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2,152 results for “outputs”
Inputs and outputs for bilayers simulations in "Accurate Simulations of Lipid Monolayers Require a Water Model With Correct Surface Tension"
<p>Inputs and outputs file for simulations of POPC and DPPC Bilayers at various temperatures. For details see:</p>
Data and output files for the paper, "Dynamical systems inference at scale reveals intrinsic instability in the dysbiotic microbiome"
<p>The above files contain the output of the model, which we call MDSINE2, developed in the paper titled, "<em>Dynamical systems inference at scale reveals intrinsic instability in the dysbiotic microbiome</em>" (See https://github.com/gerberlab/MDSINE2_Paper for implementation details). More specifically, forward_sims.tgz contains the output of forward simulations in .npy format; mixed_prior_fixed.tgz contains the pickle files corresponding to the output of the MCMC simulations for fixed topology; mixed_prior_unfixed.tgz contains the pickle files corresponding to the output of the MCMC simulations for unfixed topology; other_files contains information on eigenvalues and cycles, results for Phylogenetic Neighborhood Analysis, Keystoneness Analysis and the output in .npy format for comparator methods; ASV_OTU_aggregate_and_plylogeneteic trees contains the plots of trajectories (relative abundance) of OTUS and their constituent ASVs, and of phylogenetic trees. </p>
Output 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 output data (results) of the paper "Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach".</p>
WRF Output for 2 km Simulation of Spiral Gravity Waves
<p>WRF output file for the 2km Spiral Gravity Wave simulation of Nolan and Onderlinde (2022).</p>
WRF Output for 0.5 km Simulation of Spiral Gravity Waves
<p>WRF Output for 0.5 km Simulation of Spiral Gravity Waves</p>
WRF Output for 1 km Simulation of Spiral Gravity Waves with 25 Levels
<p>WRF Output for 1 km Simulation of Spiral Gravity Waves with 25 Levels</p>
WRF Output for 1.5 km Simulation of Spiral Gravity Waves
<p>WRF Output for 1.5 km Simulation of Spiral Gravity Waves</p>
WRF Output for 1 km Simulation of Spiral Gravity Waves
<p>WRF Output for 1 km Simulation of Spiral Gravity Waves, gzipped.</p>
Output files from tauRAMD simulations for the dissociation of ligands from T4 lysozyme mutants
<p>The zip files contain the output files generated by tauRAMD to simulate the dissociation of ligands from T4 lysozyme (T4L) mutants.</p> <p>Directories:</p> <p>bnz-l99a-ph5: benzene bound to T4L L99A; simulations at 20 °C<br> bnz-l99a-ph5-t10: benzene bound to T4L L99A; simulations at 10 °C<br> bnz-l99a-ph5-t30: benzene bound to T4L L99A; simulations at 30 °C<br> bnz-m102a-ph5: benzene bound to T4L M102A<br> bnz-f104a-ph5: benzene bound to T4L F104A<br> ind-l99a-ph5: indole bound to T4L L99A</p> <p>Further details about the method can be found in the publication "Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from τRAMD simulations" (doi: https://doi.org/10.1016/j.crstbi.2021.04.001).</p> <p> </p>
Computational output dataset for monoterpene glycoside hydrolysis: Supplement to 10.5281/zenodo.5346588
<p>Computational data resulting from optimisation and vibrational analysis of monoterpenes, geraniol and linalool, resulting from acid-catalysed hydrolysis of geraniol glucoside. Supplementary dataset for the computation described in:</p> <p>Hixson, Josh, Pisaniello, Lisa, Parker, Mango, Grebneva, Yevgeniya, Bilogrevic, Eleanor, Stegmann, Robin, & Francis, Leigh. (2021, August 31). Methods for predicting and assessing flavour evolution during white wine ageing. https://doi.org/10.5281/zenodo.5346589</p> <p>Structures were drawn in Avogadro (version 1.2.0), then optimised (UFF) and systematic rotor conformer search performed, with the resulting geometry used to create initial input files for GAMESS (Linux distribution, version 2019 R2, University of Iowa, USA) running on the University of South Australia’s High Performance Compute Cluster. Calculations were first performed for molecules in the gas phase (RHF/3-21G) to provide starting geometries which were then used as the input for density functional theory (B3LYP/6-311G(d)) equilibrium geometry calculations in water (SMD solvent model). Local minima were confirmed by vibrational analysis and the presence of all real frequencies. Each calculation was performed in parallel across eight cores.</p> <p>The chemical structures input into Avagadro and the naming used in computational files can be found in 'starting_structures_naming.png' and the scheme of glucoside hydrolysis and rearrangement from the original manuscript has been re-published here as 'glycoside_hydrolysis_scheme.png'. Two computational output files (.log) are present for each structure: an output of the geometry optimisation ending in '_B3LYP_6311G_aq.log', and; the optimised geometry analysed by vibrational analysis, including thermochemistry, denoted with '_vibrational.log'. </p> <p>The compounds, compound file naming (starting_structures_naming.png) and structure numbering in the original work (glycoside_hydrolysis_scheme.png) are as follows: geraniol glucoside, geraniol_gluc, compound 1; protonated geraniol glucoside, geraniol_gluc_protonated, compound 2; linalyl or geranyl cation (identical structures found after geometry optimisation), linalyl_cation or geranyl cation, compound 3, protonated geraniol, geraniol_protonated, compound 4; geraniol, geraniol, compound 5; protonated linalool, linalool_protonated, compound 6; linalool, linalool, compound 7.</p> <p> </p> <p> </p>
Climate and air quality relevant output diagnostics from UKESM1 for the additional AerChemMIP simulation ssp370SSTpdEmis
<p>This dataset contains model output data for radiative fluxes, emissions, aerosols and ozone produced from an additional AerChemMIP model experiment where anthropogenic precursor emissions and trace gas constituents were held fixed at 2014 values. This model experiments was conducted by UKESM1, a model contributing to CMIP6, and was run over the period 2015 to 2100 to investigate the effect of anthropogenic emissions on near-term climate forcers. The atmosphere only CMIP6 configuration of UKESM1 was used to run this experiment. Model simulations were conducted at a global resolution of 1.875° x 1.25°.</p>
Figure 2 in Sex change and reproductive output of the protandric shrimp Merguia rhizophorae (Rathbun, 1900) (Decapoda, Merguiidae)
Figure 2. Anatomical and morphological characteristics of the male phase (a–c) and female phase (d– f) individuals of Merguia rhizophorae (Rathbun 1900). (a) Close-up of the endopod of the first pleopod (arrow points to the cincinnuli); (b) Endopod of the second pleopod (upper and lower arrows point to the appendix interna and the appendix masculina, respectively); (c) Gonopore on the coxae of the fifth pereiopod (arrow points to the gonopore); (d) Close-up of the endopod of the first pleopod (arrow points to the area where cincinnuli would be located in a male phase); (e) Endopod of the second pleopod, lacking an appendix masculina (arrow points to the appendix interna); (f) Coxae of the fifth pereiopod, lacking a gonopore (arrow points to the area where gonopore would be located in a male phase). Scale bar: A–D, 0.2 mm; B–E, 1.0 mm; C–F, 0.5 mm. Legend: CI, cincinnuli; AI, appendix interna; AM, appendix masculina; GO, gonopore; P, pereiopod.
Figure 1 in Sex change and reproductive output of the protandric shrimp Merguia rhizophorae (Rathbun, 1900) (Decapoda, Merguiidae)
Figure 1. (a) Occurrence of Merguia rhizophorae along the western Atlantic. The red circle shows Vaza- Barris estuarine region of Sergipe State, northeastern coast of Brazil and blue circle shows the previous records of the species (obtained from literature and Global Biodiversity Information Facility – GBIF; http://www.gbif.org); (b–c) Sampling site in a fringe mangrove forest; (d) Excavation of the burrows using a spatula; (e) Burrows (white arrows) inhabited by mud shrimps (yellow arrow) distributed on the sediment of the intertidal zone; (f) lateral view of Merguia rhizophorae (Rathbun 1900). Photo credits: Douglas F.R. Alves.
Figure 3 in Sex change and reproductive output of the protandric shrimp Merguia rhizophorae (Rathbun, 1900) (Decapoda, Merguiidae)
Figure 3. Sex change in Merguia rhizophorae (Rathbun 1900). (a) Size frequency distribution of body size (CL, mm); (b) Adjustment of the logistic function, indicating the carapace length (CL, mm) where 50% of the female phase shrimps are morphologically mature. Legend: MP, male phase; FPNO, female phase non-ovigerous; FPO, female phase ovigerous.
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
<p>Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. </p>
Outputs of the Jupyter Notebook - MODIS MOD021KM and FIRMS
<p>The dataset contains the outputs of the notebook "MODIS MOD021KM and FIRMS" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samuel Jackson (author), Science & Technology Facilities Council, <a href="https://github.com/samueljackson92">@samueljackson92</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <p>MOD021KM</p> <ul> <li> <p>MODIS Characterization Support Team (MCST)</p> </li> <li> <p>MODIS Adaptive Processing System (MODAPS)</p> </li> </ul> <p>Firms</p> <ul> <li> <p>University of Maryland</p> </li> </ul> <p><em>Dataset authors</em></p> <p>MOD021KM</p> <ul> <li> <p>MODIS Science Data Support Team (SDST)</p> </li> </ul> <p>Firms</p> <ul> <li> <p>NASA’s Applied Sciences Program</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Louis Giglio, Wilfrid Schroeder, Joanne V. Hall, and Christopher O. Justice. MODIS Collection 6 Active Fire Product User’s Guide Revision B. Technical Report, NASA, 2018. URL: <a href="https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf">https://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf</a>.</p> </li> </ul> <p> </p>
GCAM-LAC v5.3-stash Output Data
<p>This dataset includes output data from the GCAM LAC (v5.3-stash) model. The GCM and RCP selected for the study of Colombia Energy-Water-Land Systems are GFDL-ESM2M and RCP 2.6.</p>
Transcriptomics processed data outputs for Sofen et al 2022
<p>Processed data ouputs for transcripomtics analysis of Southern Ocean Time Series eukaryotic microbial communities. Files include: protein and HMM database used for iron stress biomarker screening, Proteins that hit to the HMM database curated to search the metatranscriptomic dataset for iron stress biomarker proteins with protein ID's, taxonomic annotation (EUKulele, MMTESP) and calculated transcripts per million (TPM) values, DNA-directed RNA Polymerase (RPB1) proteins used for taxonomic charecterization of the surface eukaryotic community with protein ID's, taxonomic annotation (EUKulele, MMTESP) and calculated transcripts per million (TPM) values.</p>
Sea Ice Rheology Experiment (SIREx) - Model output data
<p>Sea-ice model output analyzed in the Sea Ice Rheology Experiment (SIREx) Part I and Part II. </p> <p>There is one netCDF file per model, per year (1997 and/or 2008). Each netCDF file contains daily output (as means or instantaneous values -- as indicated in the name) for January-February-March of the given year. Please see below for more information on what is included and how to cite. </p> <p> </p> <p><strong>1. Naming convention of the files</strong></p> <p>"Model simulation label" + _ + "year" + _ + "daily_means" OR "instant" </p> <p> </p> <p><strong>2. Variables included</strong></p> <ul> <li><em>U,V</em> : sea-ice velocity in the x,y directions (in m/s);</li> <li><em>A</em>: ice concentration per grid cell (0 -- 1);</li> <li><em>h</em>: mean ice thickness per grid cell (in m);</li> <li>Grid spacing information: centered on u-points (<em>DXU, DYU</em>), v-points (<em>DXV, DYV</em>), or t-points (<em>DXT, DYT</em>) (in m); </li> <li>Grid point coordinates: for u-points (<em>ULON, ULAT</em>), v-points (<em>VLON, VLA</em>T), or t-points (<em>TLON, TLAT</em>) (in degrees); </li> <li><em>maskT</em>: land mask at t-points (0/1 = ocean/land);</li> <li><em>time</em>: time axis (in days since 1901-01-01 00:00:00).</li> </ul> <p> </p> <p><strong>3. Recommended citation usage</strong></p> <p>If <em>all</em> simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx papers (Bouchat et al., 2022, Hutter et al., 2022 ). If only <em>selected </em>simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).</p>
HYSPLIT ensemble model output for case studies
<p>Each folder named like a date (YYYYmmddHHMM) has a case study and it's named after eruption start time.</p> <p> * <strong>201208061200 </strong>- Historical case study for eruption starting at 2012-08-06T12:00 UTC<br> * <strong>202107150000</strong> - Operational case study for eruption starting at 2021-07-15T00:00 UTC</p> <p>Each case study folder has :<br> - <strong>input</strong> folder with the eruption source parameters, sampled with Latin Hyspercube technique, used to configure each HYSPLIT run.<br> - <strong>output </strong>folder with the HYSPLIT modelling results processed to cumulative deposited ash thickness netcdf file.</p> <p>Code to process results is in this <a href="http://github.com/yannikbehr/ashfall_forecasts/">Git repository</a></p> <p> </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.