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Automatic message sequence chart creation from simulation run of the Chandy-Lamport algorithm modeled by colored Petri net
<p>Videos of two message sequence chart creation from simulation runs of the Chandy-Lamport algorithm modeled by colored Petri net using the CPN tool.</p> <p><strong>Message Sequence Chart Of Model With Automatic Simulation Run_SuppInfo.mp4</strong>: This video shows the automatic generation of a message sequence chart of a proposed colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.1. The video has been generated by the authors' updated extension server of the CPN tool. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition in an automatic simulation run.</p> <p><strong>Message Sequence Chart Of Model With Step-By-Step Simulation Run_SuppInfo.mp4:</strong> This video shows the automatic generation of a message sequence chart of the proposed colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.1. We fired our selected enabled transition of the model to create this video.</p>
Fermi-GBM and Swift-BAT Data Release Related to Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run
<p>This material contains the data products associated with A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run [1]. It is based, in part, on data products associated with GWTC-2.1 [2][3] and GWTC-3 [4][5] from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration provided under a Creative Commons Attributional 4.0 International license. For more information, see the paper (<a href="https://arxiv.org/abs/2308.13666">https://arxiv.org/abs/2308.13666</a>) and the included README.txt.</p> <p>The data release includes the following directories:</p> <ul> <li><strong>3sigma_upper_limit_maps:</strong> 3 sigma flux upper limits reported by GBM as a function of sky position over a 10-1000 keV energy range.</li> <li><strong>5sigma_upper_limit_maps: </strong>5 sigma flux upper limits reported by GBM and BAT as a function of sky position over a 15-350 keV energy range.</li> <li><strong>gbm_targeted_results: </strong>GRB candidates from the GBM Targeted Search.</li> <li><strong>gbm_temporal_offset_analysis: </strong>input files to the time offset analysis applied to GBM on-board triggers and candidates from the GBM Untargeted Search.</li> <li><strong>bbh_model_fluxes: </strong>predicted gamma-ray fluxes over the 10-1000 keV energy range for likely BBH mergers.</li> <li><strong>gw_localizations:</strong> GW localization files used to overlay the 90% credible area onto GBM upper limits as a function of sky position.</li> <li><strong>m1_m2_contours: </strong>90% credible region paths for the GW component masses m1, m2.</li> </ul> <p>The data release also includes a set of example scripts to show how the contents of the data files are used. Refer to the included README.txt for more details.</p> <p><strong>References:</strong></p> <p><a href="https://arxiv.org/abs/2308.13666">[1] Fletcher, C. et al 2023, arXiv, 2308.13666</a><br> <a href="https://doi.org/10.5281/zenodo.6513631">[2] LIGO Scientific Collaboration and Virgo Collaboration. 2022, Zenodo, 6513631</a><br> <a href="https://doi.org/10.5281/zenodo.5759108">[3] LIGO Scientific Collaboration and Virgo Collaboration. 2021, Zenodo, 5759108</a><br> <a href="https://doi.org/10.5281/zenodo.5546663">[4] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration. 2021, Zenodo, 5546663</a><br> <a href="https://doi.org/10.5281/zenodo.5546665">[5] LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration 2021, Zenodo, 5546665</a></p> <p><br> </p>
Environmental DNA highlights the influence of salinity and agricultural run-off on coastal fish assemblages in the Great Barrier Reef region
<p class="MsoNormal">Agricultural run-off in Australia's Mackay-Whitsunday region is a major source of nutrient and pesticide pollution to the coastal and inshore ecosystems of the Great Barrier Reef. While the effects of run-off are well documented for the region's coral and seagrass habitats, the ecological impacts on estuaries, the direct recipients of run-off, are less known. This is particularly true for fish communities, which are shaped by the physico-chemical properties of the coastal waterways that vary greatly in tropical regions. To address this knowledge gap, we used environmental DNA (eDNA) metabarcoding to examine teleost and elasmobranch fish assemblages at four locations (three estuaries and a harbour) subjected to varying levels of agricultural run-off during a wet and dry season. Pesticide and nutrient concentrations were markedly lower during the sampled dry season. With the influx of freshwater and agricultural run-off during the wet season, teleost and elasmobranch taxa richness significantly decreased in all three estuaries, along with pronounced changes in fish community composition which were largely associated with environmental variables (particularly salinity). In contrast, the nearby Mackay Harbour exhibited a far more stable community structure, with no marked changes in fish assemblages observed between the sampled seasons. Within the wet season, differing compositions of fish communities were observed among the four sampled locations, with this variation being significantly correlated with environmental variables (salinity, chlorophyll, DOC) and contaminants from agricultural run-off, i.e., nutrients (nitrogen and phosphorus) and pesticides. Historically contaminated and relatively unimpacted estuaries each demonstrated distinct fish communities, reflecting their associated catchment use. Our findings emphasise that while seasonal effects (e.g., changes in salinity) play a key role in shaping the community structure of estuarine fish in this region, agricultural contaminants (nutrients and pesticides) are also important contributors in some systems.</p>
Collection of runs with VIA genomics workload on RISC-V architectures
<p>This is a repository of results obtained experimenting with VIA genomics workload on RISC-V architectures. The current repository will be moved into a proper web-site with tools to properly visualize the data gathered. However, for now, we provide the results as txt files with the output of the workload for the several cases we examined so far. Within the dataset there is a README explaining the nomenclature used to save the results.</p>
GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run — Data behind the figures
<p>This material is part of several data products associated with GWTC-3, the third Gravitational-Wave Transient Catalog from the <a href="https://www.ligo.org/">LIGO</a> Scientific Collaboration, the <a href="https://www.virgo-gw.eu/">Virgo</a> Collaboration, and the <a href="https://gwcenter.icrr.u-tokyo.ac.jp/en/">KAGRA</a> Collaboration. For more information, see the paper (<a href="https://dcc.ligo.org/LIGO-P2000318/public">dcc.ligo.org/LIGO-P2000318/public</a>), the related material linked from this page, and the GWTC-3 data release documentation (<a href="https://www.gw-openscience.org/GWTC-3/">www.gw-openscience.org/GWTC-3/</a>).</p> <p><br> <strong>Data behind the figures</strong></p> <p>This page contains the data behind various paper figures. The material for each figure is contained in a tar file. A short description can be found below. Figures not included here are associated with one of the other GWTC-3 data releases.</p> <p> </p> <p><strong>Figure 1</strong></p> <ul> <li>Figure01.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 1. This shows the number of candidates with probability of astrophysical origin > 50% as a function of surveyed time–volume.</p> <p>The dates for the first observing run (<a href="https://doi.org/10.1103/PhysRevX.6.041015">O1</a>) and second observing run (<a href="https://doi.org/10.1103/PhysRevX.9.031040">O2</a>) candidates are hard-coded into the script, and the dates for third observing run (<a href="https://arxiv.org/abs/2108.01045">O3a</a> and O3b) candidates are included in two text files. The effective binary neutron star time–volume (BNS-VT) for each observing run is stored in separate .csv files.</p> <p>Each .csv file contains two columns, the first is the GPS time and the second is the cumulative effective BNS VT in Mpc<sup>3</sup> kyr (this is converted to Gpc<sup>3</sup> yr in the included script).</p> <p>The included script reproduces Figure 1 from GWTC-3 using the supplied data.</p> <p> </p> <p><strong>Figure 2</strong></p> <ul> <li>Figure02.tar.gz</li> </ul> <p>Data and plotting scripts for GWTC-3: Figure 2. The figure shows sensitivity curves for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script reads the .txt files containing strain data for Hanford, Livingston, and Virgo and saves figures as PDF files.</p> <p>The sensitivity curves are representative of performance during O3b. Further examples of sensitivity curves across observing runs can be found from the <a href="https://www.gw-openscience.org/detector_status/">Gravitational Wave Open Science Center</a>.</p> <p> </p> <p><strong>Figure 3</strong></p> <ul> <li>Figure03.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 3. The left panel shows the <a href="https://doi.org/10.1088/1361-6382/abd594">binary neutron star inspiral range</a> of LIGO Hanford, LIGO Livingston, and Virgo versus time. The right panel shows histograms of the binary neutron star ranges for LIGO Hanford, LIGO Livingston, and Virgo.</p> <p>The Python script (figure_3.py) reads the range.txt files and the histogram.txt files to produce each panel and saves them as PDF files.</p> <p>Further summary information about the O3b run can be obtained from the <a href="https://www.gw-openscience.org/detector_status/O3b/">Gravitational Wave open Science Center</a>.</p> <p> </p> <p><strong>Figure 4</strong></p> <ul> <li>Figure04.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 4. This plot shows the rate of <a href="https://doi.org/10.1088/1361-6382/abfd85">non-Gaussian noise transients (glitches)</a> in the LIGO Hanford, LIGO Livingston and Virgo data across O3b. The recorded glitches are identified by the <a href="https://virgo.docs.ligo.org/virgoapp/Omicron/">Omicron</a> pipeline with signal-to-noise ratio of > 6.5. There is a reduction in the LIGO glitch rate after the introduction of <a href="https://doi.org/10.1088/1361-6382/abc906">reaction chain (RC) tracking</a>, which reduced the incidence of scattered light (slow scattering) glitches.</p> <p>The script glitch_rates_GWTC-3_Fig_4.py produces Figure 4 of GWTC-3 making use of the glitch rates stored in the glitch_rates_GWTC-3_Fig_4.h5 file. Run the script within an <a href="https://computing.docs.ligo.org/conda/environments/igwn-py37/">igwn-py37</a> or <a href="https://computing.docs.ligo.org/conda/environments/igwn-py38/">igwn-py38</a> Conda environment, paying attention to having the hdf5 file glitch_rates_GWTC-3_Fig_4.h5 in the same directory of the script. Pass the argument -v or --verbose for additional info about the rates.</p> <p> </p> <p><strong>Figure 5</strong></p> <ul> <li>Figure05.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 5. This figure illustrates the time–frequency structure of two common types of glitch seen in O3: <a href="https://doi.org/10.1088/1361-6382/abc906">slow scattering</a> and <a href="https://doi.org/10.1088/1361-6382/ac1ccb">fast scattering</a>. Both are caused by light scattering within the LIGO detectors.</p> <p>The Python script scattering_GWTC-3_Fig_5.py produces Figure 5 in the GWTC-3 Catalog paper using <a href="https://www.gw-openscience.org/O3/">open data</a>. The script saves the plot as a PDF file namely, scattering_GWTC-3_Fig_5.pdf and the data used to generate the plot in the files data_fast_scattering.txt and data_slow_scattering.txt.</p> <p>For further examples of the time–frequency structure of glitches, the community-science project <a href="https://gravityspy.org/">Gravity Spy</a> catalogs visualizations of glitches in gravitational-wave data.</p> <p> </p> <p><strong>Figure 12</strong></p> <ul> <li>Figure12.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 12. This plots results of the waveform consistency test (as does Figure 13), plotting the match between waveform templates and minimally modeled reconstructions. The on-source results are for the candidate signals, while the off-source results are for simulated signals with compatible properties.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The Python script (figure_12.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB to produce the corresponding match–match plots (PDF files).</p> <p> </p> <p><strong>Figure 13</strong></p> <ul> <li>Figure13.tar.gz</li> </ul> <p>Data and script to produce GWTC-3: Figure 13. This plots results of the waveform consistency test (as does Figure 12), plotting the p-values for the minimally modeled waveform reconstructions. The p-values are plotted in increasing order.</p> <p>The waveform reconstructions are performed using <a href="https://git.ligo.org/lscsoft/bayeswave">BayesWave</a> and <a href="https://gwburst.gitlab.io/">cWB</a>. The two pipelines select different sets of candidates to analyze.</p> <p>The script (figure_13.py) reads data from files FittingFactor.txt for Bayeswave and FittingFactor_C01.txt for cWB (the same files used to produce Figure 12) to produce the corresponding p-value plots (PDF files).</p> <p> </p> <p><strong>Figure 14</strong></p> <ul> <li>Figure14.tar.gz</li> </ul> <p>Data and script to produce GWTC-3 Figure 14. This figure shows representative noise amplitude spectral densities for LIGO Hanford, LIGO Livingston, and Virgo during Observing Run 2 and Observing Run 3b.</p> <p>The script (figure_14.py) reads data from the .txt files included in the release to reproduce Figure 14 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 15</strong></p> <ul> <li>Figure15.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 15. This shows differences in the data used to analyze <a href="https://doi.org/10.7935/b024-1886">GW200115_042309</a>, with and without glitch subtraction. A low frequency cut (illustrated by the dotted white line) was used to remove the glitch in the <a href="https://doi.org/10.3847/2041-8213/ac082e">first analysis</a> of this candidate, whereas glitch subtraction is now used when performing parameter estimation. The curving orange line shows the approximate signal track.</p> <p>The script reads in the deglitched frame L-L1_HOFT_CLEAN_SUB60HZ_C01_T1700406_v4-1263095808-4096.gwf, downloaded from <a href="http://doi.org/10.5281/zenodo.5546679">an associated data release</a>, query raw <a href="https://www.gw-openscience.org/O3/">public LIGO Livingston data</a>, and reproduce Figure 15 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 16</strong></p> <ul> <li>Figure16.tar.gz</li> </ul> <p>Script to produce GWTC-3: Figure 16. This figure illustrates the <a href="https://gwpy.github.io/docs/latest/examples/timeseries/qscan/">time–frequency structure</a> of data containing three O3 candidates identified only by <a href="https://gwburst.gitlab.io/">cWB</a> (the same as shown in Figure 17). Each shows evidence of instrumental origin. </p> <p>The script queries <a href="http://www.gw-openscience.org/O3/">public LIGO data</a> and reproduce Figure 16 from GWTC-3 in PDF format.</p> <p> </p> <p><strong>Figure 17</strong></p> <ul> <li>Figure17.tar.gz</li> </ul> <p>Data and script for GWTC-3: Figure 17. This figure illustrates the <a href="https://doi.org/10.1088/1742-6596/363/1/012032">time–frequency structure</a> of candidate signals as reconstructed by <a href="https://gwburst.gitlab.io/">cWB</a> for three O3 candidates identified only by cWB (the same as shown in Figure 16). Each shows evidence of instrumental origin. For a compact binary coalescence signal, we would expect the signal to have a chirp structure, sweeping up from low to high frequencies.</p> <p>The script figs.py reads data (the reconstruction from cWB) from the .txt files to produce the corresponding time–frequency plots (PDF files). The script must be run three times to produce the panels of Figure 17: the event names are hardcoded into the script, which must be edited to produce the desired panel.</p> <p> </p> <p><strong>How to download all files from this page</strong></p> <p>If you would like to download all files on this page, we recommend <a href="https://gitlab.com/dvolgyes/zenodo_get">zenodo_get</a>:</p> <pre><code class="language-bash">pip install zenodo_get zenodo-get RECORD_ID_OR_DOI </code></pre> <p>where the record ID for the most recent version of this page is 5571766 and IDs for other versions can be found in the Versions section at the side of this page.</p> <p> </p> <p>For more general background on gravitational-wave data analysis, try the materials from a <a href="https://www.gw-openscience.org/workshops/">GW Open Data Workshop</a> or the <a href="https://doi.org/10.1088/1361-6382/ab685e">guide to LIGO–Virgo data analysis</a>.</p> <p> </p>
Data from: Multiple spawning run behavior and population consequences in migratory striped bass Morone saxatilis
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Run and output files from: Holocene population expansion of a tropical bee coincides with early human colonisation of Fiji rather than climate change
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Physiological data and R script for running physiology combined model for Drosophila suzukii
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Simulation code for: Clones on the run - the genomics of a recently expanded partially clonal species
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Environmental DNA highlights the influence of salinity and agricultural run-off on coastal fish assemblages in the Great Barrier Reef region
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Baltimore Ecosystem Study: Pharmaceutical concentrations for core sites in Gwynns Falls and Baisman Run
Water samples from one year of weekly samples at the Baltimore Ecosystem Study core stream sites were analyzed to measure concentrations of 92 different pharmaceutical concentrations. Note that none of the sites sampled in this dataset receive effluent from wastewater treatment plants (a common source of pharmaceutical contamination). In Baisman Run, sewage is treated by onsite systems, while in the Gwynns Falls watershed, sewage is piped across the watershed boundary and treated elsewhere.
MAIC-2 simulation run_t14
<p>Visualization of the time-dependent H<sub>2</sub>O glaciation of Mars, simulated by MAIC-2 experiment run_t14. This is the best-fit simulation #6 by <a href="https://arxiv.org/abs/0903.2688">Greve et al. (2010)</a>, extended from 20 million years ago until 10 million years into the future. North up, vertical exaggeration factor ~676. Bottom panel: Obliquity over time.</p>
Fig. 127 in The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions
Fig. 127. Elaphropeza limosa Shamshev & Grootaert, 2007, ♀ abdomen, lateral view.
Demeter Input Files for 0.05 degree LULCC from GCAM SSP/RCP/GCM Scenario Runs
<p>Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM ) and a geospatial downscaling model (Demeter) under diverse Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05°×0.05°) and is spread under more diverse SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.</p> <p>The dataset includes the inputs for Demeter to produce projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). See <a href="https://github.com/JGCRI/chen_et_al_2020a">https://github.com/JGCRI/chen_et_al_2020a</a> for details on how to reproduce this experiment.</p> <p> </p>
Figure 24. When running a in DELTA for Beginners. An introduction into the taxonomy software package DELTA
Figure 24. When running a directive file the program asks for an export of the directive files.
Model sensitivity runs for Southern Ocean studies
<p>The present generation of global climate models is characterized by insufficient reflection of short-wave radiation over the Southern Ocean due to misrepresentation of clouds. This is a significant concern as it leads to excessive heating of the ocean surface, sea surface temperature biases, and subsequent problems with atmospheric dynamics. Using the recent version of the Met Office's Unified Model, we show that modifying the cloud micro-physics scheme with a more realistic value for the shape parameter of atmospheric ice-crystals suggested by theory and observations, benefits the simulation of short-wave radiation. In the control model, for calculating the growth rate through deposition process, it is assumed that all ice particles are spherical in shape. By approximating the value for plates or aggregates of ice crystals, along with modified ice nucleation temperatures, we show that there is a reduction in the annual-mean short-wave cloud radiative effect over the Southern Ocean. </p>
Plant-animal interactions between carnivorous plants, sheet-web spiders, and ground-running spiders as guild predators in a wet meadow community
<p>Plant-animal interactions are diverse and wide-spread shaping ecology, evolution and biodiversity of most ecological communities. Carnivorous plants are unusual in that they can be simultaneously engaged with animals in multiple mutualistic and antagonistic interactions including reversed plant-animal interactions where they are the predator. Competition with animals is a potential antagonistic plant-animal interaction unique to carnivorous plants when they and animal predators consume the same prey.</p> <p>The goal of this field study was to test the hypothesis that under natural conditions, sundews and spiders are predators consuming the same prey thus creating an environment where interkingdom competition can occur.</p> <p>Over 12 months, we collected data on 15 dates in the only protected Highland Rim Wet Meadow Ecosystem in Kentucky where sundews, sheet-web spiders and ground-running spiders co-exist. One each sampling day, we attempted to locate fifteen sites with: 1) both sheet-web spiders and sundews; 2) sundews only; and where neither occurred. Sticky traps were set at each of these sites to determine prey (springtails) activity-density. Ground-running spiders were collected on sampling days. DNA extraction was performed on all spiders to determine which individuals had eaten springtails and comparing this to the density of sundews where the spiders were captured. </p> <p>Sundews and spiders consumed springtails. Springtail activity-densities were lower the higher the density of sundews. Both sheet-web and ground-running spiders were found less often where sundew densities were high. Sheet-web size was smaller where sundews densities were high. </p> <p>The results of this study suggest that asymmetrical exploitative competition occurs between sundews and spiders. Sundews appear to have a greater negative impact on spiders, where spiders probably have little impact on sundews. In this example of interkingdom competition where the asymmetry should be most extreme, amensalism where one competitor experiences no cost of interaction may be occurring. </p>
REMIND 2.1 paper - model runs
<p>output of the scenarios of the REMIND2.1 paper</p>
The extensibility of the plantar fascia influences the windlass mechanism during human running
<p>The arch of the human foot is unique among hominins as it is compliant at ground-contact but sufficiently stiff to enable push-off. These behaviours are partly facilitated by the ligamentous plantar fascia whose role is central to two mechanisms. The ideal windlass mechanism assumes that the plantar fascia has a nearly constant length to directly couple toe dorsiflexion with a change in arch shape. However, the plantar fascia also stretches and then shortens throughout gait as the arch-spring stores and releases elastic energy. We aimed to understand how the extensible plantar fascia could behave as an ideal windlass when it has been shown to strain throughout gait, potentially compromising the one-to-one coupling between toe arc length and arch length. We measured foot bone motion and plantar fascia elongation using high-speed x-ray during running. We discovered that toe plantarflexion delays plantar fascia stretching at foot-strike, which likely modifies the distribution of the load through other arch tissues. Through a pure windlass effect in propulsion, a quasi-isometric plantar fascia's shortening is delayed to later in stance. The plantar fascia then shortens concurrently to the windlass mechanism, likely enhancing arch recoil at push-off.</p>
Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?
Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.
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.