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2,152 results for “outputs”
Artificial Intelligence for EU Decision-Making: Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy
<p>The uploaded dataset was used for the statistical analysis of the pre-print "Artificial Intelligence for EU Decision-Making: Effects on Citizens’ Perceptions of Input, Throughput and Output Legitimacy" (Permanent identifier: <a href="https://arxiv.org/abs/2003.11320">arXiv:2003.11320</a>)</p> <p>A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (1) independent human decision-making (HDM); (2) independent algorithmic decision-making (ADM) by AI-based systems; and (3) hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most democratic (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making.</p>
Effect of changing ocean circulation on deep ocean temperature in the last millennium: simulation output data
<ul> <li>This dataset contains the output of model simulations used in the paper:<br> Scheen, Jeemijn and Stocker, Thomas F., "Effect of changing ocean circulation on deep ocean temperature in the last millennium", Earth System Dynamics Discussions, https://doi.org/10.5194/esd-11-925-2020, 2020 </li> <li>All figures can be reproduced when combining this dataset with the published analysis code. <br> </li> <li>In addition this dataset contains the data behind Fig. 2 of the paper:<br> Gebbie, G. and Huybers, P. : "The Little Ice Age and 20th-century deep Pacific cooling", Science, 363, 70-74, https://doi.org/10.1126/science.aar8413, 2019<br> </li> <li>Download either the small (unzipped 5 Gb) or large (unzipped 22 Gb) version of the dataset. <strong>Warning: this needs to be loaded into memory when running the notebook.</strong> You only need the small version to run the github notebook and reproduce the figures, but you are free to explore additional variables in the large version.</li> </ul> <p>Overview of doi's:</p> <ul> <li>paper: <a href="https://doi.org/10.5194/esd-11-925-2020">https://doi.org/10.5194/esd-11-925-2020</a></li> <li>code (analysis and figures): <a href="https://doi.org/10.5281/zenodo.4022947">https://doi.org/10.5281/zenodo.4022947</a></li> <li>data (simulation output): <a href="https://doi.org/10.5281/zenodo.4022927">https://doi.org/10.5281/zenodo.4022927</a></li> </ul>
Time series of electricity output for large grid connected photovoltaic installations in Chile
<p>These data sets accompany the paper "Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?". They include capacity factors (values 0 to 1) in hourly temporal resolution of 103 large PV installations in Chile derived from official sources as well as simulated capacity factors using PV_LIB with ERA5-land and MERRA-2 reanalysis data. The data covers the period 2014-2018 and simulations were performed assuming either a "fixed" system with orientation towards north and inclination equal to the latitude (i.e. optimal inclination) or a horizontal single axis “tracking” system with backtracking. Furthermore, accuracy indicators (Pearson’s correlation, mean bias error and root mean square error) are provided, comparing the simulated time series with capacity factors derived from official sources. Capacity factors were calculated for the 103 installations and both alternative configurations (“fixed” and “tracking”) but only a subset of 23 installations has reference data of sufficient quality to allow for a validation (for a more detailed description, see the paper). Indicators were also calculated for all installations and configurations but should be only compared for installations inside a particular configuration: there are 14 systems classified as “tracking” and 9 as “fixed”. Further details are available in the paper and the entire Python and R code is available on github at <a href="https://github.com/inwe-boku/PV_from_era5">https://github.com/inwe-boku/PV_from_era5</a>. </p>
Relations in the Biographical Dictionary of Republican China - Raw NLP Output
<p>This file provides the data on relations as extracted from the Biographical Dictionary of Republican China (BDRC) with CoreNLP. This is the raw output before any form of processing and cleaning.</p>
Example BOUT++ output from blob2d
<p>Output files for the blob2d example from BOUT++ (https://github.com/boutproject/BOUT-dev/tree/next/examples/blob2d). Created with BOUT++ at commit 999793fe363e062fd47da20bcd8ff679c85a8b30.</p>
Example BOUT++ output from hasegawa-wakatani
<p>Output files for the hasegawa-wakatani example from BOUT++ (https://github.com/boutproject/BOUT-dev/tree/next/examples/hasegawa-wakatani). Created with BOUT++ at commit 999793fe363e062fd47da20bcd8ff679c85a8b30.</p>
Princeton Handbook for Reproducible Neuroimaging: Sample Output
<p>This archive contains sample output files for the <a href="http://doi.org/10.5281/zenodo.3677090">sample data</a> accompanying the <a href="https://brainhack-princeton.github.io/handbook/">Princeton Handbook for Reproducible Neuroimaging</a>. Outputs include the NIfTI images converted using <a href="https://github.com/nipy/heudiconv">HeuDiConv</a> (v0.8.0) and organized according to the <a href="https://bids.neuroimaging.io/">BIDS</a> standard, quality control evaluation using <a href="https://mriqc.readthedocs.io/">MRIQC</a> (v0.15.1), data preprocessed using <a href="https://fmriprep.readthedocs.io/en/stable/">fMRIPrep</a> (v20.2.0), and other auxiliary files. All outputs were created according to the procedures outlined in the handbook, and are intended to serve as a didactic reference for use with the handbook. The sample data from which the outputs are derived were acquired (with informed consent) using the<a href="https://github.com/ReproNim/reproin"> ReproIn</a> naming convention on a Siemens Skyra 3T MRI scanner. The sample data include a T1-weighted anatomical image, four functional runs with the “prettymouth” spoken story stimulus, and one functional run with a block design emotional faces task, as well as auxiliary scans (e.g., scout, soundcheck). The “prettymouth” story stimulus created by <a href="https://doi.org/10.1177%2F0956797616682029">Yeshurun et al., 2017</a> and is available as part of the<a href="https://openneuro.org/datasets/ds002345"> Narratives</a> collection, and the emotional faces task is similar to<a href="https://doi.org/10.1016/j.nicl.2015.05.004"> Chai et al., 2015</a>. The brain data are contributed by author S.A.N. and are authorized for non-anonymized distribution.</p>
Example Cloud-Resolving Model Output (Dec 2013 run)
<p>This dataset contains a selection of hourly, domain-mean quantities from the large-ensemble of realistic cloud-resolving model experiments described here: https://acp.copernicus.org/articles/20/6291/2020/</p>
Global physical input-output tables for iron and steel (2008-2017).
<p><strong>Dataset:</strong> Global physical input-output tables for iron and steel</p> <p><strong>Years:</strong> 2008-2017</p> <p><strong>Base classification:</strong> 32 regions, 39 processes and 30 flows</p> <p><strong>Associated journal article: </strong>The PIOLab - Building global physical input-output tables in a virtual laboratory (forthcoming, Journal for Industrial Ecology)</p> <p><strong>Associated GitHub repository</strong>: www.github.com/fineprint-global/PIOLab</p> <p><strong>Contact:</strong> hanspeter.wieland@wu.ac.at</p> <p>The folder <em>RawData</em> contains the unprocessed results of the reconciliation run in the PIOLab. These tables (in the Tvy format) form the basis for the R scripts that are available from the GitHub repository mentioned above. Please note the instructions on GitHub for further information and how i.e. where the content of <em>RawData</em> needs to be stored in your local repository.</p> <p>The folder <em>gPSUT</em> contains the processed physical supply-use tables, including final use matrices and boundary input and output blocks. The variable names are described in detail in the method section of the journal article.</p> <p>The folder <em>gPIOT</em> contains the process-by-process IO model, which was used for the calculation of the footprint indicators in the Journal article. Please read the information on the footprint calculus in the journal article.</p> <p>The folder<em> Diagnostics </em>contains, for all years of the time series, results from the analyses of the constraint realization. The journal article presents only the diagnostic test for the year 2008.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL soybean simulations
<p>This data set contains output data from simulations with the model LPJmL for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations
<p>This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL maize simulations
<p>This data set contains output data from simulations with the model LPJmL for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Model output from the Bern3D model of pre-industrial and Last Glacial Maximum
<p>The here presented datasets contain the model results of the Bern3D model to be published in Pöppelmeier et al. (2021) <em>Climate of the Past Discussions</em> (https://doi.org/10.5194/cp-2020-135).</p> <p>The datasets of the five performed simulations are presented: PI_CTRL, LGM_CTRL, LGM_BS, LGM_BS+wind, LGM_BS+wind+tidal. For detailed information on the model set ups please see section 2 and Table 1 of Pöppelmeier et al. (2021).</p>
Open Access journals charging publication fees for a) the ten countries with the highest output of Open Access journals and b) sociological journals (2012, 2014)
<p>The files represent data on Open Access journals charging publication fees for a) the ten countries with the highest output of Open Access journals and b) all sociological journals indexed in the Directory of Open Access Journals DOAJ. The data covers a time span of about 18 months, it was collected in November 2012 and June 2014.</p>
RAPID input and output files corresponding to "RAPID Applied to the SIM-France Model"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RAPID input and output files that were used in the study reported in:</p> <ul> <li>David, Cédric H., Florence Habets, David R. Maidment and Zong-Liang Yang (2011), RAPID applied to the SIM-France model, Hydrological Processes, 25(22), 3412-3425. DOI: 10.1002/hyp.8070. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Time format</strong></p> <p>The times reported in this description all follow the ISO 8601 format. For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1<sup>st</sup> 2000 (2000-01-01), Central Standard Time (-06:00). Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step. For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps. The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1<sup>st</sup> 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1<sup>st</sup> 2000, Universal Time.</p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The hydrographic network of SIM-France, as published in Habets, F., A. Boone, J. L. Champeaux, P. Etchevers, L. Franchistéguy, E. Leblois, E. Ledoux, P. Le Moigne, E. Martin, S. Morel, J. Noilhan, P. Quintana Seguí, F. Rousset-Regimbeau, and P. Viennot (2008), The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, Journal of Geophysical Research: Atmospheres, 113(D6), DOI: 10.1029/2007JD008548.</li> <li>The observed flows are from Banque HYDRO, Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations. Available at http://www.hydro.eaufrance.fr/index.php.</li> <li>Outputs from a simulation using SIM-France (Habets et al. 2008). The simulation was run by Florence Habets, and produced 3-hourly time steps from 1995-08-01T00:00+02:00 to 2005-07-31T21:02+00:00. Further details on the inputs and options used for this simulation are provided in David et al. (2011).</li> </ul> <p> </p> <p><strong>Software</strong></p> <p>The following software were used to produce files in this dataset:</p> <ul> <li>The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.1.0. Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).</li> <li>ESRI ArcGIS (http://www.arcgis.com). </li> <li>Microsoft Excel (https://products.office.com/en-us/excel). </li> <li>The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers). </li> </ul> <p> </p> <p><strong>Study domain</strong></p> <p>The files in this dataset correspond to one study domain:</p> <ul> <li>The river network of SIM-France is made of 24264 river reaches. The temporal range corresponding to this domain is from 1995-08-01T00:00+02:00 to 2005-07-31 T21:00+02:00.</li> </ul> <p> </p> <p><strong>Description of files </strong></p> <p>All files below were prepared by Cédric H. David, using the data sources and software mentioned above. </p> <ul> <li><em>rapid_connect_France.csv.</em> This CSV file contains the river network connectivity information and is based on the unique IDs of the SIM-France river reaches (the IDs). For each river reach, this file specifies: the ID of the reach, the ID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the IDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of ID. The values were computed based on the SIM-France FICVID file. This file was prepared using a Fortran program.</li> <li><em>m3_riv_France_1995_2005_ksat_201101_c_zvol_ext.nc. </em>This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005/07/31T21:00+02:00. The values were computed using the outputs of SIM-France. This file was prepared using a Fortran program.</li> <li><em>kfac_modcou_1km_hour.csv.</em> This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>. The values were computed based on the following information: ID, size of the side of the grid cell, Equation (5) in David et al. (2011), and using a wave celerity of 1 km/h. This file was prepared using a Fortran program.</li> <li><em>kfac_modcou_ttra_length.csv. </em>This CSV file contains a second guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same IDs and are sorted similarly to <em>rapid_connect_France.csv</em>. The values were computed based on the following information: ID, size of the side of the grid cell, travel time, and Equation (9) in David et al. (2011).</li> </ul> <ul> <li><em>k_modcou_0.csv.</em> This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> </ul> <ul> <li><em>k_modcou_1.csv.</em> This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_2.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_3.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_4.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_a.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_b.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>k_modcou_c.csv. </em>This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on the following information: <em>kfac_modcou_1km_hour.csv </em>and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_0.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_1.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_2.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_3.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_4.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_a.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_b.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>x_modcou_c.csv.</em> This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to <em>rapid_connect_San_Guad.csv</em>. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li><em>rivsurf_France.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the SIM-France domain. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_adour.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Adour River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_allier.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Allier River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_ardeche.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Ardeche River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_dordogne.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Dordogne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_garonne.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_garonne_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_garonneariege.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Garonne and Ariege River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_herault.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Herault River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loir.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loir River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loire.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loire_amont_nevers.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_loire_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Loire River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_lot.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Lot River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_meuse.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Meuse River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_oise.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Oise River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_rhone.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_rhone_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_rhone_suisse.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Rhone River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_saone.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Saone River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_seine.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_seine_amont.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, upstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_seine_reste.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Seine River Basin, downstream. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_tarn.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Tarn River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>rivsurf_vienne.csv. </em>This CSV file contains the list of unique IDs of SIM-France river reaches in the Vienne River Basin. The river reaches are sorted from upstream to downstream. The values were computed using the ID field. This file was prepared using Excel.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p1_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p2_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p3_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_p4_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pa_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pb_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_3653days_pc_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 2005-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_p0_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s_pougny.nc. </em>This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same IDs and are sorted similarly to <em>rivsurf_France.csv</em>. The time range for this file is from 1995-08-01T00:00+02:00 to 1996-07-31-21:00+02:00. The values were computed using the Muskingum method with parameters of Table (2) in David et al. (2011). This file was prepared using RAPID v1.1.0 running with the preonly ILU solver on one core.</li> <li><em>gage_id_1995_1996_full.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>gage_id_1995_1996_full_nash.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with full daily data record and for which RAPID simulations led to a positive efficiency value. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>gage_id_1995_2005_70.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and with 70% daily data record. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>Qobs_1995_1996_full.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full.csv</em>. The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_1996_full_nash.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>. The time range for the daily values is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_1996_full_nash_93.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>. The time range for the daily values is from 1995-11-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobs_1995_2005_70.csv. </em>This CSV file contains daily averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_2005_70.csv</em>. The time range for the daily values is from 1995-08-01T00:00+02:00 to 2005-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>Qobsbarrec_1995_1996_full_nash.csv. </em>This CSV file contains the reciprocal of the averaged measured stream flow (in cubic meters per second). The river reaches have the same IDs and are sorted similarly to <em>gage_id_1995_1996_full_nash.csv</em>. The time range for the computation of the average is from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID, and the observations from SCHAPI. This file was prepared using a Fortran program and Excel.</li> <li><em>forcingtot_id_1995_1996_full.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the SIM-France domain. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_garonne_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Garonne River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_loire_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Loire River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_rhone_pougny.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream of Lake Geneva. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_rhone_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Rhone River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>forcinguse_id_seine_reste.csv. </em>This CSV file contains the list of IDs of rivers containing SCHAPI gauges and used as forcing instead of RAPID simulations for the Seine River Basin, downstream. The river reaches are sorted in increasing value of ID. The time range used for determining a full record is daily from 1995-08-01T00:00+02:00 to 1996-07-31T21:00+02:00. The values were computed using the following field: ID. This file was prepared using a Fortran program, and Excel.</li> <li><em>Qfor_1995_1996_full.csv. </em>This CSV file is identical to <em>Qobs_1995_1996_full.csv.</em></li> <li><em>Qfor_1995_1996_full_93.csv. </em>This CSV file is identical to <em>Qobs_1995_1996_full_nash_93.csv.</em></li> <li><em>Qinit_93.csv. </em>This CSV file contains the final state of RAPID after a simulation ending on 1995-11-31T00:00+02:00</li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>A small bug in RAPID v1.1.0 was discovered and fixed on 2011-07-16 that had an impact on the optimization of parameters when using forcing data to replace upstream simulations. This bug led to erroneous results for only two of the basins where upstream forcing was used: Garonne River Basin, downstream; and Rhone River Basin, downstream. The bug had no influence on: Loire River Basin, downstream, and Seine River Basin, downstream; or on any of the other simulations. This should not affect the conclusions of David et al. (2011) since only a few locations were impacted. </p> <p> </p> <p><strong>Funding</strong></p> <p>This work was partially supported by the French Mines Paristech, by the French Agence Nationale de la Recherche under the Vulnérabilité de la nappe du Rhin (VulNaR) project, by the French Programme Interdisciplinaire de Recherche sur l’Environnement de la Seine (PIREN-Seine) project, by the U.S. National Aeronautics and Space Administration under the Interdisciplinary Science Project NNX07AL79G, by the U.S. National Science Foundation under project EAR-0413265: CUAHSI Hydrologic Information Systems, and by the American Geophysical Union under a Horton (Hydrology) Research Grant.</p>
Output files corresponding to "Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to the output files that were produced for the study reported in:</p> <ul> <li>Sawyer, Audrey H., Cédric H. David, and James S. Famiglietti, (2016), Continental patterns of submarine groundwater discharge reveal coastal vulnerabilities, Science, 353(6300), 705-707. DOI:10.1126/science.aag1058. </li> </ul> <p> </p> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p> </p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Regions used are: Northeast (NE: 01), Mid-Atlantic (MA: 02), South-Atlantic North (SAN: 03N), South-Atlantic South (SAS: 03S), South-Atlantic West (SAW: 03W), Lower Mississippi (MS: 08), Texas (TX: 12), California (CA: 18), and Pacific Northwest (NW: 17).</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2010 Census dataset (CENSUS 2010), obtained from: http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>NHDFlowline_CONUS_coastline.zip. </em>This zip file contains a shapefile with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in regions used. </li> <li><em>Catchment_CONUS_coastline.zip. </em>This zip file contains a shapefile with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in regions used. </li> <li><em>Catchment_CONUS_coastline_centroid.zip</em>.<em> </em>This zip file contains a shapefile with the centroids of the above catchments. </li> <li><em>SGD_Coastal_Vulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID. Unique feature identifier in NHDPlusV2 (-).</li> <li>LENGTHkm. Length of coastline feature (km).</li> <li>REACHCODE. Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature (-).</li> <li>AREAsqkm. Area of coastal catchment feature (km<sup>2</sup>).</li> <li>REGION. NHDPlusV2 region: NE = Northeast, MA = Mid-Atlantic, SAN = South Atlantic North, SAS = South Atlantic South, SAW = South Atlantic West, TX = Texas, MS = Lower Mississippi, CA = California, PN = Pacific Northwest (-).</li> <li>RLENGTHkm. Total length of coastline accumulated by REACHCODE (km).</li> <li>RAREAsqkm. Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>BGRUNkgpsqm. Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>SGDsqmpy. Average annual fresh SGD rate for REACHCODE (m<sup>2</sup>/y).</li> <li>RCOUNT. Number of features by REACHCODE (-).</li> <li>PDENpsqkm. Population density for coastal catchment feature (km<sup>-2</sup>).</li> <li>SWIVULN. Vulnerability to saltwater intrusion: - 1 = vulnerable, 0 = not vulnerable (-).</li> <li>PCTDEV11. Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>CONTVULN. Vulnerability to offshore contamination associated with direct groundwater discharge: - 1 = vulnerable, 0 = not vulnerable (-).</li> </ul> </li> </ul> <p> </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>No bugs have been unveiled since publication of this dataset or the associated manuscript. Vulnerability thresholds are subjective and could be adjusted for different applications, refer to published manuscript for approaches used here.</p> <p> </p> <p><strong>Funding</strong></p> <p>This work was supported by the Ohio State University School of Earth Sciences, and NSF grant EAR-1446724 (A.H.S); the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA, and grants from the NASA SWOT and Sea Level Science Teams (C.H.D. and J.S.F.).</p>
Tonsil Atlas: CellRanger outputs
<p>This repository contains all the expression and accessibility matrices for each of the five data modalities (scRNA-seq, scATAC-seq, Multiome, CITE-seq+scVDJ-seq, and spatial transcriptomics) associated with the article "An Atlas of Cells in The Human Tonsil," published in Immunity in 2024. In particular, each modality was produced with different platforms from 10X Genomics and processed with different versions of CellRanger. This repository contains the most important outputs from each CellRanger run of each library. In addition, it also includes de expression and accessiblity matrices for the two mantle cell lymphoma (MCL) patients analyzed in the paper.</p> <p>Version 4.0 of this repository adds all high-quality TIFF images of the Visium slides, as requested by a user.</p>
Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"
<p>Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a></p> <p> </p> <p>The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling. </p> <p> </p> <p>Regarding the raster files, please note:</p> <p>a) All values in these datasets have been multiplied by 10,000 to optimize file sizes.</p> <p>b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym.</p> <p>c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.</p> <p> </p> <p>If you have any questions about the dataset or its use, please don't hesitate to contact us.</p> <p> </p> <p> </p>
Circularity3 DDOMP - Project tracksheets: scholarly publications, non-peer-reviewed digital outputs, dataset log, and software log.
<p>Project tracking sheets for the Circularity3 project. </p> <p>The following tracking sheets are provided:</p> <ol> <li>Scholarly Publications </li> <li>Non-peer-reviewed digital outputs</li> <li>Dataset log </li> <li>Software log </li> </ol> <p>For further specifications and explanations of the tracking sheets refer to the Circularity3 DDOMP here: https://doi.org/10.5281/zenodo.11047951</p> <p> </p> <p>This tracking sheets reference widely the PARSEC research teams tracking sheets, to whom we are very grateful for their transparent and insightful documentation.</p> <p>Stall, Shelley, Specht, Alison, Corrêa, Pedro Luiz Pizzigatti, David, Romain, Edmunds, Rorie, Mabile, Laurence, Machicao, Jeaneth, Miyairi, Nobuko, Murayama, Yasuhiro, O'Brien, Margaret, Wyborn, Lesley, & Vellenich, Danton Ferreira. (2023). PARSEC Data and Digital Output Management Plan and Workbook. Zenodo. <a href="https://doi.org/10.5281/zenodo.3891426">https://doi.org/10.5281/zenodo.3891426</a></p>
SQUID manuscript workflow with outputs
<p>This repository reproduces the analysis performed in for our method <strong>SQUID</strong> (<strong>S</strong>urrogate <strong>Qu</strong>antitative <strong>I</strong>nterpretability for <strong>D</strong>eepnets; Seitz, McCandlish, Kinney and Koo). It contains tools to apply SQUID on several previously-published genomic models, and compare its results to existing attribution methods.</p><p>The scripts contained in this release are a replica of those found in the current release of our GitHub repository (https://github.com/evanseitz/squid-manuscript as of commit 5b47a7d), with the addition that all intermediate and final outputs are provided here.</p>
ScienceDex guides
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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.