cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.q8dxne
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lamium purpureum",
 "Geometry POLYGON ((4.350227 50.78323, 4.350227 50.79168, 4.3597 50.79168, 4.3597 50.78323, 4.350227 50.78323))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027293-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kcxvj4r34z
title: Out-of-Pocket Outpatient Expenditure and Sleep Duration among Middle-aged and Older Adults in China

description: This dataset accompanies the manuscript “Out-of-Pocket Outpatient Expenditure and Sleep Duration among Middle-aged and Older Adults in China”. It contains cleaned CHARLS data and the full R code used to generate all analyses (Tables 1–4, Figures 1–3). The dataset allows full replication of the published results.
DOI: 10.17632/kcxvj4r34z.1
title: Out-of-Pocket Outpatient Expenditure and Sleep Duration among Middle-aged and Older Adults in China

description: This dataset accompanies the manuscript “Out-of-Pocket Outpatient Expenditure and Sleep Duration among Middle-aged and Older Adults in China”. It contains cleaned CHARLS data and the full R code used to generate all analyses (Tables 1–4, Figures 1–3). The dataset allows full replication of the published results.
DOI: 10.15468/dl.rtpeyq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zosterops atriceps",
 "Country is one of (Myanmar, Thailand, Lao People’s Democratic Republic, Cambodia, Viet Nam, Malaysia)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027294-250827131500795/datasets/export for details.
DOI: 10.15468/dl.ntzq8a
title: Occurrence Download

description: A dataset containing 739 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Galiniera saxifraga",
 "Geometry POLYGON ((-17.62504 -34.81917, 51.13387 -34.81917, 51.13387 37.34999, -17.62504 37.34999, -17.62504 -34.81917))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 739 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027296-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2l8p9w
title: CCDC 2391628: Experimental Crystal Structure Determination

description: Related Article: qilong chen|2024|CSD Communication|||
description: Related Article: Qi-Long Chen, Le Mao, Yi-Fan Pan, Bin Wang, Ka Lu, Zhiwei Jiao, Fu-Min Zhang, Xiao-Ming Zhang, Yong-Qiang Tu|2025|Chem Cat.|5|101414|doi:10.1016/j.checat.2025.101414
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2m2r2k
title: CCDC 2415708: Experimental Crystal Structure Determination

description: Related Article: Shuai Hou, Qing-Xuan Yu, Dong-En Zhang, Xue-Meng Jia, Xin Wang, Yu-Hui Luo|2025|J.Solid State Chem.|352|125583|doi:10.1016/j.jssc.2025.125583
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.ubyvbj
title: Occurrence Download

description: A dataset containing 999 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "Geometry POLYGON((-47.36697 -15.55001,-48.17223 -15.54436,-48.17788 -15.57917,-48.27289 -16.03919,-47.38014 -16.03166,-47.31711 -15.69676,-47.36697 -15.55001))",
 "OccurrenceStatus is Present",
 "TaxonKey is Colibri serrirostris"
 ]
}

 The dataset includes 999 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027297-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aembwx
title: Occurrence Download

description: A dataset containing 42011 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Hieracium laevigatum"
 ]
}

 The dataset includes 42011 records from 327 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027292-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2n2pk0
title: CCDC 2445452: Experimental Crystal Structure Determination

description: Related Article: Shoufeng Jiao, Yadong Zhai, Huan Zhang, Peng Li|2025|Polyhedron|281|117751|doi:10.1016/j.poly.2025.117751
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.17049028
title: Lammertink Gomez Ferreyra Bodrati Cockle_Breeding Biology Canebrake Groundcreeper Clibanornis dendroclaptoides

description: raw data for manuscript submitted to Ornithology Research
DOI: 10.5281/zenodo.17049027
title: Lammertink Gomez Ferreyra Bodrati Cockle_Breeding Biology Canebrake Groundcreeper Clibanornis dendroclaptoides

description: raw data for manuscript submitted to Ornithology Research
DOI: 10.15468/dl.f2f8hw
title: Occurrence Download

description: A dataset containing 16573 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Hieracium amplexicaule"
 ]
}

 The dataset includes 16573 records from 227 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027298-250827131500795/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.30057844.v1
title: Data Set - Educators' Perceptions of Trauma in Higher Education (Responses) Data Set.pdf

description: This data set accompanies the article, "Educators' Perceptions of Trauma in Higher Education." This is a qualitative research study that used open-response survey data to examine educators’ perceptions of trauma and learning in a higher education setting. Twenty-one participants from one Massachusetts college completed the survey anonymously, and the research team used a structured tabular approach for analysis. <br>
DOI: 10.6084/m9.figshare.30057844
title: Data Set - Educators' Perceptions of Trauma in Higher Education (Responses) Data Set.pdf

description: This data set accompanies the article, "Educators' Perceptions of Trauma in Higher Education." This is a qualitative research study that used open-response survey data to examine educators’ perceptions of trauma and learning in a higher education setting. Twenty-one participants from one Massachusetts college completed the survey anonymously, and the research team used a structured tabular approach for analysis. <br>
DOI: 10.17632/24zzk556mb
title: Приложение/application (Методология трансформации разнородных величин/Methodology for transforming heterogeneous quantities)

description: Исходная таблица анализа крови и этапы ее преобразования
DOI: 10.17632/24zzk556mb.1
title: Приложение/application (Методология трансформации разнородных величин/Methodology for transforming heterogeneous quantities)

description: Исходная таблица анализа крови и этапы ее преобразования
DOI: 10.17632/yhtx43ny38
title: Data and Python Code for Implementing Boosting Machines to Predict Local Buckling Strength of CFS Channels with Staggered Web Perforations

description: This repository contains the dataset and reproducible Python notebook used to benchmark design-code (DSM) calculations against machine-learning (ML) models for cold-formed steel channel members with and without staggered slots. The data files provide the master experimental dataset and DSM-based comparison sheets for the two specimen families (slotted and solid). The Jupyter notebook loads these files, performs preprocessing, trains/evaluates the ML models, and produces figures/tables comparing model accuracy to DSM.

File inventory

Data.xlsx — Master dataset used for ML training/validation. Contains geometric/material descriptors and the measured target response (bending capacity 
𝑀
M, units as in the paper).

data_DSM_slots.xlsx — DSM benchmark calculations for slotted specimens (inputs + DSM-predicted capacities).

data_DSM_slot_1.xlsx — A curated subset of the slotted dataset (used for ablation/cross-checks or quick runs).

data_DSM_solids.xlsx — DSM benchmark calculations for solid (unperforated) specimens.

data_DSM_solid_1.xlsx — A curated subset of the solid dataset (used for ablation/cross-checks or quick runs).

ML paper-Copy1.ipynb — Reproducible Jupyter notebook: data loading, preprocessing, model training (e.g., XGBoost and baselines), metrics, and plots; includes DSM vs ML comparisons.

Notes

Typical feature columns include geometric descriptors and material properties (e.g., web/flange/lip dimensions, thickness, slot geometry descriptors, yield stress 𝑓𝑦 and the measured capacity 𝑀. Exact column names/units are documented in the notebook cells.

All spreadsheets are UTF-8 .xlsx files; the notebook targets Python 3.9+
DOI: 10.17632/yhtx43ny38.1
title: Data and Python Code for Implementing Boosting Machines to Predict Local Buckling Strength of CFS Channels with Staggered Web Perforations

description: This repository contains the dataset and reproducible Python notebook used to benchmark design-code (DSM) calculations against machine-learning (ML) models for cold-formed steel channel members with and without staggered slots. The data files provide the master experimental dataset and DSM-based comparison sheets for the two specimen families (slotted and solid). The Jupyter notebook loads these files, performs preprocessing, trains/evaluates the ML models, and produces figures/tables comparing model accuracy to DSM.

File inventory

Data.xlsx — Master dataset used for ML training/validation. Contains geometric/material descriptors and the measured target response (bending capacity 
𝑀
M, units as in the paper).

data_DSM_slots.xlsx — DSM benchmark calculations for slotted specimens (inputs + DSM-predicted capacities).

data_DSM_slot_1.xlsx — A curated subset of the slotted dataset (used for ablation/cross-checks or quick runs).

data_DSM_solids.xlsx — DSM benchmark calculations for solid (unperforated) specimens.

data_DSM_solid_1.xlsx — A curated subset of the solid dataset (used for ablation/cross-checks or quick runs).

ML paper-Copy1.ipynb — Reproducible Jupyter notebook: data loading, preprocessing, model training (e.g., XGBoost and baselines), metrics, and plots; includes DSM vs ML comparisons.

Notes

Typical feature columns include geometric descriptors and material properties (e.g., web/flange/lip dimensions, thickness, slot geometry descriptors, yield stress 𝑓𝑦 and the measured capacity 𝑀. Exact column names/units are documented in the notebook cells.

All spreadsheets are UTF-8 .xlsx files; the notebook targets Python 3.9+
DOI: 10.17632/6w9g62xc67
title: Arabic Sentiment Datasets

description: This dataset is specifically designed for sentiment analysis (SA) in the Arabic language, serving as a crucial resource for developing and evaluating various SA models. The dataset contains [briefly describe the content, e.g., number of entries, types of sentiments (positive, negative, neutral), sources of the data like social media, reviews, etc.]. It has been curated to meet the unique linguistic characteristics of Arabic text, facilitating the training, validation, and benchmarking of machine learning and natural language processing models. While there are several sentiment analysis datasets available in multiple languages, this dataset focuses on Arabic, supporting research aimed at understanding sentiment in Arabic-speaking communities.
To ensure the effectiveness of feature selection approaches in sentiment analysis, the dataset can be used alongside well-known datasets such as those available from the UCI Machine Learning Repository (https://archive.ics.uci.edu/), which provides a range of datasets commonly employed for evaluating feature selection techniques.

The dataset aligns with previous work such as Al-Moslmi et al.'s construction of an Arabic sentiment lexicon for public use, which contributed significantly to Arabic sentiment analysis resources [1]. Additionally, this dataset draws inspiration from established Arabic corpora such as the Opinion Corpus for Arabic (OCA) by Rushdi-Saleh et al. [2], and Ar-Twitter, a corpus designed for sentiment analysis on Arabic tweets, as demonstrated by Abdulla et al. [3].

References:
Al-Moslmi, T., Albared, M., Al-Shabi, A., Omar, N., Abdullah, S.: Arabic sentilexicon: Constructing publicly available language resources for Arabic sentiment analysis. Journal of Information Science, 44(3), 345–362 (2018).
Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M.: OCA: Opinion corpus for Arabic. Journal of the American Society for Information Science and Technology, 62(10), 2045–2054 (2011).
Abdulla, N., Mahyoub, N., Shehab, M., Al-Ayyoub, M.: Arabic sentiment analysis: Corpus-based and lexicon-based. In: Proceedings of The IEEE Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (2013).
DOI: 10.17632/6w9g62xc67.2
title: Arabic Sentiment Datasets

description: This dataset is specifically designed for sentiment analysis (SA) in the Arabic language, serving as a crucial resource for developing and evaluating various SA models. The dataset contains [briefly describe the content, e.g., number of entries, types of sentiments (positive, negative, neutral), sources of the data like social media, reviews, etc.]. It has been curated to meet the unique linguistic characteristics of Arabic text, facilitating the training, validation, and benchmarking of machine learning and natural language processing models. While there are several sentiment analysis datasets available in multiple languages, this dataset focuses on Arabic, supporting research aimed at understanding sentiment in Arabic-speaking communities.
To ensure the effectiveness of feature selection approaches in sentiment analysis, the dataset can be used alongside well-known datasets such as those available from the UCI Machine Learning Repository (https://archive.ics.uci.edu/), which provides a range of datasets commonly employed for evaluating feature selection techniques.

The dataset aligns with previous work such as Al-Moslmi et al.'s construction of an Arabic sentiment lexicon for public use, which contributed significantly to Arabic sentiment analysis resources [1]. Additionally, this dataset draws inspiration from established Arabic corpora such as the Opinion Corpus for Arabic (OCA) by Rushdi-Saleh et al. [2], and Ar-Twitter, a corpus designed for sentiment analysis on Arabic tweets, as demonstrated by Abdulla et al. [3].

References:
Al-Moslmi, T., Albared, M., Al-Shabi, A., Omar, N., Abdullah, S.: Arabic sentilexicon: Constructing publicly available language resources for Arabic sentiment analysis. Journal of Information Science, 44(3), 345–362 (2018).
Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M.: OCA: Opinion corpus for Arabic. Journal of the American Society for Information Science and Technology, 62(10), 2045–2054 (2011).
Abdulla, N., Mahyoub, N., Shehab, M., Al-Ayyoub, M.: Arabic sentiment analysis: Corpus-based and lexicon-based. In: Proceedings of The IEEE Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (2013).
DOI: 10.17632/ybyhxv4czd.2
title: A Sensor based turning dataset for data-driven surface roughness estimation

description: Machining data for a particularly difficult-to-machine material, namely Inconel-625 is presented. Inconel is widely used in aerospace applications and is difficult to machine as unlike other materials, Inconel does not get softer with increasing temperature. This data set comprises of 27 sets of Accelerometer data and Dynamometer data acquired during the machining of Inconel -625 on a Kirloskar Turnmaster 40 Lathe. A Mitutoyo Surface Roughness Tester was used to measure the surface roughness after each turning operation.
DOI: 10.17632/vf2f2bsvd4.2
title: Predicting Gamow–Teller β-Decay Matrix Elements: Reproducible ML Benchmark (R Markdown Run-Book, RData Workspace, and Tab-Delimited Data)

description: This repository accompanies the manuscript submitted to the Nuclear Physics A and provides a fully scripted, reproducible workflow for predicting Gamow–Teller (M(GT)) β-decay matrix elements from tabulated nuclear data. The package contains:

a cache-aware R Markdown run-book that reproduces all offline analyses, tables, and figures from a clean R session (instant rendering when the workspace file is present),

a complementary non-cached R Markdown that retrains all models under the same protocol,

a pre-trained RData workspace (models, preprocessed test matrices/targets, theoretical baselines), and

the tab-delimited input table used in the study.

The online M(GT) calculator referenced in the manuscript is a demonstration layer built on the same code base provided here; it is not used to compute the reported benchmark metrics.

Methods (concise): We evaluate multiple learners (random forests, Cubist, kernel SVM, linear-XGBoost, BRNN) under a stratified 10-fold cross-validation and held-out testing protocol. Hyperparameters are tuned strictly within training folds; final metrics are reported exclusively on unseen test partitions. Post-hoc explanations (e.g., SHAP) are included for interpretability. A fixed random seed and a clean parallel backend are used for deterministic runs.

Data provenance: The tab-delimited TXT table aggregates observations from the databases cited in the article (as detailed in the manuscript’s Data section). Column names match those used in the paper (e.g., A1, Z1, A2, Z2, J, N, Time, QEN, IBE, LOGft, EXPT) alongside theoretical baselines (USDB, IMSRG, CCEI, CEFT). Please refer to the article for the exact sources and curation details.

Files and Descriptions (File manifest)
reproduce_cached.Rmd — Primary, cache-aware run-book.
Loads pre-trained objects from the accompanying .RData when present and reproduces all offline results (metrics, tables, Taylor diagrams) without retraining; otherwise falls back to training.
reproduce.Rmd — Full retraining run-book.
Regenerates all models and figures from scratch under the same protocol (use when the .RData is not available or to re-train deliberately).
RIMAGE_reduced.rdata — Pre-trained workspace (cache).
Contains model lists (RF, Cubist, SVM-Radial, XGBoost-Linear, BRNN), corresponding preprocessed test matrices/targets, and theoretical baselines for both Full and Reduced feature regimes.
veri.txt — Tab-delimited input table (as used in the manuscript).
Aggregated from the databases cited in the paper (NUBASE2016, NNDC); includes experimental target values and the theoretical baselines (USDB, IMSRG, CCEI, CEFT). Column names match those referenced in Methods.
Related Article
Manuscript submitted to the Journal of Computational Science (JoCS) for evaluation. The repository mirrors the exact workflow used for the offline results reported in the paper. The online M(GT) calculator derives from the same scripts but is not used to produce benchmark metrics.
DOI: 10.17632/9c5sdb88kn
title: Direct surface wave cross-correlations, dispersion data and inverted Vs model of the Chinese Mainland

description: This dataset contains the Rayleigh wave direct surface wave cross-correlations (CF1-5.zip) , phase velocity dispersion data (Disper.zip) and inverted shear wave velocity model of lithosphere (Zhao2025_VsModel.dat) obtained from the dense China Regional Seismic Networks. For details, please refer to readme.txt.
DOI: 10.17632/9c5sdb88kn.1
title: Direct surface wave cross-correlations, dispersion data and inverted Vs model of the Chinese Mainland

description: This dataset contains the Rayleigh wave direct surface wave cross-correlations (CF1-5.zip) , phase velocity dispersion data (Disper.zip) and inverted shear wave velocity model of lithosphere (Zhao2025_VsModel.dat) obtained from the dense China Regional Seismic Networks. For details, please refer to readme.txt.
DOI: 10.17632/8mprk8br23.1
title: The Development of a Low-cost Photometric-stereo-based Material Scanner

description: This dataset accompanies the HardwareX manuscript “The Development of a Low-cost Photometric-stereo-based Material Scanner”.

Abstract:
Capturing accurate texture maps from physical materials remains a challenge in digital prototyping and projection-based augmented reality (P-SAR). This paper presents an open-source material scanning system based on photometric stereo, designed for affordability, simplicity, and efficient operation. The system combines a consumer-grade digital camera, Multifaceted Reflector (MR)16 LED lighting, and Arduino-controlled automation to acquire material data up to A4 size within 15 seconds. Accurate colour reproduction is achieved through a hybrid calibration workflow that integrates camera profiling with a 3D lookup table. The resulting images are processed in a streamlined Substance 3D Designer pipeline to generate albedo and normal maps compatible with Physically Based Rendering (PBR). To evaluate performance under realistic conditions, two fabric samples were scanned and qualitatively compared with professionally digitised references. Albedo maps were assessed based on dominant colour accuracy using CIEDE2000, while normal maps were evaluated through visual rendering comparisons and directional distribution analysis. Scanning and processing times were also measured to verify workflow efficiency. Results demonstrate that the proposed system produces perceptually consistent textures suitable for real-time rendering applications while offering a low-cost and customisable solution for material digitisation.

This dataset includes the design files and software scripts required to reproduce the hardware and workflow described in the paper.