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477 results for “input data”
Input data for the Resources for the Future Socioeconomic Projections (RFF-SPs)
<p>This repository contains large input data files for generating the RFF-SPs, and small accompanying files with data necessary for interpreting the larger files.</p> <p>--------------------------------------------------------------------------------<br> /MSW_GDP/<br> --------------------------------------------------------------------------------</p> <p>-MSW.mat is a MATLAB data file based on Müller, Stock, and Watson (forthcoming) containing two series:<br> 1) Year-by-sample draws "F-paths" representing frontier values of ln(GDP/capita) for the OECD, and <br> 2) Year-by-country-by-sample deviations from those F-paths, denoted as the U-path.</p> <p>A country's ln(GDP/capita) is equal to the sum of the sample's F-path and the country-sample's U-path. There are 418 years representing 1900-2317, 113 countries with names in country_names.txt, and 2000 sample trajectories. GDP/capita is measured in 2011$.</p> <p>-country_names.txt contain iso codes for the 113 countries in the order in which they appear in MSW.mat's U-path object. OECD countries are indicated with an "x" for reference.</p> <p>This information was transmitted via email from Ulrich Mueller (Princeton) to Kevin Rennert (RFF) on January 28, 2020, with a link to the file on Mueller's website at http://www.princeton.edu/~umueller/matlabJan2020.mat.</p> <p>--------------------------------------------------------------------------------<br> /Raftery population/<br> --------------------------------------------------------------------------------</p> <p>-pop_trajectories_probmig.csv contains country-by-year-by-sample (here, a sample is labelled a "Trajectory") population estimates based on Raftery and Ševčíková (2021). Population is in units of thousands of people, with 1000 samples. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on August 11, 2021.</p> <p>-death_rates.csv contains country-by-year-by-sample death rates in units of annual deaths per one thousand people, with 1000 samples corresponding to the sample order of samples in pop_trajectories_probmig.csv. This data was transmitted via email by Hana Sevcikova (University of Washington) to Kevin Rennert (RFF) on October 7, 2021.</p> <p>-iso_key.csv contains a key mapping ISO alpha-3 country codes ("ISO3") to ISO numeric-3 country codes (denoted "NumericCode" in this file and "LocID" in pop_trajectories_probmig.csv). This data was acquired from a search on https://www.iso.org/obp/ui#search in 2021. The "country codes" box was checked and no keywords were used. The list was trimmed to the 184 countries included in the current version of the GIVE model.</p> <p>--------------------------------------------------------------------------------<br> Information<br> --------------------------------------------------------------------------------</p> <p>The probabilistic economic and emissions projections are from Müller, Stock, and Watson (forthcoming). </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>--------------------------------------------------------------------------------<br> References <br> --------------------------------------------------------------------------------</p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: https://www.sciencedirect.com/science/article/pii/S0169207021001394 </p>
DEMSI v0.0.1 input data
<p>DEMSI v0.0.1 test case data set</p>
input files for " Martini bead form factors for nucleic-acids and their application in the refinement of protein/nucleic-acid complexes against SAXS data".
<p>input files for " Martini bead form factors for nucleic-acids and their application in the refinement of protein/nucleic-acid complexes against SAXS data", cf plumed-nest</p>
The input files and processed output data for the OSSEs using particle filter for the forecast of cloud and precipitation
<p>DART_input.tar.gz contains the input files for the DART system for the control run, Exp-1 ~ Exp-5.</p> <p>WRF&WRS_namelist.tar.gz contains the input namelist files for the WPS and WRF model for the nature run, the control run, and Exp-1 ~ Exp-5.</p> <p>cloud_data.tar.gz contains the procssed output data for the WRF/DART system corresponding to the nature run, the control run, and Exp-1 ~ Exp-5. Untarring the file will generate several subdirectories named after the UTC time. For example, 202008200200 denotes the results for at 02:00 UTC, 20 August, 2020. Because the data for all times are quite large (~10G), we only uploaded the data at 02:00 UTC, 20 August, 2020, 10:30 UTC, 20 August, 2020, 19:00 UTC, 20 August, 2020, 03:30 UTC, 21 August, 2020, and 12:00 UTC, 21 August, 2020. Each subdirectory contains preassim_mean.nc and postassim_mean.nc, which mean the posterior and prior estimate of atmosphere state variables and other processed variables. Either preassim_mean.nc or postassim_mean.nc contains the cloud water path (CWP, unit:kgm-2), cloud water content (CWC, which is the sum of the mixing ratio of six cloud hydrometeors, unit: kgkg-1), RE_CLOUD(unit:um), RE_ICE(unit:um), QVAPOR(unit:kgkg-1), T(the perturbation of potential temperature, unit:K).</p> <p>rain_rate.tar.gz contains the procssed output data for the rain rate corresponding to the nature run, the control run, and Exp-4 ~ Exp-5.</p>
Niche differentiation in the bank vole: Maxent input and output data
<p>Species-level environmental niche modelling has been crucial in efforts to understand how species respond to climate variation and change. However, species often exhibit local adaptation and intraspecific niche differences that may be important to consider in predicting responses to climate. Here, we explore if phylogeographic lineages of the bank vole originating from different glacial refugia (Carpathian, Western, Eastern and Southern) show niche differentiation, which would suggest a role for local adaptation in biogeography of this widespread Eurasian small mammal. We first model the environmental requirements for the bank vole using species-wide occurrences (210 filtered records) and then model each lineage separately to examine niche overlap and test for niche differentiation in geographical and environmental space. We then use the models to estimate past [Last Glacial Maximum (LGM) and mid-Holocene] habitat suitability to compare to previously hypothesized glacial refugia for this species. Environmental niches are statistically significantly different from each other for all pairs of lineages in geographical as well as environmental space and these differences cannot be explained by habitat availability within their respective ranges. Together with the inability of most of the lineages to correctly predict the distributions of other lineages, these result support intraspecific ecological differentiation in the bank vole. Model projections of habitat suitability during the LGM support glacial survival of the bank vole in the Mediterranean region as well as in central and western Europe. Niche differences between lineages and the resulting spatial segregation of habitat suitability suggest ecological differentiation has played a role in determining the present phylogeographic patterns in the bank vole. Our study illustrates that models pooling lineages within a species may obscure the potential for different response to climate change among populations.</p>
Input data from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>These files are a 1GB fragments of a compressed archive containing the data used in the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance".</p> <p>To simplify the uploading process, the file was split into chunks using the `split` utility in Linux. They can be joined back together with the command `cat input_data* > input_data.tar.gz`</p> <p>All data used is either publicly available or generated by this project. The subsets of the Recount3 and GTEx that we used are not owned by us, so putting them in this creative commons repository should not be construed as re-licensing them. </p> <p> </p>
Data from: Do microorganism stoichiometric alterations affect carbon sequestration in paddy soil subjected to phosphorus input?
Ecological stoichiometry provides a powerful tool for integrating microbial biomass stoichiometry with ecosystem processes, opening far-reaching possibilities for linking microbial dynamics to soil carbon (C) metabolism in response to agricultural nutrient management. Despite its importance to crop yield, the role of phosphorus (P) with respect to ecological stoichiometry and soil C sequestration in paddy fields remains poorly understood, which limits our ability to predict nutrient-related soil C cycling. Here, we collected soil samples from a paddy field experiment after 7 years of superphosphate application along a gradient of 0, 30, 60, 90 (P-0 through P-90, respectively) kg P ha-1 y-1 in order to evaluate the role of exogenous P on soil C sequestration through regulating microbial stoichiometry. P fertilization increased soil total organic C and labile organic C by 1-14% and 4-96%, respectively, while rice yield is a function of the activities of soil β-1, 4-glucosidase (BG), acid phosphatase (AP) and the level of available soil P through a stepwise linear regression model. P input induced C limitation as reflected by decreases in the ratios of C:P in soil and microbial biomass. An ecoenzymatic ratio indicating microbial investment in C versus P acquisition, i.e., ln(BG):ln(AP), changed the ecological function of microbial C acquisition and was stoichiometrically related to P input. This mechanism drove a shift in soil resource availability by increasing bacterial community richness and diversity, and stimulated soil C sequestration in the paddy field by enhancing C degradation-related bacteria for the breakdown of plant-derived carbon sources. Therefore, the decline in the C:P stoichiometric ratio of soil microorganism biomass under P input was beneficial for soil C sequestration, which offered a "win-win" relationship for the maximum balance point between C sequestration and P availability for rice production in the face of climate change.
Input data for Sampedro et al
<p>Input data for reproduction of results and figures shown in Sampedro et al: "Residential energy demand, emissions, and expenditures at region and income-decile level for alternative SSP scenarios"</p>
Data for "Reactive nitrogen input and low rainfall mitigate soil respiration responses to warming"
<p>This dataset is used to make tables and figures for the paper entitled "Reactive nitrogen input and low rainfall mitigate soil respiration responses to warming" submitted to Ecology Letters in July 2022. It contains field monitoring data on soil respiration and ecosystem productivity and a meta-analysis database.</p>
Input data and model output for study about wind changes and impact on the Subtropical Front
<p>This dataset contains:</p> <p>Model data for the CONTROL simulation (CONTROL.gz)</p> <p>Model data for the SHIFT simulation (SHIFT.gz) where the westerly winds have been shifted by 1degree per decade</p> <p>Model data for the INCREASE simulation (INCREASE.gz) where the westerly winds have been incresaed by 1 percent per decade</p> <p>Reference dataset are provided (Argo.gz and Modiz.gz)</p>
Simulation Input Data for "Atomic Origins of Biomass Recalcitrance in Organic Solvents"
<p>This is the reduced data behind an upcoming manuscript investigating lignocellulosic interactions in plant secondary wall, when exposed to different organic solvent pretreatment. The data is taken directly from the directory structure that contains both the simulation and analysis, with excluded trajectory files and intermediate products to fit within the zenodo upload limit. The tar command used to generate this tarball was:</p> <pre><code class="language-bash">tar -zcvf lignincelluloseindustrialsolvent.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*log" --exclude="*[0-9].out" --exclude="*npz" --exclude="*pkl" --exclude="*npy" --exclude="*png" --exclude="*bmim*" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*txt" --exclude="*state_prev.cpt" --exclude="*ppm" --exclude="Simulations" FaceDifferences</code></pre> <p>Within the FaceDifferences directory, there are 2 primary subdirectories:</p> <ul> <li><strong>Build </strong>contains the scripts and files to build the individual lignin cellulose in organic solvent molecular systems.</li> <li><strong>NewSolventSimulations</strong> contains the all-atom MD simulation inputs and the analysis scripts (subdirectory <strong>Analysis</strong>)</li> </ul>
Input data for Bayesian and information theoretic model selection and similarity analysis
<p>This data serves as input to the codes found in the following repository https://github.com/MariaFMoralesOreamuno/Bayesian_Information_theoretic_model_selection.git</p> <p> </p>
Thermochronology data in Ebro basin and model input parameters for computing cooling histories
<p>Two files (word and excel) containing Table DR1 that refer to the model input parameters and Table DR2 with details of the (U-Th-Sm)/He analyses. </p>
MIROC4-ACTM: Model setup, input and output data for CH4 LETKF (Bisht et al., GMD-D, 2022)
<p>Details in :</p> <p>Bisht, J. S. H., Patra, P. K., Takigawa, M., Sekiya, T., Kanaya, Y., Saitoh, N., and Miyazaki, K.: Estimation of CH<sub>4</sub> emission based on advanced 4D-LETKF assimilation system, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-719, 2022.</p>
Input data for manuscript "SNR-based GNSS reflectometry for coastal sea-level altimetry – Results from the first IAG inter-comparison campaign"
<p>GNSS data collected at station GTGU for one year (2015.5-2016.5) and nearby tide gauge data.</p> <p>Note: the antenna position in the header file, expressed in global Cartesian coordinates, corresponds inadvertently to integer values of latitude, longitude, and altitude (57.0°, 11.0°, 0.0 m); non-truncated values are as follows: 57.3929549°, 11.9134886°, 40.420 m.</p>
Human placental MeRIP-seq data-Input
<p>MeRIP-seq for huamn placental tissues from different gestational age. Early, first trimester; Middle, second trimester; Late, third trimester. </p>
Input data for predicted sedimentary environments on the Norwegian continental margin
<p>Input and output data relating to R workflow for predicting sedimentary environments on the Norwegian continental margin (https://github.com/diesing-ngu/SedEnv). The following files are included:</p><p><strong>SedEnv_4km_MaxCombArea_point_20230622.shp</strong> - Point shapefile of the response data (substrate type). Note that these data points were derived from mapped products and are not sample points as such. </p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p>
Input data for predicted sediment accumulation rates on the Norwegian continental margin
<p>Input data relating to R workflow for predicting sediment accumulation rates on the Norwegian continental margin (https://github.com/diesing-ngu/SedRates). The following files are included:</p><p><strong>norway_sar_2023-08-28.csv</strong> - Data on sediment accumulation rates from the <a href="https://doi.org/10.5194/essd-15-4105-2023">MOSAIC v2.0</a> database</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>mud_2023-06-30.tif </strong>- Georeferenced TIFF-file of predicted mud content. Used as an additional predctor.</p><p><strong>SedEnv3_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the prediction probabilities of the predicted sedimentary environments. Used as additional predctors.</p><p><strong>SedEnv3_max_probabilities_2023-07-01.tif </strong>- Georeferenced Tiff-file of the maximum probabilities, i.e., the probability of the class that was mapped.</p><p><strong>SedEnv3_AOA_2023-07-01.tif </strong>- Georeferenced Tiff-file of the area of applicability of the model <a href="https://doi.org/10.1111/2041-210X.13650">(Meyer & Pebesma, 2021)</a></p>
Input data for predicted dry bulk densities on the Norwegian continental margin
<p>Input data relating to R workflow for predicting dry bulk densities on the Norwegian continental margin (https://github.com/diesing-ngu/DBD). The following files are included:</p><p><strong>DBD_2023-07-21.csv </strong>- Data on dry bulk densities in surface sediments</p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p><p><strong>GrainSizeReg_folk8_classes_2023-06-28.tif</strong> - Georeferenced TIFF-file of predicted substrate classes. Used to update the area of interest (exclude areas mapped as Rock and boulders).</p><p><strong>GrainSizeReg_folk8_probabilities_2023-06-28.tif</strong> - Georeferenced TIFF-file of the prediction probabilities of the predicted substrate classes. Used as additional predctors.</p>
Input data for predicted substrate types on the Norwegian continental margin
<p>Input data relating to R workflow for predicting substrate types on the Norwegian continental margin (https://github.com/diesing-ngu/GrainSizeReg). The following files are included:</p><p><strong>AoI_Harris_mod </strong>- Polygon shapefile delimiting the area of interest</p><p><strong>GrainSize_4km_MaxCombArea_folk8_point_20230628</strong> - Point shapefile of the response data (substrate type). Note that these data points were derived from mapped products and are not sample points as such. </p><p><strong>predictors_ngb.tif </strong>- Multi-band georeferenced TIFF-file of predictor variables</p><p><strong>predictors_description</strong>.txt - Information on variables stored in predictor_ngb.tif including units, statistics, time period and sources.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.