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477 results for “input data”
Dynamics of island mass effect: detection input & output data
<p>"dynamic_IMEdetection.zip" contains a dynamic island mass effect detection software package.</p> <p>To run:</p> <ul> <li>Unzip "dynamic_IMEdetection.zip"</li> <li>Download input data</li> <li>change path, region to process, and options in "main_dynamicIME_detection.m" to work with the location of the unzipped "dynamic_IME_input_output_data" directory.</li> <li>Run "main_dynamicIME_detection.m"</li> </ul> <p>"dynamic_IME_input_output_data.zip" contains the input data and ouput IME and BO masks of the "dynamic_IMEdetection" software for four case studies:</p> <ul> <li>I04 = Rapa Nui</li> <li>I07 = Society Islands</li> <li>I09 = Samoa</li> <li>I18 = Fiji-Tonga</li> </ul> <p>Input data are custom multi-satellite products computed using the custom-made satellite binning software found here: <a href="../doi/10.5281/zenodo.13376824">10.5281/zenodo.13376824</a>.</p> <p>Output IME and BO masks are computed using the dynamic IME detection software (i.e. "dynamic_IMEdetection") which works with the original naming (i.e. "I04", "I07", "I09", and "I18") and subdirectory path of of the share data "dynamic_IME_input_output_data".</p> <p>Output IME/BO mask, chlorophyll concentration, and modelled surface current timeseries in *.gif format:</p> <ul> <li>RapaNui_8Dbin_20160520-0712_20161205-2230_map-time-series.gif = Rapa Nui between 2016/05/20 and 2016/12/05</li> <li>SocietyIsl_8Dbin_20160622-1012_20170209-0945_map-time-series.gif = Society Islands between 2016/06/22 and 2017/02/09</li> <li>Samoa_8Dbin_20160810-1115_20170226-1030_map-time-series.gif = Samoa between 2016/08/10 and 2017/02/26</li> <li>FijiTonga_8Dbin_20170218-1240_20170906-1205_map-time-series.gif = Fiji - Tonga between 2017/02/18 and 2017/09/06</li> </ul>
Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks
<p>Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively.</p> <p>Filenames denote the structure of the SS profile: </p> <p>???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}<br> </p> <p>LT = elastic lithosphere thickness (km)</p> <p>UMV = upper mantle viscosity (1E21 Pa s)</p> <p>LMV = lower mantle viscosity (1E21 Pa s)</p> <p>i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile</p> <p>96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity</p> <p> </p> <p>The columns of the input files are as follows:</p> <p>LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0)</p> <p>units for the above are as follows:</p> <p>km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m, mm/year</p> <p>where 'days since 2000' assumes exactly 365.25 days per year.</p>
Electron concentration profiles calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data
<p>The files contain electron concentration <i>Ne</i> profiles during solar X-ray flares that occurred on 9-11 June 2014. The altitude range is 50-90 km.</p><p>Values of electron concentration were calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data (MSIS neutral atmosphere model and Aura satellite measurements). Results are obtained for ten VLF paths: from European transmitters ICV, TBB, GQD, GBZ, and DHO to Mikhnevo geophysical observatory (55°N 38°E) and A118 SID station (43°N 1°E).</p><p>The data is presented as MATLAB files. Each .mat file contains data and variable "description" with data's structure information.</p>
Input data and scenarios outputs of the Water Resources Research paper "Optimal economic spatial and temporal allocation of green and grey investment to address water security threats : Case Study - Velhas River Basin, Brazil"
Open the record for dataset details and reuse information.
Input and output data and code for PPS2 on 333 vertebrate and111 plant genome assemblies, and for DDS2+ on fungal genome assemblies
<p>The datasets contain PPS2 and DDS2+ source and binary code, other scripts, and some input and output data. Please see the README file after unpacking it. The absolute paths in the scripts need to be modified in order to duplicate the results in this dataset. Most of the input and output data have to be removed from the datasets for quick uploading and downloading; otherwise, the datasets would exceed the 50-Gb size limit.</p>
Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:</p><p>--uniformize=1</p><p>--predictor_norm_type_string="z_score"</p><p>--vector_target_norm_type_string=""</p><p>--scalar_target_norm_type_string=""</p><p> </p><p>Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
Model data and input files
<p>The zip file contains the matlab code of the model and the input data that was used to run the model. The zip file contains a README.docx that explains the contents of the zip file</p>
The Dutch National Flexible Groundwater Model prototype input data
<p>This dataset includes the necessary model input data to run the Netherlands' National Flexible Groundwater Model prototype. For this, pre-processing is required using the quad2d tools, provide by https://github.com/verkaik/quad2d/releases/tag/quad2d_v0.1.</p>
Planning resource adequacy of wind- and solar-based electricity systems: Input data and results files
<p>This record contains the input data and raw results files for the study titled "<a href="https://www.sciencedirect.com/science/article/pii/S2666792424000234">Planning reliable wind- and solar-based electricity systems</a>."</p> <p>Tyler H. Ruggles, Edgar Virgüez, Natasha Reich, Jacqueline Dowling, Hannah Bloomfield, Enrico G.A. Antonini, Steven J. Davis, Nathan S. Lewis, Ken Caldeira, "Planning reliable wind- and solar-based electricity systems," Advances in Applied Energy, 2024, https://doi.org/10.1016/j.adapen.2024.100185.</p> <p>Additionally, csv files are provided to recreate the associated figures in the paper in the "Figures_files.zip" file.</p> <p>The input data contains wind and solar generation availability profiles and electricity demand profiles for the contiguous US. The profiles cover the years 1950-2022 and are calculated from the ERA5 dataset. The study only used the satellite era data from the year 1979 onward. Input profiles are presented at 4 resolutions: hourly, 2-hour, 3-hour, and 4-hour resolution.</p> <p>The results files contain some keys indicating the modeling scenario used: "SWB" = "Solar+Wind+Battery"; "SWBNG" = "Solar+Wind+Battery+Natural Gas generation"; and "SWBPGP" = "Solar+Wind+Battery+Power-to-H2-to-Power Loop". The files can be grouped into multiple categories:</p> <ol> <li>The main analysis including the initial energy system optimization results and the secondary system performance testing results. <ol> <li>Initial optimization results are found in zip files titled "Initial_Optimization_Jan29v1_*.zip"</li> <li>The testing of the optimized systems are found in the zip file "Lost_Load_Decade_Testing_Jan29v1.zip"</li> </ol> </li> <li>A secondary analysis compared systems optimized on a single year of data and tested on a single other year of data. Those results are in "Matrix_Figure_NYrs1_Aug04v1.zip"</li> <li>A supplementary analysis compared the modeled results using input data with the 4 different time resolutions. These results can be found in the zip files titled "DeltaT_Test_July08v1dt*.zip"</li> </ol>
[VISIR-2: input data for benchmark runs] OpenCPN
<p>Bathymetric data, namelists, as well as current and wind fields needed for the VISIR-2 ship weather routing model benchmark run against OpenCPN.</p>
Data and analysis process for Microbiome processing of organic nitrogen input supports growth and cyanotoxin production of Microcystis aeruginosa cultures
<p>Data and analysis process for manuscript titled "Microbiome processing of organic nitrogen input supports the growth and cyanotoxin production of Microcystis aeruginosa cultures"</p>
Code and exemplificative data for publication titled: Synaptic inputs to motor neurons underlying muscle co-activation for functionally different tasks have different spectral characteristics.
<p>Code implemented in the publication</p> <p>Titled: Synaptic inputs to motor neurons underlying muscle co-activation for functionally different tasks have different spectral characteristics</p> <p>Authors: Borzelli,D; Vieira,TMM; Botter,A; Gazzoni,M; Lacquaniti,F; d’Avella,A</p> <p>Published in: Journal of Neurophysiology</p> <p>Year: 2024</p> <p>When running the function 'coherenceAnalysis_Borzelli_et_al_JNeurophysiol_2024.m', the code load the firings of the motor units identified during two exemplificative trials performed by a participant, saved in 'data_Borzelli_et_al_JNeurophysiol_2024'. One trial was directed toward an horizontal target of the perturbed block and the other was directed toward a supination target of the baseline block.</p> <p>Then the code calculates the coherence between the MUs identified on the BB and on the TB (cross-muscle coherence) and the cross-muscle coherence after excluding the components synchronized with the norm of the endpoint force.</p> <p>Finally, the code plots the results.</p> <p>This code could easily be modified to compute all the relevant analyses presented in the paper, such as the total within muscle coherence and the within muscle coherences after excluding the components synchronized with the norm of the force or the sum of the firings of the antagonist muscle.</p>
Code and input data related to "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector"
<p>Input data and code for the submitted article: "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector" <br><br>by Alissa Ganter <sup>1,†</sup>, Paula Baumann <sup>1,2,†</sup>, Veis Karbassi <sup>2</sup>, Giovanni Sansavini <sup>1,*</sup></p> <p><sup>1 </sup>Reliability and Risk Engineering, Institute of Process and Energy Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland</p> <p><sup>2 </sup>School of Business and Economics, RWTH Aachen University, Kackertstraße 7, 52072 Aachen, Germany</p> <p><sup>† </sup>These authors contributed equally</p> <p><sup>*</sup>Corresponding author: sansavig@ethz.ch</p>
Simulation Input Data for "Quantifying acetylation-induced changes in the plant secondary cell wall structure and dynamics"
<p>This is the reduced data behind an upcoming manuscript investigating impact of acetylation on plant secondary cell wall. The data is taken directly from the directory structure that contains both the simulation and analysis, with excluded trajectory files and intermediate products to fit within the zenodo upload limit. The tar command used to generate this tarball was: </p> <p> </p> <pre><code>tar -zcvf Acetylatedcellwall.tar.gz --exclude="*BAK" --exclude="*dcd" --exclude="*poster*" --exclude="*old" --exclude="*out" --exclude="*vel" --exclude="*ppm" --exclude="*mp4" --exclude="*txt" --exclude="*tga" --exclude="*vmd" --exclude="*log" --exclude="fixed*png" --exclude="frame*png" --exclude="nonacetylation*png" . </code></pre>
Input data and outputs for the OCALM project
<p>This repository contain a zip file with input and output data for an experiment with OCALM (<a href="https://github.com/fanavarro/ocalm">https://github.com/fanavarro/ocalm</a>):</p> <ul> <li>input <ul> <li>ontologies: ontologies used as input (FoodOn, LKIF, GeneOntology), together with their normalized form.</li> <li>text <ul> <li>food_text: natural language text corpus about food, including the original and the processed text.</li> <li>gene_text: natural language text corpus about genetics, including the original and the processed text.</li> <li>legal_text: natural language text corpus about legal topics, including the original and the processed text.</li> </ul> </li> </ul> </li> <li>results: the results derived from comparing each ontology with each natural language text corpus by using OCALM.</li> <li>NCBO_Recommender_results: the results of the NCBO Recommender with the same experiment performed by OCALM.</li> <li>analysis.R: R script to get figures summarizing the results.</li> </ul> <p>The SNOMED ontology and the medical text corpus used for input were not included due to licensing issues; however, the results are included in this repository.</p>
Input data for ENERWAT-GLOB - Water Treatment
<p>Input data for the 5 arcmin modelling framework calculating the global energy consumption of water treatment technologies (conventional drinking water treatment, desalination, wastewater treatment). Contains the following: 1) global data (folder "global_data"); and 2) inputs related to the various analyzed technologies (folder "water_treatment").</p> <p>Folder "global_data" contains: 1) 5 arcmin maps of world countries, HydroBASINS, population (total, rural, urban) and Gross Domestic Product; 2) country-level Total Gross Domestic Product and population statistics reported by the United Nations; 3) validation data, i.e. country-level energy consumption data from the U.S. Energy Information Administration for 2015 (primary energy and electricity), country-level estimates of energy consumption from Liu et al. (2016) and validation data compiled from literature reviewed by Chini et al. (2021). </p> <p>Folder "water_treatment": 1) folder "desalination" contains DesalData v2019 (edited to show bare country and region information as it is licensed data) and points used to model the increasing energy efficiency of Reserve Osmosis over time (Elimelech et al. (2011) as actual model input and Liu et al. (2016) for validation); 2) folder "dwt" contains data for conventional drinking water treatment in 2015, i.e. 5 arcmin municipal water withdrawals from PCR-GLOBWB 2 (Sutanudjaja et al. 2018) calculated by Wada et al. (2014), 5 arcmin water withdrawal sources modelled by PCR-GLOBWB 2, desalination plants online in 2015 with drinking water purposes from DesalData (edited as above), water access statistics from AQUASTAT (basic water access) and the World Bank (safely managed water access); 3) folder "wwt" contains data for wastewater treatment, i.e. global wastewater treatment plants from HydroWASTE (Ehalt Macedo et al. 2022) supplemented with data on Chinese wastewater treatment plants from Chen et al. (2019), 5 arcmin wastewater production and treatment rates from Jones et al. (2021) and data on energy consumption of 328 secondary wastewater treatment plants from Longo et al. (2016). </p> <p>For more information on the specific data sources, please refer to the manuscript "Global energy consumption of water treatment technologies". </p> <p> </p> <p> </p> <div> </div> <div> </div>
Input data for the Community Water Model (CWatM) - a regional dataset covering Israel and the Ayalon Basin
<p>This data was used for the paper: 'Wastewater matters: Incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08)'. When using this data pleas cite the paper, alongside this Zenodo repository.</p> <p>Fridman, D., Smilovic, M., Burek, P., Tramberend, S., and Kahil, T. 2024. Wastewater matters: Incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08). <em>Geoscientific Model Development Discussions</em>, 1-26. [Preprint]</p> <p><strong><span>Background</span></strong></p> <ul> <li>This dataset was developed as part of the IIASA WINTER project, aiming to run high resolution hydrological<br>simulations in the river basins in Israel (Water Futures and Solutions for Israel (WFaS-Israel) | IIASA).</li> <li>Conducting a high-resolution (30 arcseconds) simulation can rely on a mix of upscaled coarse global, high-resolution global, and local datasets. Specifically, Hanasaki et al. (2022) stress the importance of local water management and use data for hyper-resolution hydrologic simulations.</li> <li>The dataset covers the terrestrial area of Israel and the Palestinian Authority. It also includes the cross-border and upstream river basin in the neighboring countries Egypt, Jordan, Syria, and Lebanon. The selected river basins of Ayalon and Sorek are in the central coastal area of Israel and vary by topography, land cover, and water management. The Ayalon stream drains the western downslopes of the Judea and Samaria mountains and outlets into the Yarkon stream (to the North), which later reaches the Mediterranean Sea. The Sorek stream drains the hills around South-West Jerusalem and flows Westwards until reaching the Mediterranean Sea.</li> </ul> <p><strong><span>Further Information</span></strong></p> <ul> <li>For details about the contents of this dataset please refer to the readme.txt</li> <li>For details about the data soruces and processing please refer to the dataset_description.pdf</li> </ul> <p>In case of additional questions please do not hesitate to write to us fridman@iiasa.ac.at</p>
Input data for https://github.com/CCSB-DFCI/TF_isoforms_paper
<p>The input data for the code at https://github.com/CCSB-DFCI/TF_isoforms_paper which generates the figures in the paper "Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms" to be published in Molecular Cell in 2025. This data directory is just to reproduce the analysis and figures. The supplementary data tables contain the original data in a cleaner format than these files.</p>
Input data for estimating dimensionless number (Reynolds, Swimming and Strouhal number) of swimming penguin
<p>Propulsion performance of swimming and flying animals is often evaluated by using dimensionless numbers, such as the Strouhal and Reynolds numbers. They have been shown to allow better understanding of locomotion efficiency, using relatively simple approaches and avoiding overly complex computational models. Specifically, it has been reported that efficient propulsion is more likely to occur when Strouhal number values – estimated from propulsive frequencies and amplitudes – are within a relatively narrow range, depending on the corresponding Reynolds number, broadly expressing the fluid resistance to the animal motion. We have estimated both Strouhal and Reynolds numbers for seven species of penguins after analysing relevant kinematic data taken from the literature. The obtained values neatly indicate that, as expected, penguins employ efficient propulsion mechanisms. Additionally, by comparing these values with those for alcids – seabirds that can also fly – we have found that penguins swim at least as efficiently as alcids. However, we have also found that the swimming number – proportional to the product of Strouhal and Reynolds numbers – neatly correlates to the diving abilities of the considered species and apparently indicates, in a straightforward hierarchical manner, the gains in diving due to the loss of flying abilities. Within the penguin species, a clear correlation is also observed between diving performance and drag coefficient values.</p>
Input data for Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach
<p>This dataset contains all necessary input data to reproduce the results of the paper "Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach".</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.