2 adsorption and activation on corundum surfaces: Implications for transition metal redox" by Mengning Xi, Chi Zhang*, Zhiqiang Wang, Hanzhong Jia*
DOI: 10.17632/smfhndcy95.1
title: Atomistic mechanisms of reactive oxygen species generation via O2 adsorption and activation on corundum surfaces: Implications for transition metal redox

description: Data associated with submitted manuscript "Atomistic mechanisms of reactive oxygen species generation via O2 adsorption and activation on corundum surfaces: Implications for transition metal redox" by Mengning Xi, Chi Zhang*, Zhiqiang Wang, Hanzhong Jia*
DOI: 10.17632/7pvdhb8tzc
title: FTT

description: F-18-FTT animal image data.
DOI: 10.17632/7pvdhb8tzc.1
title: FTT

description: F-18-FTT animal image data.