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2,152 results for “outputs”
APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r6)
<p>APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r6)</p>
APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r3)
<p>APECOSM outputs used in the Earth's Future "Past and Future of Marine Ecosystems" special issue (historical-r3)</p>
Watters et al. 2020 reanalysis output and code
<p>Three .csv analysis output files from the Watters et al. 2020 paper (<a href="https://doi.org/10.1038/s41598-020-59223-9">https://doi.org/10.1038/s41598-020-59223-9</a>). The analyses (using the code which imputes missing krill biomass values for years where no acoustic surveys were conducted) take several hours to run, with each file representing the output from a reformulated model.</p> <p> </p> <p>1. hr.derived_s_Watters_original.csv : these represent the "as-is" output using the original code, data sources and model formulation</p> <p>2. hr_derived_s_37.csv : modified in line with the provided manuscript i.e. rescaling of the area for which Local Krill Biomass and Local Harvesting Rates are generated during the summer (i.e. SSMU as opposed to gSSMU scales), and removal of chinstrap and Adelie penguin performance indices over winter, to reflect large-scale migration outside the key fishing grounds. This modification also included March in winter, to demonstrate the effect of an increasingly winter-orientated fishery in subarea 48.1 (that is, the fishery ramps up catch from the middle of March, when penguins have finished breeding). </p> <p>3. hr_derived_s_37b.csv : as for case #2 above, but with March considered in summer (as in the original model formulation.</p> <p>The included R script is annotated with the code changes relating to the three cases, as well as the DOI for the original paper and the raw data sources, should people wish to check the .csv files above. </p> <p> </p>
Data from: Contrasting effects of land cover on nesting habitat use and reproductive output for bumble bees
<p><span><span><a name="_Hlk64216363">Understanding habitat quality is central to understanding the distributions of species on the landscape, as well as to conserving and restoring at-risk species. Although it is well-known that many species require different resources throughout their life cycles, pollinator conservation efforts focus almost exclusively on forage resources. In this study, we evaluate nesting habitat for bumble bees by locating nests directly on the landscape. We compared colony density and colony reproductive output for <i>Bombus impatiens, </i>the common eastern bumble bee, across three different land cover types (hay fields, meadows, and forests). We also assessed nesting habitat associations for all <i>Bombus</i> nests located during surveys to tease apart species-specific patterns of habitat use. We found that <i>B. impatiens</i> nested under the ground in two natural land cover types, forests and meadows, but found no <i>B. impatiens</i> nests in hay fields. Though <i>B. impatiens</i> nested at similar densities in both meadows and forests, colonies in forests had much higher reproductive output<i>. </i></a>In contrast, <i>B. griseocollis</i> tended to nest on the surface of the ground and was almost always found in meadows. <i>B. perplexis</i> was the only species to nest in all three habitat types, including hay fields. For some bumble bee species in this system, meadows, the habitat type with abundant forage resources, may be sufficient to maintain them throughout their life cycles. However, <i>B. impatiens</i> might benefit from heterogeneous landscapes with forests and meadows. Results for <i>B. impatiens</i> emphasize the longstanding notion that habitat use is not always positively correlated with habitat quality (as measured by reproductive output). Our results also show that habitat selection by bumble bees at one spatial scale may be influenced by resources at other scales. Finally, we demonstrate the feasibility of direct nest searches for understanding bumble bee distribution and ecology. </span></span></p>
MITgcm model setup and output for "Antarctic Slope Current modulates ocean heat intrusions towards Totten Glacier"
<p>MITgcm model setup and output for "Antarctic Slope Current modulates ocean heat intrusions towards Totten Glacier</p> <p>Here, it contains the results of the East Antarctic simulation from 1992-2016. Model grid is lat-lon similar to LLC1080 grid resolution roughly 3-4 km in the region. See Nakayama et al., submitted to GRL for detail. </p> <p><strong>(Contents)</strong><br> code.zip (code to run this simulation)<br> input.zip (input file required for this simulation)<br> results_zenodo.zip (due to size limit of 50GB, please check <a href="https://ecco.jpl.nasa.gov/drive/files/ECCO2/LatLon_East_Antartic">https://ecco.jpl.nasa.gov/drive/files/ECCO2/LatLon_East_Antarctic</a> for complete model output. Complete datasets can also be obtained by rerunning the simulation.)</p> <p><strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..<br> mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /nobackup/hzhang1/forcing/era_xx/ .<br> cp ../build/mitgcm_uv .<br> qsub run_omp_high_t1.pbs</p>
Chinese Provincial Multi-Regional Input-Output database for 2012, 2015, and 2017
<p>Please note that the tables are constructed by CEADs. More information and the latest updated can be found in the CEADs website (https://www.ceads.net/data/input_output_tables/)</p> <p>If any question, please contact Heran Zheng (zhengheran@foxmail.com)</p> <p> </p> <p> </p> <p> </p>
NEXMIF encephalopathy: DeNovogear output of WES data of the family
<p>The developmental and epileptic encephalopathies (DEE) are the most severe group of epilepsies. Recently, <i>NEXMIF</i> mutations have been shown to cause a DEE in females, characterized by myoclonic–atonic epilepsy and recurrent nonconvulsive status. Here we used advanced neuroimaging techniques in a patient with a novel <i>NEXMIF</i> de novo mutation presenting with recurrent absence status with eyelid myoclonia, to reveal brain structural and functional changes that can bring the clinical phenotype to alteration within specific brain networks. Indeed, the alterations found in the patient involved the visual pericalcarine cortex and the middle frontal gyrus, regions that have been demonstrated to be a core feature in epilepsy phenotypes with visual sensitivity and eyelid myoclonia with absences.</p>
ED2-hydro model output from simulations at Barro Colorado Island
<p>Monthly model output from the Ecosystem Demography model version 2 with hydrodynamics (ED2; Medvigy et al., 2009; Powell et al., 2018) run at Barro Colorado Island (BCI). ED2 was initialized from bare ground at BCI and run with recycled 2008-2014 observed meteorology (i.e. “BASE”) until predictions of above ground biomass (AGB) reached dynamic equilibrium after 700 years (Powell et al., 2018; referred to as simulation year 0 in the simulations presented here). The four data sets provided here all start after the spin up period and each provide a 50 year (monthly time step) time series of selected ED2 history variables under a particular precipitation scenario: 1) a synthetic El Niño time series (Powell <em>et al.</em>, 2017) which produces two exceptionally strong El Niño events within 30 years (the “ENSO” scenario), 2) a “WET” scenario where precipitation increased 30% compared to BASE precipitation, and 3) a “DRY-DS” scenario where dry season (January–April) precipitation was reduced 75% compared to BASE and 4) a continuation of BASE meteorology.</p> <p><strong><strong>Medvigy D</strong>, <strong>Wofsy SC</strong>, <strong>Munger JW</strong>, <strong>Hollinger DY</strong>, <strong>Moorcroft PR</strong></strong>. <strong>2009</strong>. Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2. <em>Journal of Geophysical Research: Biogeosciences</em> <strong>114</strong>: 1–21.</p> <p><strong><strong><strong>Powell TL</strong>, <strong>Koven CD</strong>, <strong>Johnson DJ</strong>, <strong>Faybishenko B</strong>, <strong>Fisher RA</strong>, <strong>Knox RG</strong>, <strong>McDowell NG</strong>, <strong>Condit R</strong>, <strong>Hubbell SP</strong>, <strong>Wright SJ</strong>, <em>et al.</em></strong></strong> <strong>2018</strong>. Variation in hydroclimate sustains tropical forest biomass and promotes functional diversity. <em>New Phytologist</em> <strong>219</strong>: 932–946.</p> <p><strong><strong>Powell T</strong>, <strong>Kueppers L</strong>, <strong>Paton S</strong></strong>. <strong>2017</strong>. Seven years (2008-2014) of meteorological observations plus a synthetic El Nino drought for BCI Panama.</p> <p> </p> <p> </p>
US-SB2 2016-2020 ReddyProc Output
<p>Post processed data from Ameriflux site US-SB2 (Switchgrass).</p>
FragHub: OMSL output results -093025
<h1>FragHub 1.4.1 integration results</h1> <h2>input OMSL:</h2> <ul> <li><a href="https://zenodo.org/records/16984129">MSnlib</a></li> <li><a href="https://external.gnps2.org/gnpslibrary">GNPS2 </a></li> <li><a href="https://zenodo.org/records/14887271">ISDB</a></li> <li><a href="https://systemsomicslab.github.io/compms/msdial/main.html#MSP">systemomicslab</a></li> <li><a href="https://mona.fiehnlab.ucdavis.edu/downloads">MoNA</a></li> <li><a href="https://github.com/MassBank/MassBank-data/releases/tag/2025.05.1">MassBank</a></li> <li><a href="https://www.metaboanalyst.ca/docs/Databases.xhtml">Metaboanalyst</a></li> </ul> <h2><br>Database integration workflow:</h2> <ol> <li>Deduplicate spectra (splash keys)</li> <li>apply default filters (all but entropy)</li> <li>normalize metadata </li> <li>add chemical ontology</li> </ol> <h2>Output results available in .csv .json and .msp:</h2> <ul> <li>Spectra NEG experimental: 1140117</li> <li>unique INCHIKEY: 113410</li> <li>Spectra POS experimental: 3731277</li> <li>unique INCHIKEY: 210777</li> </ul> <p>unique INCHIKEY2D experimental: 248245</p> <ul> <li>Spectra NEG<em> in silico</em>: 2737159</li> <li>unique INCHIKEY: 1536985</li> <li>Spectra POS <em>in silico</em>: 2644561</li> <li>unique INCHIKEY: 1529720</li> </ul> <p>unique INCHIKEY2D <em>in silico</em>: 1611201</p>
Databases for "A multiscale approach to identify key factors determining rural WaSH service level and sustainability" (ShinyApp). Application to SIASAR conceptual model v1, outputs obtained from Nicaragua, Honduras, Panama and Dominican Republic SIASAR databases (last accessed: December 21, 2016)".
<p>Attached databases contain the results obtained by applying the SIASAR composite indicators v1 (available at http://upcommons.upc.edu/handle/2117/77587) to data presented in http://doi.org/10.5281/zenodo.571351</p> <p>These files can be directly exploited by using the ShinyApp “A multiscale approach to identify key factors determining rural WaSH service level and sustainability”.</p> <p>Each *.csv file contains one country's database. </p>
Motor context dominates output from Purkinje cell functional regions during reflexive visuomotor behaviors
<p>Example calcium imaging dataset for Knogler et al., 2018</p>
The effect of past saturation changes on noble gas reconstructions of mean ocean temperature: Model Output Data
<p>This dataset contains model output for the simulations presented in "<em>The effect of past saturation changes on noble gas reconstructions of mean ocean temperature</em>".</p> <p>The NetCDF4 files contain the following variables:</p> <ul> <li>Potential Temperature</li> <li>Salinity</li> <li>Sea-ice area</li> <li>Ar, Kr, Xe, and N<sub>2</sub> concentrations</li> <li>AMOC strength and Northern-Sourced Water fraction (only transient runs)</li> </ul>
Assessing the atmospheric response to subgrid surface heterogeneity in CESM2 - code, output, and analysis script
<p>This dataset contains the raw model output, analysis scripts, and CESM source code (containing modifications to include subgrid-scale surface heterogeneity impacts in CAM) that were used to produce the figures in a manuscript targeting the Journal of Advances in Modeling Earth Systems (JAMES) for submission, "Assessing the atmospheric response to subgrid surface heterogeneity in CESM2". For additional information on CESM2 and the most up to date source code, see https://github.com/ESCOMP/CESM. </p> <p>The pre-print of the submitted article is available here: <a href="https://doi.org/10.1002/essoar.10512837.1">https://doi.org/10.1002/essoar.10512837.1</a></p>
model output of Cumulus convection over land in cloud-resolving simulations with a coupled ray tracer
<p><strong>768_*</strong>: Output of cloud resolving simulations of a diurnal cycle (Augst 15th, 2016) with shallow cumulus clouds over Cabauw, the Netherlands. All simulations are performed with MicroHH, with radiative transfer computed either with a two-stream approximation or with ray tracing. </p> <ul> <li>2s_het: main simulation with two-stream solver</li> <li>2s_hom_sw: two-stream solver, horizontally averaged surface solar radiative fluxes</li> <li>2s_hom_hrsw: two-stream solver, horizontally averaged heating rates</li> <li>rt_het: main simulation with ray tracing, 256 samples per pixel </li> <li>rt_hom_sw: ray tracing, horizontally averaged surface solar radiative fluxes, 256 samples per pixel </li> <li>rt_hom_hrsw: ray tracing, horizontally averaged heating rates, 256 samples per pixel </li> <li>rt_het_128: ray tracing, 128 samples per pixel</li> <li>rt_het:_64 ray tracing, 64 samples per pixel</li> <li>rt_het:_32 ray tracing, 32 samples per pixel</li> </ul>
Processed model output used in 'The impact of winds on AMOC in a fully-coupled climate model'
<p>Processed model output from wind-nudging experiments used to investigate the Atlantic Meridional Overturning Circulation. </p> <p> </p> <p>For further details, see </p> <p>Roach, L. A, Blanchard-Wrigglesworth E. Ragen, S., Cheng, W., Armour, K. and Bitz, C. M.. (2022). The impact of winds on AMOC in a fully-coupled climate model. In review at Geophysical Research Letters</p>
The supplementary (hourly output data & computational performance log) for OMARE of the Ocean Dynamics manuscript
<p>The supplementary (hourly output data & computational performance log) for OMARE of the Ocean Dynamics manuscript with the title: Computational performance and scalability Analysis for the flexible ocean Modeling framework of OMARE (Ocean Modeling with Adaptive REsolution).</p> <p>amr_wbc.tar.gz: The hourly output data of core prognostics (u, v, ssh, ts) of experiments X-AMR, X, S for OMARE in TianHe platform, including the picture results (png format).</p> <p>Computational_Performance.zip: The computational performance logs of the M, S, X, L-M-50, M-S-30, M-S-100, S-X-10, for OMARE in ThianHe and IntelCluster platform. The computational performance logs of X-AMR and I/O in TianHe platform.</p>
Results and assumptions for: Modeling the Circular Economy in Environmentally Extended Input-Output Tables: methods, software and case study
<p>This dataset presents supplementary information for <em>"Modeling the Circular Economy in Environmentally Extended Input-Output Tables: methods, software and case study" </em><a href="https://doi.org/10.1016/j.resconrec.2019.104508">https://doi.org/10.1016/j.resconrec.2019.104508</a></p> <p>The data was processed and results were obtained through https://cmlplatform.github.io/pycirk/</p> <p> </p> <p><br> Annex I: Scenario assumptions and modeling choices and complete results (file Annex_I.xlsx) <br> Annex II: Contains analysis of other software, database modifications, and list of affected categories (file Annex_II.docx) <br> Results: settings, assumptions, results and their analysis from the case study presented in the paper (file Donati_CE_EEIO_Mo_SI.tar.gz)</p>
Benchmark output of different 3D scanner applications
<p>Visualized outputs, BLK360 with 3DR 360, software from Leica and scanner apps for the iPad</p>
SWMF - Idealized CIR Event output
<p>Simultation output from the BATS-R-US component of the SWMF.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.