cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5946866
title: Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples

description: # Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples" SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes. SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results. SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID. SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows:<br> * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020.<br> * rab.all: median clr value for all samples in the feature<br> * rab.win.conditionA: median clr value for the condition A of samples<br> * rab.win.conditionB: median clr value for the condition B of samples<br> * diff.btw: median difference in clr values between A and B conditions<br> * diff.win: median of the largest difference in clr values within A and B conditions<br> * effect : median effect size: diff.btw / max(diff.win) for all instances<br> * overlap : proportion of effect size that overlaps 0 (i.e. no effect)<br> * we.ep: Expected P value of Welch’s t test<br> * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test<br> * wi.ep: Expected P value of Wilcoxon rank test<br> * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test<br> * parts: gene name<br> * conditionA: label of condition A that is compared against condition B<br> * conditionB: label of condition B that is compared against condition A<br> * conditions.A.vs.B: label to explain condition A compared against condition B<br> NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences. SD6: FASTA file containing all unique consensus sequences reported in the manuscript. SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.
description: {"references": ["Martiny, Hannah-Marie, et al. \"Global distribution of mcr gene variants in 214,095 metagenomic samples.\" (2021)."]}
DOI: 10.5281/zenodo.5946865
title: Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples

description: # Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples" SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes. SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results. SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID. SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows:<br> * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020.<br> * rab.all: median clr value for all samples in the feature<br> * rab.win.conditionA: median clr value for the condition A of samples<br> * rab.win.conditionB: median clr value for the condition B of samples<br> * diff.btw: median difference in clr values between A and B conditions<br> * diff.win: median of the largest difference in clr values within A and B conditions<br> * effect : median effect size: diff.btw / max(diff.win) for all instances<br> * overlap : proportion of effect size that overlaps 0 (i.e. no effect)<br> * we.ep: Expected P value of Welch’s t test<br> * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test<br> * wi.ep: Expected P value of Wilcoxon rank test<br> * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test<br> * parts: gene name<br> * conditionA: label of condition A that is compared against condition B<br> * conditionB: label of condition B that is compared against condition A<br> * conditions.A.vs.B: label to explain condition A compared against condition B<br> NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences. SD6: FASTA file containing all unique consensus sequences reported in the manuscript. SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.
description: {"references": ["Martiny, Hannah-Marie, et al. \"Global distribution of mcr gene variants in 214,095 metagenomic samples.\" (2021)."]}
DOI: 10.6084/m9.figshare.12130980.v39
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130980.v38
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.3761562.v1598
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1597
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.11911428.v2
title: Dataset - BSISO in SP GCMs: effects of air-sea coupling and mean-states biases

description: The effect of air-sea coupling on simulated boreal summer intraseasonal oscillation (BSISO) is examined using atmosphere–ocean-mixed-layer coupled (SPCAM3-KPP) and uncoupled configurations of the superparameterized (SP) Community Atmospheric Model, version 3 (SPCAM3). The coupled configuration is constrained to either observed ocean mean state or the mean state from the SP coupled configuration with a dynamic ocean (SPCCSM3), to understand the effect of mean-state biases on the BSISO. All configurations overestimate summer mean subtropical rainfall and its intraseasonal variance. All configurations simulate realistic BSISO northward propagation over the Indian Ocean and western Pacific, in common with other SP configurations. Prescribing the 31-day smoothed sea surface temperature (SST) from the SPCAM3-KPP simulation in SPCAM3 worsens the overestimated BSISO variance. In both coupled models, the phase relationship between intraseasonal rainfall and SST is well captured. This suggests that air-sea coupling improves the amplitude of simulated BSISO and contributes to the propagation of convection. Constraining SPCAM3-KPP to the SPCCSM3 mean state also reduces the overestimated BSISO variability, but weakens BSISO propagation. Using the SPCCSM3 mean state also introduces a one-month delay to the BSISO seasonal cycle compared to SPCAM3-KPP with the observed ocean mean state, which matches well with observation. Based on a Taylor diagram, both air-sea coupling and SPCCSM3 mean-state SST biases generally lead to higher simulated BSISO fidelity, largely due to their abilities to suppress the overestimated subtropical BSISO variance.
DOI: 10.6084/m9.figshare.11911428.v1
title: Dataset - BSISO in SP GCMs: effects of air-sea coupling and mean-states biases

description: The effect of air-sea coupling on the simulated boreal summer intraseasonal oscillation (BSISO) is examined using atmosphere—ocean-mixed-layer coupled (SPCAM3-KPP) and uncoupled configurations of the Super-Parameterized (SP) Community Atmospheric Model, version 3 (SPCAM3). The coupled configuration is constrained to either the observed ocean mean state or the mean state from the SP coupled configuration with a dynamic ocean (SPCCSM3), to understand the effect of mean state biases on the BSISO in the latter. All configurations overestimate summer mean subtropical rainfall and its intraseasonal variance. All configurations simulate realistic BSISO northward propagation over the Indian Ocean and western Pacific, in common with other SP configurations.Constraining SPCAM3-KPP to the SPCCSM3 mean state reduces the overestimated BSISO variability, but also weakens BSISO propagation. Using the SPCCSM3 mean state also introduces a one-month delay to the BSISO seasonal cycle compared to SPCAM3-KPP with the observed ocean mean state, which matches well with the reanalysis. The phase relationship between intraseasonal rainfall and sea surface temperature (SST) is captured by all coupled models, but with a shorter delay between suppressed convection and warm SST relative to the reanalysis. Prescribing the 31-day smoothed SSTs from the SPCAM3-KPP simulations in SPCAM3 worsens the overestimated BSISO variance. This suggests that air-sea coupling improves the amplitude of the simulated BSISO. Based on a Taylor diagram, SPCCSM3 mean state SST biases and air-sea coupling both lead to higher simulated BSISO fidelity, largely due to their ability to suppress the overestimated subtropical BSISO variance.
DOI: 10.6084/m9.figshare.3761562.v1600
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1599
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.7033577.v10
title: Deep image reconstruction from human brain activity

description: Shen, Horikawa, Majima, and Kamitani (2019) Deep image reconstruction from human brain activity. PLos Comput Biol. (bioRxiv preprint)<br>Here we provide DNN features decoded from human brain activity (decodedfeatures-*.mat files) as well as preprocessed fMRI data used for the decoding (sub-*.h5 files).<br><br>Raw fMRI data are available at OpenNeuro.<br><br>Demo code is available at GitHub.<br><b>History</b><br>2020-09-09: True DNN features (unit activations) of VGG19 for natural image training and test images were added.<br>2020-09-08: True DNN features (unit activations) of VGG19 (except conv1) for natural image training images were added.<br><br>2020-08-05: fMRI data version 2, which applied slightly different preprocessng and ROIs from the original study, were added (for the natural image experiment only; *_perceptionNaturalImageTraining/Test_VC_v2.h5).<br><br>2020-05-18: The following fMRI data files of artificial images, letters, and imagery experiments contained incorrect samples and were fixed.<br>- sub-01_perceptionLetterImage_original_VC.h5- sub-02_perceptionArtificialImage_original_VC.h5<br>- sub-02_perceptionLetterImage_original_VC.h5- sub-02_imagery_original_VC.h5<br>- sub-03_perceptionArtificialImage_original_VC.h5- sub-03_perceptionLetterImage_original_VC.h5- sub-03_imagery_original_VC.h5<br>
DOI: 10.6084/m9.figshare.12937172.v1
title: Newsvendor EEG

description: The files in the .mat format are the raw EEG data collected by the NetStation software. Each file is collected from one subject. The dataset contains 42 files in total. The .xls file is the behavioural data collected by the Eprime software and then reformatted in Excel.
DOI: 10.6084/m9.figshare.12798296.v7
title: Data and code from Rizzuto et al. “Forage stoichiometry predicts home range size in a small terrestrial herbivore"

description: Data and code used in Rizzuto et al. "Forage stoichiometry predicts home range size in a small terrestrial herbivore”.<br>The file that produces the Supporting Code, and presents the analyses in human-readable format, is “SupportingCode.Rmd”.<br>For correct compilation of the .Rmd files, please organize the files in this repository as follows:<br>- all .Rmd and .R files in a "../Code/“ folder- all .csv files in a "../Data/" folder- all .tiff files in a “../Data/StDMs_Rasters/Ratios/" folder- unzip the SamplePoints.zip file in a "../Data/GridPoints/" folder- all .rds files in a "../Results/" folder<br>Where the Code, Data, and Results folder all share the same root.<br>Please note that access to some shapefiles used to produce the maps in the manuscript, SI, or Supporting Code document was regulated by agreements with the Government of Newfoundland and Labrador and the Federal Government of Canada. As such, they could not be publicly shared. This does not impact the analyses, only the production of some visual supporting material (e.g., study area maps). Please contact the corresponding author (M. Rizzuto) for any question. <br> See README.md for details on the repository’s contents.
DOI: 10.6084/m9.figshare.12798296.v6
title: Data and code from Rizzuto et al. “Forage stoichiometry predicts home range size in a small terrestrial herbivore"

description: Data and code used in Rizzuto et al. "Forage stoichiometry predicts home range size in a small terrestrial herbivore”.<br>The file that produces the Supporting Code, and presents the analyses in human-readable format, is “StoichiometryOfHomeRanges.Rmd”.<br>For correct compilation of the .Rmd files, please organize the files in this repository as follows:<br>- all .Rmd and .R files in a ../Code/ folder- all .csv files in a ../Data folder- all .tiff files in a ../Data/StDMs_Rasters/Ratios folder- unzip the SamplePoints.zip file in a ../Data/GridPoints folder- all .rds files in a ../Results folder<br>Where the Code, Data, and Results folder all share the same root.<br>Please note that access to some shapefiles used to produce the maps in the manuscript, SI, or Supporting Code document was regulated by agreements with the Government of Newfoundland and Labrador. As such, they could not be publicly shared. This does not impact the analyses, only the production of some visual supporting material (e.g., study area maps). Please contact the corresponding author (M. Rizzuto) for any question. <br> See README.md for details on the repository’s contents.
DOI: 10.6084/m9.figshare.12798296.v9
title: Data and code from Rizzuto et al. “Forage stoichiometry predicts home range size in a small terrestrial herbivore"

description: Data and code used in Rizzuto et al. "Forage stoichiometry predicts home range size in a small terrestrial herbivore”.<br>The file that produces the Supporting Code, and presents the analyses in human-readable format, is “SupportingCode.Rmd”.<br>For correct compilation of the .Rmd files, please organize the files in this repository as follows:<br>- all .Rmd and .R files in a "../Code/“ folder- all .csv files in a "../Data/" folder- all .tiff files in a “../Data/StDMs_Rasters/Ratios/" folder- unzip the SamplePoints.zip file in a "../Data/GridPoints/" folder- all .rds files in a "../Results/" folder<br>Where the Code, Data, and Results folder all share the same root.<br>Please note that access to some shapefiles used to produce the maps in the manuscript, SI, or Supporting Code document was regulated by agreements with the Government of Newfoundland and Labrador and the Federal Government of Canada. As such, they could not be publicly shared. This does not impact the analyses, only the production of some visual supporting material (e.g., study area maps). Please contact the corresponding author (M. Rizzuto) for any question. <br> See README.md for details on the repository’s contents.
DOI: 10.6084/m9.figshare.12798296.v8
title: Data and code from Rizzuto et al. “Forage stoichiometry predicts home range size in a small terrestrial herbivore"

description: Data and code used in Rizzuto et al. "Forage stoichiometry predicts home range size in a small terrestrial herbivore”.<br>The file that produces the Supporting Code, and presents the analyses in human-readable format, is “SupportingCode.Rmd”.<br>For correct compilation of the .Rmd files, please organize the files in this repository as follows:<br>- all .Rmd and .R files in a "../Code/“ folder- all .csv files in a "../Data/" folder- all .tiff files in a “../Data/StDMs_Rasters/Ratios/" folder- unzip the SamplePoints.zip file in a "../Data/GridPoints/" folder- all .rds files in a "../Results/" folder<br>Where the Code, Data, and Results folder all share the same root.<br>Please note that access to some shapefiles used to produce the maps in the manuscript, SI, or Supporting Code document was regulated by agreements with the Government of Newfoundland and Labrador and the Federal Government of Canada. As such, they could not be publicly shared. This does not impact the analyses, only the production of some visual supporting material (e.g., study area maps). Please contact the corresponding author (M. Rizzuto) for any question. <br> See README.md for details on the repository’s contents.
DOI: 10.6084/m9.figshare.3761562.v1608
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1607
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1610
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1609
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.12838124.v2
title: corfiles_NS

description: Corrected segmentation data (.csv files) for immunofluorescence images (fixed samples, Nestor Saiz) for embryos analyzed in the paper by Morgani <i>et al.</i>, in <i>Developmental Biology </i>(2018) https://doi.org/10.1016/j.ydbio.2018.06.017
DOI: 10.6084/m9.figshare.12838124.v1
title: corfiles_ns

description: Corrected segmentation data (.csv files) for immunofluorescence images (fixed samples, Nestor Saiz) for embryos analyzed in the paper by Morgani <i>et al.</i>, in <i>Developmental Biology </i>(2018) https://doi.org/10.1016/j.ydbio.2018.06.017