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380 results for “Pressure Data”
Data set associated to simulations performed within the manuscript "Endosperm turgor pressure both promotes and restricts seed growth and size".
<p>This dataset contains the following files</p> <ul> <li>2F4-Col0-iku2-Fig.3f and Supp. Fig.9 <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose unesterified pectins are labelled with the 2F4 antibody (channel 2)</li> <li>2 genotypes: Col0 and iku2</li> <li>3 independent experiments</li> </ul> </li> <li>JIM5-Col0-ap2-Fig. 4d and Supp Fig. 12b <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the JIM5 antibody (channel 2)</li> <li>2 genotypes: Col0 and ap2-6</li> <li>2 independent experiments</li> </ul> </li> <li>JIM5-Col0-iku2-Fig. 3h and Supp Fig. 10 <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the JIM5 antibody (channel 2)</li> <li>2 genotypes: Col0 and iku2</li> <li>3 independent experiments</li> </ul> </li> <li>LM19-Col0-ap2-Fig. 4b and Supp Fig. 12a <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the LM19 antibody (channel 2)</li> <li>2 genotypes: Col0 and ap2-6</li> <li>2 independent experiments</li> </ul> </li> <li>LM19-Col0-iku2-Fig. 3d and Supp Fig. 8 <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the LM19 antibody (channel 2)</li> <li>2 genotypes: Col0 and iku2</li> <li>3 independent experiments</li> </ul> </li> <li>pELA1-VENUS-Col0-iku2-Fig3c <ul> <li>Confocal stack of developing seeds expressing <em>pELA1::3X-VENUS-N7</em></li> <li>2 genotypes: Col0 and iku2</li> <li>1 independent experiments</li> </ul> </li> <li>pELA1-VENUS-Col0-iku2-Supp Fig. 6 <ul> <li>Confocal stack of developing seeds expressing <em>pELA1::3X-VENUS-N7</em></li> <li>2 genotypes: Col0 and iku2</li> <li>1 independent experiments</li> </ul> </li> <li>Seed-size-ap26-iku2-Fig.5e-Supp-Fig. 12c <ul> <li>Pictures of dry seeds</li> <li>4 genotypes: Col0, iku2, ap2-6 and iku2 ap2-6</li> <li>2 independent experiments</li> </ul> </li> <li>Wall-rupture-Col0-iku2-Fig3k <ul> <li>Confocal stack of developing seeds expressing <em>LTi6b-GFP </em>(First channel) and dyed with FM4-64 (second channel) imaged after a 40 µm indentation to break testa wall</li> <li>2 genotypes: Col0 and iku2</li> <li>2 replicates</li> </ul> </li> <li>Wall-rupture-Col0-iku2-Supp Fig. 11 <ul> <li>Confocal stack of developing seeds expressing <em>LTi6b-GFP </em>(First channel) and dyed with FM4-64 (second channel) imaged after a 30 µm or a 50µm indentation to break testa wall</li> <li>2 genotypes: Col0 and iku2</li> <li>2 replicates</li> </ul> </li> <li>181211-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10 DPA</li> <li>2 genotypes: Col0 and iku2</li> </ul> </li> <li>190125-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10 DPA (no 9DPA)</li> <li>2 genotypes: Col0 and iku2</li> </ul> </li> <li>190523-Col0-ede13-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 9DPA</li> <li>2 genotypes: Col0 and <em>ede1-3</em></li> </ul> </li> <li>200724-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10DPA</li> <li>2 genotypes: Col0 and <em>iku2</em></li> </ul> </li> <li>200921-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10DPA</li> <li>2 genotypes: Col0 and <em>iku2</em></li> </ul> </li> <li>201023-Col0-ede13-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10DPA</li> <li>2 genotypes: Col0 and <em>ede1-3</em></li> </ul> </li> <li>210519-Col0-Timelapse-invitro-Sorbitol <ul> <li>Picture of developing WT seeds (Col-0) at 3, 6, 9 and 12DPA</li> <li>Fruits were grown <em>in planta </em>(uncut) or <em>in vitro</em> from 3DPA onwards</li> <li>Fruits growing <em>in vitro</em> were cultivated in ½ MS + 1% Sucrose + 0.1X PPM (Plant Preservative Medium) + 1X Gamborg Vitamins + 0 to 200mM Sorbitol</li> <li>Note that the file containing the pictures from this experiment was too large for Zenodo, the pictures were thus transformed into 8 bit black/white pictures, a 2x2 binning was applied, before a saving into tiff. The original pictures are available at: <a href="http://flower.ens-lyon.fr/">http://flower.ens-lyon.fr/</a></li> </ul> </li> <li>210709-Col0-Timelapse-invitro-Sorbitol <ul> <li>Picture of developing WT seeds (Col-0) at 3, 6, 9 and 12DPA</li> <li>Fruits were grown <em>in planta </em>(uncut) or <em>in vitro</em> from 3DPA onwards</li> <li>Fruits growing <em>in vitro</em> were cultivated in ½ MS + 1% Sucrose + 0.1X PPM (Plant Preservative Medium) + 1X Gamborg Vitamins + 0 to 200mM Sorbitol</li> <li>Note that the file containing the pictures from this experiment was too large for Zenodo, the pictures were thus transformed into 8 bit black/white pictures, a 2x2 binning was applied, before a saving into tiff. The original pictures are available at: <a href="http://flower.ens-lyon.fr/">http://flower.ens-lyon.fr/</a></li> </ul> </li> <li>data.zip file: <ul> <li>Measurements of seed growth tracking experiments performed on WT plants as well as iku2 and ede1-3 mutants.</li> <li>Measurements of endosperm pressure performed on WT plants and iku2 mutants.</li> <li>Simulation results from a parameter space exploration of the system of ODEs we studied within the scope of the work described in the manuscript.</li> </ul> </li> </ul>
Data on chemical kinetics of high-temperature high-pressure combustion
<p><span>Key thermal reaction speed constant data for main temperature range</span></p>
Data and models to accompany "Turgor pressure affects transverse stiffness and resonant frequencies of buzz-pollinated poricidal anthers"
<p>Data sets and models to accompany the paper "Turgor pressure affects transverse stiffness and resonant frequencies of buzz-pollinated poricidal anthers". </p>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
Accelerometer, gyroscope and pressure data associated with behaviors of free-ranging hawksbill sea turtles (Eretmochelys imbricata)
<p> </p> <p> </p> <div> <div> <div> <div> <div> <p>In this paper, we explored the use of transfer learning across species and taxa, employing fully convolutional neural networks to predict the behaviors of critically endangered hawksbill sea turtles from acceleration data. For this purpose, fully convolutional neural networks (V-net and U-net) were pre-trained on a dataset of green turtles (Zenodo link) and human data (Intensive Care Unit (ICU) HAR dataset, <a href="https://doi.org/10.24432/C54S4K" target="_new" rel="noreferrer">https://doi.org/10.24432/C54S4K</a>) before being fine-tuned on the hawksbill dataset. The results reveal a 8% and 4% improvement in F1-score with transfer learning from the green turtle and human datasets, respectively, compared to training the models from random weight initialization (without transfer learning). </p> </div> </div> </div> </div> </div> <p> </p> <p>The dataset comprised the raw acceleration, gyroscope and depth sequence of 6 free-ranging hawksbill sea turtles associated with the behaviors. The indiviuals were equipped with a on-board video recorder combined with an accelerometer, gyroscope, magnetometer and luminosity, temperature and depth sensors using four suction cups and an automatic release system over a two-day periods (see Jeantet et al. 2020 for details and the associated article). The accelerometer, gyroscope, magnetometer recorded at 20 Hz and the pressure, temperature and luminosity sensors at 1 Hz. The cameras were programmed to record until nightfall (6 pm) and resume at daybreak (6 am). The magnetometer, luminosity and temperature data are provided but not used in the associated study. </p> <p> </p> <div> <div> <div> <div> <div> <p>For each individual, the data collected by the devices was correlated with observed behaviors from video recordings. Unlabeled sequences, mostly comprising night recordings, were excluded, resulting in the creation of one file per day of deployment for each individual. </p> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <p>To process the depth data and increase the sampling rate to 20 Hz, we used a linear interpolation technique. We called this new variable "Pressure_corr". Additionally, we calculated the pressure difference ("Pressure_diff") between each measuring point (originally at 1 Hz).</p> </div> </div> </div> </div> </div> <p> </p> <p>"In total, 69.7 hours of multi-sensor sequences were labelled from six different hawksbill turtles (approximately 11.6 hours of recording per individual, max = 17.8 hours, min = 6.3 hours, standard deviation = 3.6 hours). The predominant behavior observed in the videos was <em>Feeding</em>, totaling over 38.6 hours, followed by <em>Resting</em> and <em>Swimming</em>, with 19.1 hours and 7.9 hours, respectively. The other behaviors were expressed in minority (<em>Breathing</em>: 2.2 hours, <em>Gliding</em>: 1 hour, <em>Scratching</em>: 0.8 hour and <em>Other</em>: 0.1 hour). " </p> <p> </p> <p>The folder contains 10 Python matrices, each with 15 columns (AccX, AccY, AccZ, GyrX, GyrY, GyrZ, MagX, MagY, MagZ, Depth, Light, Temperature, Pressure_corr, Pressur_diff, Behavior) and a number of rows corresponding to the deployment duration. The title of each file indicates the camera number used (CC-09-XX) and the deployment day (DD-MM-YYYY), with the last digit specifying whether the matrix corresponds to the first or second day of deployment.</p> <p> </p> <p>The folder also contains two dictionaries (behInd_to_behName, behName_to_behInd) that specify the behaviors associated with each number used as a label in the Behavior column. Additionally, there is a dictionary (dico_info) that provides the names of the matrix columns and the frequence of recording.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Adaptations to climate-mediated selective pressures in humans.
<p>This dataset contains the genotype data in PLINK binary format for the 5 populations genotyped in the Di Rienzo lab and published in </p> <p>Hancock AM, Witonsky DB, Alkorta-Aranburu G, Beall CM, Gebremedhin A, Sukernik R, Utermann G, Pritchard JK, Coop G, Di Rienzo A (2011) Adaptations to climate-mediated selective pressures in humans. PLoS Genet. 7(4):e1001375</p>
Data from: Responses of population structure and genomic diversity to climate change and fishing pressure in a pelagic fish
<p><span>The responses of marine species to environmental changes and anthropogenic pressures (e.g. fishing) interact with ecological and evolutionary processes that are not well understood. Knowledge of changes in the distribution range and genetic diversity of species and their populations into the future is essential for the conservation and sustainable management of resources.</span><span> Almaco jack (<em>Seriola rivoliana</em>) is<em> </em>a pelagic fish with high importance to fisheries and aquaculture in the Pacific Ocean. </span><span>In this study, we assessed contemporary genomic diversity and structure in loci that are putatively under selection (outlier loci) and determined their potential functions. Utilizing a combination of genotype-environment association, spatial distribution models, and demogenetic simulations, we modeled the effects of cl</span><span>imate change (under three different RCP scenarios) and fishing pressure on the species' geographic distribution and genomic diversity and structure to 2050 and 2100.</span><span> Our results show that most of the outlier loci identified were related to biological and metabolic processes that may be associated with temperature and salinity. Contemporary genomic structure showed three populations—two in the Eastern Pacific (</span><span>Cabo San Lucas </span><span>and Eastern Pacific) and one in the Central Pacific (</span><span>Hawaii</span><span>). Future projections suggest a loss of suitable habitat and potential range contractions for most scenarios, while fishing pressure decreased population connectivity. Our results suggest that future climate change scenarios and fishing pressure will affect the genomic structure and genotypic composition of <em>S. rivoliana</em> and lead to loss of genomic diversity in populations distributed in the eastern-central Pacific Ocean, which could have profound effects in fisheries that depend on this resource.</span></p>
Supplementary Data: Human settlement pressure drives slow-moving landslide exposure
<p>These datasets (.Rmd, .Rroj., .rds) are ready to use within the R software for statistical programming with the R Studio Graphical User Interface (https://posit.co/download/rstudio-desktop/). Please copy the folder structure into one single directory and follow the instructions given in the .Rmd file. Files and folders are described in the README.md file</p> <p>0_ferrer_etal_2024.Rproj</p> <p>README.md</p> <p>1_R_Notebook:</p> <ul> <li>SlowMovingLandslide_Exposure.rmd</li> <li>Ferrer2024_FloodExposureAnalysis.Rmd</li> </ul> <p>2_data:</p> <ul> <li>1_input: <ul> <li>sm_database_2023_01_17.csv</li> <li>sm_database_references.pdf</li> <li>gmba_v2</li> <li>ipcc_regions</li> <li>natrual_earth</li> </ul> </li> <li>2_processed: <ul> <li>model_data_2023_01_17.csv</li> <li>summary_database.csv</li> </ul> </li> </ul> <p>3_results:</p> <ul> <li>1_model <ul> <li>2023_12_31.model.rds</li> </ul> </li> <li>2_figures: <ul> <li>main_figure_elements</li> </ul> </li> <li>3_ext_figures</li> </ul> <p>4_manuscript_figures:</p> <ul> <li>1_main figures</li> <li>2_extended_figures</li> </ul> <p><br>5_floding_analysis:</p> <ul> <li>flood_comp_mod.csv</li> <li>flood_comp_model.rds</li> </ul>
Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation (Supporting data)
<p>The dataset contains sample numerical results associated with the manuscript under the same title, "Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation", published in Journal of Computational Physics (2018), https://doi.org/10.1016/j.jcp.2018.04.028.</p>
Replication Data for: Magnetic measurements on micron-size samples under high pressure using designed NV centers
<pre><em>Data description.pdf </em>describes the uploaded data. <em>Figure Data.xlsx</em> contains the data represented in the figures of the main text and supplementary information. <em>Iron.zip and MgB2.zip </em>include the raw experimental data. <em>PlotIronData.m, PlotMgB2Data.m, SimulatedIronMagneticField.m</em>, <em>SphereField.m</em>, <em>CarttoPol.m</em>, <em>PoltoCart.m </em>are Matlab codes used to process and fit the data. </pre>
Speciation data for "Pressure-induced coordination changes in a pyrolitic silicate melt from ab initio molecular dynamics simulations"
<p>With <em>ab initio</em> molecular dynamics simulations on pyrolite melt, we examine the detailed changes in elemental coordination as a function of pressure and temperature. We consider the average coordination as well as the proportion and distribution of coordination environments at pressures and temperatures encompassing the conditions at which molten silicates may exist in present-day Earth and those of the Early Earth's magma ocean. At ambient pressure and 2000 K, we find that the average coordination of cations with respect to oxygen is 4.0 for Si-O, 4.0 for Al-O, 3.7 for Fe-O, 4.6 for Mg-O, 5.9 for Na-O and 6.2 for Ca-O. Although the coordination for iron with respect to oxygen is underestimated, the coordination number for all other cations are consistent with experiments. At the base of the upper mantle (~15 GPa and 2000 K), the average coordination for Si-O remains at 4.0, but increases to 4.1 for Al-O, 4.2 for Fe-O, 4.9 for Mg-O, 8.0 for Na-O and 6.8 for Ca-O. The coordination environment for Na-O remains approximately constant up to core-mantle boundary conditions (135 GPa and 4000 K), but increases to about 6 for Si-O, 6.5 for Al-O, 6.5 for Fe-O, 8 for Mg-O, 9.5 for Ca-O. Our results have implications for melt properties, such as viscosity, transport coefficients, thermal conductivities and electrical conductivities, and will help interpret experimental results on silicate glasses.</p> <p>Detailed speciation statistics for pyrolite melt were determined using <em>a</em><em>b initio</em> molecular dynamics simulations with the Vienna Ab Initio Simulation Package (VASP) (Kresse and Furthmuller, 1996). Simulations were performed with a time step of 0.5-2 femtoseconds for 10-50 picoseconds, depending on the temperature and density. The composition of the Bulk Silicate Earth was modeled with a pyrolite melt with the stoichiometry NaCa<sub>2</sub>Fe<sub>4</sub>Mg<sub>30</sub>Al<sub>3</sub>Si<sub>24</sub>O<sub>89</sub>. Bond distances were determined from the pair distribution functions, which describe the probability of finding an atom type at a given distance from the reference atom. We used the first peak in the pair distribution function to approximate the average bond length; the distance at which the first minimum occurs marks the radius of the first coordination sphere of atoms that are directly bonded to the reference atom. We used this value to define the bond criterion between two atom types. Additional computational details can be found in the manuscript.</p>
Data of the subfigures of article "Intracellular dry mass density increases under growth-induced pressure"
<p>Data from the article "Intracellular dry mass density increases under growth-induced pressure". Each excel file tab corresponds to the data plotted in the different sub-figures. </p>
Data for "To heal or not to heal? Part I: The effect of pore fluid pressure on the frictional healing behavior of Oklahoma lithologies"
<p>This dataset includes the original data files for each experiment in csv format, the mat version with an additional friction column, the hold picks, the velocity step picks, and the RSFitting results. They have the following names:</p> <ul> <li>UC####.csv</li> <li>UC####.mat</li> <li>UC##_hold_picks.mat</li> <li>UC##_healing_picks.mat</li> <li>UC##_VS_RSFit.mat</li> </ul> <p>The CSV and mat files include the on-sample shear displacement data labeled LVDT1 and LVDT2 and the on-sample radial displacement data labeled LVDT3. All data files except for the RSFit include an OG_Index column which is consistent across files for each experiment, such that it provides a unique indicator for each datapoint. Note that all experiment numbers are available in the main text, except for UC0094, which is shown in the supplement text S6.</p>
Experimental and theoretical data to study intrusion pressure and contact angle for a lyophobic material, Cu2L
<p>/* **********<br>/* This work is licensed under a Creative Commons Attribution 4.0 International License.<br>/* **********<br> </p> <p>Open access to theoretical and experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union). Research pertaining to Task 3.1 (WP3).<br>Underlying data for the publication Merchiori, S. et al. Counterintuitive Trend of Intrusion Pressure with Temperature in the Hydrophobic Cu2(tebpz) MOF. Small 2024, 20,2402173. https://doi.org/10.1002/smll.202402173. Data related to Figures 1, 2.</p>
Experimental data for Pseudomonas aeruginosa from experimental evolution under different bottleneck sizes and antibiotic selection pressures
<p>We here combined evolution experiments with genomic and genetic analyses to assess whether bottleneck size and antibiotic-induced selection influences the evolutionary path to resistance in pathogenic<i> Pseudomonas aeruginosa</i>, one of the most problematic opportunistic human pathogens. Two sets of evolution experiments were performed across 16 transfers, in which either the aminoglycoside gentamicin or the fluoroquinolone ciprofloxacin were used as antibiotic. The evolutionary response was studied using counts of bacterial cells at the end of each transfer period (i.e., yield) or by calculating the growth rate from regular optical density measurement during the respective transfer periods. For the populations at the end of the evolution experiments, we also determined their antibiotic resistance with the help of standardized dose response curves. Moreover, we performed whole genome sequencing to assess the frequency of variants, which emerged and spread during the evolution experiments. We further focused on variants in two specific genes, which were selectively favoured in the gentamicin evolution experiments, and assessed their relative fitness using competition experiments. We found that antibiotic resistance is favoured under high antibiotic selection and weak bottlenecks, but also under low antibiotic selection and severe bottlenecks. We found that the absence of high resistance under low selection and weak bottlenecks is caused by the spread of low-resistance variants with high competitive fitness under these conditions. We conclude that bottlenecks in combination with drug-induced selection are currently neglected key determinants of pathogen evolution and antibiotic treatment outcome.</p>
Supporting data of "Temperature and barometric pressure affect the activity intensity and movement of an endangered thermoconforming lizard"
<p>JOURNAL PUBLICATION CITATION: Daniel Ariano-Sánchez, Rasmus M. Mortensen, Rory P. Wilson, Peder Bjureke, Stefanie Reinhardt and Frank Rosell. Temperature and barometric pressure affect the activity intensity and movement of an endangered thermoconforming lizard. <em>Ecosphere.</em></p> <p><em>------------</em></p> <p>Raw data in RDS format and R codes of movement paths and step-selection function analyses</p> <p>-------------</p> <p>CleanDBALMM.R – R script of the LMM for VeDBA from obtained from the daily diaries in the backpack system.</p> <p>CleanGPSHeloderma.R – R script for calculation of step-lengths, bearings and turning angles from the GPS data of the Guatemalan Beaded Lizards tracked, including the LMM and circular-linear regressions.</p> <p>CleanSSF.R – R script of the step-selection functions.</p> <p>DBAHeloderma.rds – Raw data of the Guatemalan Beaded Lizard movement obtained from the daily diaries in the backpack system.</p> <p>GPSData.rds - Raw data of the Guatemalan Beaded Lizard movement obtained from GPS tags.</p> <p>ssf3.rds – Dataset for the step-section functions.</p>
Imprints of Ocean Chaotic Intrinsic Variability on Bottom Pressure and Implications for Data and Model Analyses
<p>These data are used for the manuscript "Imprints of Ocean Chaotic Intrinsic Variability on Bottom Pressure and Implications for Data and Model Analyses" to be submitted to Geophysical Research Letter. Uploaded data include:</p> <p>[1] ext_int_quater_deg.nc: atmospherically driven and intrinsic variations for subseasonal, intra-annual and mean seasonal bottom pressure signals at model original resolution.</p> <p>[2] ext_int_3deg.nc: atmospherically driven and intrinsic variations for smoothed subseasonal and intra-annual bottom pressure signals at 3*3 degree resolution.</p> <p>[3] ext_int_10deg.nc: atmospherically driven and intrinsic variations for smoothed intra-annual bottom pressure signals at 10*10 degree resolution.</p> <p>[4] meanseason_timeseries.nc: Time series of mean seasonal bottom pressure signals from all 50 ensemble members over Agulhas Current region and Argentine Basin.</p>
High-pressure x-ray diffraction data for claudetite II
<p>High-pressure x-ray diffraction data for claudetite II collected at the European Synchrotron Radiation Facility.</p>
High-pressure x-ray diffraction data for arsenolite and its inclusion compound with helium
<p>X-ray diffraction data for arsenolite and its inclusion compound collected at the European Synchrotron Radiation Facility.</p>
X-ray diffraction data for urotropine under high pressure - part 3 - data from ID27 beamline
<p>X-ray diffraction data for urotropine collected at the ID27 beamline of the European Synchrotron Radiation Facility under high pressure.</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.