cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5417547
title: Reproduction data and code for "The social cost of carbon dioxide under climate-economy feedbacks and temperature variability"

description: ### Data for Kikstra et al. 2021, ERL, The social cost of carbon dioxide under climate-economy feedbacks and temperature variability This repository contains data and scripts for the main text figures in the article Kikstra, J.S., Waidelich, P., Rising J., Yumashev, D., Hope, C., Brierley, C.M. (2021) The social cost of carbon dioxide under climate-economy feedbacks and temperature variability, Environmental Research Letters. DOI https://doi.org/10.1088/1748-9326/ac1d0b ##### Authors<br> - Jarmo S. Kikstra<br> - Paul Waidelich<br> - James Rising<br> - Dmitry Yumashev<br> - Chris Hope<br> - Chris M. Brierly Correspondence: kikstra@iiasa.ac.at ##### Data repository structure<br> - data # hosts all data that is used to produce the main text figures<br> - growth-effects # hosts model data for results from model runs with persistent damages, for figure 3 and figure 4<br> - PAGE-ANN-Growth_ModelResults_PersistenceDistribution # annual PAGE version, empirical persistence distribution<br> - PAGE-arVAR-Growth_ModelResults_PersistenceDistribution # annual PAGE version with temperature variability, empirical persistence distribution<br> - PAGE-Growth_ModelResults_FixedPersistence # main version, with 10 time steps, fixed persistence levels<br> - PAGE-Growth_ModelResults_LowPassFilterPersistenceDistribution # main version, with 10 time steps, low pass filter persistence distribution<br> - PAGE-Growth_ModelResults_PersistenceDistribution # main version, with 10 time steps, empirical persistence distribution<br> - page-ice # model data for results from PAGE-ICE base model runs<br> - scco2-montecarlo # for figure 6 data<br> - update-page # for figure 2 data<br> - variability # for figure 5 data<br> - extended data # hosts other data used in the manuscript or which we simply want to publish alongside. Example for naming convention for each variable in […] behind the files.<br> - economic-impact # model output variables “EquityWeighting_ te_totaleffect{_ann_yr}.csv” [PAGE-ICE_SSP1-1.9_discountedimpacts.csv]<br> - montecarlo-draws # model output variable “trialdata.csv” [PAGE-ICE_SSP1-1.9_trialdata.csv]<br> - sensitivity # hosts selected sensitivity analysis data used in the appendix<br> - temperature # model output variable “ClimateTemperature_rt_g_globaltemperature.csv” [PAGE-ICE_SSP1-1.9_globaltemperature.csv]<br> - scripts # has one script for each main text figure,<br> - figures # hosts main text figures as they come out of the scripts<br> - README.md # explanation for the repository
DOI: 10.5281/zenodo.5116563
title: AWASH Water Stress Metrics

description: This data repository contains county-level water stress results from the analysis performed in "The importance of infrastructure and national demand to represent constraints on water supply in the United States". The largemaps.pdf file contains the figures from the paper in a larger format. summary.csv shows for each county the median water stress (across years) as well as the range of water stress, for each of the four scenarios (local runoff, river network only, with canals, and with canals and reservoirs).
DOI: 10.5281/zenodo.3249468
title: The Small World of Global Marine Fisheries: The Cross-Boundary Consequences of Larval Dispersal

description: This dataset contains the key intermediate inputs and tabular results generated as part of the paper "The Small World of Global Marine Fisheries". The main directories are as follows: - transitions: Matrices describing the probability of particle transitions. - spawn: Collated information on spawning locations and larval dynamics. - spawn-transits: Species-level transition probabilities. - weights: Collated information on EEZs and on sovereign regions. - atrisk: Import and export flows, and corresponding risk factors. - economics: Data files to support economic calculations - saudata: Data from Sea Around Us. - shapefiles: Geospatial information.
DOI: 10.5281/zenodo.2636744
title: The Small World of Global Marine Fisheries: The Cross-Boundary Consequences of Larval Dispersal

description: This dataset contains the key intermediate inputs and tabular results generated as part of the paper "The Small World of Global Marine Fisheries". The main directories are as follows: - transitions: Matrices describing the probability of particle transitions. - spawn: Collated information on spawning locations and larval dynamics. - spawn-transits: Species-level transition probabilities. - weights: Collated information on EEZs and on sovereign regions. - atrisk: Import and export flows, and corresponding risk factors. - economics: Data files to support economic calculations - saudata: Data from Sea Around Us. - shapefiles: Geospatial information.
DOI: 10.5281/zenodo.5557838
title: Watkins RenSeq assemblies 4

description: RenSeq assemblies of wheat lines including Watkins landraces generated using CLC assembler.
DOI: 10.5281/zenodo.809469
title: US Water Network Observed and Inferred Flows

description: This dataset provides a synthesized national record of river gauges. It includes inferred flows which are based on the structure of the river network and empirical flow relationships. In total, the network contains 22619 gauges (or virtual junction gauges) and over 1 million years of monthly data. The dataset is provide as a saved data file for R, flowdata.RData, containing two variables: nodes and allflow. `nodes` describes each gauges or virtual junction gauge in the water flow network, representing a combination of gauges from multiple sources. The source is specified by the `collection` column, as follows: `rivdis`: From the RivDIS dataset, at http://iridl.ldeo.columbia.edu/SOURCES/.UNH/.CSRC/.RivDIS/index.html?Set-Language=en `usgs`: GAGES II river gauge `reservoir`: National Inventory of Dams reservoir `usgsres`: USGS gauged reservoir `canal`: Canal cross county boundaries `junction`: Junction node added to capture the structure of rivers from HydroSHEDS The `colid` specifies the ID of the gauge within its collection, and combined `collection` and `colid` are unique. The location of the gauge is specified by the latitude `lat` and longitude `lon`. The elevation is provided in meters in column `elev`. The flow data is contained in the `allflow` matrix. This matrix has a column for each node (as many columns as there are rows in `nodes)`, and a row for each month starting from Jan. 1915. All the flows are in units of m^3 / s.
DOI: 10.5281/zenodo.809468
title: US Water Network Observed and Inferred Flows

description: This dataset provides a synthesized national record of river gauges. It includes inferred flows which are based on the structure of the river network and empirical flow relationships. In total, the network contains 22619 gauges (or virtual junction gauges) and over 1 million years of monthly data. The dataset is provide as a saved data file for R, flowdata.RData, containing two variables: nodes and allflow. `nodes` describes each gauges or virtual junction gauge in the water flow network, representing a combination of gauges from multiple sources. The source is specified by the `collection` column, as follows: `rivdis`: From the RivDIS dataset, at http://iridl.ldeo.columbia.edu/SOURCES/.UNH/.CSRC/.RivDIS/index.html?Set-Language=en `usgs`: GAGES II river gauge `reservoir`: National Inventory of Dams reservoir `usgsres`: USGS gauged reservoir `canal`: Canal cross county boundaries `junction`: Junction node added to capture the structure of rivers from HydroSHEDS The `colid` specifies the ID of the gauge within its collection, and combined `collection` and `colid` are unique. The location of the gauge is specified by the latitude `lat` and longitude `lon`. The elevation is provided in meters in column `elev`. The flow data is contained in the `allflow` matrix. This matrix has a column for each node (as many columns as there are rows in `nodes)`, and a row for each month starting from Jan. 1915. All the flows are in units of m^3 / s.
DOI: 10.5281/zenodo.834755
title: RAM Legacy Stock Assessment Database Geospatial Regions

description: This data archive describes region definitions for the RAM Legacy Stock Assessment Database. Within the RAM Legacy database, stock assessments are associated with named areas. We approximate coordinates and bounding boxes for each of these areas, using country EEZs and fishing area shapefiles when appropriate. In addition, we develop a simple language to encode the GIS shapes of the areas, along with an interpreter to translate these codes into polygons. The syntax supports using political entities, shapefile regions, circles and rectangles, clipped versions of these, and combinations of these. The archive contains the following contents: - syntax.pdf: This document describes the geocoding syntax, and lists all of the geocoding descriptions for the assessment regions. - results: This folder contains a shapefile of assessment regions (ram.shp) and a summary file of each region's centroid and size. - sources: This folder contains shapefiles for FAO regions and New Zealand fishing regions, used by the syntax system, and latlon.csv which contains the region descriptions for each assessment region. - code: load_areas.R contains functions that interpret the geocoding syntax and genshape.R generates the ram.shp shapefile.
DOI: 10.5281/zenodo.834754
title: RAM Legacy Stock Assessment Database Geospatial Regions

description: This data archive describes region definitions for the RAM Legacy Stock Assessment Database. Within the RAM Legacy database, stock assessments are associated with named areas. We approximate coordinates and bounding boxes for each of these areas, using country EEZs and fishing area shapefiles when appropriate. In addition, we develop a simple language to encode the GIS shapes of the areas, along with an interpreter to translate these codes into polygons. The syntax supports using political entities, shapefile regions, circles and rectangles, clipped versions of these, and combinations of these. The archive contains the following contents: - syntax.pdf: This document describes the geocoding syntax, and lists all of the geocoding descriptions for the assessment regions. - results: This folder contains a shapefile of assessment regions (ram.shp) and a summary file of each region's centroid and size. - sources: This folder contains shapefiles for FAO regions and New Zealand fishing regions, used by the syntax system, and latlon.csv which contains the region descriptions for each assessment region. - code: load_areas.R contains functions that interpret the geocoding syntax and genshape.R generates the ram.shp shapefile.
DOI: 10.5281/zenodo.1134020
title: Travel Times by Transportation Mode in Nairobi, Kenya

description: This directory contains data files that describe travel times in<br> Nairobi, Kenya. Three different modes of transportation are included: - Walking, according to MapQuest and filled in with Google Maps where<br> MapQuest fails to find a route. - Driving, under normal traffic conditions. - Matatus, the informal public transportation network. The travel times are computed from a gridded origin location to a<br> collection of gridded destination locations. In the directories, each<br> file contains destination location times for a given origin location.<br> These are titled &lt;mode&gt;-###.csv. The CSV is organized from north to<br> south, along rows, and west to east, along columns. An additional<br> &lt;mode&gt;.csv file in each directory gives the organization of the<br> numbered files, again from north to south, along rows, and west to<br> east, along columns. There are also "extended" versions of each grid, with larger<br> destination grids for each origin point, formed by "daisy-chaining"<br> together the original destination grids. The directory also contains source files for working with the data: - calc_access.R shows how to calculate a simple mobility measure,<br> using the functions in tools_calc_access.R - tools_analyze.R are a more general set of functions for performing<br> arbitrary callback-based operations on the travel time data. - tools_calc_access.R contains underlying functions for setting up<br> access to the grids and calculating access. - tools_map.R contains the grid origin and destination definitions<br> and functions for plotting.
DOI: 10.5281/zenodo.1134019
title: Travel Times by Transportation Mode in Nairobi, Kenya

description: This directory contains data files that describe travel times in<br> Nairobi, Kenya. Three different modes of transportation are included: - Walking, according to MapQuest and filled in with Google Maps where<br> MapQuest fails to find a route. - Driving, under normal traffic conditions. - Matatus, the informal public transportation network. The travel times are computed from a gridded origin location to a<br> collection of gridded destination locations. In the directories, each<br> file contains destination location times for a given origin location.<br> These are titled &lt;mode&gt;-###.csv. The CSV is organized from north to<br> south, along rows, and west to east, along columns. An additional<br> &lt;mode&gt;.csv file in each directory gives the organization of the<br> numbered files, again from north to south, along rows, and west to<br> east, along columns. There are also "extended" versions of each grid, with larger<br> destination grids for each origin point, formed by "daisy-chaining"<br> together the original destination grids. The directory also contains source files for working with the data: - calc_access.R shows how to calculate a simple mobility measure,<br> using the functions in tools_calc_access.R - tools_analyze.R are a more general set of functions for performing<br> arbitrary callback-based operations on the travel time data. - tools_calc_access.R contains underlying functions for setting up<br> access to the grids and calculating access. - tools_map.R contains the grid origin and destination definitions<br> and functions for plotting.
DOI: 10.5281/zenodo.3889144
title: Data produced for "Crop switching reduces agricultural losses from climate change in the United States by half"

description: This data archive includes all of the results from the models used to<br> produce the paper "Crop switching reduces agricultural losses from<br> climate change in the United States by half". It contains three main<br> archives: 1. inputs: The temperature and water stress indicators for each crop,<br> along with county-level log-yields. Both the Bayesian and OLS<br> models are fit to this data. Static covariates are available in<br> us-bioclims-new.csv.<br> <br> Files:<br> <br> data-inputs-futureedds1.zip - data-inputs-futureedds3.zip:<br> GCM-specific quantifications of the exceedance degree days used as<br> temperature predictors.<br> <br> data-inputs-other.zip: All other input files. 2. bayes: Each of the variables fit in the Bayesian model, for each<br> MCMC draw from the posterior distribution. The contained directory<br> includes for each crop versions with constant variance (-variance)<br> and under cross-validation (-cv). The counties are ordered<br> according to fips-usa.csv. Files: data-bayes-checks: Posterior predictive check outputs. data-bayes-full-constvar-*.zip: MCMC draws for the all-years fit<br> assuming constant variance. Divided to make the files more<br> manageable.<br> <br> data-bayes-full-varvar.zip: MCMC draws for the all-years fit<br> assuming county-specific variances. data-bayes-cv-constvar.zip: MCMC draws for the cross-validation fit<br> assuming constant variance. data-bayes-cv-varvar-*.zip: MCMC draws for the cross-validation fit<br> assuming county-specific variances. Divided to make the files more<br> manageable. 3. optim: Optimization model results. The results/ directory contains<br> the optimization results for each set of assumptions presented in<br> the appendix, applied to the average yield levels. The results-mc/<br> directory contains profit, yield, and optimization results for each<br> draw of the MCMC independently. Files:<br> <br> data-optim-inputs-mc.zip: Locally optimal cropping files, under<br> MCMC draws of the model.<br> <br> data-optim-results-mc.zip: Constrainted optimization model results,<br> under MCMC draws of the model.<br> <br> data-optim-results.zip: Constrained optimization model results,<br> using the average parameters of the model.<br> <br> data-optim-other.zip: Other outputs of the optimization process.<br>
DOI: 10.5281/zenodo.3544653
title: Data produced for "Crop switching reduces agricultural losses from climate change in the United States by half"

description: This data archive includes all of the results from the models used to<br> produce the paper "Crop switching reduces agricultural losses from<br> climate change in the United States by half". It contains three main<br> archives: 1. inputs: The temperature and water stress indicators for each crop,<br> along with county-level log-yields. Both the Bayesian and OLS<br> models are fit to this data. Static covariates are available in<br> us-bioclims-new.csv.<br> <br> Files:<br> <br> data-inputs-futureedds1.zip - data-inputs-futureedds3.zip:<br> GCM-specific quantifications of the exceedance degree days used as<br> temperature predictors.<br> <br> data-inputs-other.zip: All other input files. 2. bayes: Each of the variables fit in the Bayesian model, for each<br> MCMC draw from the posterior distribution. The contained directory<br> includes for each crop versions with constant variance (-variance)<br> and under cross-validation (-cv). The counties are ordered<br> according to fips-usa.csv. Files: data-bayes-checks: Posterior predictive check outputs. data-bayes-full-constvar-*.zip: MCMC draws for the all-years fit<br> assuming constant variance. Divided to make the files more<br> manageable.<br> <br> data-bayes-full-varvar.zip: MCMC draws for the all-years fit<br> assuming county-specific variances. data-bayes-cv-constvar.zip: MCMC draws for the cross-validation fit<br> assuming constant variance. data-bayes-cv-varvar-*.zip: MCMC draws for the cross-validation fit<br> assuming county-specific variances. Divided to make the files more<br> manageable. 3. optim: Optimization model results. The results/ directory contains<br> the optimization results for each set of assumptions presented in<br> the appendix, applied to the average yield levels. The results-mc/<br> directory contains profit, yield, and optimization results for each<br> draw of the MCMC independently. Files:<br> <br> data-optim-inputs-mc.zip: Locally optimal cropping files, under<br> MCMC draws of the model.<br> <br> data-optim-results-mc.zip: Constrainted optimization model results,<br> under MCMC draws of the model.<br> <br> data-optim-results.zip: Constrained optimization model results,<br> using the average parameters of the model.<br> <br> data-optim-other.zip: Other outputs of the optimization process.<br>
DOI: 10.5281/zenodo.5154106
title: Common Inputs for the CIL Projection System

description: This data repository provides basic input data for the projection system, https://gitlab.com/ClimateImpactLab/Impacts/impact-calculations/ During the installation process for the projection system, this data should be stored in the "shared directory", and the system pointed to it. The repository contains the following directories: - climate: Example climate data files (only data for the year 2000 of CCSM4 is included).<br> - regions: Input files specifying the structure of impact regions.<br> - social: Socioeconomic data used as inputs to the projection system. This repository was prepared by the Climate Impact Lab `socioeconomics/sharing/make_archive.sh` script.
DOI: 10.5281/zenodo.5150989
title: Common Inputs for the CIL Projection System

description: This data repository provides basic input data for the projection system, https://gitlab.com/ClimateImpactLab/Impacts/impact-calculations/ During the installation process for the projection system, this data should be stored in the "shared directory", and the system pointed to it. The repository contains the following directories: - climate: Example climate data files (only data for the year 2000 of CCSM4 is included).<br> - regions: Input files specifying the structure of impact regions.<br> - social: Socioeconomic data used as inputs to the projection system. This repository was prepared by the Climate Impact Lab `socioeconomics/sharing/make_archive.sh` script.
DOI: 10.25934/pr00007440
title: 20211008d_PRODSmoketest_RB

description: q
DOI: 10.6084/m9.figshare.16637008.v21
title: hCoV-19 / North America / USA / Florida

description: Variant Submissions - Mu GH (B.1.621 + B.1.621.1) North America / USA / FloridaFor Dates:20SEP2021 21SEP202122SEP2021<br>23SEP2021 A.M. & P.M06OCT2021<br>Variant Submissions - Delta GK/478K.V1 (B.1.617.2+AY.x) North America / USA / FloridaFor Dates:23SEP2021 - 28SEP202129SEP2021 - 01OCT2021 02OCT2021 - 05OCT202106OCT2021 - 07OCT2021REFERENCE: GISAID. I would like to Gratefully acknowledge all data contributors, i.e. the Authors, the Originating laboratories responsible for obtaining the specimens, and the Submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based.<br>
DOI: 10.6084/m9.figshare.16777786.v1
title: Angiotensin II.pzfx

description: Angiotensin - ELISA
DOI: 10.25824/redu/o7ljc5
title: Resultados IVC e Kappa - 2 Rodada.tab

description: Resultados IVC e Kappa - 2 Rodada
DOI: 10.25824/redu/irjvck
title: Resultados IVC e Kappa - 1 Rodada.tab

description: Resultados IVC e Kappa - 1 Rodada
DOI: 10.25824/redu/u0hzuf
title: RDBCI.tab
DOI: 10.25824/redu/oqpmez
title: Resultados IVC e Kappa.xlsx
DOI: 10.25824/redu/i8bn5z
title: Adaptação e validação do inventario para evaluación de competencias en enfermeras de práctica avanzada

description: Banco de dados referente à avaliação das equivalências semântica, idiomática, cultural, conceitual, bem como clareza e relevância, por um Comitê de Especialistas, na validação de conteúdo da versão adaptada do IECEPA para a cultura brasileira.
DOI: 10.6084/m9.figshare.16777954
title: How Emotional Intelligence Influences Study Habits among Students in Blended Learning Environments during COVID-19: The Mediating Role of Cognitive Engagement

description: data set <br>
DOI: 10.6084/m9.figshare.16777786.v2
title: Angiotensin II.pzfx

description: Angiotensin - ELISA
DOI: 10.5281/zenodo.5555932
title: JASPAR TFBS LOLA databases - Part 1

description: This repository contains the first part of the JASPAR 2022 LOLA databases used by the JASPAR TFBS enrichment tool. For each organism, we provide the LOLA databases for all JASPAR 2022 TFBS sets as compressed directories containing a set of .RDS R objects. Databases are organised by genome assembly. Due to file sizes, we had to split the repository into two different parts. Part 2 of the repository containing the databases for human can be found here.
DOI: 10.6084/m9.figshare.16777786.v3
title: DATA ARTICLE

description: Angiotensin - ELISATubular measurementsIntertubular measurementsLicenceCountsGene expressions
DOI: 10.6084/m9.figshare.16766893
title: Figure6_ephys_data.mat

description: ephys dataset displayed in figure 6
DOI: 10.6084/m9.figshare.16766893.v1
title: Figure6_ephys_data.mat

description: ephys dataset displayed in figure 6
DOI: 10.6084/m9.figshare.16777963
title: SuppFigure6_ephys_data.mat

description: Ephys data shown in supp figure 6
DOI: 10.6084/m9.figshare.16777963.v1
title: SuppFigure6_ephys_data.mat

description: Ephys data shown in supp figure 6
DOI: 10.6084/m9.figshare.16777786.v4
title: DATA ARTICLE - GMJC

description: Angiotensin - ELISATubular measurementsIntertubular measurementsLicenceCountsGene expressions