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1,600 results for “input”
MD Analysis Pre-Processed Data Input Files: Towards a New Model for the TREX1 Exonuclease, Hemphill et al.
<p>All atoms molecular dynamics simulations were performed for TREX1 apoenzyme, TREX1-ssDNA complex, and TREX1-dsDNA complex, with four replicate simulations per system. Trajectory and initial condition output files were pre-processed in VMD to generate time-step sampled mol2 files of the macromolecules in each simulation. The mol2 files were used to calculate phi/psi angles of the protein backbone, and to extract all-atoms coordinate information over time, and this information was organized into an RData file for each simulation system, including all simulation replicates. These RData files are provided, and they were analyzed with the scripts described in the affiliated manuscript. </p>
SC2 input files
<p>Old set of input files used for a deprecated version of the OPENSC2. Input files, however, can still be useful therefore are saved in this repository. To be checked and updated.</p>
Planar Graph Classifier - Ego Twitch input
<p>Ego Twitch social network graphs acquired from the Stanford Large Network Dataset Collection.</p>
Planar Graph Classifier - GD Rome input
<p>Rome Graph dataset acquired from the GD benchmark graph set.</p>
Planar Graph Classifier - GD planar input
<p>Planar Graph dataset acquired from the House of Graphs</p>
Input and Output for CDGRe
<p>The input directory contains DREAM5 datasets for GRN inference and single-cell datasets including hESC and mDC.</p> <p>The output directory contains GRN inference results for each dataset.</p>
Taylor Valley meteorological input data for ICEMELT model
<p>These input files were too large to be included in ICEMELT-Cross GitHub repository (https://doi.org/10.5281/zenodo.6808770). To run the ICEMELT model, copy and paste the contents of this repository into the 'input' directory.</p>
Input data for deep learning model-analog
<p>This repository contains input data required to run the Deep Learning Model-Analog (<a href="https://github.com/kinyatoride/DLMA">GitHub</a>), as presented in the paper titled "Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts" by Toride et al. A preprint is available at <a href="https://arxiv.org/abs/2404.15419">https://arxiv.org/abs/2404.15419</a>.</p> <p>The <code>cesm2</code> directory contains the Community Earth System Model Version 2 Large Ensemble (<a href="https://doi.org/10.26024/kgmp-c556" rel="nofollow">CESM2-LE</a>), while the <code>real</code> directory contains the Ocean Reanalysis System 5 (<a href="https://doi.org/10.24381/cds.67e8eeb7" rel="nofollow">ORAS5</a>) datasets. These datasets have been processed to provide detrended monthly anomalies and have been interpolated to two different resolutions: 2° × 2° and 5° × 5°. The 5°×5° files are used as input, while the 2°×2° files are used for analog forecasting.</p>
LAMMPS input files and analysis for estimating the glass transition temperature using ensemble molecular dynamcs of a selection of epoxy resins using different protocols for computing the density-temperature behaviour.
<p>LAMMPS input files and analysis for estimating the glass transition temperature using ensemble molecular dynamcs of a selection of epoxy resins using different protocols for computing the density-temperature behaviour. </p>
Galactic Component Classification For the all-sky PLATO Input Catalog
<div> <h1>PLATO Targets Classification</h1> <p>This repository contains the full list of PLATO targets, classified into their Galactic Component membership. The basis for the catalogue is the all-sky PLATO input catalog (asPIC) by Montalto2021, crossmatched with Gaia DR3.</p> <h2>Files</h2> <p>There are four files included in this repository:</p> <ol> <li> <p><strong>targets_classified</strong>:</p> <ul> <li>Description: The entire sample, classified into thin disk, thick disk candidate, thick disk, halo candidate, and halo.</li> <li>Note : Classification is only performed if relative uncertainties no greater than 20% in the following columns: "ra", "dec", "pmra", "pmdec", "parallax", and "radial_velocity". For targets where this requirement is not fulfilled, the related columns are filled with NaN.</li> <li>Format: 2,675,538 rows, 61 columns</li> </ul> </li> <li> <p><strong>LOPS2</strong>:</p> <ul> <li>Description: A subselection of targets_classified in the LOPS2 field.</li> <li>Galactic Coordinates: l=255.9375°, b=-24.62432°</li> <li>Additional Column: "n_cameras" specifying the number of PLATO cameras that will observe the target.</li> <li>Format: 169,438 rows, 62 columns</li> </ul> </li> <li> <p><strong>LOPN1</strong>:</p> <ul> <li>Description: A subselection of targets_classified in the LOPN1 field.</li> <li>Galactic Coordinates: l=81.56250°, b=24.62432°</li> <li>Format: 173,735 rows, 62 columns</li> </ul> </li> <li> <p><strong>special_target_list</strong>:</p> <ul> <li>Description: A selection of 47 targets, kinematically classified as halo stars, within the high-SNR P1 PLATO sample.</li> <li>Additional Column: "n_cameras" specifying the number of PLATO cameras that will observe the target, and "Field" specifying if target belongs to LOPS2 or LOPN1 field</li> <li>Format: 47 rows, 63 columns</li> </ul> </li> </ol> <h2>Classification Criteria</h2> <p>We classify only those stars into Galactic components that have relatively certain kinematic parameters. Specifically, we require no more than 20% relative uncertainty in the following columns: "ra", "dec", "pmra", "pmdec", "parallax", "radial_velocity". This results in 2,283,538 out of the total 2,675,539 stars having a component classification.</p> <h2>Analysis Conditions</h2> <p>For the analysis in our paper, we applied the following additional conditions:</p> <ul> <li>Population.notnull()</li> <li>Stellar type = FGK</li> <li>Radius > 0</li> <li>Mass > 0</li> <li>Teff > 0</li> <li>Logg.notnull()</li> <li>[Fe/H].notnull()</li> </ul> <p>Applying these conditions results in 1,830,288 entries remaining.</p> <h2>Columns</h2> <p>The columns in the dataset are as follows:</p> <table> <tbody> <tr> <th>Column Name</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>gaiaID_DR2</td> <td>Gaia DR2 ID (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>gaiaID_DR3</td> <td>Gaia DR3 ID (matched to DR2 catalog, based on angular distance)</td> </tr> <tr> <td>parallax</td> <td>Parallax of target (mas), from Gaia DR3</td> </tr> <tr> <td>e_parallax</td> <td>Error on parallax</td> </tr> <tr> <td>ra</td> <td>Right Ascension (deg) from Gaia DR3</td> </tr> <tr> <td>e_ra</td> <td>Error on Right Ascension</td> </tr> <tr> <td>dec</td> <td>Declination (deg) from Gaia DR3</td> </tr> <tr> <td>e_dec</td> <td>Error on declination</td> </tr> <tr> <td>pmra</td> <td>Proper motion in right ascension direction (mas/yr), from Gaia DR3</td> </tr> <tr> <td>e_pmra</td> <td>Error on proper motion in right ascension direction</td> </tr> <tr> <td>pmdec</td> <td>Proper motion in declination direction (mas/yr), from Gaia DR3</td> </tr> <tr> <td>e_pmdec</td> <td>Error on proper motion in declination direction</td> </tr> <tr> <td>radial_velocity</td> <td>Radial velocity (km/s), from Gaia DR3</td> </tr> <tr> <td>e_radial_velocity</td> <td>Radial velocity error</td> </tr> <tr> <td>[alpha/Fe]</td> <td>Alpha elemental abundance from Gaia RV spectra, alphafe_gspspec in Gaia DR3</td> </tr> <tr> <td>e_[alpha/Fe]_lower</td> <td>Lower error on alpha element abundance</td> </tr> <tr> <td>e_[alpha/Fe]_upper</td> <td>Upper error on alpha element abundance</td> </tr> <tr> <td>[Fe/H]</td> <td>Metallicity estimate, either from RVS spectra (mh_gsspec in Gaia DR3, with first 13 quality flags equal to 0, Recio-Blanco2023) or XGBOOST (Andrae2023)</td> </tr> <tr> <td>[Fe/H]_source</td> <td>Source of the metallicity estimate</td> </tr> <tr> <td>e_[Fe/H]_lower</td> <td>Lower error on the metallicity estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>e_[Fe/H]_upper</td> <td>Upper error on the metallicity estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>logg</td> <td>log g estimate, either from RVS spectra (logg_gsspec in Gaia DR3, with first 13 quality flags equal to 0, Recio-Blanco2023) or XGBOOST (Andrae2023)</td> </tr> <tr> <td>logg_source</td> <td>Source of the log g estimate</td> </tr> <tr> <td>e_logg_lower</td> <td>Lower error on the log g estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>e_logg_upper</td> <td>Upper error on the log g estimate (NaN in case of Andrae2023)</td> </tr> <tr> <td>[Fe/H]_apogee</td> <td>Metallicity estimate from APOGEE-DR17</td> </tr> <tr> <td>e_[Fe/H]_apogee</td> <td>Error on APOGEE metallicity estimate</td> </tr> <tr> <td>[alpha/M]_apogee</td> <td>Alpha abundance estimate from APOGEE-DR17</td> </tr> <tr> <td>e_[alpha/M]_apogee</td> <td>Error on APOGEE alpha estimate</td> </tr> <tr> <td>logg_apogee</td> <td>log g estimate from APOGEE-DR17</td> </tr> <tr> <td>e_logg_apogee</td> <td>Error on APOGEE log g estimate</td> </tr> <tr> <td>[Fe/H]_galah</td> <td>Metallicity estimate from GALAH DR3</td> </tr> <tr> <td>e_[Fe/H]_galah</td> <td>Error on GALAH metallicity</td> </tr> <tr> <td>[alpha/Fe]_galah</td> <td>Alpha abundance estimate from GALAH DR3</td> </tr> <tr> <td>e_[alpha/Fe]_galah</td> <td>Error on GALAH alpha abundance</td> </tr> <tr> <td>logg_galah</td> <td>log g estimate from GALAH DR3</td> </tr> <tr> <td>e_logg_galah</td> <td>Error on GALAH log g</td> </tr> <tr> <td>GLON</td> <td>Galactic longitude (deg)</td> </tr> <tr> <td>GLAT</td> <td>Galactic latitude (deg)</td> </tr> <tr> <td>U</td> <td>Heliocentric velocity in the direction of the Galactic center (km/s), calculated from ra, dec, parallax, pmra, pmdec, radial_velocity using galpy, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>V</td> <td>Heliocentric velocity in the direction of the Galactic rotation (km/s), calculated from ra, dec, parallax, pmra, pmdec, radial_velocity using galpy, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>W</td> <td>Heliocentric velocity in the direction of the North Galactic Pole (km/s), calculated from ra, dec, parallax, pmra, pmdec, radial_velocity using galpy, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>UW</td> <td>Total non-circular velocity UW = sqrt(U^2 + W^2) (km/s), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>R</td> <td>Distance from the Galactic center in the Galactic plane (kpc), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>Z</td> <td>Distance from the Galactic plane (kpc), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>gaiaV</td> <td>V-band magnitude (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_gaiaV</td> <td>Error on V-band magnitude</td> </tr> <tr> <td>Gmag</td> <td>Gaia G magnitude (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Gmag</td> <td>Error on G magnitude</td> </tr> <tr> <td>Radius</td> <td>Radius of star (solar radii) (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Radius</td> <td>Error on radius</td> </tr> <tr> <td>Mass</td> <td>Mass of star (solar masses) (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Mass</td> <td>Error on mass</td> </tr> <tr> <td>Teff</td> <td>Effective temperature of star (K) (from asPIC catalog, Montalto2021)</td> </tr> <tr> <td>e_Teff</td> <td>Error on effective temperature</td> </tr> <tr> <td>Stellar Type</td> <td>Classification into M or FGK type, based on asPIC catalog (Montalto2021)</td> </tr> <tr> <td>u1</td> <td>The first quadratic limb-darkening parameter, based on grid by Morello2022</td> </tr> <tr> <td>u2</td> <td>The second quadratic limb-darkening parameter, based on grid by Morello2022</td> </tr> <tr> <td>TD/D</td> <td>Probability ratio thick disk/thin disk (see Section 2.2. in our paper), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>TD/H</td> <td>Probability ratio thick disk/halo (see Section 2.2 in our paper), only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>Population</td> <td>Galactic component classification (Thin Disk, Thick Disk, Halo, Thick Disk Candidate, Halo Candidate), see paper for definitions, only available if Population could be determined (see Classification Criteria)</td> </tr> <tr> <td>n_cameras</td> <td>Number of PLATO cameras observing the target (Additional Column for LOPS2, LOPN1, and Special Target List)</td> </tr> <tr> <td>field</td> <td>Flag for LOPS2 or LOPN1 fields (Additional Column for Special Target List)</td> </tr> </tbody> </table> </div>
Organoid tiny dataset for stitching (SearchFirst, tif input)
<p><span>Dataset acquired on Yokogawa CV8000 on 2024.06.07 by Nicole Repina, Friedrich Miescher Institute for Biomedical Research</span></p> <p>Dataset contains 11 fields of view (each 1000 x 1000 pix) acquired with SearchFirst*. Touching fields have 50 pix overlap. </p> <p>60x water objective, 2x2 binning, zyx spacing (6, 0.21667, 0.21667) um per pixel. Each field of view has 6 z-slices.</p> <p>Mouse small intestinal organoids (day 4) immunostained with the following dyes (2 fluorescence channels):</p> <p>Channel 1 (C01, 405nm) = DAPI nuclear stain<br>Channel 2 (C02, 488nm) = B-catenin membrane stain</p> <p>*In SearchFirst, the sample is first imaged with a first-pass low-magnification objective (e.g. 4x). An object detection algorithm is then used to identify object-containing regions. Next, the second-pass acquisition is performed with a higher magnification objective that only acquires object-containing regions. The objects are thus imaged not in a tiled grid. Further, some fields may be standalone, while others may have one or more touching fields that are acquired with the specified pixel overlap. </p>
CMAQ 5.0 Adjoint Input Data
<p>The data set that is used for testing the adjoint model.</p>
The monthly-average input variables (T, S, DO, N, P, Si) and retrieved pH
<p>The monthly-average input variables (T, S, DO, N, P, Si) from the Changjiang Biology Finite-Volume Coastal Ocean Model and retrieved pHT values from 2000 to 2016 on the East China Sea shelf and three cruises data during 2018 used to evaluate the ANN model</p>
Protein structure file as input 2AC0.sce (YASARA scene)
<p>Protein structure file as input for predicting the effect of mutations on protein structure</p> <p>2AC0.sce, adapted structure<br> This is a part of a tetrameric complex of the transcription factor P53 bound to DNA. 3 of the 4 P53 structures have been removed for simplicity and visualized some nice features.<br> <br> 2AC0_Repaired.sce, minimized structure, saved as YASARA scene object</p>
Molecular Dynamics trajectories and portable binary run input (TPR) files for the dual role of anionic lipids in amyloid aggregation
<h1>About this repository</h1> <p>We conducted Coarse-Grained molecular dynamics simulations to study the aggregation of the amyloid-beta fragment, Aβ16-22 (K16LVFFAE22), on mixed lipid bilayers composed of POPC and POPS. Three bilayer compositions were examined: 0% PS-100% PC, 10% PS-90% PC and 30% PS-70% PC. Simulations were performed using GROMACS 2019.4, employing the WEPROM forcefield for peptide modeling and WEPMEM for lipid modeling. For each POPS percentage, four independent replicas were run for 3000 ns. </p> <h1><strong>Contents</strong></h1> <h3>> Abeta_PCPS.zip </h3> <p>This zip file contains three main folders: 0PS, 10PS, and 30PS. These represent different percentages of PS present in the lipid bilayer.</p> <p>Each folder contains:</p> <p>1. Initial structures:<br> - em1.gro: Lipid bilayer without peptides<br> - eq1.gro: Lipid bilayer at 95 Ų area-per-lipid (APL)<br> - em2.gro: Lipid bilayer at 95 Ų APL with 16 peptides<br> - eq2.gro: Final equilibrated structure with peptides (used for production)</p> <p>2. Supporting files:<br> - index.ndx: Index groups for peptides and lipid bilayer<br> - run.pdf: Structure file for VMD visualization</p> <p>3. Four replica folders (replica1 to replica4), each containing:<br> - red.tpr: Binary input for analysis (peptides and lipid bilayer only)<br> - full_trj_pbc.xtc: Trajectory file (peptides and lipid bilayer only, centered in box)</p> <p>Note: The `.tpr` files were created using the `gmx convert-tpr` tool.</p> <p><strong>To access the source files with which these simulations were set-up, and a brief tutorial, see: </strong><a href="https://github.com/suhasgotla/heparin_amyloid_self-assembly">https://github.com/meenaljainumd/PCPS_amyloid_aggregation</a></p>
Nutrient loading from a sustainably certified aquaculture operation dwarfs annual nutrient inputs from a large multi-use watershed, Lake Yojoa, Honduras (DATA)
<p><strong>Dataset for manuscript entitled: "Nutrient loading from a sustainably certified aquaculture operation dwarfs annual nutrient inputs from a large multi-use watershed, Lake Yojoa, Honduras"</strong></p> <p><strong>Abstract: </strong>Net-pen aquaculture is a popular and increasingly prevalent method for producing large quantities of low-fat protein in freshwater ecosystems across the tropics. While there are numerable social and economic advantages associated with aquaculture, there are also challenges related to the environmental sustainability of aquaculture operations which supply pens with externally produced feed. For example, excessive nutrient loading which can drive rapid eutrophication in aquatic ecosystems is a major risk. In this study we compare the estimated annual nutrient loads from the six principal tributaries that contribute to Lake Yojoa to the estimated nutrient load of a large net-pen Tilapia operation located in the central region of the lake. The Tilapia farm was responsible for ~ 86% of the nitrogen (N) and ~95% of the phosphorus (P) contributions to Lake Yojoa for the year of our study. This disproportionate nutrient loading of both N and P suggests that this single aquaculture operation, more so than changes in nutrient inputs from the watershed, was responsible for the previously documented deterioration of Lake Yojoa. This study shows the potential for net-pen aquaculture to have disproportionately negative impacts on freshwater ecosystems, even when operations meet the current sustainability certifications standards. We suggest shifts in metrics that could improve the impact of the certification process so that best practices can reduce the impact of net-pen aquaculture on freshwater ecosystems and arrive at the intended goal of long-term environmental sustainability.</p> <p>Data files: </p> <ul> <li>rating_curves_2018_2019: Rating cruves for tributaries Helado, Balas, Varsovia, and Raices</li> <li>sensor_2018_2019: Pressure transducer data for Healdo, Balas, Varsovia, and Raices</li> <li>Staff_TS_27MAY21: Manual stage measurements for Helado, Balas, Varsovia, and Raices</li> <li>13OCT21_Rivers: In stream nutrient data for all tributaries</li> <li>q_cacao_2019: Discharge for Cacao calculated using Manning's equation (methods provided in text)</li> <li>q_yure_2019: Discharge for Yure calculated using Manning's equation (methods provided in text)</li> <li>N_P_aqua: Nutrient contributions from aquaculture (calculations provided in Supplemental Text 4)</li> <li>precip: Yojoa watershed precipitation</li> </ul> <p> </p>
Inputs and outputs of nep in GPUMD
<p>See the GPUMD manual for the descriptions of the inputs and outputs of the nep executable within GPUMD: https://gpumd.zheyongfan.org/index.php/Main_Page.</p>
input file/output data for Liu et al.,2021
<p>This is the input file, fault kinematic table, and full model output.</p> <p>MODEL_DATA_0100-MODEL_DATA_0500 are modeling results of the <em>basin</em> stage.<br> MODEL_DATA_AFTER_PHASE_01-MODEL_DATA_AFTER_PHASE_08 are modeling results of <em>margin</em> stage.<br> MODEL_DATA_1700-MODEL_DATA_10000 are modeling results of the<em> post-rift </em>stage.</p>
Sector-coupled input data for optimizing the German transmission grid
<p>This repository contains input data for the open-source python tool <a href="https://github.com/openego/eTraGo">eTraGo</a> (<strong>e</strong>lectricity <strong>Tra</strong>nsmission <strong>G</strong>rid <strong>o</strong>ptimization) version 0.9.0.<br> This data will be uploaded to the <a href="https://openenergy-platform.org/">OpenEnergy Platform</a> which can be accessed by eTraGo. This dataset is an intermediate solution until the data is uploaded.</p> <p>The published data includes the sector-coupled transmission grid data for two scenarios (eGon2035 and eGon2035_lowlfex). It was created with the open-source tool <a href="https://github.com/openego/eGon-data/">eGon-data</a> within the research project <a href="https://ego-n.org">eGon</a>. All input data sets as well as the code are available under open source licenses.</p> <p>The data is stored as a PostgreSQL database in the attached backup file. First, the required schemas and extensions have to be created within the database by running the following SQL statements:</p> <p><code>CREATE SCHEMA grid;<br> CREATE SCHEMA boundaries;<br> CREATE EXTENSION postgis;</code></p> <p>Afterwards the data can be restored by using e.g. pgAdmin or via PostgreSQL's <a href="https://www.postgresql.org/docs/current/app-pgrestore.html">pg_restore</a> command (replace <code>HOST</code>, DATABASE_<code>NAME, PORT</code> and <code>USER</code> by your settings):</p> <p><code>pg_restore --host HOST --port PORT --username USER --no-password --dbname </code>DATABASE_<code>NAME --no-owner --no-privileges --verbose "etrago_data_egon2035.backup"</code></p>
Input and result datasets related to "Mannhardt et al. (2022): Overcoming the energy crisis in Europe: Collaborative and selfish mitigation strategies of the gas shortage" (submitted)
<p>Datasets and raw results for the submitted article:</p> <p>"Overcoming the energy crisis in Europe: Collaborative and selfish mitigation strategies of the gas shortage" </p> <p>by Jacob Mannhardt, Paolo Gabrielli, Giovanni Sansavini (sansavig@ethz.ch)<br> Reliability and Risk Engineering Lab, Institute of Energy and Process Engineering, ETH Zurich</p> <p>All rights lie with the authors. Refer to the README.txt for a description of the datasets and their usage in the article.</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.