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Input data and scenarios outputs of the Water Resources Research paper "Optimal economic spatial and temporal allocation of green and grey investment to address water security threats : Case Study - Velhas River Basin, Brazil"

Open the record for dataset details and reuse information.

Input files and movie visualizations for convection models discussed in Becker and Fuchs, "Generation of evolving plate boundaries and toroidal flow from visco-plastic damage-rheology mantle convection and continents", manuscript revised for G-Cubed

These input files are for the CitcomS software as available on github.com/geodynamics/citcoms and used in the version under commit 2bda530. They can be used to recreate the models discussed in Becker and Fuchs (revised manuscript submitted to G-Cubed, 11/2023), with model codes discussed and listed in Table 1 of the preprint as provided here. We also provide selected animations of the time dependence of model output, referenced to the same model names.

Input and output data and code for PPS2 on 333 vertebrate and111 plant genome assemblies, and for DDS2+ on fungal genome assemblies

The datasets contain PPS2 and DDS2+ source and binary code, other scripts, and some input and output data. Please see the README file after unpacking it. The absolute paths in the scripts need to be modified in order to duplicate the results in this dataset. Most of the input and output data have to be removed from the datasets for quick uploading and downloading; otherwise, the datasets would exceed the 50-Gb size limit.

Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"

The tar file contains two directories: data and models.  Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data).  The data in these directories are unnormalized.  The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file.  These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively.  To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths.  The other arguments should be as follows:--uniformize=1--predictor_norm_type_string="z_score"--vector_target_norm_type_string=""--scalar_target_norm_type_string="" Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data.  To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.

Model data and input files

The zip file contains the matlab code of the model and the input data that was used to run the model. The zip file contains a README.docx that explains the contents of the zip file

opencc-by-nc-sa-4.0Nov 2023View details →

RRR input and output files corresponding to "Global patterns in river water storage dependent on residence time"

Corresponding peer-reviewed publication This dataset corresponds to all the RRR input and output files that were used in the study reported in: <ul> <li>Collins, E. L., C. H. David, R. Riggs, G. H. Allen, T. M. Pavelsky, P. Lin, M. Pan, D. Yamazaki, R. K. Meentemeyer, and G. M. Sanchez (2024), Global patterns in river water storage dependent on residence time.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Input datasets The dataset herein benefited from and was built upon other datasets: <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/ZRIHVF29X43C">10.5067/ZRIHVF29X43C</a></li> <li>GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/QN80TO7ZHFJZ">10.5067/QN80TO7ZHFJZ</a></li> <li>GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/SGSL3LNKGJWW">10.5067/SGSL3LNKGJWW</a></li> </ul> Known bugs and limitations in this dataset or the associated manuscript In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, two river reaches do not have a corresponding catchment: COMID=31000001 and COMID=61000003. These two river reaches were removed from our analysis, hence bringing the total number of river reaches used from 2,938,143 to 2,938,141, the same value as the total number of catchments. In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, the attribute table for the river reach with COMID=67072432 contains only four upstream reaches (COMID=64072433, COMID=64072735, COMID=64073772, and COMID=64073786), while the reach actually has five upstream reaches on the map (those above and COMID=64073793). This was modified herein because all connectivity in this dataset was created by associating upstream and downstream nodes rather than using the attribute table. <div> <div> <div> Our monthly dataset of river gauges with 95% daily data availability for 1980–2009 and with average discharge greater than or equal to 100 m3/s contains 1,148 stations. After mapping gauges to the MERIT Hydro river network using a joint criteria of 1) within a distance of 0.05 degrees and 2) uncorrected 30-yr average simulated discharge of the river reach within one order of magnitude relative to the observed average discharge of the gauge; the number of gauges reduces to 1129. In cases of multiple gauges per reach, we selected the gauge that had the closest observed average discharge to the uncorrected 30-yr averages simulated, resulting in 1,001 gauges. The dataset was further split in calibration gauges (702) and validation gauges (299). Three specific gauges are located in places with 0 accumulation of runoff in their specific sub-basin, they are located at river reaches with ID 41003992, 41001545, and 24005996. These three gauges were removed from the overall analysis at all gauges, resulting in 998 total gauges. All three gauges are part of the 299 validation gauges, not the 702 calibration gauges. They were retained in the analysis of 299 validation gauges.    </div> </div> </div> Files included in this version All monthly discharge observation files for 1148 gauges, in shapefile and CSV formats: <ul> <li>sites_1980-01_2009-12_100cms_095p.zip</li> <li>timeseries_obs_1980-01_2009-12_100cms_095p_monthly.zip</li> </ul> All CSV files corresponding to the hydrography of all pfaf_level_02 regions of MERIT-Hydro v0.7 Basins v1.0 (i.e. the 61 values of ii): <ul> <li>coords_pfaf_ii.zip</li> <li>kfac_pfaf_ii_1km_hour.zip</li> <li>k_pfaf_ii_low.zip</li> <li>k_pfaf_ii_nrm.zip</li> <li>k_pfaf_ii_hig.zip</li> <li>rapid_catchment_pfaf_ii.zip</li> <li>rapid_connect_pfaf_ii.zip</li> <li>rapid_coupling_pfaf_ii_GLDAS.zip</li> <li>riv_bas_id_pfaf_ii_topo.zip</li> <li>sort_pfaf_ii_topo.zip</li> <li>xfac_pfaf_ii_0.1.zip</li> <li>x_pfaf_ii_low.zip</li> <li>x_pfaf_ii_nrm.zip</li> <li>x_pfaf_ii_hig.zip</li> </ul> All netCDF4 files with monthly surface and subsurface runoff that were combined from GLDAS 2.0 and corresponding to the following land surface models for the years 1980 to 2009 (both included): <ul> <li>GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_ENS_M_1980-01_2009-12_utc.zip (the 3-model ensemble average based on the three above models).</li> </ul> All netCDF4 files with monthly lateral inflows (based on the GLDAS data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly discharge (based on the m3_riv data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>Qout_pfaf_ii_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly storage (based on the Qout_pfaf_ii_GLDAS_ENS data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig): <ul> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_hig.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above): <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_GLDAS_ENS.zip</li> </ul> All monthly discharge observation files for 1148 gauges, in shapefile format, with observed monthly mean appended: <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ.zip</li> </ul> All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, in shapefile and CSV formats, corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR.zip</li> <li>obs_tot_id_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> <li>Qobs_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> </ul> All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, separated into calibration (CAL) and validation (VAL) gauges, in shapefile and CSV formats, corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_CAL.zip</li> <li>obs_bas_id_1980-01_2009-12_100cms_095p_meanQ_CAL_pfaf_ii.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_VAL.zip</li> </ul> All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for calibration (CAL) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for all correction (COR) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly storage (based on the Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig): <ul> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_hig.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip data above): <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_GLDAS_COR.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) catchments that have been dissolved and of which the perimeter was extracted, corresponding to each one of the 61 values of ii, the pfaf_level_02 code; as well as global combined files: <ul> <li>cat_pfaf_ii_MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_pfaf_ii_MERIT_Hydro_v07_Basins_v01_perim.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_perim.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network retaining only those reaches that flow to the global coast, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_coast.zip</li> </ul>

The Dutch National Flexible Groundwater Model prototype input data

This dataset includes the necessary model input data to run the Netherlands' National Flexible Groundwater Model prototype. For this, pre-processing is required using the quad2d tools, provide by https://github.com/verkaik/quad2d/releases/tag/quad2d_v0.1.

opencc-by-4.0Oct 2023View details →

HiPACE++ input scripts for "Emittance-preserving acceleration of high-quality positron beams using warm plasma filaments"

This dataset presents the <a href="https://github.com/Hi-PACE/hipace/">HiPACE++</a> input scripts required to re-create the data of the figures in the article "Emittance-preserving acceleration of high-quality positron beams using warm plasma filaments"

Downsampled AnnData input file - Galaxy Training Material

Downsampled input dataset for the Single Cell Data Formats Conversion tutorial on Galaxy Training Network.

CDS input for Monocle3 tutorial - Galaxy Training Material

CDS input file for Monocle3 trajectory analysis tutorial. Created from AnnData object from the upstream pre-processing.

openDec 2023View details →

Input datasets for the Galactic Tomography (G-Tomo) EXPLORE app

Dust extinction density cubes at different spatial resolutions and extends

openDec 2023View details →

Input datasets for the Galactic Tomography 3D Visualisation (G-Tomo-3D) EXPLORE app

Dust extinction density cubes at different resampling levels

Planning resource adequacy of wind- and solar-based electricity systems: Input data and results files

This record contains the input data and raw results files for the study titled "<a href="https://www.sciencedirect.com/science/article/pii/S2666792424000234">Planning reliable wind- and solar-based electricity systems</a>." Tyler H. Ruggles, Edgar Virgüez, Natasha Reich, Jacqueline Dowling, Hannah Bloomfield, Enrico G.A. Antonini, Steven J. Davis, Nathan S. Lewis, Ken Caldeira, "Planning reliable wind- and solar-based electricity systems," Advances in Applied Energy, 2024, https://doi.org/10.1016/j.adapen.2024.100185. Additionally, csv files are provided to recreate the associated figures in the paper in the "Figures_files.zip" file. The input data contains wind and solar generation availability profiles and electricity demand profiles for the contiguous US. The profiles cover the years 1950-2022 and are calculated from the ERA5 dataset. The study only used the satellite era data from the year 1979 onward. Input profiles are presented at 4 resolutions: hourly, 2-hour, 3-hour, and 4-hour resolution. The results files contain some keys indicating the modeling scenario used: "SWB" = "Solar+Wind+Battery"; "SWBNG" = "Solar+Wind+Battery+Natural Gas generation"; and "SWBPGP" = "Solar+Wind+Battery+Power-to-H2-to-Power Loop". The files can be grouped into multiple categories: <ol> <li>The main analysis including the initial energy system optimization results and the secondary system performance testing results. <ol> <li>Initial optimization results are found in zip files titled "Initial_Optimization_Jan29v1_*.zip"</li> <li>The testing of the optimized systems are found in the zip file "Lost_Load_Decade_Testing_Jan29v1.zip"</li> </ol> </li> <li>A secondary analysis compared systems optimized on a single year of data and tested on a single other year of data. Those results are in "Matrix_Figure_NYrs1_Aug04v1.zip"</li> <li>A supplementary analysis compared the modeled results using input data with the 4 different time resolutions. These results can be found in the zip files titled "DeltaT_Test_July08v1dt*.zip"</li> </ol>

Diagenesis decreasing the Mo isotopic composition in estuarine systems: Implications for constraining its riverine input to ocean

Physical and chemical characteristics of water and particulate samples in the August and December 2014 cruises and characteristics of water samples from the July 2016 and February 2017 cruises.

opencc-by-4.0Jul 2023View details →

[VISIR-2: input data for benchmark runs] OpenCPN

Bathymetric data, namelists, as well as current and wind fields needed for the VISIR-2 ship weather routing model benchmark run against OpenCPN.

opencc-by-4.0Mar 2024View details →

Oktoberfest timsTOF Input

Example input data for Oktoberfest <a href="https://github.com/wilhelm-lab/oktoberfest">https://github.com/wilhelm-lab/oktoberfest</a>

opencc-by-4.0Mar 2024View details →

Data and analysis process for Microbiome processing of organic nitrogen input supports growth and cyanotoxin production of Microcystis aeruginosa cultures

Data and analysis process for manuscript titled "Microbiome processing of organic nitrogen input supports the growth and cyanotoxin production of Microcystis aeruginosa cultures"

opencc-by-4.0Apr 2024View details →

MEGAN 3.2 input files

This document describes the contents of three zipped files for modeling BVOC emissions with MEGAN3.2   GFv7nc.zip: These are global 1km2 maps of the four growth forms. All are in netcdf format -          TC30s7.nc (tree), CC30s7 (crop), SC30s7.nc (shrub), GC30s7.nc (herbaceous)     EVT3d.zip: This is a global 1km2 map of Emission type (EVT) distribution. It is in netcdf format -          EVT3d.nc   EFP20240120.zip: These files go in the “inputs” folder of the MEGAN EFP emission factor processor. All are in Comma Separated Values ASCII text format -EFv20240120.csv contains emission factors -SpeciationCrop230822.csv, SpeciationHerb230822.csv, SpeciationShrub230822.csv, SpeciationTree230921.csv each contain plant species composition for one of the major growth form types.

opencc-by-4.0Apr 2024View details →

Code and exemplificative data for publication titled: Synaptic inputs to motor neurons underlying muscle co-activation for functionally different tasks have different spectral characteristics.

Code implemented in the publication Titled: Synaptic inputs to motor neurons underlying muscle co-activation for functionally different tasks have different spectral characteristics Authors: Borzelli,D; Vieira,TMM; Botter,A; Gazzoni,M; Lacquaniti,F; d’Avella,A Published in: Journal of Neurophysiology Year: 2024 When running the function 'coherenceAnalysis_Borzelli_et_al_JNeurophysiol_2024.m', the code load the firings of the motor units identified during two exemplificative trials performed by a participant, saved in 'data_Borzelli_et_al_JNeurophysiol_2024'. One trial was directed toward an horizontal target of the perturbed block and the other was directed toward a supination target of the baseline block. Then the code calculates the coherence between the MUs identified on the BB and on the TB (cross-muscle coherence) and the cross-muscle coherence after excluding the components synchronized with the norm of the endpoint force. Finally, the code plots the results. This code could easily be modified to compute all the relevant analyses presented in the paper, such as the total within muscle coherence and the within muscle coherences after excluding the components synchronized with the norm of the force or the sum of the firings of the antagonist muscle.