cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14288232
title: Simulation trajectories for: How sensitive are protein hydration shells to electrolyte concentration and protein composition?

description: PROJECT DESCRIPTION:We used classical, atomistic molecular dynamics simulations to investigate the hydrogen bond structure of the solvation shells of 5 halophilic and 5 mesophilic proteins in low (0.15 mol/kg) and high (2 mol/kg) aqueous KCl solutions. The simulation results reveal that, despite systematic amino acid composition differences between these two types of proteins, the hydrogen bond structure in their solvation shells is similarly robust to changes in KCl concentration.

DATASET DESCRIPTION:This dataset contains 15 of the 20 production run simulation trajectories  mentioned in the indicated related publication:- proteins 1DOI-1FRD  at both KCl concentrations (proteins are identified by their pdb ID, given in the order "halophile-mesophile");- proteins 1V9E-4CNX at both KCl concentrations; - proteins 1HZ6-2KAC at both KCl concentrations;- protein 2L28 at 2 molal KCl (halophile)- protein 2ITH at both KCl concentrations (mesophile).  

Single copies of each of the simulated proteins were simulated immersed in 0.15 molal and 2 molal aqueous KCl solutions, using TIP3P water, the AMBER99SB force field for the proteins, Joung and Cheatham parameters for the ions, and modified Lennard-Jones for interactions involving carboxylate oxygens (Geraili and Vila Verde, Biophysical Journal, Volume 120, Issue 13, 2746 - 2762, and   Kashefolgheta and Vila Verde, Phys. Chem. Chem. Phys., 2017, 19, 20593-20607)

USAGE: unzip all files from the same directory, so the directory trees are preserved.Please note, that some Microsoft Windows Installations encounter some errors while extracting the zip archives due to their depth of directories. Tools like 7zip are able to extract these directories on MS Windows.

Base directory structure:     /homes/adj781h/geraili/Ph.D._Project/001.0_Protein_folding_problem/002.0_impact_of_acidic_amino_acids/003.2_Dynamics/Dynamics/'\$molecule'/'\$conc'/'\$typ'/Protein_dynamics/Run    where     - \$molecule is one of 1DOI_1FRD  1HZ6_2KAC  1V9E_4CNX 2L28_2ITH (each of the halophilic/mesophilic protein pairs)    - \$conc is either 2molal or 0.15molal and denots the KCl concentration in the aqueous solvent    - \$typ is either Halophile or Non-Halophile

RELATED DATASET: Other simulation input and other output files for the same simulations can be found in https://doi.org/10.17877/RESOLV-2024-m32ubdo9. To use these datasets together, place all *zip files in the same directory and unzip them. The directory structure will be preserved, so it will be easy to identify which input files gave origin to each trajectory.
DOI: 10.17632/xykyffh2xv
title: Viruses coordinatively exploit host TLE1 for replication

description: This dataset delineates alterations in host genes transcriptionally regulated by Transducin-like enhancer of split 1 (TLE1) following influenza virus infection. TLE1, a transcriptional co-repressor encoded at chromosome 9q21.32, is clinically utilized as a diagnostic marker for synovial sarcoma. However, our analysis reveals that TLE1-regulated gene sets identified by ChIP enrichment are distinct post-infection, indicating its previously uncharacterized role in modulating transcriptional reprogramming during viral challenge. These findings provide novel insights for identifying host-directed antiviral targets and elucidate mechanisms by which influenza virus subverts host gene expression to facilitate replication and pathogenesis, thereby highlighting potential therapeutic interventions.
DOI: 10.17632/xykyffh2xv.1
title: Viruses coordinatively exploit host TLE1 for replication

description: This dataset delineates alterations in host genes transcriptionally regulated by Transducin-like enhancer of split 1 (TLE1) following influenza virus infection. TLE1, a transcriptional co-repressor encoded at chromosome 9q21.32, is clinically utilized as a diagnostic marker for synovial sarcoma. However, our analysis reveals that TLE1-regulated gene sets identified by ChIP enrichment are distinct post-infection, indicating its previously uncharacterized role in modulating transcriptional reprogramming during viral challenge. These findings provide novel insights for identifying host-directed antiviral targets and elucidate mechanisms by which influenza virus subverts host gene expression to facilitate replication and pathogenesis, thereby highlighting potential therapeutic interventions.
DOI: 10.15468/dl.gr8qk4
title: Occurrence Download

description: A dataset containing 442 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lychnocephalus tomentosus Mart. ex DC."
 ]
}

 The dataset includes 442 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021641-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/6xy9d4bp28
title: Data and code for: Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market

description: This dataset accompanies the study “Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market”, which proposes a framework for contagion risk prediction by comprehensively mining the features of technical charts and technical indicators.
The utilized data include the closing prices of 28 sectors in Shen wan primary industry index, the closing price of CSI-300 Index, and eight classes of trading indicators that include Turnover Rate, Price-to-Earnings Ratio, Trading Volume, Relative Strength Index, Moving Average Convergence Divergence, Moving Average, Bollinger Bands, and Stochastic Oscillator. The sample period is from 5 Jan 2007 to 30 Dec 2022. The closing prices of 28 sectors are downloaded from the Choice database. The closing price of the CSI-300 Index and eight classes of trading indicators are downloaded from the Wind database.
This dataset includes two raw data files, one predefined temporary file, and eighteen code files, which are described as follows: 
Sector_data.csv stores the closing prices of 28 sectors.
CSI_300_data.csv includes closing price of CSI-300 Index, and eight classes of trading indicators.
DCC_temp.csv is a predefined temporary file used to store correlation results.
Descriptive_code.py is utilized to calculate the statistical results.
ADF Test.py is utilized to test the stationarity of the data.
Min-max normalization.py is utilized to standardize data.
ADCC-GJR-GARCH.R is utilized to calculate dynamic conditional correlations between sectors.
MST_figure.py is used to a construct complex network that illustrates the inter-sector relationships.
Correlation.py is used to calculate inter-industry correlations. 
Corr_up.py, corr_mid.py and corr_down.py are used to calculate dynamic correlations in upstream, midstream, and downstream sectors.
Centrality.py is used to quantify the importance or influence of nodes within a network, particularly across distinct upstream, midstream, and downstream sectors.
Averaging_corr_over_a_5-day_period.py calculates 5-day rolling averages of correlation and centrality metrics to quantify contagion risk on a weekly cycle.
Convert technical charts using PIP and VG methods.py extracts significant nodes and converts them into graphical representations, and save them in Daily Importance Score.csv, Daily Threshold Matrix.csv, and Daily Technical Indicators.csv.
Convert_CSV_to_TXT.py converts Daily Importance Score.csv, Daily Threshold Matrix.csv, and Daily Technical Indicators.csv into TXT files for later use.
Four files included in the folder of Generating and normalizing the subgraphs to generate subgraphs and then normalize them. The receptive_field.py serves as the main program, which calls the other three files. The stock_graph_indicator.py calculates topological structure data for subsequent use.
Predictive_model.py takes normalized subgraphs and Y-values defined by contagion risk as inputs and performs parameter tuning to achieve optimal results.
DOI: 10.17632/6xy9d4bp28.1
title: Data and code for: Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market

description: This dataset accompanies the study “Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market”, which proposes a framework for contagion risk prediction by comprehensively mining the features of technical charts and technical indicators.
The utilized data include the closing prices of 28 sectors in Shen wan primary industry index, the closing price of CSI-300 Index, and eight classes of trading indicators that include Turnover Rate, Price-to-Earnings Ratio, Trading Volume, Relative Strength Index, Moving Average Convergence Divergence, Moving Average, Bollinger Bands, and Stochastic Oscillator. The sample period is from 5 Jan 2007 to 30 Dec 2022. The closing prices of 28 sectors are downloaded from the Choice database. The closing price of the CSI-300 Index and eight classes of trading indicators are downloaded from the Wind database.
This dataset includes two raw data files, one predefined temporary file, and eighteen code files, which are described as follows: 
Sector_data.csv stores the closing prices of 28 sectors.
CSI_300_data.csv includes closing price of CSI-300 Index, and eight classes of trading indicators.
DCC_temp.csv is a predefined temporary file used to store correlation results.
Descriptive_code.py is utilized to calculate the statistical results.
ADF Test.py is utilized to test the stationarity of the data.
Min-max normalization.py is utilized to standardize data.
ADCC-GJR-GARCH.R is utilized to calculate dynamic conditional correlations between sectors.
MST_figure.py is used to a construct complex network that illustrates the inter-sector relationships.
Correlation.py is used to calculate inter-industry correlations. 
Corr_up.py, corr_mid.py and corr_down.py are used to calculate dynamic correlations in upstream, midstream, and downstream sectors.
Centrality.py is used to quantify the importance or influence of nodes within a network, particularly across distinct upstream, midstream, and downstream sectors.
Averaging_corr_over_a_5-day_period.py calculates 5-day rolling averages of correlation and centrality metrics to quantify contagion risk on a weekly cycle.
Convert technical charts using PIP and VG methods.py extracts significant nodes and converts them into graphical representations, and save them in Daily Importance Score.csv, Daily Threshold Matrix.csv, and Daily Technical Indicators.csv.
Convert_CSV_to_TXT.py converts Daily Importance Score.csv, Daily Threshold Matrix.csv, and Daily Technical Indicators.csv into TXT files for later use.
Four files included in the folder of Generating and normalizing the subgraphs to generate subgraphs and then normalize them. The receptive_field.py serves as the main program, which calls the other three files. The stock_graph_indicator.py calculates topological structure data for subsequent use.
Predictive_model.py takes normalized subgraphs and Y-values defined by contagion risk as inputs and performs parameter tuning to achieve optimal results.
DOI: 10.17632/rxxfjjrdr2
title: Crystallizing extracellular protein reveals paths for silver mineralization and recovery

description: Heavy metal pollution calls for green recovery strategies. In response to metal stress, bacteria secret extracellular proteins (ECPs), but the specific role of individual ECP in silver (Ag2+) mineralization and recovery remains unexplored. Here, we investigated how a single ECP from silver-hypertolerant Enterobacter cloacae mediates Ag²⁺ mineralization. Proteomics (LC-MS/MS, MALDI-TOF) identified the 15.6 kDa protein as an inosine-monophosphate dehydrogenase (ImpD) homolog, whose secretion peaks in medium containing 15.9 mg L⁻¹ Ag²⁺. Partially purified ImpD crystallized at 20–30 °C. Time-resolved in-situ crystallography and X-ray diffraction captured monomers assembling into donut-shaped hexamers that weave into thread-like lattices; these ordered scaffolds template orientation-specific nucleation of Ag-rich crystals, enabling quantitative silver capture from solution. This previously unrecognized single-protein mediated biomineralization mechanism reveals ECPs as programmable bio-lixiviants, offering a low-energy, solvent-free route to selective metal recovery and expanding the toolkit for biometallurgy and environmental remediation.
DOI: 10.17632/rxxfjjrdr2.1
title: Crystallizing extracellular protein reveals paths for silver mineralization and recovery

description: Heavy metal pollution calls for green recovery strategies. In response to metal stress, bacteria secret extracellular proteins (ECPs), but the specific role of individual ECP in silver (Ag2+) mineralization and recovery remains unexplored. Here, we investigated how a single ECP from silver-hypertolerant Enterobacter cloacae mediates Ag²⁺ mineralization. Proteomics (LC-MS/MS, MALDI-TOF) identified the 15.6 kDa protein as an inosine-monophosphate dehydrogenase (ImpD) homolog, whose secretion peaks in medium containing 15.9 mg L⁻¹ Ag²⁺. Partially purified ImpD crystallized at 20–30 °C. Time-resolved in-situ crystallography and X-ray diffraction captured monomers assembling into donut-shaped hexamers that weave into thread-like lattices; these ordered scaffolds template orientation-specific nucleation of Ag-rich crystals, enabling quantitative silver capture from solution. This previously unrecognized single-protein mediated biomineralization mechanism reveals ECPs as programmable bio-lixiviants, offering a low-energy, solvent-free route to selective metal recovery and expanding the toolkit for biometallurgy and environmental remediation.
DOI: 10.17632/7ztfnd5tjg.1
title: Peers' performance feedback

description: The compressed file contains data and Stata code to replicate the paper "Peers' performance feedback".
The data comprises all penalty kicks in knockout stages of World Cup competitions in male soccer from 1982 to 2022
The purpose is to study how individual performance if affected by the preceding performance of teammates and rivals. That is, how individual productivity is influenced by feedback on peer's performance.
DOI: 10.17632/7ztfnd5tjg.2
title: Peers' performance feedback

description: The compressed file contains data and Stata code to replicate the paper "Peers' performance feedback".
The data comprises all penalty kicks in knockout stages of World Cup and European Cup competitions in male soccer from 1976 to 2024
The purpose is to study how individual performance if affected by the preceding performance of teammates and rivals. That is, how individual productivity is influenced by feedback on peer's performance.
DOI: 10.17632/7ztfnd5tjg.3
title: Peers' performance feedback

description: The compressed file contains data and Stata code to replicate the paper "Peers' performance feedback".
The data comprises all penalty kicks in knockout stages of World Cup and European Cup competitions in male soccer from 1976 to 2024
The purpose is to study how individual performance if affected by the preceding performance of teammates and rivals. That is, how individual productivity is influenced by feedback on peer's performance.
DOI: 10.17632/dr576vzsrt
title: More Than Surface: Three-Dimensional Urbanization Metrics Outweigh Two-Dimensional Metrics in Explaining Spontaneous Plant Diversity

description: This dataset primarily includes the field result data used in the paper and the corresponding analysis R code.
DOI: 10.17632/dr576vzsrt.1
title: More Than Surface: Three-Dimensional Urbanization Metrics Outweigh Two-Dimensional Metrics in Explaining Spontaneous Plant Diversity

description: This dataset primarily includes the field result data used in the paper and the corresponding analysis R code.
DOI: 10.5281/zenodo.15274013
title: Visium Spatial data of Brain section from Parkinson Mouse Model based on inducible expression of human a-syn constructs: 3-months

description: Using 3-months old mice of a inducible expression of human a-syn constructs based Parkinson mouse model, we produced a Visium Spatial V1 platform (10x Genomics) data. Sequencing was performed on a NovaSeq 6000 with PE150.

Sequences were fiducially aligned to spots using Loupe Browser ver. 8. All aligned sequences were mapped using spaceranger count 3.0.1 with a custom refence, which included sequences for the promotor and transgene (Camk2aTTA, V1S/SV2) to the mouse genome mm39. We filtered each sample of the Visium Spatial dataset based on the MAD filtering of number of reads (nUMI), number of genes (nGene), and percentage of mitochondrial genes (percent.mt). A spot was filtered out if it was outside of 3x MAD value in at least two metrics. Filtered samples were merged into one Seurat 5.1.0 object and we obtained normalized counts by the SCTransform function of Seurat. Integration was performed using Harmony 1.2.0 on 50 PCA embeddings and clustering was done using Leiden clustering based on 30 harmony embeddings. Integrated clusters were visualized using the UMAP method. Samples that were not successfully integrated (based on similarity measures of the harmony embeddings) and showed high percentage.mt or low nUMI levels compared to other samples, were removed from subsequent analysis. A final integration and clustering were performed after filtering. Regions were first annotated based on a 0.1 resolution clustering to get high level region annotation (Cortex, Hippocampus, Subcortex). Each high-level region was further annotated based on either more granular resolutions or subclustering. Marker genes from mousebrain.org and literature were used in combination with the Allen mouse brain atlas to obtain anatomically relevant annotations.
DOI: 10.5281/zenodo.15274014
title: Visium Spatial data of Brain section from Parkinson Mouse Model based on inducible expression of human a-syn constructs: 3-months

description: Using 3-months old mice of a inducible expression of human a-syn constructs based Parkinson mouse model, we produced a Visium Spatial V1 platform (10x Genomics) data. Sequencing was performed on a NovaSeq 6000 with PE150.

Sequences were fiducially aligned to spots using Loupe Browser ver. 8. All aligned sequences were mapped using spaceranger count 3.0.1 with a custom refence, which included sequences for the promotor and transgene (Camk2aTTA, V1S/SV2) to the mouse genome mm39. We filtered each sample of the Visium Spatial dataset based on the MAD filtering of number of reads (nUMI), number of genes (nGene), and percentage of mitochondrial genes (percent.mt). A spot was filtered out if it was outside of 3x MAD value in at least two metrics. Filtered samples were merged into one Seurat 5.1.0 object and we obtained normalized counts by the SCTransform function of Seurat. Integration was performed using Harmony 1.2.0 on 50 PCA embeddings and clustering was done using Leiden clustering based on 30 harmony embeddings. Integrated clusters were visualized using the UMAP method. Samples that were not successfully integrated (based on similarity measures of the harmony embeddings) and showed high percentage.mt or low nUMI levels compared to other samples, were removed from subsequent analysis. A final integration and clustering were performed after filtering. Regions were first annotated based on a 0.1 resolution clustering to get high level region annotation (Cortex, Hippocampus, Subcortex). Each high-level region was further annotated based on either more granular resolutions or subclustering. Marker genes from mousebrain.org and literature were used in combination with the Allen mouse brain atlas to obtain anatomically relevant annotations.
DOI: 10.17632/hdfpkt2y9n
title: Microsoft Dynamics 365 Finance and Operations Reports Dataset

description: The dataset consists of total 90 data points for past Dynamics 365 Finance and Operations projects. The data is collected from three organizations having CMMI level 3, located in Lahore Pakistan.
The dataset consists of Organization code, Project code for identification of data points against companies and their respective projects
9 predictor variables - Number of Fields(nF), Number of Input Parameters(nIP), Number of Data sources(nDS), Number of Tables(nT), Number of Graphs(nG), Number of Static Visual Elements(nSVE), Number of Report Designs(nRD), Number of Integrations(nI), Number of Business Units(nBU)
Actual Effort in Person Days
Expert Judgement Effort in Person Days
DOI: 10.17632/hdfpkt2y9n.1
title: Microsoft Dynamics 365 Finance and Operations Reports Dataset

description: The dataset consists of total 90 data points for past Dynamics 365 Finance and Operations projects. The data is collected from three organizations having CMMI level 3, located in Lahore Pakistan.
The dataset consists of Organization code, Project code for identification of data points against companies and their respective projects
9 predictor variables - Number of Fields(nF), Number of Input Parameters(nIP), Number of Data sources(nDS), Number of Tables(nT), Number of Graphs(nG), Number of Static Visual Elements(nSVE), Number of Report Designs(nRD), Number of Integrations(nI), Number of Business Units(nBU)
Actual Effort in Person Days
Expert Judgement Effort in Person Days
DOI: 10.17632/dgsv89g9n4.2
title: Comparison of the earliest NC and CC planetesimals: Evidence from ungrouped iron meteorites

description: This electronic supplementary material contains the content presented in Tables 1-4 and Tables S1-8 of the related manuscript, which include the data underlying the figures.
DOI: 10.17632/dgsv89g9n4.3
title: Comparison of the earliest NC and CC planetesimals: Evidence from ungrouped iron meteorites

description: This electronic supplementary material contains the content presented in Tables 1-4 and Tables S1-8 of the related manuscript, which include the data underlying the figures.
DOI: 10.17632/rn4zbzg8z2
title: AraSTEM

description: AraSTEM is a dataset designed to evaluate the knowledge of large language models (LLMs) in STEM (Science, Technology, Engineering, and Mathematics) subjects in Arabic. It consists of multiple-choice questions covering various topics and difficulty levels, requiring models to demonstrate a deep understanding of scientific Arabic. The dataset includes the question, options, correct answer, subject, level, and a link to the resource.
DOI: 10.17632/rn4zbzg8z2.5
title: AraSTEM

description: AraSTEM is a dataset designed to evaluate the knowledge of large language models (LLMs) in STEM (Science, Technology, Engineering, and Mathematics) subjects in Arabic. It consists of multiple-choice questions covering various topics and difficulty levels, requiring models to demonstrate a deep understanding of scientific Arabic. The dataset includes the question, options, correct answer, subject, level, and a link to the resource.
DOI: 10.23664/rd05-14.acdis.hiv.2017
title: AHRI HDSS.RD05-14 ACDIS HIV 2017
DOI: 10.17632/jz9vzv6bxc
title: Dataset para Revisión sistemática de la literatura de Gemelos Digitales para Ambient Assisted Living

description: El dataset contiene la hoja de cálculo resultante del proceso de extracción y síntesis de los datos de los trabajos seleccionados para su análisis en la revisión sistemática de la literatrura (SLR) llevada a cabo en el paper "Revisión sistemática de la literatura de Gemelos Digitales para Ambient Assisted Living", presentado en las V Jornadas de Investigación Predoctoral en Ingeniería Informática (JIPII 2025) de la Universidad de Cádiz
DOI: 10.17632/jz9vzv6bxc.1
title: Dataset para Revisión sistemática de la literatura de Gemelos Digitales para Ambient Assisted Living

description: El dataset contiene la hoja de cálculo resultante del proceso de extracción y síntesis de los datos de los trabajos seleccionados para su análisis en la revisión sistemática de la literatrura (SLR) llevada a cabo en el paper "Revisión sistemática de la literatura de Gemelos Digitales para Ambient Assisted Living", presentado en las V Jornadas de Investigación Predoctoral en Ingeniería Informática (JIPII 2025) de la Universidad de Cádiz