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2,152 results for “outputs”
Merged output log files from collect_environment.sh
<p>Output from scripts that gathers execution environment information on Summit, Fugaku, and Oakforest-PACS.</p>
Output from the PISCES Cobalt biogeochemical ocean model
<p>Output from the PISCES biogeochemical model used in Hawco et al. 2020 PNAS. This model was originally developed and published in Tagliabue A, et al. (2018) [The Role of External Inputs and Internal Cycling in Shaping the Global Ocean Cobalt Distribution: Insights From the First Cobalt Biogeochemical Model. Global Biogeochem Cycles 32(4):594–616.] but with the base model modified as described by Aumont O, et al. (2017) [Variable reactivity of particulate organic matter in a global ocean biogeochemical model. Biogeosciences 14(9):2321–2341.]. All fields are annual averages.</p> <p>For more information see www.pnas.org/cgi/doi/10.1073/pnas.2001393117</p>
[BIOCOM-PIPE] Example and expected output files with an Illumina dataset
<p>BIOCOM-PIPE: a new user-friendly metabarcoding pipeline for the characterization of microbial diversity from 16S, 18S and 23S rRNA gene amplicons</p> <p><strong>Summary:</strong></p> <p>This Zenodo repository encompasses the <em>d</em>emo input files and expected output files in the pipeline such that before directly applying it on large-scale datasets, a user can be assured that he/she has successfully implemented the pipeline. More precisely, we chose to use a recent dataset published (Sadet et al., 2018 – Applied Soil Ecology – DOI: 10.1016/j.apsoil.2018.02.006), with raw datasets areavailable in the EBI database system under project accession number PRJEB14258. We hope that these files will help users to efficiently test and checked the BIOCOM-PIPE pipeline. The deposited archive includes :</p> <p>- the Input.txt file with chosen parameters,</p> <p>- the project.csv file describing the composition of the library,</p> <p>- .fastq files from the EBI database system under project accession number PRJEB14258,</p> <p>- the expected result files and summary files after BIOCOM-PIPE analysis.</p> <p> </p> <p><strong>Background:</strong></p> <p>The ability to compare samples or studies easily using metabarcoding so as to better interpret microbial ecology results is an upcoming challenge. There exists a growing number of metabarcoding pipelines, each with its own benefits and limitations. However, very few have been developed to offer the opportunity to characterize various microbial communities (e.g., archaea, bacteria, fungi, photosynthetic microeukaryotes) with the same tool.</p> <p><strong>Results:</strong></p> <p>BIOCOM-PIPE is a flexible and independent suite of tools for processing data from high-throughput sequencing technologies, Roche 454 and Illumina platforms, and focused on the diversity of archaeal, bacterial, fungal, and photosynthetic microeukaryote amplicons. Various original methods were implemented in BIOCOM-PIPE to (i) remove chimeras based on read abundance, (ii) align sequences with structure-based alignments of RNA homologs using covariance models or a post-clustering tool (ReClustOR), and (iii) re-assign OTUs based on a reference OTU database. The comparison with two other pipelines (FROGS and mothur) highlighted that BIOCOM-PIPE was better at discriminating land use groups.</p> <p><strong>Conclusions:</strong></p> <p>The BIOCOM-PIPE pipeline makes it possible to analyze 16S/18S and 23S rRNA genes in the same package tool. This innovative approach defines a biological database from previously analyzed samples and performs post-clustering of reads with this reference database by using open-reference clustering. This makes it easier to compare projects from various sequencing runs. For advanced users, the pipeline was developed to allow for adding or modifying the components, the databases and the bioinformatics tools easily.</p>
Lagrangian Atmospheric Model output for the Control, Onset and Development experiments
<p><span><span><span><span><span><span><span><span><span><span><span>This dataset consists of model outputs from the Control, Onset and Development experiments. Each experiment has six ensembles which were integrated for three years. 6-hourly instantaneous surface (1000hPa) zonal and meridional wind speed (m/s) between 31°S-31°N is recorded in uv1000hPa_{Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. 6-hourly accumulated precipitation (mm) between 32°S-32°N is stored in precip_{ Control, Onset, Development}_ens{1, 2, 3, 4, 5, 6}.dat. Note the precip.*dat records accumulated rainfall for the past 6 hours. The missing data is recorded as nan.</span></span></span></span></span></span></span></span></span></span></span></p>
Model output data for "Negative density-dependent dispersal emerges from the joint evolution of density- and body condition-dependent dispersal strategies"
<p>Empirical studies have documented both positive and negative density-dependent dispersal, yet most theoretical models predict positive density dependence as a mechanism to avoid competition. Several hypotheses have been proposed to explain the occurrence of negative density-dependent dispersal, but few of these have been formally modeled. Here, we developed an individual based model of the evolution of density-dependent dispersal. This model is novel in that it considers the effects of density on dispersal directly, and indirectly through effects on individual condition. Body condition is determined mechanistically, by having juveniles compete for resources in their natal patch. We found that the evolved dispersal strategy was a steep, increasing function of both density and condition. Interestingly, although populations evolved a positive density-dependent dispersal strategy, the simulated metapopulations exhibited negative density-dependent dispersal. This occurred because of the negative relationship between density and body condition: high density sites produced low condition individuals that lacked the resources required for dispersal. Our model therefore generates the novel hypothesis that observed negative density-dependent dispersal can occur when high density limits the ability of organisms to disperse. We suggest that future studies consider how phenotype is linked to the environment when investigating the evolution of dispersal.</p>
WRF-Chem Volcano output - Etna Christmas 2018 (run L2)
<p>Output from a model run of a modified version of WRF-Chem, which we refer to as WRF-Chem Volcano (WCV). At the time of submission of this dataset, these data were the basis of the modelling part of a paper in preparation, working title: "WRF-Chem modelling and TROPOMI observations of halogen chemistry within the plume of Etna's Christmas 2018 eruption".</p> <p>The code of WCV is maintained <a href="https://github.com/LukeSurl/WCV">here</a>. Development of this code will continue after the submission of this repository, <a href="https://github.com/LukeSurl/WCV/tree/20200521">This branch</a> is a permanent record of the code as used to create this dataset.<br> This is a simulation of the chemistry of the plume of the 2018 eruption of Mount Etna. This simulation includes bromine, chlorine and mercury chemistry. The time period covered is the 24-27 December, however please note that the author considers the first 24 hours of simulation to be "spin up", and that the modelled volcanic fluxes were assigned based on observations at midday on the 25th, and were not calibrated specifically for the 26th or 27th.</p> <p>"Run L2" refers the internal serial number assigned to this model run.</p> <p><strong>Overview of files</strong></p> <p><strong><em>Output files: wrfout_d0*_YYYY-MM-DD_HH:MM:SS</em></strong></p> <p>Output data is contained in NetCDF files. Each file contains hourly snapshots of the 3D simulation with fields for various parameters. Most of the 4D (three spatial dimensions and time) variables record modelled mixing ratios of various species (in units ppm). Many other variables are standard WRF-Chem output variables; please see <a href="https://ruc.noaa.gov/wrf/wrf-chem/">WRF-Chem's documentation</a>. If the meaning of a variable is unclear, please <a href="mailto:luke.surl@latmos.ipsl.fr?subject=WCV%20run%20L2%20dataset">contact the author</a>.</p> <p>Files are separated by model domain and by time. The "d0x" value specifies the model domain. Domain 1 is the full extent of the modelled area and has grid cells of 30 km × 30 km. Domains 2, 3, 4 each model a successively smaller area subset at a 3x greater resolution than their parent domain. Please see the ACPD paper for further details on this.<br> For operational reasons, the number of hours in different output files is slightly irregular:</p> <ul> <li>Files <em>wrfout_d0x_2018-12-24_00:00:00</em> contain 24 hourly snapshots from 2018-12-24_00:00:00 -- 2018-12-24_23:00:00</li> <li>Files <em>wrfout_d0x_2018-12-25_00:00:00</em> contain 24 hourly snapshots from 2018-12-25_00:00:00 -- 2018-12-25_23:00:00</li> <li>Files <em>wrfout_d0x_2018-12-26_00:00:00</em> contain 1 snapshot, 2018-12-26_00:00:00 only</li> <li>Files <em>wrfout_d0x_2018-12-26_01:00:00</em> contain 24 hourly snapshots from 2018-12-26_01:00:00 -- 2018-12-27_00:00:00</li> <li>Files <em>wrfout_d0x_2018-12-27_01:00:00</em> contain 24 hourly snapshots from 2018-12-27_01:00:00 -- 2018-12-28_00:00:00</li> </ul> <p><strong><em>Input files</em></strong></p> <p>This repository also contains various input files for the WRF-Chem simulation.</p> <ul> <li><em>wrfinput_d0x</em> (netcdf format) defines the initial conditions assigned for the beginning of the simulation (2018-12-24 00:00:00) for each of the model domains.</li> <li><em>wrfbdy_d01</em> (netcdf format) defines the boundary conditions for the simulation.</li> <li><em>wrfchemi_d0x_YYYY-MM-DD_HH:MM:SS</em> (netcdf format) defines, for each day and domain of the simulation, anthropogenic emissions.</li> <li><em>wrfchemv_d0x</em> (netcdf format) defines the volcanic emissions. A file is specified for each domain.</li> <li><em>namelist.input</em> (text file) defines various wrf-chem runtime options </li> </ul>
CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
<p>This is the subset of CESM1.2 model simulation output that was used for analysis and visualization of Yu and Fedorov [2020] (DOI:10.1029/2020GL088381). Please refer to README for details.</p>
Outputs of GFDL AM2.1 simulations used in Wang et al. 2020 GRL
<p>30 years JJA mean air temperature, zonal wind, and meridional wind.</p> <p>ctrl: control simulation</p> <p>ctrl_SST_global: forced by aerosol-induced global SST change</p> <p>ctrl_SST_global_zm: forced by aerosol-induced global zonal mean SST change</p> <p>ctrl_SST_uniformTropicalAve: forced by aerosol-induced tropical mean SST change</p> <p>10 years daily outputs of "ctrl", "ctrl_SST_global_zm", and "ctrl_SST_uniformTropicalAve" experiments</p>
GHGF-Flux forward model outputs
<p>Outputs from a tagged tracer simulation using the Greenhouse Gas Framework – Flux (GHGF-Flux) forward model for the four regions of interest, including Northeast China, Southeast China, North Bangladesh, and North India.</p>
Example BOUT++ output from elm-pb
<p>Output files for the elm-pb example from BOUT++ (https://github.com/boutproject/BOUT-dev/tree/next/examples/elm-pb). Created with BOUT++ at commit df7c980c3bec3adee17067a3dd9190c2ff79f0db.</p>
Numerical model code, input files and output data for publication ``Mixing and Transformation in a Deep Western Boundary Current: a case study''
<p>Contains numerical model data (code, input files, selected output) to supplement publication ``Mixing and Transformation in a Deep Western Boundary current'', by Spingys and co-authors. All umerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations used in the above-referenced paper.</p>
Observations of AOD and GEOS-Chem simulation model output dataset
<p>Archive of observations of AOD (MODIS Deep Blue, MAIAC, AERONET) used to evaluate the simulations of using high resolution offline dust emission, upon submission of the following manuscript:</p> <p>Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted. </p> <p>This dataset can also be accessed via this FTP ftp://stetson.phys.dal.ca/jmeng/GMD_OfflineDust_data/ </p> <p>The folder "GMD_OfflineDust_data" contains three folders, "AERONET", "MODIS" and "GC_output", representing AERONET AOD, MODIS AOD and model outputs respectively. </p>
Output Files from Reactive Transport Models (supplement to Solid Earth Manuscript 'se-2020-99')
<p>This repository includes the output files (in Tecplot format) of the numerical models in the paper entitled "<em><strong>Understanding controls on hydrothermal dolomitisation: insights from 3D Reactive Transport Modelling of geothermal convection</strong></em>".</p> <p>A key to the output data provided here is summarised in Data_Benjakul_et_al_2020.xlsx.</p> <p> </p> <p>We used <strong>TOUGHREACT</strong>, a numerical simulation program developed by the Lawrence Berkeley National Laboratory (LBNL) which is available at: <a href="https://tough.lbl.gov/licensing-download/toughreact-licensing-download/">https://tough.lbl.gov/licensing-download/toughreact-licensing-download/</a> to numerically model the convection of seawater in a single fault within carbonate-hosted system. The parameters used in the numerical models are given in the paper.</p> <p><a href="https://doi.org/10.5194/se-11-2439-2020">https://doi.org/10.5194/se-11-2439-2020</a></p> <p>The output files from TOUGHREACT (in Tecplot format) are visualised using <strong>trexplot</strong>, a python script developed by Hamish Robertson which can be found at: <a href="https://github.com/hammytheham/trexplot">https://github.com/hammytheham/trexplot</a> (last access: 5 December 2020).</p> <p> </p> <p>Please contact me via the following e-mail addresses in case you have any questions: <a href="mailto:rb17526@bristol.ac.uk">rb17526@bristol.ac.uk</a> or <a href="mailto:rbenjaku@gmail.com">rbenjaku@gmail.com</a></p>
Amory et al. (2021), Geoscientific Model Development : data, model outputs and source code
<p><strong>Data and model outputs for the replication of the analysis made in:</strong><br> (see the published version of this article in Geoscientific Model Development, 2021 - please cite this version if you use these data)<br> C. Amory, C. Kittel, L. Le Toumelin, C. Agosta, A. Delhasse, V. Favier, and X. Fettweis: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adelie Land, East Antarctica, Geoscientific Model Development, accepted, 2021. </p> <p>See README.txt for a full description of the dataset content</p> <p>Please contact me at amory.charles@live.fr if you need other half-hourly outputs or for more details on the dataset</p>
Tethys - Input and Output Data
<p>This dataset includes input and output data for 20 combinations of GCM/RCP scenarios for the Tethys model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
Xanthos - Output Data
<p>This dataset includes output data for 20 combinations of GCM/RCP scenarios for the Xanthos model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
GCAM-USA Raw Outputs for Khan et al. 2021 - Evolution of energy-water-agriculture interconnectivity across the U.S.
<p>GCAM-USA outputs for paper: Khan et al. 2021, Evolution of energy-water-agriculture interconnectivity across the U.S.</p>
CGAM LAC - Input and Output Data
<p>This dataset includes input and output data for 20 combinations of GCM/RCP scenarios for the GCAM LAC (v5.1.3) model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
Input and output files for WRF urban simulations for the metropolitan area of Tel-Aviv
<p><span><span>We implement and verify for the first time four Weather Research and Forecasting model urban configurations, </span></span><span><span>focused on the coastal metropolitan area of Tel-Aviv (MTA) using updated land use and morphological maps. We analyze the mesoscale summertime flow and the urban canopy (UC) role in the occurrence of different hodograph dynamics observed within MTA at night. These events may be significant in air quality research. The four configurations – bulk (MM), single-layer (SLUCM), multi-layer (BEP), and BEP coupled with the building energy model – reproduce the observed diurnal temperature and wind cycles, with similar 10m wind direction bias and RMSE (15<sup>o</sup> and ~30<sup>o</sup>, respectively), with preference for MM and SLUCM at night. However, the SLUCM shows the lowest skill for the 10m wind speed (WS) (bias and RMSE 1ms<sup>-1</sup>), and the BEP shows the largest underestimation of the 2m temperature, ~-2.5<sup>o</sup>C. In the SLUCM, the WS increases over an UC and with increasing building heights. These results call for a re-examination of the SLUCM WS parameterization. The simulations show that at night, a convergence line (CL) builds up with the urban heat island, downstream of the NW flow. West of the CL, the wind continues flowing from the sea, and rotates anti-clockwise to form a non-elliptical sea-breeze hodograph. Removing MTA UC restores an elliptical hodograph. East of the CL, the UC supports an elliptical hodograph with a clockwise rotation through the NE sector, previously reported as dynamically unstable. We expect such wind hodograph dynamics within similar coastal metropolitan areas.</span></span></p>
COSIPY-WRF Daily SMB output 2014-2018
<p>Daily average values of </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.