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1,899 results for “Running”
Sacramento River winter-run Chinook salmon cohort reconstruction – data and code
Open the record for dataset details and reuse information.
Modeled ranges of California plant species and scripts to run the models for Spatial phylogenetics of the native California flora (Thornhill et al. BMC Biology)
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Data from: Role of multiple, adjustable toes in distributed control shown by sideways wall-running in geckos
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NZESM model data of simulation u-bl274 (historical run)
<p>Subset data of NZESM historical simulation u-bl274.</p>
UKESM model data of simulation u-bm456 (historical run)
<p>Subset data of UKESM historical simulation u-bm456</p>
Namelists required to run sea ice and physics configuration of MITgcm for the West Antarctic Peninsula
<p>This dataset contains the namelists that describe the initial conditions, forcing files and specific package configurations used to run a sea ice and ocean physics configuration of the MITgcm (general circulation model) for the West Antarctic Peninsula (WAP). </p>
Test data for running snakePipes : WGBS workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the WGBS workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for the mouse (<strong>GRCm38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Test data for running snakePipes : noncoding-RNA-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the noncoding-RNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>GRCm38</strong>) genome. Repeat Masker file is required for this workflow.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
GEO4962 input dataset for running CESM 2.1.0 case for the course
<p>Contains the input dataset for running CESM 2.1.0 as part of the GEO4962 course at the University of Oslo.</p>
Input files to run the Iquitos dengue simulator
<p>Input files to run the Iquitos dengue simulator</p>
FIGURE 1 in How Fast Could Tyrannosaurus rex Run?
FIGURE 1. IN A FREE-BODY DIAGRAM for a running tyrannosaur, the angles of the leg joints (top) are critical parameters. In addi- tion to the forces and torques produced by leg muscles, external forces (bottom) including the weights of the body (Ƒb), thigh (Ƒt), shank (Ƒs), and metatarsus (Ƒm), as well as the ground reaction force (GRF) that acts a distance R from the toe joint must be incorporated. (Adapted from ref. 6.)
Fig. 2. A B in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 2. A B-spline solid is a closed object whose shape can be adjusted by moving control points (dark points) that deforms the local portion of the object near the control point. The initial cylindrical shape in A is adjusted (B and C) by pulling out the points at the ends and drawing the points in the middle closer to the axis.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
FIGURE 2 in How Fast Could Tyrannosaurus rex Run?
FIGURE 2. ESTIMATED EXTENSOR MUSCLE MASS per leg needed to run fast, as a fraction of total body mass. Larger animals need relatively more leg muscle to run. This dependence is illustrated by the solid line, calculated for a chicken scaled up to the size of T. rex. For several models of T. rex anatomy, the estimates (red) all require more leg muscle for running fast than the creature was likely to have had (orange dot). (Adapted from ref. 6.)
FIGURE 1 in How Fast Could Tyrannosaurus rex Run?
FIGURE 1. IN A FREE-BODY DIAGRAM for a running tyrannosaur, the angles of the leg joints (top) are critical parameters. In addi- tion to the forces and torques produced by leg muscles, external forces (bottom) including the weights of the body (Ƒb), thigh (Ƒt), shank (Ƒs), and metatarsus (Ƒm), as well as the ground reaction force (GRF) that acts a distance R from the toe joint must be incorporated. (Adapted from ref. 6.)
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>
Urban / rural acorn ant metabolic rate and running speed
<p>Metabolic rates of ectotherms are expected to increase with global trends of climatic warming. But the potential for rapid, compensatory evolution of lower metabolic rate in response to rising temperatures is only starting to be explored. Here, we explored rapid evolution of metabolic rate and locomotor performance in acorn-dwelling ants (<i>Temnothorax curvispinosus</i>) in response to urban heat island effects. We reared ant colonies within a laboratory common garden (25 °C) to generate a lab-born cohort of workers and tested their acute plastic responses to temperature. Contrary to expectations, urban ants exhibited a higher metabolic rate compared with rural ants when tested at 25 °C, suggesting a potentially maladaptive evolutionary response to urbanization. Urban and rural ants had similar metabolic rates when tested at 38 °C, as a consequence of a diminished plastic response of the urban ants. Locomotor performance also evolved such that the running speed of urban ants was faster than rural ants under warmer test temperatures (32 and 42 °C) but slower under a cooler test temperature (22 °C). The resulting specialist-generalist tradeoff and higher thermal optimum for locomotor performance might compensate for evolved increases in metabolic rate by allowing workers to more quickly scout and retrieve resources.</p>
Input dataset for a single location in Norway ALP1 (61.0243N,8.12343E) for running CTSM/FATES EMERALD
<pre>inputdata_version2.0.0 for a single location in Norway ALP1 (61.0243N,8.12343E) for running CTSM/FATES EMERALD (created by Hui Tang, using tools from https://github.com/NordicESMhub/ctsm/tree/release-emerald-platform2.0.1/tools/emerald_sites_tools) - Same as version version1.0.0, except two additional input files required by fire module are included. - Should work with model platform:release-emerald-platform2.*.* </pre> <p> </p> <p><em>Additional reference:</em></p> <p>Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., et al. (2019). The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. Journal of Advances in Modeling Earth Systems, 11, 4245–4287. <a href="https://doi.org/10.1029/2018MS001583">https://doi.org/10.1029/2018MS001583</a></p>
The Little Things that Run the City dataset
<p>Plant-insect metanetwork dataset collected across 15 greenspaces within a densely urbanised inner-city municipality (Melbourne, Australia). </p>
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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.