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280 results for “mesa”
Long-term monitoring of reptiles and ground arthropods near the Phoenix-Mesa Gateway Airport, Mesa, Arizona, USA, ongoing since 2010
Reptiles and amphibians have been monitored at the Gateway site since 2010. The goals of the project have been to provide undergraduate and graduate students opportunities to learn hands-on wildlife techniques, follow seasonal patterns of herpetofauna and ground arthropods, and serve as a test bed for new projects and technologies, including development of a mobile app for data collection. Live trapping methods include 6 trap arrays of pitfall and funnel traps placed along drift fences. Arrays are checked daily when traps are actively open to trap animals. Lizards are given a unique toe clip code, but all other species are unmarked. Reptiles and amphibians are weighed and measured and released at point of capture. Ground arthropods are counted to the Order-level. Arrays are open typically from March to October and the years vary in trapping effort with arrays open from 2 to 68 days per year. The most common species captured are tiger whiptail (*Aspidoscelis tigris*) and common side-blotched (*Uta stansburiana*) lizards.
Grand Mesa 2017-02-01 snow depth estimate
<p>Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series of very-high-resolution commercial stereo imagery. More formal releases of data products will be available in the coming years.<br> <br> Analysis of this dataset is presented in the following publication:</p> <ul> <li>McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H-P., Painter, T., Molotch, N., Elder, K., Hiemstra, C., Brucker, L., (2019), Spatially Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations, Water Resources Research, 55 (19), 10026-10036, doi:<a href="https://doi.org/10.1029/2019WR024907">10.1029/2019WR024907</a>.</li> </ul> <p>If you use these data, please cite the above publication.</p>
MESA model files and data for: 'Stellar Neutrino Emission Across The Mass-Metallicity Plane'
<p>Example MESA model files and stellar evolution tracks for download from "Stellar Neutrino Emission Across The Mass-Metallicity Plane".</p>
Grand Mesa Study Plot
<p>Cleaned (quality checked) and continuous hourly records of snow energy and mass balance variables from Grand Mesa Study Plot (GMSP). </p> <p>GMSP is located in an opening in a pine forest on the northern rim of the Grand Mesa (39.050802, -108.061435) in west central Colorado at 3239 m. </p> <p>The site was funded by the USGS (poc: Jayne Belnap), installed by the Center for Snow and Avalanche Studies (poc: Jeff Derry), and is maintained by the Snow Optics Laboratory at NASA- JPL (poc: Thomas Painter). The study plot is primarily used to assess the spatial variability in the hydrologic impacts of dust on snow (see Skiles et al., 2015, DOI: 10.1002/hyp.10569). Real time data can be retried from MesoWest (station ID: GMSPC).</p> <p>The study plot consists of a snow profile plot that contains a tower holding the instrumentation array. Tower measurements include wind speed and direction, air temperature and relative humidity, snowpack depth, incoming and outgoing broadband (BB; 0.285–2.800 μm) and near-infrared/shortwave-infrared (NIR/SWIR; 0.695–2.800 μm) solar radiation, and incoming longwave radiation values. Precipitation is from the nearby ‘Mesa Lakes’ Snow Telemetry site (SNOTEL; NRCS), which is located approximately 500m north of GMSP at 3048m under the assumption that precipitation is similar at the two sites.</p> <p>Variables are accompanied by notes in adjacent columns to indicate if the value is measured, interpolated, or 'cleaned' (i.e snow depths are set to 0 after the snow depletion date). </p> <p>Note: Grand Mesa was a field site for the NASA SnowEx field campaign in Year 1 (2017), and will be again Year 3 (2019). GMSP is/was within airborne flight domains.</p> <p>Dataset point of contact: S. McKenzie Skiles, Department of Geography, University of Utah (m.skiles@geog.utah.edu) </p>
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the West Mesa Meteorological Station (WSMS), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Tule 222 Well Meteorological Station (WSMS). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetwsms/.
Reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars "
<p>This is a reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - II. Implementation of magnetic braking in MESA and implications for the evolution of surface rotation in OB stars" by Keszthelyi et al. (2020), https://doi.org/10.1093/mnras/staa237</p>
MESA (r11701) models with convective turnover times
<p>$\texttt{MESA r11701}$ (<a title="r11701 release paper" href="https://ui.adsabs.harvard.edu/abs/2019ApJS..243...10P/abstract" target="_blank" rel="noopener">Paxton et al. 2019</a>) stellar models in the range 0.08 - 1.3 $\rm M_{\odot}$ with a metallicity of $\rm Z_{\odot}$ (as according to <a href="https://ui.adsabs.harvard.edu/abs/2009ARA%26A..47..481A/abstract" target="_blank" rel="noopener">Asplund et al. 2009</a>) and no rotation. These models are part of a study on convective turnover times accepted to ApJ and available to read here: <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241020000G/abstract" target="_blank" rel="noopener">Gossage et al. 2024</a>.</p> <p> </p> <ul> <li>$\texttt{MESA r11701}$ may be downloaded here: <a href="https://zenodo.org/records/2665077" target="_blank" rel="noopener">https://zenodo.org/records/2665077</a></li> <li>The associated $\texttt{MESA SDK}$ may be found here: <a href="http://user.astro.wisc.edu/~townsend/static.php?ref=mesasdk-old#linux-download" target="_blank" rel="noopener">old release archive</a> (compiled under $\texttt{GCC version 8.3.0}$)</li> </ul> <p> </p> <h3><strong>Models and inlists</strong></h3> <p>The compressed archive files $\texttt{MESA_run_directories_to_XGyr.tar.gz}$ contain run directories for our models evolved up to $\texttt{X}$ Gyrs (1, 5, or 14). Each directory within has a name corresponding to the model's initial mass (e.g., with $\texttt{00101M_dir}$ corresponding to a 1.01 $\rm M_{\odot}$ model) and contains the inlist (called $\texttt{inlist_project}$) used for that run. Each directory contains a subdirectory called $\texttt{LOGS}$ that contains the run's output (stellar profiles and histories in this case). The stellar profiles are available at approximately 1 Myr, 1 Gyr, 5 Gyr and 14 Gyr, when possible. Every $\texttt{LOGS}$ directory should contain a $\texttt{final_profile.data}$ file which is the stellar profile at the final simulation step of that run. These models were produced to study the variation of the convective turnover time, according to mixing length theory (as according to <a href="https://ui.adsabs.harvard.edu/abs/1965ApJ...142..841H/abstract" target="_blank" rel="noopener">Henyey et al. 1965</a>) in 1D stellar evolution.</p> <p>In the history files, the convective turnover times provided are (in units of seconds):</p> <ol> <li>$\texttt{conv_env_turnover_time_l_hp}$, calculated one half of a (local) pressure scale height from the bottom of the convection zone (BCZ), or core in fully convective stars. This calculation corresponds to the values cited in <a href="https://arxiv.org/abs/2410.20000" target="_blank" rel="noopener">Gossage et al. 2024</a>.</li> <li>$\texttt{conv_env_turnover_time_l_hp_mid}$ at one pressure scale height from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_hp_hi}$ at two pressure scale heights from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_hp_hi2}$ at four pressure scale heights from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_hp_hi3}$ at eight pressure scale heights from the BCZ</li> <li>$\texttt{conv_env_turnover_time_l_b}$ at half pressure scale height from the BCZ, as calculated and adopted in <a href="https://ui.adsabs.harvard.edu/abs/1985ApJ...299..286G/abstract" target="_blank" rel="noopener">Giliand et al. 1985</a></li> <li>$\texttt{conv_env_turnover_time_l_t}$ at one pressure scale height from the BCZ (calculated in same manner as <a href="https://ui.adsabs.harvard.edu/abs/1985ApJ...299..286G/abstract" target="_blank" rel="noopener">Giliand et al. 1985</a>)</li> <li>$\texttt{conv_env_turnover_time_g}$ a "global" convective turnover time, calculated as a running sum of local distances divided by convective velocities (computed cell-wise) through the outer convection zone of the model</li> </ol> <p>The quantities 1-5 above are scaled by the $\texttt{inlist}$ parameter $\texttt{x_ctrl(15)}$, which is set to 0.5 by default.</p> <h3><strong>$\texttt{MESA src (run_star_extras.f90)}$ and other files </strong></h3> <p>Our $\texttt{run_star_extras.f90}$ file is provided as well. Several additional files may be needed, such as reaction networks (these are as in $\texttt{MIST v1.2}$, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...823..102C/abstract" target="_blank" rel="noopener">Choi et al. 2016</a>) that may be downloaded here: <a href="https://waps.cfa.harvard.edu/MIST/resources.html" target="_blank" rel="noopener">https://waps.cfa.harvard.edu/MIST/resources.html</a>. Our $\texttt{run_star_extras.f90}$ file is also based on that used in the $\texttt{MIST v1.2}$ models, but with some additions. </p> <h3><strong>Observational Data</strong></h3> <p>The compiled observational data used in our study is recorded in literature_sample.csv. This data is comprised of observations from several sources:</p> <ul> <li><a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...967L..36S/abstract" target="_blank" rel="noopener">Stassun & Kounkel 2024</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2011ApJ...743...48W/abstract" target="_blank" rel="noopener">Wright et al. 2011</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2022ApJ...931...45N/abstract" target="_blank" rel="noopener">Nunez et al. 2022</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...684A...9S/abstract" target="_blank" rel="noopener">Shan et al. 2024</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2019A%26A...628A..41P/abstract" target="_blank" rel="noopener">Pizzocaro et al. 2019</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2022AN....34320049M/abstract" target="_blank" rel="noopener">Magaudda et al. 2022</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2012A%26A...546A.117G/abstract" target="_blank" rel="noopener">Gondoin 2012</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.479.2351W/abstract" target="_blank" rel="noopener">Wright et al. 2018</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2013A%26A...556A..14G/abstract" target="_blank" rel="noopener">Gondoin 2013</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2016Natur.535..526W/abstract" target="_blank" rel="noopener">Wright & Drake 2016</a></li> </ul> <p>The csv contains the cross-match with Gaia DR3 (<a href="https://ui.adsabs.harvard.edu/abs/2016A%26A...595A...1G/abstract" target="_blank" rel="noopener">Gaia Collaboration et al. 2016</a>; <a href="https://ui.adsabs.harvard.edu/abs/2023A%26A...674A...1G/abstract" target="_blank" rel="noopener">Gaia Collaboration et al. 2023</a>), and we removed duplicated sources which had the same X-ray measurement. </p>
Automatic detection of clefts and corridors within the sandstone plateau - Szczeliniec Wielki & Szczeliniec Mały mesas, Poland
<p>This dataset presents the method of automatic clefts and corridors detection within areas of highly dissected relief. This methodical approach was developed for a geomorphological study on Szczeliniec Wielki and Szczeliniec Mały sandstone mesas (Stołowe Mts., SW Poland) (Migoń et al. 2023)</p> <p>Contents:</p> <ol> <li>CleftHunter_manual_v1_0.pdf - short method description</li> <li>kernel_examples.zip - set of exemplary kernel files </li> <li>Szczeliniec_Wielki_clefts_threshold_depth_2m.zip - raster dataset of automatically detected clefts within the plateau of Szczeliniec Wielki (.tif)</li> <li>Szczeliniec_Maly_clefts_threshold_depth_2m.zip - raster dataset of automatically detected clefts within the plateau of Szczeliniec Mały (.tif)</li> <li>Szczeliniec_Wielki_mesa_caprock_base.zip - Szczeliniec Wielki caprock zone (.shp, polygon)</li> <li>Szczeliniec_Maly_mesa_caprock_base.zip - Szczeliniec Mały caprock zone (.shp, polygon)</li> <li>Szczeliniec_Wielki_mesa_caprock_hillshade.zip - shaded relief of the Szczeliniec Wielki plateau (.tif)</li> <li>Szczeliniec_Maly_mesa_caprock_hillshade.zip - shaded relief of the Szczeliniec Mały plateau (.tif)</li> </ol> <p>Preferred citation:</p> <p>Migoń P., Duszyński F., Jancewicz K., Kotowska M., Porębna W. (2023), Surface-subsurface connectivity in the morphological evolution of sandstone-capped tabular hills – how much analogy to karst?. Geomorphology vol. 440, Id 108884, 1–22<br>DOI: 10.1016/j.geomorph.2023.108884</p> <p> </p> <p>This research was funded by National Science Centre, Poland, research project no. 2020/39/D/ST10/00861.</p>
Figure 5 in A new aetosaur (Archosauria: Pseudosuchia) from the upper Blue Mesa Member (Adamanian: Early-Mid Norian) of the Late Triassic Chinle Formation, northern Arizona, USA, and a review of the paratypothoracin Tecovasuchus across the southwestern USA
Figure 5. Revised regional occurrences of Tecovasuchus chatterjeei across the Chinle Formation and Dockum Group of the southwestern United States (modified from Martz 2008 and Heckert et al. 2007). Abbreviations: AZ=Arizona, NM=New Mexico, TX=Texas.
Figure 1 in A new aetosaur (Archosauria: Pseudosuchia) from the upper Blue Mesa Member (Adamanian: Early-Mid Norian) of the Late Triassic Chinle Formation, northern Arizona, USA, and a review of the paratypothoracin Tecovasuchus across the southwestern USA
Figure 1. Stratigraphic position in the Chinle Formation (A) of PFV 456 and the Placerias and Downs Quarries in Arizona (modified from Reyes et al. 2020 and Kligman et al. 2023), and their geographic occurrence (B). U-Pb ages based on Ramezani et al. (2014) and Rasmussen et al. (2020). Abbreviations: AZ=Arizona; PEFO=Petrified Forest National Park; Tr.=Triassic.
Figure 3. A in A new aetosaur (Archosauria: Pseudosuchia) from the upper Blue Mesa Member (Adamanian: Early-Mid Norian) of the Late Triassic Chinle Formation, northern Arizona, USA, and a review of the paratypothoracin Tecovasuchus across the southwestern USA
Figure 3. A. Holotype paramedian osteoderm of Kryphioparma caerula gen. et sp. nov. in comparison to that of other stagonolepidoid taxa documented within the Placerias Quarry and PFV 456, UCMP 165173. B. Desmatosuchus, PEFO 49568. C. Calypotosuchus wellesi, PEFO 46222. Orientation: All in dorsal view. Abbreviations: Ant.=Anterior, Ant. bar=Anterior bar, Ant. lam.=Anterior lamina, Dors. em.=Dorsal eminence. Small, unlabeled arrows indicate lateral direction.
MESA models with magnetic braking
<p>Output history files of MESA models run for <a href="https://ui.adsabs.harvard.edu/abs/2021ApJ...912...65G/abstract">Gossage et al. 2021</a> (MESA models with magnetic braking), and associated MESA input files (inlist templates and modified run_star_extras.f90 source code).</p>
Figure 4 in A new aetosaur (Archosauria: Pseudosuchia) from the upper Blue Mesa Member (Adamanian: Early-Mid Norian) of the Late Triassic Chinle Formation, northern Arizona, USA, and a review of the paratypothoracin Tecovasuchus across the southwestern USA
Figure 4. Paramedian osteoderms of Adamanian typothoracines documented within the Chinle Formation (A, B, D, E, H, I, L, M) and Dockum Group (C, F, G, J, K, N). Kryphioparma caerula gen. et sp. nov., UCMP 165173 (A, B). Tecovasuchus chatterjeei, PEFO 49404 (D, E), NCSM 35011 (H, I), UMMP 9600 (C, F, G), and TTU-P 9222 (J). Ambiguous paratypothoracin, MNA V3202 (L-M). Paratypothorax sp., TTU-P 9169 (K). Typothorax coccinarum, TTU-P 9214 (N). Orientations: Dorsal (A, C, D, H, J, K, L, N), Ventral (G), posterior (F), medial cross-section (B, M), lateral cross-section (E, I) views. Abbreviations: Ant.=Anterior, Ant. bar=Anterior bar, A.l.p.=Anterolateral process, A.m.p.=Anteromedial process, Bvl.=Beveled edge, Dors. em.=Dorsal eminence, Vent. strt.=Ventral strut. Small, unlabeled arrows indicate lateral direction.
Figure 2 in A new aetosaur (Archosauria: Pseudosuchia) from the upper Blue Mesa Member (Adamanian: Early-Mid Norian) of the Late Triassic Chinle Formation, northern Arizona, USA, and a review of the paratypothoracin Tecovasuchus across the southwestern USA
Figure 2. Paramedian osteoderm fragments of Kryphioparma caerula gen. et sp. nov. Holotype and paratype specimens collected from the Placerias Quarry, UCMP 165173 (A–F) and UCMP 126847 (G–L), respectively. Referred specimens collected from PFV 456, PEFO 51662 (M–R) and PEFO 46468 (S–X). Orientations: anterior (A, G, M, S), dorsal (B, H, N, T), ventral (C, I, O, U), posterior (D, J, P, V), medial cross-section (F, K, R, W), and lateral cross-section (E, L, Q, X) views. Abbreviations: Ant.=Anterior, Ant. bar=Anterior bar, Grv.=Grooves, M.e.=Medial edge, Vent. strt.=Ventral strut. Small, unlabeled arrows indicate lateral direction.
Dataset: Mesa Laboratories, Inc. (MLAB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Mesa Air Group, Inc. (MESA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data files for the tabulated MESA equation of state in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<div> <div> <div> <p>** These tables are automatically downloaded from this repository when running phantom **<br><br>This tabulated equation of state in PHANTOM is adapted from the logPgas − Temperature equation of state tables provided with the open source package Modules for Experiments in Stellar Astrophysics MESA (Paxton et al. 2011). Details of the data, originally compiled from blends of equations of state from Saumon, Chabrier, & van Horn (1995) (SCVH), Timmes & Swesty (2000), Rogers & Nayfonov (2002, also the 2005 update), Potekhin & Chabrier (2010) and for an ideal gas, are outlined by Paxton et al. (2011).</p> <p>Code to read these tables is available as part of phantom (<a href="https://github.com/danieljprice/phantom/blob/master/src/main/eos_mesa_microphysics.f90">src/main/eos_mesa.f90</a>). The original version of these tables and the module to read them was contributed by Tom Constantino from the MUSIC code (<a href="https://ui.adsabs.harvard.edu/abs/2017A&A...600A...7Ga">Goffrey et al. 2017</a>), and the phantom implementation described in <a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. (2018)</a>.The current tables were created by Tom Reichardt for the paper Reichardt et al. (2020):<br><br><a href="https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.5333R/abstract">https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.5333R/abstract</a></p> </div> </div> </div> <p>Figure 1 in Reichardt et al. (2020) shows the pressure, temperature, Gamma and P/Pideal shown as a function of internal energy and density from these tables</p>
Linked collectors and determiners for: Colorado Mesa University, Walter A. Kelley Herbarium.
Natural history specimen data linked to collectors and determiners held within, "Colorado Mesa University, Walter A. Kelley Herbarium". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/53b690a5-1d43-4368-8ace-75dbad510e1c">https://bionomia.net/dataset/53b690a5-1d43-4368-8ace-75dbad510e1c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/53b690a5-1d43-4368-8ace-75dbad510e1c">https://gbif.org/dataset/53b690a5-1d43-4368-8ace-75dbad510e1c</a>. Formatted as a Frictionless Data package.
Figs 263-265 in Afrotropical taxa of the genus Mesa SAUSSURE 1892 (Hymenoptera, Tiphiidae, Myzininae)
Figs 263-265: Mesa xanthogramma: (263) habitus; (264) basal hind tarsomerus; (265) epipygium. Figs 266-271: Mesa xanthogramma: (266) head, frontal aspect; (267) pronotum, dorsal aspect; (268) epipygium, dorsal aspect; (269) gonosquama; (270) volsella; (271) aedeagus; (263: scale bar: 5mm; 265-267: scale bar: 1mm; 264, 268-271: scale bar: 0.5mm).
Figs 249-255 in Afrotropical taxa of the genus Mesa SAUSSURE 1892 (Hymenoptera, Tiphiidae, Myzininae)
Figs 249-255: Mesa silvana: (249) head, frontal aspect; (250) pronotum, dorsal aspect; (251) pronotum, lateral aspect; (252) epipygium, dorsal aspect; (253) gonosquama; (254) volsella; (255) aedeagus; (249-251: scale bar: 1 mm; 252-255: scale bar: 0.5mm).
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.