Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

1,600

datasets available to search

ShareScore release 0.9.0

Dataset results

1,600 results for “input”

Learn how ShareScore rates datasets ↗

zenodo44/100

Artificial Intelligence for EU Decision-Making: Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy

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>) 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.

opencc-by-4.0Mar 2020View details →

zenodo44/100

`hectordata` inputs: Reduced Complexity Model Intercomparison (RCMIP) Phase 1 Emissions and Concentrations

The attached data was downloaded on April 30, 2020 from <a href="https://www.rcmip.org/">https://www.rcmip.org/</a>.  These data have no formal citation or DOI.  Since we use this specific version as inputs to the `hectordata` R package (<a href="https://github.com/JGCRI/hectordata">https://github.com/JGCRI/hectordata</a>), we offer these as an archived resource.  The `hectordata` package is used to prepare the inputs used in the simple climate model Hector (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>). The datasets contained were listed as "Version 4.0.0, 31st December 2019" for both Emissions and Concentrations.  Units are described in the files.

opencc-by-4.0Apr 2020View details →

zenodo44/100

Global physical input-output tables for iron and steel (2008-2017).

Dataset: Global physical input-output tables for iron and steel Years: 2008-2017 Base classification: 32 regions, 39 processes and 30 flows Associated journal article: The PIOLab - Building global physical input-output tables in a virtual laboratory (forthcoming, Journal for Industrial Ecology) Associated GitHub repository: www.github.com/fineprint-global/PIOLab Contact: hanspeter.wieland@wu.ac.at The folder RawData 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 RawData needs to be stored in your local repository. The folder gPSUT 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. The folder gPIOT 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. The folder Diagnostics 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.

opencc-by-4.0Dec 2020View details →

zenodo44/100

Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"

Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018 There are 2 files in this upload: 1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron. 2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’. The subfolder ‘Functions’ contains functions called by the main codes. The main folder contains the following codes: Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1. Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2. Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3. Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4. Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1. Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.   The subfolder ‘Data’ contains the data structures used for the different figures: data_figure1.mat data_figure2.mat data_figure3.mat data_figure4_MECA.mat data_figure4_Activation.mat data_figure4_Inactivation.mat data_figureS2_Activation.mat data_figureS2_Inactivation.mat data_Axon.mat   The data structures contain the following fields: Mouse_Name : name of the mouse. Mouse_DateOfBirth: date of birth of the mouse (YMD). Mouse_Sex: sex of the mouse (F or M). Mouse_Genotype: genotype of the mouse. Mouse_Drug: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’). Mouse_Virus: virus injected if any. Cell_Counter; cell recorded in a given mouse. Cell_Type: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP). Cell_Depth: depth of the recorded cell relative to pia (µm). Cell_TargetedBrainArea: cortical area targeted (C2 column of the barrel cortex = C2). Cell_Fluorescence: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf Mouse_Genotype) with Cell_Fluorescence=TRUE is considered as a VIP neuron (cf Cell_Type). Sweep_Counter: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s). Sweep_Type: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’;  Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’;  ). Sweep_Start_Time: time at the beginning of the sweep recording (YMDHms). Sweep_WhiskerAngle: C2 whisker angular position extracted from simultaneous high-speed video filming (deg). Sweep_WhiskerAngle_SamplingRate: sampling rate of the whisker angle trace. Sweep_WhiskingOnset_Time: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s). Sweep_MembranePotential: membrane potential recording (mV) after cutting of the APs. Sweep_MembranePotential_SamplingRate: sampling rate of the membrane potential signal (pt.s-1). Sweep_CurrentInjected: current injected into the cell (pA). Sweep_CurrentInjected_SamplingRate: sampling rate of current signal (pt.s-1). Sweep_WhiskerStim_Name: whisker to which the magnetic stimulus was applied to (C2 or B2&C2). Sweep_WhiskerStim_Time: onset times of the whisker stimulus (s). Sweep_OptoStim_Power: light power applied for optogenetic manipulations (% of the max power). Sweep_OptoStim_Time: onset times of the light pulses for optogenetic manipulations (s). Cell_ID: unique cell identifier (= Mouse_Name+Cell_Counter). SpikeThreshold: spike threshold used to detect APs (mV). Trial_WhiskingOnset: data structure containing the cut signals used to compute averaged responses around whisking onset times. Trial_WhiskerStim: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times. Trial_WhiskerStim_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements. Trial_WhiskerStim_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements. Trial_Opto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times. Trial_OptoAndWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements. Trial_OptoAndWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements. Trial_OnlyWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements. Trial_OnlyWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements. Trial_OnlyOpto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model

Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.

opencc-by-4.0Dec 2020View details →

zenodo44/100

A join of the Huber et al. (2014) catalog of stellar parameters and the Kepler Input Catalog

This a join of the <a href="http://arxiv.org/abs/1312.0662">Huber et al. (2014)</a> and the <a href="http://arxiv.org/abs/1102.0342">Kepler Input Catalog</a>

opencc-zeroDec 2014View details →

zenodo44/100

RAPID input and output files corresponding to "RAPID Applied to the SIM-France Model"

Corresponding peer-reviewed publication This dataset corresponds to all the RAPID input and output files that were used in the study reported in: <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> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Time format 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 1st 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 1st 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1st 2000, Universal Time. Data sources The following sources were used to produce files in this dataset: <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> Software The following software were used to produce files in this dataset: <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> Study domain The files in this dataset correspond to one study domain: <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> Description of files All files below were prepared by Cédric H. David, using the data sources and software mentioned above. <ul> <li>rapid_connect_France.csv. 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>m3_riv_France_1995_2005_ksat_201101_c_zvol_ext.nc. 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 rapid_connect_France.csv. 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>kfac_modcou_1km_hour.csv. 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 rapid_connect_France.csv. 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>kfac_modcou_ttra_length.csv. 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 rapid_connect_France.csv. 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>k_modcou_0.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> </ul> <ul> <li>k_modcou_1.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_2.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_3.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_4.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_a.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_b.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>k_modcou_c.csv. 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 rapid_connect_San_Guad.csv. The values were computed based on the following information: kfac_modcou_1km_hour.csv and using Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_0.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_1.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_2.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_3.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_4.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_a.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_b.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>x_modcou_c.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Table (2) in David et al. (2011). This file was prepared using a Fortran program.</li> <li>rivsurf_France.csv. 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>rivsurf_adour.csv. 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>rivsurf_allier.csv. 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>rivsurf_ardeche.csv. 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>rivsurf_dordogne.csv. 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>rivsurf_garonne.csv. 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>rivsurf_garonne_reste.csv. 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>rivsurf_garonneariege.csv. 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>rivsurf_herault.csv. 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>rivsurf_loir.csv. 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>rivsurf_loire.csv. 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>rivsurf_loire_amont_nevers.csv. 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>rivsurf_loire_reste.csv. 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>rivsurf_lot.csv. 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>rivsurf_meuse.csv. 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>rivsurf_oise.csv. 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>rivsurf_rhone.csv. 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>rivsurf_rhone_reste.csv. 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>rivsurf_rhone_suisse.csv. 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>rivsurf_saone.csv. 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>rivsurf_seine.csv. 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>rivsurf_seine_amont.csv. 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>rivsurf_seine_reste.csv. 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>rivsurf_tarn.csv. 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>rivsurf_vienne.csv. 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>Qout_France_201101_c_zvol_ext_3653days_p1_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_3653days_p2_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_3653days_p3_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_3653days_p4_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_3653days_pa_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_3653days_pb_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_3653days_pc_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_366days_p0_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s.nc. 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 rivsurf_France.csv. 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>Qout_France_201101_c_zvol_ext_366days_pb_dtR1800s_pougny.nc. 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 rivsurf_France.csv. 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>gage_id_1995_1996_full.csv. 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>gage_id_1995_1996_full_nash.csv. 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>gage_id_1995_2005_70.csv. 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>Qobs_1995_1996_full.csv. 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 gage_id_1995_1996_full.csv. 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>Qobs_1995_1996_full_nash.csv. 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 gage_id_1995_1996_full_nash.csv. 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>Qobs_1995_1996_full_nash_93.csv. 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 gage_id_1995_1996_full_nash.csv. 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>Qobs_1995_2005_70.csv. 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 gage_id_1995_2005_70.csv. 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>Qobsbarrec_1995_1996_full_nash.csv. 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 gage_id_1995_1996_full_nash.csv. 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>forcingtot_id_1995_1996_full.csv. 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>forcinguse_id_garonne_reste.csv. 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>forcinguse_id_loire_reste.csv. 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>forcinguse_id_rhone_pougny.csv. 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>forcinguse_id_rhone_reste.csv. 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>forcinguse_id_seine_reste.csv. 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>Qfor_1995_1996_full.csv. This CSV file is identical to Qobs_1995_1996_full.csv.</li> <li>Qfor_1995_1996_full_93.csv. This CSV file is identical to Qobs_1995_1996_full_nash_93.csv.</li> <li>Qinit_93.csv. This CSV file contains the final state of RAPID after a simulation ending on 1995-11-31T00:00+02:00</li> </ul> Known bugs and limitations in this dataset or the associated manuscript. 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. Funding 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.

opencc-by-4.0Oct 2011View details →

zenodo44/100

Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - registered sections (input data)

These are the 3500x3500 ROI data, selected from complete scans. Following procedure was applied: - Coarse registration (Ulrich et al, 2014) - ROI selection, 4k x 4k regions - Normalisation - Fine-grain registration (Lobachev et al, 2016) - Crop to the center to obtain 3500x3500 size. We estimated the slice thickness to be 7 µm.

opencc-by-4.0Jul 2016View details →

zenodo44/100

Selkie GIS Techno-Economic Tool input datasets

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in theIrish and Welsh maritime areas. Read more here:https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/ <blockquote>This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.</blockquote> ********************File Formats********************Results are presented in three file formats: tif Can be imported into a GIS software (such as ARC GIS)csv Human-readable text format, which can also be opened in Excelpng Image files that can be viewed in standard desktop software and give a spatial view of results  ******************Input Data******************All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data. Hourly Data from 2000 to 2019 - Wind -Copernicus ERA5 dataset17 by 27.5 km grid  10m wind speed - Wave -Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset3 by 5 km grid  *********************Accessibility*********************The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.  The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month. Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if  the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month. Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.  Wind hourly data is from the ERA 5 dataset.    ********************Availability********************A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather  windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the  relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation betweenaccessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer. The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the  environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.  Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined  by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total  number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationshipbetween the two. A mature technology reliability was assumed.  **********************Weather Window**********************The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the  given duration for the month. The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset  (0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer. The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.  The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep? Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather  windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain  suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable  weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available  at any given point in the month. *****************************Extreme Wind and Wave*****************************The Extreme wave layers show the highest significant wave height expected to occur during the given return period.The Extreme wind layers show the highest wind speed expected to occur during the given return period.   To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced  portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model  that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values  for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold. The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution. The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). Thesecond input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected  extremes and used to calculate the extreme value for the selected return period.

opencc-by-4.0Nov 2023View details →

zenodo44/100

Input data for ismip6-gris-results-processing

This archive provides the input data used for scalar processing of ISMIP6 Greenland ice sheet output data. Processing scripts are available on github (https://github.com/ismip/ismip6-gris-results-processing) and have been additionally archived on zenodo (https://zenodo.org/records/3939115).Results are related to publication "The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6" , Goelzer et al., 2020

opencc-by-4.0Nov 2023View details →

zenodo44/100

S1Data: ChIP-seq Data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"

ChIP data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"

opencc-by-4.0Nov 2023View details →

zenodo44/100

Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"

These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded). These files provide insight into the AI-supported screening process and the choices made by the human reviewer.

opencc-by-4.0Jan 2024View details →

zenodo44/100

Bubble/Foam Simulations for Malej et al. 2023, source codes, input files, matlab files, data files

.F are source codes, *.m are matlab scripts for analysis and postprocessing, .txt are data files including bathymetry and data from sensitivity tests

opencc-by-4.0Oct 2023View details →

zenodo44/100

Data for :Nitrogen availability in digestates from full-scale biogas plants following soil application as affected by operation parameters and input feedstocks

This archive contains data for the paper "Nitrogen availability in digestates from full-scale biogas plants following soil application as affected by operation parameters and input feedstocks". Obtained from a soil incubation experiment for 80 days.

opencc-by-4.0Oct 2023View details →

zenodo44/100

Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft

Open the record for dataset details and reuse information.

opengpl-3.0-or-laterAug 2024View details →

zenodo44/100

Dataset and Input Files for the "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA" Manuscript

Datasets and input files used for the Ecology and Society manusript "Sustainability and Resilience Through Connection: The Economic Metacommunites of the Western USA".

opencc-by-4.0Nov 2024View details →

zenodo44/100

Data inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al.

This data packet provides inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al., currently in the submission process. This repository will eventually be updated to link to the published manuscript.   =============================================================================== =============================================================================== Overview =============================================================================== =============================================================================== Data and model products associated with the manuscript "Mammal niches are not  conserved over continental scales" by Goldstein et al.  Files are organized into two subdirectories. The first, "model_inputs/",  contains 8 data files intended to be used as part of the reproducible code  repository at  https://github.com/dochvam/Mammal_SVCs_ISDM_reproducible.  The second subdirectory, "model_outputs/", contains modeled products giving estimated spatially varying niche relationships and predictions of relative abundance. Below, we describe the contents of each file type. See the main manuscript  for full methodology, data sources, and discussions of spatial scales. NOTE: Version 1 of this dataset contained some errors that have been corrected in Version 2. Version 2 was used as the input dataset for the analyses in the associated manuscript. Version 1 should not be used. =============================================================================== =============================================================================== Subdirectory 1: "model_inputs/" =============================================================================== =============================================================================== Two versions of each of four files are provided, corresponding to analyses  that do or do not consider ancient genetic lineages as potential sources of  spatial nonstationarity in mammal niches. Each file type is formatted the same, and the versions are differentiated by either the suffix "nolineage" or  "lineage" in the filename.  =============================================================================== File 1: gridcell_covars_lineage.csv and gridcell_covars_nolineage.csv =============================================================================== These files are .csvs giving spatial covariate data for each scale 2 cell in North America, summarized to 5000 m. All percentage values are given in  10ths of a percent (scale of 0-1000). The following columns are provided: - grid_cell: Scale 2 cell ID - Arable: pct arable land (Jung et al. 2020) - EVI_mean: mean enhanced vegetation index (Didan 2021) - EVI_Q95: 95th quantile of EVI (Didan 2021) - Forest: Pct forest cover (Jung et al. 2020) - Grassland: pct grassland (Jung et al. 2020) - Pastureland: pct pastureland (Jung et al. 2020) - Pop_den: Human population density, from Gridded Population of the World (CIESIN 2018) - Precipitation: avg annual precip. (Vega et al. 2017) - Shrubland: pct shrubland (Jung et al. 2020) - Temp_max: Average maximum daily temperature (Vega et al. 2017) - Terrain_roughness (Amatulli et al. 2018) - Wetlands: pct wetlands (Jung et al. 2020) - is_land: Whether or not the cell is on land vs. ocean, used for filtering - Agriculture: Pct. agricultural land (Jung et al. 2020) - Pop_den_sqrt: Square root of human population density (CIESIN 2018) - EVI_variability: Distance btw the 95% inner quantiles of EVI (Didan 2021)   =============================================================================== File 2: inat_cts_lineage.csv and inat_cts_nolineage.csv =============================================================================== These files give summaries of iNaturalist sampling effort and detections for target species. The following columns are provided: - grid_cell: Scale 3 cell ID - n: Total iNaturalist effort in the cell (number of obs. of all mammals) - The remaining columns are named for species. Each column gives the count     of observations of the species in the cell. =============================================================================== File 3: ct_datlist_lineage.RDS and ct_datlist_nolineage.RDS =============================================================================== The ct_datlist files contain R objects that are lists of lists. These objects ultimately contain all of the camera detection histories and camera-level covariate data used in modeling. We use the nice data type "unmarkedFrameOccu" from the unmarked R package to organize these detection data. Each outer list is of length equal to the number of species. The ith element of each list contains the following named slots: - species: a string giving the name of the ith species - umf: an unmarkedFrameOccu object. This object has three important slots:     - y: a (# deployments) x (max # replicates) matrix giving 1s, 0s, or NAs indicating          whether the target species was observed in that 10-day window;     - siteCovs: a (# deployments) x 2 data frame with the following columns:         - site_ID: A unique ID of the exact location, shared by deployments with the same            coordinates         - subproject_ID: A unique ID indicating which camera array is associated                 with this deployment     - obsCovs: a (# deployments * max # replicates) x 6 data frame with the following columns:             - year: the year of survey, relative to 2020 (zero-year is 2020)             - yday_scaled: the (scaled) Julian date of the beginning of the window             - yday_scaled_sq: yday_scaled^2, for use in estimating a quadratic effect             - log_roaddist_scaled: Scaled distance to nearest road (Meijer et al. 2018)             - Canopy_height_scaled: Scaled canopy height (Potapov et al. 2021)             - obs_len_scaled: Scaled duration of window, to account for some windows                                being cut off at < 10 days - coords: a data frame. Originally, this file gave the exact position for each camera,       but these exact locations have been scrubbed for privacy. See the original sources           cited in the manuscript for full details. This data frame contains the following column:     - scale2_grid_ID: the ID of the Scale-2 5000 m grid cell containing the camera =============================================================================== File 4: grid_translator_wspecs_nolineage.csv and grid_translator_wspecs_lineage.csv =============================================================================== These files are used for bookkeeping to track the relationships between the  three spatial scales in this study. Each row corresponds to a single "scale 2" cell, giving the ID of the corresponding S3 and S4 grid and also an ID for each species indicating whether and where it is in the species' range.  Note that the scale names in the code don't match the manuscript. In the code, "scale 1" is the level of an individual camera, "scale 2" is the 5 km intensity grid, "scale 3" is the 50 km iNaturalist grid, and "scale 4" is the 100 km SVC grid. The following columns are provided: - scale2_grid_ID: unique ID for each cell in the 5 km intensity grid - scale3_grid_ID: unique ID for each cell in the 50 km iNaturalist aggregation - scale4_grid_ID: unique ID for each cell in the 100 km SVC grid - GRID_ID_[species]: for each species, a column is provided on the S4 scale                      counting each cell in the species' modeled range. NAs                  indicate that the S2 cell defined in the row is not              included in the species' modeled range. =============================================================================== =============================================================================== Subdirectory 2: "model_outputs/" =============================================================================== =============================================================================== =============================================================================== File 1: svc_estimates.csv =============================================================================== This file gives an estimate of the effect of each covariate on each species' intensity, and the uncertainty in that estimate, for each species/covariate pair. Results correspond to lineage models for species with phylogeographies and non-lineage species otherwise. Each row represents the effect of one  covariate on one species' relative intensity process within one 100 km cell g.  Note that many estimates of beta_g are uncertain even for strong spatial  effects---the model is often confident that a spatial process is supported  while estimates of the realized process are uncertain. The following columns are provided: - x: the x-coordinate of the 100 km cell - y: the y-coordinate of the 100 km cell - species - parname: the name of the covariate - mean: the mean of the posterior samples of beta_g - 2.5%: the 2.5th quantile of the posterior samples of beta_g - 50%: the 50th quantile of the posterior samples of beta_g - 97.5%: the 97.5th quantile of the posterior samples of beta_g The following spatial projection is used to define X/Y coordinates: "+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83" =============================================================================== File 2: predicted_intensity.tif =============================================================================== This file contains a raster "brick" giving the predicted intensity surface  and uncertainty in this surface for each species. All predictions are generated using models that do *not* account for lineage information---this means that  predictions for species with lineages are not from the models reported in the main manuscript. The reason for this is that we found that lineages were  overall unsupported, so better predictions can be arrived at by excluding this source of uncertainty in the underlying intensity process. The raster brick has 66 layers. Each layer provides either the mean predicted log intensity in each grid cell across the species range or else provides the standard error of that predicted log intensity. Layer names indicate output type and species associated with each layer. =============================================================================== =============================================================================== References =============================================================================== =============================================================================== Camera data are obtained from the following sources, which can be consulted to obtain the original raw camera data - Cove, Michael V., et al. "SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States." (2021): e03353. - Kays, Roland, et al. "SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID‐19 pandemic." (2022): e3775. - Shamon, H., et al. “SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.” Ecology, 105.6 (2024): e4318. - Rooney, B., et al. “SNAPSHOT USA 2019–2023: The first five years of data from a coordinated camera trap survey of the United States.” In Press (2024). - Kays, Roland, et al. "Does hunting or hiking affect wildlife communities in protected areas?." Journal of Applied Ecology 54.1 (2017): 242-252. - Roberts, R. California Department of Fish and Wildlife, Bobcat Program Initiative. wildlifeinsights.org (2023). - Lasky, Monica, et al. "CAROLINA CRITTERS: a collection of camera trap data from wildlife surveys across North Carolina." Ecology 102.7 (2021): e03372. - Forrester, T. (2000). Urban to Wild Project. http://n2t.net/ark:/63614/w12004302. Accessed via wildlifeinsights.org on 2024-08-29. - McMurry, S. et al. In review (2024). - Forrester, T. (2011) Okaloosa S.C.I.E.N.C.E. Project. http://n2t.net/ark:/63614/w12004287.  - Myers, J. (2014) Tyson Research Center ForestGEO Project. http://n2t.net/ark:/63614/w12004295. - McMurry, S., and Kays, R.(2023). Calloway Forest Preserve. http://n2t.net/ark:/63614/w12006449. Accessed via Wildlife Insights on 2024-08-29. - McMurry, S., Parsons, A., Lasky, M., Luongo, K.,  Clark, J., McShea, W., Scher, L., Kays, R., Spurlin, J., Martin, G., Frech, G., Barajas-Salazar, K.,  Snider, M. (2022). Last updated October 2023. Calloway Forest Preserve. http://n2t.net/ark:/63614/w12004251. Accessed via wildlifeinsights.org on 2024-08-29. - Kays, R.. (2008). Last updated March 2024. Albany Area Camera Trapping Project. http://n2t.net/ark:/63614/w12003860. Accessed via wildlifeinsights.org on 2024-08-29. - Kays, R., Snider, M., McMurry, S., Alyetama, M. (2024). Last updated April 2024. Pilot Mountain Density 2024. http://n2t.net/ark:/63614/w12007160. Accessed via wildlifeinsights.org on 2024-08-29. - Malleshappa, V., Smithsonian, E., Kays, R., Schuttler, S. (2015). Last updated December 2022. Museums Connect Mexico. http://n2t.net/ark:/63614/w12004298. Accessed via wildlifeinsights.org on 2024-08-29. Covariate data are obtained from the following sources: - Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017). - Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020). - Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018). - Center For International Earth Science Information Network-CIESIN-Columbia University. Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. (2018) doi:10.7927/H45Q4T5F. - Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13A2.061 (2021). - Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018). - Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021). - Jensen, A. J. et al. Geographic barriers but not life history traits shape the phylogeography of North American mammals. Glob. Ecol. Biogeogr. e13875 (2024). iNaturalist data are obtained from inaturalist.org via the data exporter (see manuscript for details).

opencc-by-4.0Nov 2024View details →

zenodo44/100

Effect of spatial input data quality on SWAT modelling in the Porijõgi catchment

The Porijõgi Catchment near Tartu, Estonia is the study area for this research. Four model setups were created using global/regional level data (HWSD soil, CORINE), and local high-resolution spatial data including the new Estonian high-resolution EstSoil-EH soil dataset and the Estonian Topographic Database (ETAK). The study employed statistical criteria to assess SWAT model performance for monthly simulated stream flows from 2007 to 2019. Data deposit in preparation for article: Effect of spatial input data quality on the uncertainty of the SWAT model, submitted 2022 Alexander Kmoch, Desalew Meseret Moges, Mahdiyeh Sepehrar, Balaji Narasimhan and Evelyn Uuemaa contact: alexander.kmoch@ut.ee

opencc-by-4.0Mar 2022View details →

zenodo44/100

Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns

Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device. For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.

opencc-by-4.0Mar 2022View details →

zenodo44/100

Global spatially explicit critical nitrogen surpluses and critical nitrogen inputs, and their exceedances

Data files belonging to manuscript: Schulte-Uebbing, LF, AHW Beusen, AF Bouwman & W de Vries (2022): From planetary to regional boundaries for agricultural nitrogen pollution. Nature, Vol 610 (7932), https://doi.org/10.1038/s41586-022-05158-2 * Input datafiles: Contains complete set of input files used in the calculations of global, spatially explicit critical nitrogen surpluses and critical nitrogen inputs. All input files are output from the IMAGE-GNM model. For further information on IMAGE-GNM, see:  Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollón, J. M., & Middelburg, J. J. (2015). Coupling global models for hydrology and nutrient loading to simulate nitrogen and phosphorus retention in surface water - Description of IMAGE-GNM and analysis of performance. Geoscientific Model Development, 8(12), 4045–4067. https://doi.org/10.5194/gmd-8-4045-2015 * Output datafiles: Selection of output datafiles, supporting results presented in the paper.  For more information, see file "README.xlsx".

opencc-by-4.0Dec 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record