cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10390382
title: Supplementary data and code for "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"

description: This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". Files are password protected during the revision process. A completely public version of the repository will be availble after the revision process is completed. 

In detail, the uploaded archive folder contains the following data sources:



the relevant code and supporting data (code_to_upload and supporting_data);

supplementary materials of the paper, including:



individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results);

comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results);

overrepresented network edges in categories of networks (overrepresented_structures)
DOI: 10.5281/zenodo.10390383
title: Supplementary data and code for "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"

description: This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". Files are password protected during the revision process. A completely public version of the repository will be availble after the revision process is completed. 

In detail, the uploaded archive folder contains the following data sources:



the relevant code and supporting data (code_to_upload and supporting_data);

supplementary materials of the paper, including:



individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results);

comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results);

overrepresented network edges in categories of networks (overrepresented_structures)
DOI: 10.25318/3310075001-eng
title: Ability for the business or organization to take on more debt, fourth quarter of 2023

description: Ability of the business or organization to take on more debt, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2023.
DOI: 10.6084/m9.figshare.24832866
title: imputation_tambaqui

description: A 1K SNP array was developed and validated for tambaqui, which can be applied together with imputation techniques for the use in breeding programs.
DOI: 10.6084/m9.figshare.24832866.v1
title: imputation_tambaqui

description: A 1K SNP array was developed and validated for tambaqui, which can be applied together with imputation techniques for the use in breeding programs.
DOI: 10.25318/3310075001-fra
title: Capacité des entreprises ou organismes à s’endetter davantage, quatrième trimestre de 2023

description: Capacité des entreprises ou des organismes à s’endetter davantage, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2023.
DOI: 10.4121/4126c919-1d0c-4ba9-80fa-3960f49e8cd7.v1
title: Data underlying the publication: "A model of dyadic merging interactions explains human drivers' behaviour from input signals to decisions"

description: This dataset contains simulations of a model of two human drivers interacting in a top-down-view merging scenario. This merging scenario is a simplified version of highway merging. In this scenario, two vehicles approach a pre-defined merge point. In a previous experiment, we asked two participants to stick to their initial velocity yet avoid collisions. The data in this dataset contains model simulations that describe this human interactive behaviour during driving.
DOI: 10.25318/3310075101-eng
title: Percentage of workforce anticipated to work on-site or remotely over the next three months, fourth quarter of 2023

description: Percentage and average percentage of workforce anticipated to work on-site or remotely over the next three months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2023.
DOI: 10.6084/m9.figshare.24680484
title: Gish_et_al_raw_data

description: Does aversion to insects affect insecticide use?
DOI: 10.25318/3310075101-fra
title: Pourcentage de l’effectif censé travailler sur place ou à distance au cours des trois prochains mois, quatrième trimestre de 2023

description: Pourcentage et pourcentage moyen de l’effectif censé travailler sur place ou à distance au cours des trois prochains mois, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2023.
DOI: 10.25318/3310075201-eng
title: Future outlook over the next 12 months, fourth quarter of 2023

description: Future outlook over the next 12 months, fourth quarter of 2023, by percentage ranges, North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2023.
DOI: 10.7274/24833259
title: Minerva Awardee List

description: As found in Chpt. 8, n243 of Cult of the Irrelevant
DOI: 10.7274/24833259.v1
title: Minerva Awardee List

description: As found in Chpt. 8, n243 of Cult of the Irrelevant
DOI: 10.5281/zenodo.10067514
title: Test dataset
title: Test dataset
DOI: 10.5281/zenodo.10067515
title: Test dataset
title: Test dataset
DOI: 10.5281/zenodo.10067522
title: Test dataset
title: Test dataset
DOI: 10.5281/zenodo.10391372
title: (1000G phase 3 hg38)  Linkage Desequilibrium Matrices by ancestry for RAISS

description: Linkage desequilibrium matrices generated using 1000G phase 3 data hg38 using plink

These matrices can use as input file for the RAISS python packages, which allows for the imputation of GWAS summary statistique.

RAISS Python package:

https://gitlab.pasteur.fr/statistical-genetics/raiss

RAISS paper:

https://academic.oup.com/bioinformatics/article/35/22/4837/5512360?login=true 

Preprint providing RAISS performances by ancestry (Table S3):

https://www.biorxiv.org/content/10.1101/2023.06.23.546248v1
DOI: 10.5281/zenodo.10391371
title: (1000G phase 3 hg38)  Linkage Desequilibrium Matrices by ancestry for RAISS

description: Linkage desequilibrium matrices generated using 1000G phase 3 data hg38 using plink

These matrices can use as input file for the RAISS python packages, which allows for the imputation of GWAS summary statistique.

RAISS Python package:

https://gitlab.pasteur.fr/statistical-genetics/raiss

RAISS paper:

https://academic.oup.com/bioinformatics/article/35/22/4837/5512360?login=true 

Preprint providing RAISS performances by ancestry (Table S3):

https://www.biorxiv.org/content/10.1101/2023.06.23.546248v1
DOI: 10.25318/3310075201-fra
title: Perspectives d’avenir au cours des 12 prochains mois, quatrième trimestre de 2023

description: Perspectives d’avenir au cours des 12 prochains mois, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2023.
DOI: 10.21942/uva.24829896.v2
title: Elucidating the role of water in collagen self assembly by isotopically modulating collagen hydration

description: This zip file contains data, and analysis for the paper.
DOI: 10.5281/zenodo.10356954
title: LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 1 of 3

description: Summary

This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.

Description

The uploaded content includes reconstructed images for noise levels 1 and 2. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:



Noise1 and Noise2 (this one) https://zenodo.org/records/10356955

Noise3 and Noise4 https://zenodo.org/records/10391277

Noise5 and Reference https://zenodo.org/records/10391412


The scanning parameters for all noise levels are summarized in the table below:




 
Averaged Projs
Exposure Time/ms
Scan Time/min
Voltage/kV
Current/uA


Reference
6
333
59.3
140
180


Noise Level 1
1
333
9.9
140
180


Noise Level 2
1
333
9.9
140
90


Noise Level 3
1
333
9.9
140
45


Noise Level 4
1
333
9.9
140
23


Noise Level 5
1
333
9.9
140
12




Additional link

The code for supervised learning-based denoising is available code .

Acknowledgment

This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing).
DOI: 10.5281/zenodo.10356955
title: LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 1 of 3

description: Summary

This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.

Description

The uploaded content includes reconstructed images for noise levels 1 and 2. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:



Noise1 and Noise2 (this one) https://zenodo.org/records/10356955

Noise3 and Noise4 https://zenodo.org/records/10391277

Noise5 and Reference https://zenodo.org/records/10391412


The scanning parameters for all noise levels are summarized in the table below:




 
Averaged Projs
Exposure Time/ms
Scan Time/min
Voltage/kV
Current/uA


Reference
6
333
59.3
140
180


Noise Level 1
1
333
9.9
140
180


Noise Level 2
1
333
9.9
140
90


Noise Level 3
1
333
9.9
140
45


Noise Level 4
1
333
9.9
140
23


Noise Level 5
1
333
9.9
140
12




Additional link

The code for supervised learning-based denoising is available code .

Acknowledgment

This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing).
DOI: 10.5285/d1c61edc4f554ee09ad370f6b52f82ce
title: DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022

description: This dataset contains cloud images from the NCAS Camera 12, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12), captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.

These cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.

The images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later.
DOI: 10.18112/openneuro.ds004874.v1.0.0
title: M1 PAS-EEG dataset
DOI: 10.15151/esrf-dc-1431859433
title: Stress-induced structural changes in high-temperature superconducting YBCO

description: We used x-ray diffraction on uniaxially pressurized YBa2Cu3O6.67 to follow the evolution of the 2D charge density wave and the emergence of the 3D charge density wave state while simultaneously monitoring the modifications of the lattice. The datasets are from sample yb2.
DOI: 10.5281/zenodo.10360913
title: EPFL HYMEX_LTE_SOP4 32 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 32 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/32.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.5281/zenodo.10360914
title: EPFL HYMEX_LTE_SOP4 32 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 32 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/32.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.7274/24833652
title: PM 2017 Topline Data

description: NDISC/TRIP PM survey topline
DOI: 10.7274/24833652.v1
title: PM 2017 Topline Data

description: NDISC/TRIP PM survey topline
DOI: 10.18112/openneuro.ds004141.v1.0.4
title: Targeting Dynamic Facial Processing Mechanisms in Superior Temporal Sulcus Using fMRI Neurofeedback
DOI: 10.5281/zenodo.10360915
title: EPFL HYMEX_LTE_SOP4 10 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 10 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/10.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.5281/zenodo.10360916
title: EPFL HYMEX_LTE_SOP4 10 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 10 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/10.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.25345/c5s756w5h
title: MassIVE Reanalysis RMSV000000696.1 - An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing
title: An analytical comparison of the MSstats and MSstatsBig workflows applied to the same quantification results by FragPipe. The MSstatsBig workflow employs strategies to reduce the size of the input files and works without loading the full dataset into memory. Files named Procedure 2A were processed with MSstats and Procedure 2B were processed with MSstatsBig. Both R scripts and the output of each step are included. FragPipe quantification results were included, both in MSstats format (msstats.csv) and DIA-NN output (resport.tsv). An additional subset file ("testing_input.csv") is included for testing purposes. Metadata is included by default in the msstats.csv file and is not required in a separate file.
DOI: 10.5281/zenodo.10360917
title: EPFL HYMEX_LTE_SOP4 12 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 12 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/12.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.5281/zenodo.10360918
title: EPFL HYMEX_LTE_SOP4 12 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 12 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/12.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.5285/b839ae53abf94e23b0f61560349ccda1
title: DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022

description: This dataset contains cloud images from the NCAS Camera 11, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12) captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.

These cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.

The images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later.
DOI: 10.5281/zenodo.10360919
title: EPFL HYMEX_LTE_SOP4 11 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 11 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/11.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.5281/zenodo.10360920
title: EPFL HYMEX_LTE_SOP4 11 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 11 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/11.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.6084/m9.figshare.24833791
title: Auto extract script from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Matlab script used to extract singing mouse songs from audio recording files
DOI: 10.6084/m9.figshare.24833791.v1
title: Auto extract script from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Matlab script used to extract singing mouse songs from audio recording files
DOI: 10.5281/zenodo.10360921
title: EPFL HYMEX_LTE_SOP4 31 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 31 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/31.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.5281/zenodo.10360922
title: EPFL HYMEX_LTE_SOP4 31 disdrometer station data

description: Disdrometer measurements of the EPFL HYMEX_LTE_SOP4 31 station. This dataset is part of the DISDRODB project. Station metadata are available at https://github.com/ltelab/disdrodb-data/blob/main/DISDRODB/Raw/EPFL/HYMEX_LTE_SOP4/metadata/31.yml . The software to easily process and standardize the raw data into netCDF files is available at https://github.com/ltelab/disdrodb .
DOI: 10.6084/m9.figshare.24833794
title: Song analysis script from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Matlab script used to measure acoustic features of extracted song recordings
DOI: 10.6084/m9.figshare.24833794.v1
title: Song analysis script from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Matlab script used to measure acoustic features of extracted song recordings
DOI: 10.6084/m9.figshare.24637646.v1
title: Tables S2 and S3 from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Statistical tables for females and males
DOI: 10.6084/m9.figshare.24637649.v1
title: Read me text file from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Text file containing summary of files and summary of column headings in data file
DOI: 10.6084/m9.figshare.24637652.v1
title: Data file from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Comma-delimited file containing adult mass, activity and metabolic rate data
DOI: 10.6084/m9.figshare.24637658.v1
title: R code from Temperature and nutrition do not interact to shape the evolution of metabolic rate

description: Text file containing the R script to run statistical analyses
DOI: 10.6084/m9.figshare.24799513
title: Data from Geographical and taxonomic patterns in aerobic traits of marine ectotherms

description: zipped file of code to analyze data and produce figures
DOI: 10.6084/m9.figshare.24799519
title: Table S2 from Geographical and taxonomic patterns in aerobic traits of marine ectotherms

description: Table of analyzed marine ectotherm phyla and classes from the OBIS database
DOI: 10.6084/m9.figshare.24799513.v1
title: Data from Geographic and taxonomic patterns in aerobic traits of marine ectotherms

description: zipped file of code to analyze data and produce figures
DOI: 10.6084/m9.figshare.24799519.v2
title: Table S2 from Geographical and taxonomic patterns in aerobic traits of marine ectotherms

description: Table of analyzed marine ectotherm phyla and classes from the OBIS database
DOI: 10.6084/m9.figshare.24799519.v1
title: Table S2 from Geographic and taxonomic patterns in aerobic traits of marine ectotherms

description: Table of analyzed marine ectotherm phyla and classes from the OBIS database
DOI: 10.5066/p93dhtrj
title: Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1950-2022