cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15479/at:ista:17471
title: Data for "Enhanced many-body quantum scars from the non-Hermitian Fock skin effect"

description: Mechanisms for suppressing thermalization in disorder-free many-body systems, such as Hilbert space fragmentation and quantum many-body scars, have recently attracted much interest in foundations of quantum statistical physics and potential quantum information processing applications. However, their sensitivity to realistic effects such as finite temperature remains largely unexplored. Here, we have utilized IBM's Kolkata quantum processor to demonstrate an unexpected robustness of quantum many-body scars at finite temperatures when the system is prepared in a thermal Gibbs ensemble. We identify such robustness in the PXP model, which describes quantum many-body scars in experimental systems of Rydberg atom arrays and ultracold atoms in tilted Bose--Hubbard optical lattices. By contrast, other theoretical models which host exact quantum many-body scars are found to lack such robustness, and their scarring properties quickly decay with temperature. Our study sheds light on the important differences between scarred models in terms of their algebraic structures, which impacts their resilience to finite temperature.
DOI: 10.17632/dxfs9yps45.2
title: Development and optimization of a Muliplex real-time for SARS-CoV-2

description: The study presented a standardized diagnostic method for SARS-CoV-2 using a multiplex amplification, using different source of primers and probes. This repository contains the raw data of PCR runs during the standardization process and the comparison of results using clinical samples.
DOI: 10.17632/n76t439f65.1
title: GPVS-Faults: Experimental Data for fault scenarios in grid-connected PV systems under MPPT and IPPT modes

description: Overview The Grid-connected PV System Faults (GPVS-Faults) data are collected from lab experiments of faults in a PV microgrid system. There are 16 data files in ‘.mat’ and also ‘.csv’, each for one experiment scenario, including photovoltaic array faults; inverter faults; grid anomalies; feedback sensor fault; and MPPT controller faults of various severity. GPVS-Faults data can be used to design/ validate/ compare various algorithms of fault detection/ diagnosis/ classification for PV system protection and reactive maintenance. Description The faults were introduced manually halfway during the experiments. The high-frequency measurements are noisy; with disturbances and variations of temperature and insolation during and between the experiments; MPPT/IPPT modes have adverse effects on the detection of low-magnitude faults. After critical faults, the operation is interrupted and the system may shut-down; the challenge is to detect the faults before a total failure. We refer interested researchers to read and cite the following references for detailed descriptions of: - GPVS-Faults scenarios, experiments, and data collection procedures are described in [1] with results for comparisons. - System description, energy management system, control/ communication of the PV system are detailed in [2]. -The description of the static multiblock version of the fault detection algorithm was presented in [3]. References [1]. A. Bakdi, W. Bounoua, A. Guichi, S. Mekhilef, (2020). Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL Divergence. International Journal of Electrical Power & Energy Systems. [2]. A. Guichi, A. Talha, E. M. Berkouk, S. Mekhilef, S. Gassab, (2018). A new method for intermediate power point tracking for PV generator under partially shaded conditions in hybrid system. Sol. Energy. 170, 974-987. https://doi.org/10.1016/j.solener.2018.06.027. [3]. A. Bakdi, W. Bounoua, S. Mekhilef, L. M. Halabi, (2019). Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV. Energy, 116366. https://doi.org/10.1016/j.energy.2019.116366. Structure GPVS-Faults data files are labelled as “Fxy”, where: x∈{0,1,…,7} represents the fault scenario: '0' is a fault-free experiment. '1',…,'7' are the 7 types of faults. y∈{'L','M'} is the operation mode: 'L' is Limited power mode (IPPT) 'M' is Maximum power mode (MPPT) e.g. “F4M” is fault F4 in MPPT mode, “F1L” is fault F1 in IPPT mode. Each data file includes the following columns: Time: Time in seconds, average sampling T_s=9.9989 μs. Ipv: PV array current measurement. Vpv: PV array voltage measurement. Vdc: DC voltage measurement. ia, ib, ic: 3-Phase current measurements. va, vb, vc: 3-Phase voltage measurements. Iabc: Current magnitude. If: Current frequency. Vabc: Voltage magnitude. Vf: Voltage frequency.
DOI: 10.17863/cam.97144
title: Supporting data for 'Superconductivity beyond the conventional Pauli limit in high-pressure CeSb2'

description: This deposit holds all the data underlying the figures shown in the linked publication 'Superconductivity beyond the conventional Pauli limit in high-pressure CeSb2'. Please also see readme file for more details
DOI: 10.24381/cds.c6459d3a
title: Climate variables for cities in Europe from 2008 to 2017
DOI: 10.3886/icpsr01014.v1
title: Market Volatility
title: Version 1

description: These data and/or computer programs are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the INVESTIGATOR(S) if further information is desired.
description: Datasets: 
 
 DS1: Dataset
DOI: 10.5281/zenodo.11683281
title: An aperiodic chiral tiling by topological molecular self-assembly

description: Data used in preparation of publication 'An aperiodic chiral tiling by topological molecular self-assembly'
DOI: 10.5281/zenodo.12515544
title: Generic residue numbering of the GAIN domain of adhesion GPCRs

description: The dataset corresponding to the publication:



Generic residue numbering of the GAIN domain of adhesion GPCRs



Florian Seufert, Guillermo Pérez-Hernández, Gáspár Pándy-Szekeres, Ramon Guixà-González, Tobias Langenhan, David E. Gloriam, Peter W. Hildebrand

ReasearchSquare

https://doi.org/10.21203/rs.3.rs-4761600/v1




The archive consists of three files:



gaingrn_data.tgz


Contains the underlying data and object structures for the GAIN-GRN creation process and the python package availabe in https://github.com/FloSeu/GAIN-GRN. This can be downloaded manually and extracted in the respective GAIN-GRN/ directory or automatically retrieved via a dedicated function



agpcr_gains.tgz


Contains the best (rank 1) PDB model of the ColabFold/AlphaFold2 workflow for each adhesion GPCR GAIN domain, named by its UniProt accession number.



pkd_gains.tgz


Contains the best (rank 1) PDB model of the ColabFold/AlphaFold2 workflow for each polycystic kidney disease (PKD1) / PKD1-like 1 protein (PKD1L1) GAIN domain, named by its UniProt accession number.
DOI: 10.5281/zenodo.13341420
title: HERFD-XANES and vtc-XES data acquired during CO2RR conditions for Ni single atom catalysts

description: HERFD-XANES and vtc-XES data (.h5 files) relative to the pubblication: "Unveiling the Adsorbate Configurations in Ni Single Atom Catalysts during CO2 Electrocatalytic reduction using Operando XAS, XES and Machine Learning".
DOI: 10.5281/zenodo.13629499
title: Participation in the Czech Pirate Party - voting turnout

description: To analyze participation, we gathered data from the Pirate Party Forum, focusing specifically on consultation threads that occurred between 2017 and 2023. The objective of this data collection was to provide a quantitative assessment of various aspects of these consultations. In total, 103 consultations were analyzed. The dataset includes several key metrics. The first metric is voter turnout, which represents the number of members who participated in the voting process for each consultation. We also recorded the number of eligible voters, defined as the total number of party members with voting rights. Another important metric is the number of comments within the consultation threads, used as an indicator of member engagement and the extent of discussions surrounding each consultation topic. Additionally, we examined the number of votes received by the winning options in each consultation, which reveals the level of support for the winning variant and helps determine whether the results exhibit a plebiscitary character. Finally, we tracked the number of days during which voting was open for each consultation. For each of these metrics, annual averages were calculated to identify trends and changes over time.

 

More information: michal.maly@fsv.cuni.cz
DOI: 10.5281/zenodo.14004291
title: Regionalpcs ROSMAP Differential Methylation Results

description: This dataset contains differential methylation results for the manuscript titled

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

Differential methylation analyses were performed using methylation summarized as averages, regional PCs, and CpGs across multiple gene regions: full gene region, gene body, preTSS, and promoters.

Files are named according to the gene region:



[gene region]_full_DM_results.csv: Comprehensive differential methylation results.

[gene region]_all_counts.csv: Summarized counts of significant differential methylation results.
DOI: 10.5281/zenodo.14016038
title: Regionalpcs ROSMAP GWAS Integration

description: This archive contains the GWAS integration results for the study: 

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

The GWAS integration includes colocalization and PTWAS analyses, organized within subdirectories in the main tar file.

Colocalization results: These include the output from FastENLOC for each cell type (astrocytes, endothelial cells, neurons, oligodendrocytes/OPCs and bulk) across different region types (full gene, gene body, preTSS, and promoters). Results are provided separately for averages (avgs) and regionalpcs (pcs).

PTWAS results: PTWAS output is similarly organized by cell types, region type, and summary types, providing output from the PTWAS method.
DOI: 10.5281/zenodo.14027678
title: ROSMAP meQTL Results for Endothelial cells with regionalpcs and averages

description: This dataset contains methylation quantitative trait loci (meQTL) results for the following study:

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).

Contents:



Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. parquet1 includes chromosomes 1-10, and parquet2 includes chromosomes 11-22.

cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.

cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.

cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.

cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.


This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.
DOI: 10.5281/zenodo.14027753
title: ROSMAP meQTL Results for Bulk tissue cells with CpGs

description: This dataset contains methylation quantitative trait loci (meQTL) results for the following study:

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).

Contents:



Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. parquet1 includes chromosomes 1-10, and parquet2 includes chromosomes 11-22.

cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.

cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.

cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.

cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.


This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.
DOI: 10.5281/zenodo.14027807
title: ROSMAP meQTL Results for Bulk cells with regionalpcs and averages

description: This dataset contains methylation quantitative trait loci (meQTL) results for the following study:

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).

Contents:



Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. parquet1 includes chromosomes 1-10, and parquet2 includes chromosomes 11-22.

cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.

cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.

cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.

cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.


This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.
DOI: 10.5281/zenodo.14027962
title: ROSMAP meQTL Results for Astrocytes with CpGs

description: This dataset contains methylation quantitative trait loci (meQTL) results for the following study:

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).

Contents:



Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. parquet1 includes chromosomes 1-10, and parquet2 includes chromosomes 11-22.

cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.

cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.

cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.

cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.


This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.
DOI: 10.5281/zenodo.14028091
title: ROSMAP meQTL Results for Astrocytes with regionalpcs and averages

description: This dataset contains methylation quantitative trait loci (meQTL) results for the following study:

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).

Contents:



Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. parquet1 includes chromosomes 1-10, and parquet2 includes chromosomes 11-22.

cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.

cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.

cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.

cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.


This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.
DOI: 10.5281/zenodo.14029154
title: ROSMAP meQTL Results for Oligodendrocytes with regionalpcs and averages

description: This dataset contains methylation quantitative trait loci (meQTL) results for the following study:

"regionalpcs improve discovery of DNA methylation associations with complex traits"

Tiffany Eulalio*1, Min Woo Sun1, Olivier Gevaert1, Michael D. Greicius2, Thomas J. Montine3, Daniel Nachun*‡3, Stephen B. Montgomery*‡1,3

‡ These authors contributed equally as senior authors

* Corresponding authors: Tiffany Eulalio (eulalio@alumn.stanford.edu), Daniel Nachun (dnachun@stanford.edu), Stephen B. Montgomery (smontgom@stanford.edu)

 Author affiliations:

1.     Department of Biomedical Data Science, Stanford University, Stanford, CA

2.     Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA

3.     Department of Pathology, Stanford University, Stanford, CA

 

Dataset description:

This dataset contains QTL results generated from FastQTL, organized by region type (full gene, gene body, preTSS, and promoters) and summary types (averages and regional principal components).

Contents:



Parquet tar files: These compressed archives contain output files in Parquet format from FastQTL, split by chromosome. parquet1 includes chromosomes 1-10, and parquet2 includes chromosomes 11-22.

cis_qtl_summary_stats.csv: Provides summary statistics for each phenotype-variant pair, including effect sizes, p-values, TSS distances, and additional details.

cis_qtl.signif_pairs.csv: Contains the significant QTL results identified by FastQTL.

cis_qtls.time_to_run.txt: Reports the running time for FastQTL analysis.

cis_qtls.cis_qtl.txt.gz: Contains comprehensive results for all cis QTLs.


This dataset is intended to support replication and further exploration of QTL associations across different genomic regions and summary methods.