cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18459144
title: Hotspots of multidimensional biodiversity are overlooked by protected areas

description: This folder contains data and R code to reproduce the analyses and results presented in the manuscript entitled "Hotspots of multidimensional biodiversity are overlooked by protected areas". It also contains SI Appendix Dataset S1 [specieslist.csv]
DOI: 10.7910/dvn/8veuuu
title: Academic and psychological determinants of drug‑calculation competence among nursing students in Palestine: a cross-sectional study

description: This study aimed to assess the drug-calculation skills of Palestinian nursing students and to identify academic and psychological factors influencing their performance. A cross-sectional study was conducted among 562 nursing students in their third and fourth academic years across multiple accredited nursing schools. Data were collected using a structured questionnaire that included demographic and academic variables, prior training in drug calculations, self-rated knowledge and confidence, the Abbreviated Math Anxiety Scale, the General Self-Efficacy Scale, and 15 scenario-based drug-calculation items.
DOI: 10.82220/686
title: ชุดข้อมูลการประชุมสภาผู้แทนราษฎร ชุดที่ 1 (พ.ศ. 2475-2476)

description: ชุดข้อมูลการประชุมสภาผู้แทนราษฎร ชุดที่ 1 (พ.ศ. 2475-2476) ประกอบด้วย 1. รายการการประชุมสภาผู้แทนราษฎร ชุดที่ 1 ตั้งแต่ ปี พ.ศ. 2475 ถึง ปี พ.ศ. 2476 จำนวนทั้งสิ้น 89 ครั้ง ประกอบด้วย - รายการการประชุมสภาผู้แทนราษฎร ชุดที่ 1 ปีที่ 1 สมัยสามัญ จำนวน 59 ครั้ง - รายการการประชุมสภาผู้แทนราษฎร ชุดที่ 1 ปีที่ 2 สมัยสามัญ จำนวน 14 ครั้ง - รายการการประชุมสภาผู้แทนราษฎร ชุดที่ 1 ปีที่ 2 สมัยวิสามัญ จำนวน 16 ครั้ง ซึ่งจัดเก็บและให้บริการในคลังสารสนเทศรัฐสภา รายละเอียดของแต่ละรายการประกอบด้วย ชื่อเรื่อง วันที่ประชุม ครั้งที่ประชุม สภาชุดที่ สภาปีที่ สมัยประชุม ครั้งที่ของสมัยประชุม ประเภทการประชุม ผู้แต่ง ปีพิมพ์ ภาษา สำนักพิมพ์ เจ้าของสิทธิ์ ชนิดของสื่อ แหล่งจัดเก็บต้นฉบับ URI (ซึ่งสามารถเชื่อมโยงรายการไปยังข้อมูลการประชุมสภาในคลังสารสนเทศรัฐสภาแต่ละครั้งได้ ทุกรายการสามารถเชื่อมโยงไปยังข้อมูลการประชุมสภาผู้แทนราษฎรในรูปแบบเอกสารฉบับเต็มในคลังสารสนเทศรัฐสภา มีประกอบด้วย 1. รายงานการประชุม 2. บันทึกการประชุม (ถ้ามี) 3. สรุปผลการประชุม (ถ้ามี) 4. คำวินิจฉัย (ถ้ามี) 5. สรุปเหตุการณ์ (ถ้ามี) 6. บันทึกการออกเสียงและลงคะแนน (ถ้ามี) 2. จำนวนครั้งที่การประชุมสภาผู้แทนราษฎร ชุดที่ 1 (พ.ศ. 2475-2476) แยกตามสมัยการประชุม เช่น รายการการประชุมสภาผู้แทนราษฎร ครั้งที่ 1/วันที่ 28 มิถุนายน 2475 สามารถคลิกที่ URI: https://hdl.handle.net/20.500.14156/375674 ซึ่งเป็นข้อมูลรายการ (item) ของ ข้อมูลการประชุมสภาผู้แทนราษฎร ครั้งที่ 1/วันที่ 28 มิถุนายน 2475 เป็นต้น
DOI: 10.5281/zenodo.18401353
title: Methylation data for "The DNA methylation landscape of naturally short-lived killifish"

description: Annual killifish of the genus Nothobranchius are naturally short-lived vertebrates that exhibit a wide range of aging characteristics typically observed in mammals. Here, we provide the first comprehensive whole-genome methylation map for two species, revealing local age-related as well as tissue-specific dynamics in the DNA methylome. Our maps and results add a strong foundation for the annual killifish as a model organism and further highlight its value for studying ageing and making health status predictions in vertebrates.

 

This dataset provides methylation data from WGBS.
DOI: 10.5281/zenodo.18401352
title: Methylation data for "The DNA methylation landscape of naturally short-lived killifish"

description: Annual killifish of the genus Nothobranchius are naturally short-lived vertebrates that exhibit a wide range of aging characteristics typically observed in mammals. Here, we provide the first comprehensive whole-genome methylation map for two species, revealing local age-related as well as tissue-specific dynamics in the DNA methylome. Our maps and results add a strong foundation for the annual killifish as a model organism and further highlight its value for studying ageing and making health status predictions in vertebrates.

 

This dataset provides methylation data from WGBS.
DOI: 10.6084/m9.figshare.31239421
title: <b>Malaria among children at a tertiary paediatric referral hospital in Freetown, Sierra Leone</b>

description: <b>Malaria among children at a tertiary paediatric referral hospital in Freetown, Sierra Leone</b>
DOI: 10.6084/m9.figshare.31239421.v1
title: <b>Malaria among children at a tertiary paediatric referral hospital in Freetown, Sierra Leone</b>

description: <b>Malaria among children at a tertiary paediatric referral hospital in Freetown, Sierra Leone</b>
DOI: 10.5281/zenodo.14003216
title: Data from: Case report: Using accelerometers for tracking loggerhead and green sea turtle behaviour.

description: Data and R Scripts for labelling Green turtle and Loggerhead turtle accelerometer data, calculating summary metrics (for different smoothing windows and sampling frequencies) and running a random forest model.

X.1-Label the accelerometer data with behavioural labels from BORIS

X.2-Create summary metrics and resample frequencies. This includes code adapted from (Clark, 2019; Clark et al., 2022). https://ore.exeter.ac.uk/repository/handle/10871/120152, https://www.int-res.com/abstracts/meps/v701/p145-157/ X.2 produces labelled summary metrics (2hztrainingmetrics200p2seglength_loggerheadTurtles.csv and 2hztrainingmetrics200p2seglength_greenTurtles.csv to input into X.3)

X.3-Run random forest for each frequency and smoothing window (here only final model (2Hz, 2 second smooth and third scute) has been supplied. Please contact jessica.harveycarroll@gmail.com for additional datasets/models.

Final Models    pos2_2HzRF200smooth_bestRF_greenTurtles.RDS is the final random forest model for green turtles (2Hz, 2 second smooth, third scute).

pos2_2HzRF200smooth_bestRF_loggerheadTurtles.RDS is the final random forest model for Loggerhead turtles (2Hz, 2 second smooth, third scute).

Breakdown of all 2 second smoothed model results can be found in: combined_behaviouraccuracy.csv
DOI: 10.5281/zenodo.14003215
title: Data from: Case report: Using accelerometers for tracking loggerhead and green sea turtle behaviour.

description: Data and R Scripts for labelling Green turtle and Loggerhead turtle accelerometer data, calculating summary metrics (for different smoothing windows and sampling frequencies) and running a random forest model.

X.1-Label the accelerometer data with behavioural labels from BORIS

X.2-Create summary metrics and resample frequencies. This includes code adapted from (Clark, 2019; Clark et al., 2022). https://ore.exeter.ac.uk/repository/handle/10871/120152, https://www.int-res.com/abstracts/meps/v701/p145-157/ X.2 produces labelled summary metrics (2hztrainingmetrics200p2seglength_loggerheadTurtles.csv and 2hztrainingmetrics200p2seglength_greenTurtles.csv to input into X.3)

X.3-Run random forest for each frequency and smoothing window (here only final model (2Hz, 2 second smooth and third scute) has been supplied. Please contact jessica.harveycarroll@gmail.com for additional datasets/models.

Final Models    pos2_2HzRF200smooth_bestRF_greenTurtles.RDS is the final random forest model for green turtles (2Hz, 2 second smooth, third scute).

pos2_2HzRF200smooth_bestRF_loggerheadTurtles.RDS is the final random forest model for Loggerhead turtles (2Hz, 2 second smooth, third scute).

Breakdown of all 2 second smoothed model results can be found in: combined_behaviouraccuracy.csv
DOI: 10.5281/zenodo.18413372
title: ViralClust: A Nextflow pipeline to cluster viral sequences - raw data and results

description: This is the repository for all raw data and results of the publication:

Triebel, S., Lamkiewicz, K., Marz, M. Reducing haystacks to needles - ViralClust: A Nextflow pipeline to cluster viral sequences. bioRxiv (2026). https://www.biorxiv.org/content/10.64898/2026.01.30.702815v1

The files include:



raw_data.tar.gz - raw sequences download from NCBI

Clustering results using ViralClust



Orthoebolavirus - orthoebolavirus_viralclust.tar.gz

Hepacivirus hominis (HCV) - hcv_viralclust.tar.gz

Orthoflavivirus - flavivirus_viralclust.tar.gz

Flaviviridae - flaviviridae_viralclust.tar.gz

Alphainfluenzavirus influenzae (formerly Influenza A virus, IAV) - iav_viralclust.tar.gz

Monkeypox virus - mpox_viralclust.tar.gz
DOI: 10.5281/zenodo.18413373
title: ViralClust: A Nextflow pipeline to cluster viral sequences - raw data and results

description: This is the repository for all raw data and results of the publication:

Triebel, S., Lamkiewicz, K., Marz, M. Reducing haystacks to needles - ViralClust: A Nextflow pipeline to cluster viral sequences. bioRxiv (2026). https://www.biorxiv.org/content/10.64898/2026.01.30.702815v1

The files include:



raw_data.tar.gz - raw sequences download from NCBI

Clustering results using ViralClust



Orthoebolavirus - orthoebolavirus_viralclust.tar.gz

Hepacivirus hominis (HCV) - hcv_viralclust.tar.gz

Orthoflavivirus - flavivirus_viralclust.tar.gz

Flaviviridae - flaviviridae_viralclust.tar.gz

Alphainfluenzavirus influenzae (formerly Influenza A virus, IAV) - iav_viralclust.tar.gz

Monkeypox virus - mpox_viralclust.tar.gz
DOI: 10.58073/sruc.31136338
title: Database of Community Right to Buy Applications in Scotland 2003 to 2025.

description: Database of Scottish Community Right to Buy applications under Parts 2, 3, and 3a of the Land Reform (Scotland) Act (as amended), and Part 5 of the Land Reform (Scotland) Act 2016 (as amended). Information provided on a range of social characteristics, including postcode, rural classification and Scottish Index of Multiple Deprivation. Partial information on size of land (to 2015).
DOI: 10.58073/sruc.31136338.v1
title: Database of Community Right to Buy Applications in Scotland 2003 to 2025.

description: Database of Scottish Community Right to Buy applications under Parts 2, 3, and 3a of the Land Reform (Scotland) Act (as amended), and Part 5 of the Land Reform (Scotland) Act 2016 (as amended). Information provided on a range of social characteristics, including postcode, rural classification and Scottish Index of Multiple Deprivation. Partial information on size of land (to 2015).
DOI: 10.34810/data40
title: Feed intake and performance of calves fed a milk replacer supplemented with different AA combinations

description: The effects on growth performance of supplementation of four different AA combinations in a milk replacer (MR, 25.4% CP and 20.3% fat) based on skimmed milk powder and whey protein concentrate were evaluated in 76 Holstein male calves (3 ± 1.7 d old). The 4 MR were: CTRL with no AA supplementation; PG supplying additional 0.3% Pro and 0.1% Gly; FY supplying additional 0.2% Phe and 0.2% Tyr; KMT providing additional 0.62% Lys, 0.22% Met, and 0.61% Thr. All calves were fed the same milk allowance program and were weaned at 56 d of study. Concentrate intake was limited to minimize interference of potential differences in solid feed intake among treatments. Animals were weighed weekly, intakes recorded daily, and blood samples obtained at 2, 5, and 7 wk of study to determine serum urea and plasma AA concentrations. The file presents data of calf performance. Data was collected at Torre Marimon IRTA calf facilities (Caldes de Montbui, Spain) during 2017.
description: Agency Name: Ministerio de Economía y Competitividad
ID: AGL2015-68463-C2-1-P and AGL2015-68463-C2-2
DOI: 10.5281/zenodo.17338912
title: DSVM Simulation and Validation Package (Supplementary Material)

description: This repository contains the full simulation and estimation framework of the Dynamic Social Value Model (DSVM).It includes Python source code for political, emergency, consumer, and symbolic-behavioral dynamics; parameter estimation routines (Bayesian inference); and data preparation templates for replication.The files are provided for non-commercial academic use and are intended to reproduce the empirical and simulation results reported in The Dynamic Social Value Model (Aby Housh, 2025).
DOI: 10.5281/zenodo.17338913
title: DSVM Simulation and Validation Package (Supplementary Material)

description: This repository contains the full simulation and estimation framework of the Dynamic Social Value Model (DSVM).It includes Python source code for political, emergency, consumer, and symbolic-behavioral dynamics; parameter estimation routines (Bayesian inference); and data preparation templates for replication.The files are provided for non-commercial academic use and are intended to reproduce the empirical and simulation results reported in The Dynamic Social Value Model (Aby Housh, 2025).
DOI: 10.5281/zenodo.18223323
title: Phase Transitions in Unsupervised Feature Selection

description:  

Dataset description

This dataset contains all the data required to reproduce the analyses presented in:

Fiorentino J., Monti M., Vrachnos D. M., Del Tatto V., Laio A., Tartaglia G. G., Phase Transitions in Unsupervised Feature Selection. arXiv, 2026 https://doi.org/10.48550/arXiv.2602.00660

The data support a systematic study of backward feature elimination using the Differentiable Information Imbalance (DII) across multiple protein datasets, feature types, and controlled correlation regimes.

Data organization

The DATA folder is organized as follows:

DATASETS/





Physico-chemical (82 features) and structural (67 features) descriptors computed for the human proteome




Train and test datasets for four protein classes:

 





LLPS (liquid–liquid phase separating proteins)




RBP (RNA-binding proteins)




MEMBRANE proteins




ENZYME proteins



 




Separate datasets are provided for physico-chemical and structural feature sets



Subsamples/





Random balanced subsamples of increasing size




Used to study scaling properties and phase-transition-like behavior in backward feature elimination




Each subsample is generated independently and consistently across feature sets and protein classes



DIIvsF/





Values of the Differentiable Information Imbalance (DII) during the backward feature elimination process




Reported for both training and test subsamples




Provided for all four protein datasets



weightsvsF/





Optimal feature weights identified by the DII at each step of the backward feature elimination process




Reported for all four protein datasets and feature sets



LassoFull/





Results of backward feature elimination using the DII applied to the full protein datasets




Enables comparison between subsampled and full-dataset regimes



CorrelationTuning/





Results of backward feature elimination using the DII under controlled feature statistics:





Feature correlation tuned via parameter β ∈ [−1, 1]




Feature variance heterogeneity tuned via parameter α ∈ [0, 1] at β = −1 (complete feature decorrelation)



 




Used to disentangle the role of correlations and variance heterogeneity in the observed phase transition



BinaryClassification/





Binary classification performance metrics obtained by training a multilayer perceptron (MLP)




Classifiers are trained using features selected by the DII at each step of the backward feature elimination process




Results are provided for:





all random subsamples




all four protein datasets




all feature elimination stages



 



Reproducibility

All data are designed to be used in conjunction with the accompanying GitHub repository, which provides:





the full analysis code,




environment specifications,




and a Jupyter notebook to reproduce all figures and results.



License

This dataset is released under the Creative Commons Attribution 4.0 (CC BY 4.0) license.
DOI: 10.5281/zenodo.18223324
title: Phase Transitions in Unsupervised Feature Selection

description:  

Dataset description

This dataset contains all the data required to reproduce the analyses presented in:

Fiorentino J., Monti M., Vrachnos D. M., Del Tatto V., Laio A., Tartaglia G. G., Phase Transitions in Unsupervised Feature Selection. arXiv, 2026 https://doi.org/10.48550/arXiv.2602.00660

The data support a systematic study of backward feature elimination using the Differentiable Information Imbalance (DII) across multiple protein datasets, feature types, and controlled correlation regimes.

Data organization

The DATA folder is organized as follows:

DATASETS/





Physico-chemical (82 features) and structural (67 features) descriptors computed for the human proteome




Train and test datasets for four protein classes:

 





LLPS (liquid–liquid phase separating proteins)




RBP (RNA-binding proteins)




MEMBRANE proteins




ENZYME proteins



 




Separate datasets are provided for physico-chemical and structural feature sets



Subsamples/





Random balanced subsamples of increasing size




Used to study scaling properties and phase-transition-like behavior in backward feature elimination




Each subsample is generated independently and consistently across feature sets and protein classes



DIIvsF/





Values of the Differentiable Information Imbalance (DII) during the backward feature elimination process




Reported for both training and test subsamples




Provided for all four protein datasets



weightsvsF/





Optimal feature weights identified by the DII at each step of the backward feature elimination process




Reported for all four protein datasets and feature sets



LassoFull/





Results of backward feature elimination using the DII applied to the full protein datasets




Enables comparison between subsampled and full-dataset regimes



CorrelationTuning/





Results of backward feature elimination using the DII under controlled feature statistics:





Feature correlation tuned via parameter β ∈ [−1, 1]




Feature variance heterogeneity tuned via parameter α ∈ [0, 1] at β = −1 (complete feature decorrelation)



 




Used to disentangle the role of correlations and variance heterogeneity in the observed phase transition



BinaryClassification/





Binary classification performance metrics obtained by training a multilayer perceptron (MLP)




Classifiers are trained using features selected by the DII at each step of the backward feature elimination process




Results are provided for:





all random subsamples




all four protein datasets




all feature elimination stages



 



Reproducibility

All data are designed to be used in conjunction with the accompanying GitHub repository, which provides:





the full analysis code,




environment specifications,




and a Jupyter notebook to reproduce all figures and results.



License

This dataset is released under the Creative Commons Attribution 4.0 (CC BY 4.0) license.
DOI: 10.5281/zenodo.15017669
title: esCorpiusDialog: A Large-Scale Multilingual Dialogue Dataset in Spanish, Catalan, Basque, and Galician

description: In response to the growing need for comprehensive conversational datasets, we introduce esCorpiusDialog, a large-scale, diverse dialogue corpus designed to facilitate the development of conversational models in Spanish, Catalan, Basque, and Galician. Addressing the critical shortage of high-quality conversational data for these languages, esCorpiusDialog encompasses an extensive 15.1 GiB corpus containing 26,900,187 dialogues with a total of 116,524,110 conversational turns and 1,382,496,364 tokens. On average, each dialogue includes 4.3 turns, enabling effective modeling of multi-turn interactions.

esCorpiusDialog aggregates data from multiple rich sources: movie subtitles (OpenSubtitles), newsgroups (Usenet), online forums (Mediavida, Reddit), and literature (Project Gutenberg). Specifically, the dataset comprises approximately 26.6 million dialogues in Spanish, 116 thousand in Basque, over 92 thousand in Catalan, and 63 thousand in Galician, making it the most extensive multilingual conversational dataset currently available for these languages.

The dataset has undergone meticulous processing to clearly define conversational turns and accurately segment dialogues, ensuring its suitability for training robust, open-domain conversational systems. With its breadth of topics and varied dialogue styles, esCorpiusDialog represents an invaluable resource for researchers and practitioners aiming to enhance the dialogue capabilities and generalization of fine-tuned large language models (LLMs) across diverse conversational applications.
description: Acknowledgments: This dataset is a result of the project CONVERSA (TED2021-132470B-I00) funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR".

 

RESTRICTED: Restricted due to third-party platform Terms of Service: earlier versions (v1.0–v1.1) included forum-derived archives (reddit.zip, mediavida.zip) that cannot be redistributed in full-text form. Access is limited while we replace them with metadata-only (dehydrated) identifiers and rehydration code to support reproducibility under user-side ToS compliance
DOI: 10.5281/zenodo.18466361
title: Simple unilateral rupture of the great Mw 8.8 2025 Kamchatka earthquake

description: The slip model of the great Mw 8.8 2025 Kamchatka earthquake.
DOI: 10.5281/zenodo.18466362
title: Simple unilateral rupture of the great Mw 8.8 2025 Kamchatka earthquake

description: The slip model of the great Mw 8.8 2025 Kamchatka earthquake.
DOI: 10.5281/zenodo.18466210
title: DATE: Site-Specific Hydrogen Isotope Fractionation in Lunar Minerals: Atomic-Scale Insights from First-Principles Calculations

description: DATE of Site-Specific Hydrogen Isotope Fractionation in Lunar Minerals: Atomic-Scale Insights from First-Principles Calculations
DOI: 10.5281/zenodo.18466209
title: DATE: Site-Specific Hydrogen Isotope Fractionation in Lunar Minerals: Atomic-Scale Insights from First-Principles Calculations

description: DATE of Site-Specific Hydrogen Isotope Fractionation in Lunar Minerals: Atomic-Scale Insights from First-Principles Calculations
DOI: 10.5281/zenodo.18466452
title: Intelligent Assessment and Enhancement of Urban Flood Resilience from the Comprehensive Perspective of Driving Force-Pressure-State-Response

description: Existing research has predominantly examined discrete phases of flood disasters in isolation, it has largely overlooked the interdependencies among key flood risk indicators and consequently failed to integrate these indicators holistically into flood resilience assessments. To address this limitation, this study develops a process-oriented flood resilience assessment framework that synergistically integrates natural-economic-social-hazard (NESH) model with driving force–pressure–state–response (DPSR) model, explicitly aligning both frameworks across full temporal sequence of disaster cycle. Index grid data are processed for weight prediction using multi-scale convolutional neural network (CNN), multi-head spatial attention mechanism (MHA), gated feature fusion network (GFN), and deep weight prediction head (DWPH). To reconcile subjective expert judgments with objective data-driven weights, a game-theoretic method (GTM) is employed to derive regionally optimized integrated weights. This study systematically assesses spatial distribution patterns of urban flood resilience across multiple rainfall intensity scenarios using improved multi-factor comprehensive evaluation model coupled with spatial autocorrelation analysis. Results reveal state resilience displays a distinct ring-shaped spatial pattern. In contrast, response resilience exhibits a core-periphery gradient, peaking in the city center and diminishing monotonically with distance from the core. Concurrently, the mean resilience index declines steadily—from 0.592 under low-intensity rainfall to 0.538 under high-intensity rainfall—revealing a robust attenuation trend with increasing hydrological stress. Pipe network dredging and enhanced vegetation coverage substantially reduce inundation depth and spatial extent of 5-year return period flood events; however, their risk-mitigation efficacy declines progressively under higher-intensity rainfall scenarios—particularly for events exceeding the 10-year return period. This study advances an actionable foundation for flood risk governance.