cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11911389.v2
title: Dataset: effects of air-sea coupling and ocean mean state on BSISO in SP GCMs

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.11911287
title: Fuzzing: On the Exponential Cost of Vulnerability Discovery

description: This data accompanies the publication "Fuzzing: On the Exponential Cost of Vulnerability Discovery" by Marcel Böhme and Brandon Falk at ESEC/FSE 2020. A preprint can be found at the authors' homepage. Please use the following bibtex to cite our paper and data:<br>@inproceedings{empiricalLaw, author = {B{\"o}hme, Marcel and Falk, Brandon}, title = {Fuzzing: On the Exponential Cost of Vulnerability Discovery}, booktitle = {Proceedings of the 14th Joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering}, series = {ESEC/FSE}, year = {2020}, pages = {1-12}, numpages = {12}}<br>
DOI: 10.6084/m9.figshare.11911284
title: rawdata.rar

description: In this study, we collected peripheral blood samples from 10 LN patients who were confirmed by renal biopsy and 10 healthy controls. The TCR repertoire of each sample was assessed by high-throughput sequencing to examine the distinction between LN and healthy controls.
DOI: 10.6084/m9.figshare.11911284.v1
title: rawdata.rar

description: In this study, we collected peripheral blood samples from 10 LN patients who were confirmed by renal biopsy and 10 healthy controls. The TCR repertoire of each sample was assessed by high-throughput sequencing to examine the distinction between LN and healthy controls.
DOI: 10.6084/m9.figshare.11911272
title: WN18RR

description: WN18RR is derived from WN18, with data removed to eliminate test-set leakage due to inverse relations. WN18RR contains 93003 triples, connecting 40943 entities via 11 relations.<br><br>Info about WN18: This WORDNET TENSOR DATA consists of a collection of triplets (synset, relation_type, triplet) extracted from WordNet 3.0 (http://wordnet.princeton.edu). This data set can be seen as a 3-mode tensor depicting ternary relationships between synsets.<br>All *.txt files contain one triplet per line, with 2 synset_ids and relation type identifier in a tab separated format. The first element is the synset_id of the left hand side of the relation triple, the third one is the synset_id of the right hand side and the second element is the name of the type of relations between them.<br>The WN18RR.zip file contains the other files, with more compression than the default "download all".<br>
DOI: 10.6084/m9.figshare.11911269
title: FB15k-237

description: FB15k-237 dataset is derived from FB15k, with data removed to eliminate test-set leakage due to inverse relations. WN18RR contains 310116 triples, connecting 14541 entities via 237 relations.<br><br>Information about FB15k: This FREEBASE FB15k DATA consists of a collection of triplets (synset, relation_type, triplet) extracted from Freebase (http://www.freebase.com). This data set can be seen as a 3-mode tensor depicting ternary relationships between synsets. <br>All *.txt files contain one triplet per line, with 2 mids (unique Freebase entity identifier) and relation type identifier in a tab separated format. The first element is the mid of the left hand side (of head) of the relation triple, the third one is the mid of the right hand side (or tail) and the second element is the name of the relationship between them. <br><br>The FB15k-237.zip file contains the other files, with more compression than the default "download all".
DOI: 10.6084/m9.figshare.11911197
title: Supplementary-data-wangpraseurt-2020NatCom

description: <b>File name: </b>Supplementary data 1<b></b><b>Description: </b>STL file of <i>Pocillopora damicornis</i> tissue expanded<b> </b><b>File name: </b>Supplementary data 2<b></b><b>Description: </b>STL file of intermediate bionic skeleton <b>File name: </b>Supplementary data 3<b></b><b>Description: </b>STL file of final simplified skeleton <b>File name: </b>Supplementary data 4<b></b><b>Description: </b>STL file of final simplified tissue<b> </b><b>File name: </b>Supplementary data 5<b></b><b>Description: </b>STL file of skeleton of <i>Pavona cactus</i> <b>File name: </b>Supplementary data 6<b>Description: </b>STL file of coenostal spines of <i>Pocillopora damicornis</i> <b>File name: </b>Supplementary data 7<b>Description: </b>STL file of tissue surface of <i>Pavona cactus</i> <b>File name: </b>Supplementary data 8<b>Description: </b>STL file of tissue surface of <i>Favites flexuosa</i>
DOI: 10.6084/m9.figshare.11911149.v1
title: OCIM2_KvHIGH_noHe.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911143.v1
title: OCIM2_KvHIGH_KiLOW_He.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911146.v1
title: OCIM2_KvHIGH_KiLOW_noHe.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911140.v1
title: OCIM2_KvHIGH_KiHIGH_noHe.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911137.v1
title: OCIM2_KvHIGH_He.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911128.v1
title: OCIM2_KiHIGH_noHe.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911131.v1
title: OCIM2_KiLOW_He.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911125.v1
title: OCIM2_KiHIGH_He.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911101
title: SourceData_Final

description: Source data for "Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands" by Cornblath et al.
DOI: 10.6084/m9.figshare.11911104.v1
title: OCIM2_CTL_He.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911122.v1
title: OCIM2_CTL_noHe.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911104.v2
title: OCIM2_CTL_He.bson

description: See AIBECS.jl<br>
DOI: 10.6084/m9.figshare.11911017
title: NT_Plants_Verts_SSI_rcp26_mean.tif

description: Spatial prioritization for plants and vertebrates in the Neotropics under climate change. Values in each prioritization dataset are 0 to 1 representing the rank order of each cell (1 = highest priority) scaled by the total number of cells in the solution. Prioritizations were produced with Zonation conservation planning software v4.0.0 with setting to optimize planning for climate change (see methods description in Hannah <i>et al</i>. 2020 <i>Ecography </i>https://doi.org/10.1111/ecog.05166). Existing protected areas as defined by the World Database of Protected Areas and areas of intensive agriculture or urban land use were excluded from the prioritization and appear as NoData.
DOI: 10.6084/m9.figshare.11911026
title: NT_Plants_Verts_SSI_rcp85_mean.tif

description: Spatial prioritization for plants and vertebrates in the Neotropics under climate change (RCP 8.5). Values in each prioritization dataset are 0 to 1 representing the rank order of each cell (1 = highest priority) scaled by the total number of cells in the solution. Prioritizations were produced with Zonation conservation planning software v4.0.0 with setting to optimize planning for climate change (see methods description in Hannah <i>et al</i>. 2020 <i>Ecography </i>https://doi.org/10.1111/ecog.05166). Existing protected areas as defined by the World Database of Protected Areas and areas of intensive agriculture or urban land use were excluded from the prioritization and appear as NoData.
DOI: 10.6084/m9.figshare.11911005
title: AT_Plants_Verts_SSI_rcp26_mean.tif

description: Spatial prioritization for plants and vertebrates in the Afrotropics under climate change (RCP 2.6). Values in each prioritization dataset are 0 to 1 representing the rank order of each cell (1 = highest priority) scaled by the total number of cells in the solution. Prioritizations were produced with Zonation conservation planning software v4.0.0 with setting to optimize planning for climate change (see methods description in Hannah <i>et al</i>. 2020 <i>Ecography </i>https://doi.org/10.1111/ecog.05166). Existing protected areas as defined by the World Database of Protected Areas and areas of intensive agriculture or urban land use were excluded from the prioritization and appear as NoData
DOI: 10.6084/m9.figshare.11911008
title: IM_Plants_Verts_SSI_rcp26_mean.tif

description: Spatial prioritization for plants and vertebrates in the Asia tropics under climate change (RCP 2.6). Values in each prioritization dataset are 0 to 1 representing the rank order of each cell (1 = highest priority) scaled by the total number of cells in the solution. Prioritizations were produced with Zonation conservation planning software v4.0.0 with setting to optimize planning for climate change (see methods description in Hannah <i>et al</i>. 2020 <i>Ecography </i>https://doi.org/10.1111/ecog.05166). Existing protected areas as defined by the World Database of Protected Areas and areas of intensive agriculture or urban land use were excluded from the prioritization and appear as NoData.
DOI: 10.6084/m9.figshare.11911014
title: Dataset for the paper Earnings Distributions of Scalable vs. Non-Scalable OccupationsDataset for the paper Income distribution of scalable vs. non-scalable occupations

description: Dataset for the paper Income distribution of scalable vs. non-scalable occupations
DOI: 10.6084/m9.figshare.11911002
title: AT_Plants_Verts_SSI_rcp85_mean.tif

description: Spatial prioritization for plants and vertebrates in the Afrotropics under climate change (RCP 8.5). Values in each prioritization dataset are 0 to 1 representing the rank order of each cell (1 = highest priority) scaled by the total number of cells in the solution. Prioritizations were produced with Zonation conservation planning software v4.0.0 with setting to optimize planning for climate change (see methods description in Hannah <i>et al</i>. 2020 <i>Ecography </i>https://doi.org/10.1111/ecog.05166). Existing protected areas as defined by the World Database of Protected Areas and areas of intensive agriculture or urban land use were excluded from the prioritization and appear as NoData
DOI: 10.6084/m9.figshare.11910996
title: IM_Plants_Verts_SSI_rcp85_mean.tif

description: Spatial prioritization for plants and vertebrates in the Asia tropics under climate change (RCP 8.5). Values in each prioritization dataset are 0 to 1 representing the rank order of each cell (1 = highest priority) scaled by the total number of cells in the solution. Prioritizations were produced with Zonation conservation planning software v4.0.0 with setting to optimize planning for climate change (see methods description in Hannah <i>et al</i>. 2020 <i>Ecography </i>https://doi.org/10.1111/ecog.05166). Existing protected areas as defined by the World Database of Protected Areas and areas of intensive agriculture or urban land use were excluded from the prioritization and appear as NoData.
DOI: 10.6084/m9.figshare.11910819
title: Supplementary Data 3.xlsx

description: Metabolites in wild-type and gpmA mutant salmonella after peroxide treatment<br>
DOI: 10.6084/m9.figshare.11910819.v1
title: Supplementary Data 3.xlsx

description: Metabolites in wild-type and gpmA mutant salmonella after peroxide treatment<br>
DOI: 10.6084/m9.figshare.11910798
title: Supplementary Data 2

description: transposons of Salmonella under selection after H2O2 treatment<br>
DOI: 10.6084/m9.figshare.11910798.v1
title: Supplementary Data 2

description: transposons of Salmonella under selection after H2O2 treatment<br>
DOI: 10.6084/m9.figshare.11910774
title: Chakraborty Table S1.xlsx

description: Tn5 Salmonella mutants differentially selected after 2h of treatment with 2.5 mM peroxide
DOI: 10.6084/m9.figshare.11910405
title: Cuyama climate 2016-2017

description: Data from California Irrigation Management Information System weather station #88. Years compiled to support concurrent ecological research projects.Lat 34.942525Long -119.6738
DOI: 10.6084/m9.figshare.11910405.v1
title: Cuyama climate 2016-2017

description: Data from California Irrigation Management Information System weather station #88. Years compiled to support concurrent ecological research projects.Lat 34.942525Long -119.6738
DOI: 10.6084/m9.figshare.11909781
title: Panoche climate 2014-2019

description: Data from California Irrigation Management Information System weather station #56. Years compiled to support concurrent ecological research projects.Lat 37.096694Long -120.7539
DOI: 10.6084/m9.figshare.11908419
title: Is Any Job Better than No Job? Life Satisfaction and Re-employment

description: Estimation of job transitions between good jobs and bad jobs.
DOI: 10.6084/m9.figshare.11908422
title: Data S13. Binary Conversion

description: R script for binary conversion from raw outputs of Habitat Suitability Modelling (updated from version in Chiarenza et al. 2019)
DOI: 10.6084/m9.figshare.11908419.v1
title: Is Any Job Better than No Job? Life Satisfaction and Re-employment

description: Estimation of job transitions between good jobs and bad jobs.
DOI: 10.6084/m9.figshare.11908422.v1
title: Data S13. Binary Conversion

description: R script for binary conversion from raw outputs of Habitat Suitability Modelling (updated from version in Chiarenza et al. 2019)
DOI: 10.6084/m9.figshare.11908407
title: Data S11 - Original MaxEnt outputs

description: Original MaxEnt outputs for single clade Habitat Suitability Models in MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908416
title: Data S12 - Ascii outputs from arcgis (combined from MaxEnt)

description: Scenarios-averaged MaxEnt outputs from single clade Habitat Suitability Modelling results in MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908416.v1
title: Data S12 - Ascii outputs from arcgis (combined from MaxEnt)

description: Scenarios-averaged MaxEnt outputs from single clade Habitat Suitability Modelling results in MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908407.v1
title: Data S11 - Original MaxEnt outputs

description: Original MaxEnt outputs for single clade Habitat Suitability Models in MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908374
title: Data S10 - Convex hull_bias_file_for_maxent

description: Bias file based convex hull background selection for MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908365
title: Data S8. Raw Ensemble results for single clade Habitat Suitability Modelling for each scenario

description: Raw Ensemble results for single clade Habitat Suitability Modelling for each scenario
DOI: 10.6084/m9.figshare.11908368
title: Data S9. Fossil collections_bias_file_for_maxent

description: Bias file based on fossil collection localities for MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908368.v1
title: Data S9. Fossil collections_bias_file_for_maxent

description: Bias file based on fossil collection localities for MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908374.v1
title: Data S10 - Convex hull_bias_file_for_maxent

description: Bias file based convex hull background selection for MaxEnt (java platform version 3.4.1) analysis
DOI: 10.6084/m9.figshare.11908365.v1
title: Data S8. Raw Ensemble results for single clade Habitat Suitability Modelling for each scenario

description: Raw Ensemble results for single clade Habitat Suitability Modelling for each scenario
DOI: 10.6084/m9.figshare.11908311
title: Data S7. Raw Ensemble output results for all clades from Ensemble Habitat Suitability Modelling

description: Raw Ensemble output results for all clades from Ensemble Habitat Suitability Modelling
DOI: 10.6084/m9.figshare.11908266
title: Data S5. Ensemble results figures for each clade

description: Ensemble results for single clade Habitat Suitability Modelling for each scenario.<br>
DOI: 10.6084/m9.figshare.11908305
title: Data S6. Ensemble averaged results for all clades from Ensemble

description: Scenarios averaged results for all clades from Ensemble Habitat Suitability Modelling.<br>
DOI: 10.6084/m9.figshare.11908311.v1
title: Data S7. Raw Ensemble output results for all clades from Ensemble Habitat Suitability Modelling

description: Raw Ensemble output results for all clades from Ensemble Habitat Suitability Modelling
DOI: 10.6084/m9.figshare.11908266.v1
title: Data S5. Ensemble results figures for each clade

description: Ensemble results for single clade Habitat Suitability Modelling for each scenario.<br>
DOI: 10.6084/m9.figshare.11908305.v1
title: Data S6. Ensemble averaged results for all clades from Ensemble

description: Scenarios averaged results for all clades from Ensemble Habitat Suitability Modelling.<br>
DOI: 10.6084/m9.figshare.11908227
title: Data S3. Environmental variables for extinction scenarios

description: Environmental variables for control and extinction scenarios<br>
DOI: 10.6084/m9.figshare.11908203
title: Data S2. Dinosauria dataset for MaxEnt

description: Data S2. Dinosauria dataset for MaxEnt
DOI: 10.6084/m9.figshare.11908179
title: Data S1. Dinosauria dataset - Supp Data S1_ biomod formatted occurrences

description: Data S1. Dinosauria dataset - Supp Data S1_ biomod formatted occurrences
DOI: 10.6084/m9.figshare.11908203.v1
title: Data S2. Dinosauria dataset for MaxEnt

description: Data S2. Dinosauria dataset for MaxEnt
DOI: 10.6084/m9.figshare.11908170
title: Taxonomic and phylogenetic composition show biotic resistance to exotic invasion in acid seep springs

description: This is the updated set of files for Barfknecht and Gibson's acid seep spring research. Is includes files involved in an updated analysis looking at species abundance data from 2017.
DOI: 10.6084/m9.figshare.11908170.v1
title: Taxonomic and phylogenetic composition show biotic resistance to exotic invasion in acid seep springs

description: This is the updated set of files for Barfknecht and Gibson's acid seep spring research. Is includes files involved in an updated analysis looking at species abundance data from 2017.
DOI: 10.6084/m9.figshare.11908131
title: R and data file archive for Unexpected Diversity Roots, Merten Ehmig & Peter Linder 2020, NewPhytol

description: Datasets and R files used for the publication: <b>Unexpected diversity and evolutionary lability in root architectural ecomorphs in the rushes of the hyperdiverse Cape flora</b>by Merten Ehmig, H Peter Linder,published in New Phytologist, accepted 19 Feb. 2020<br>
DOI: 10.6084/m9.figshare.11908131.v1
title: R and data file archive for Unexpected Diversity Roots, Merten Ehmig & Peter Linder 2020, NewPhytol

description: Datasets and R files used for the publication: <b>Unexpected diversity and evolutionary lability in root architectural ecomorphs in the rushes of the hyperdiverse Cape flora</b>by Merten Ehmig, H Peter Linder,published in New Phytologist, accepted 19 Feb. 2020<br>
DOI: 10.6084/m9.figshare.11908008
title: Supplemental Material, The Asymmetric Brazilian Input-Output Network

description: Supplemental material for the paper The asymmetric Brazilian input-output network
DOI: 10.6084/m9.figshare.11908008.v1
title: Supplemental Material, The Asymmetric Brazilian Input-Output Network

description: Supplemental material for the paper The asymmetric Brazilian input-output network
DOI: 10.6084/m9.figshare.11907987
title: Primary research papers using androstadienone and/or estratetraenol from Reproducible research into human chemical communication by cues and pheromones: learning from psychology's renaissance

description: An Excel spreadsheet with a list of primary research articles reporting experiments using androstadienone and/or estratetraenol as the stimuli given to human subjects.
DOI: 10.6084/m9.figshare.11907984
title: DISTRIBUTION AND HABITABILITY OF (META)STABLE BRINES ON PRESENT-DAY MARS.

description: Figures and accompanying data for Rivera-Valentin et al. (2020) Nature Astronomy, doi: 10.1038/s41550-020-1080-9. All data is provided as .mat files, which are in the binary data container format that the MATLAB program uses. This file type is also readable by Python. The data files includes '_arrays', which contain the information for the x and y axis of the figures, and '_matrix', which contains the information for the z-axis component of the figures, which are shown in color. <br>%%%%%%%%%%%%%%%%%%%%%%%%%<br>%%% Figure1.mat%%%%%%%%%%%%%%%%%%%%%%%%%<br>This file includes:Temperature_array = The information for the x-axis. Temperature is in units of Kelvin. This array contains 100 values. WaterVaporPressure_array = The information for the y-axis. Water vapor pressure is in units of Pascals. This array contains 100 values. WaterActivity_matrix = The information for the x axis. This matrix is a 100 x 100 matrix. Rows correspond to water vapor pressure and columns to temperature. To view this figure in Matlab:[XX, YY] = meshgrid(Temperature_array, WaterVaporPressure_array);surf(XX, YY, WaterActivity_matrix)view(0, 90)shading interp<br>%%%%%%%%%%%%%%%%%%%%%%%%%<br>%%% Figure1ED.mat%%%%%%%%%%%%%%%%%%%%%%%%%This file includes:Temperature_array = The information for the x-axis. Temperature is in units of Kelvin. This array contains 151 values.WaterVaporPressure_array = The information for the y-axis. Water vapor pressure is in units of Pascals. This array contains 11 values.<br>logfrequency_matrix = The information for the z-axis. This is a 11x151 maxtrix. Values are log10 of total amount of hours the specific water vapor pressure and temperature combination occurs on Mars, as simulated by MarsWRF using a 5°x5° map. <br>[XX, YY] = meshgrid(Temperature_array, WaterVaporPressure_array);surf(XX, YY, WaterActivity_matrix)view(0, 90)shading interp<br>%%%%%%%%%%%%%%%%%%%%%%%%%<br>%%% All other .mat files%%%%%%%%%%%%%%%%%%%%%%%%%All other .mat files, for Figures 2, 3, and Extended Data Figure 4, include:Latitude_array = Information for the y-axis. Latitude is from -60° to 60°. This is an array with 25 values. Longitude_array = Information for the x-axis. Longitude is from -180° to 180°. This is an array with 72 values. Data_Matrix = Information for the z-axis, which in the Figures are shown in color. These matrices are 25x72 with columns corresponding to longitude and rows to latitude. To view the data in Matlab:[XX, YY] = meshgrid(Longitude_array, Latitude_array);surf(XX, YY, Data_Matrix)shading interpview(0, 90)<br><br>