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1,221 results for “Aggregators”
Post-copulatory sexual selection is associated with sperm aggregate quality in Peromyscus mice
<p>In some species, sperm form coordinated groups that are hypothesized to improve their swimming performance in competitive contexts or to navigate through the viscous fluids of the female reproductive tract. Here we investigate sperm aggregation across closely-related species of <i>Peromyscus </i>mice that naturally vary by mating system to test the predictions that sperm aggregates (1) are faster than solitary sperm in species that females mate multiply to aid cells in sperm competition, and (2) outperform solitary sperm cells in viscous environments. We find significant variation in the size of sperm aggregates, which negatively associates with relative testis mass, a proxy for sperm competition risk, suggesting that post-copulatory sexual selection has a stabilizing effect on sperm group size. Moreover, our results show that sperm aggregates are faster than solitary sperm in some, but not all, species, and this can vary by fluid viscosity. Of the two species that produce the largest and most frequent groups, we find that sperm aggregates from the promiscuous <i>P. maniculatus</i> are faster than solitary sperm in every experimentally viscous environment but aggregation provides no such kinematic advantage under these same conditions for the monogamous <i>P. polionotus</i>. The reduced performance of <i>P. polionotus</i> aggregates is associated with less efficient aggregate geometry and the inclusion of immotile or morphological abnormal sperm. Our cross-species comparison yields insight into the evolution of sperm social behaviors, provides evidence of extensive variation in the <i>Peromyscus</i> lineage, and reveals that differences in sperm aggregate quality associate with post-copulatory sexual selection.</p>
Probiotic Bacillus subtilis Protects against a-Synuclein Aggregation in C. elegans (fluorescence microscopy data)
<p>This project has been submitted by the Maria Doitsidou Lab.<br> <br> Project contents:<br> This project contains datasets of z-stack images of <em>C. elegans</em> strains used to study how the gut microbiome affects Parkinson’s disease. Each strain contains a chromosomal insertion containing YFP fused to α-synuclein (pkIs2386[Punc-54::α-synuclein::YFP + unc-119(+)]). The following<em> C. elegans</em> strains were used and/or created for this project:<br> NL5901 pkIs2386[Punc-54::α-synuclein::YFP + unc-119(+)]<br> MDH586 daf-2(e1370) III; pkIs2386<br> MDH585 daf-16(mu86) I; pkIs2386<br> MDH587 hsf-1(sy441) I; pkIs2386<br> MDH657 daf-2(e1370) III; daf-16(mu86) I; pkIs2386<br> MDH614 daf-2(gk390525) III; pkIs2386<br> MDH611 eat-2(ad465) II; pkIs2386<br> MDH711 lagr-1(gk331) I, pkIs2386<br> MDH725 sptl-3(ok1927) II; pkIs2386<br> MDH724 asm-3(ok1744) IV; pkIs2386.<br> <br> High magnification (40x objective) z stack images of the head region were obtained by using a Zeiss Axio imager 2 microscope.<br> <br> <br> Aim:<br> Study how a probiotic<em> B. subtilis</em> strain affects alpha-synuclein protein aggregation.<br> <br> Main results:<br> The authors showed that the probiotic<em> B. subtilis</em> strain PXN21 inhibits and clears a-synuclein aggregation in a <em>C. elegans </em>model. The bacterium acts via metabolites and biofilm formation to activate protective pathways in the host, including DAF-16/FOXO and sphingolipid metabolism.<br> <br> Contributors:<br> Maria Eugenia Goya, Feng Xue, Cristina Sampedro-Torres-Quevedo, Sofia Arnaouteli, Lourdes Riquelme-Dominguez, Andres Romanowski, Jack Brydon, Kathryn L. Ball, Nicola R. Stanley-Wall and Maria Doitsidou<br> <br> These datasets were used in the following publication:<br> <br> Probiotic Bacillus subtilis Protects against a-Synuclein Aggregation in <em>C. elegans</em><br> <br> Maria Eugenia Goya, Feng Xue, Cristina Sampedro-Torres-Quevedo, Sofia Arnaouteli, Lourdes Riquelme-Dominguez, Andres Romanowski, Jack Brydon, Kathryn L. Ball, Nicola R. Stanley-Wall and Maria Doitsidou<br> <br> Cell Reports January 14, 2020 30 367-380; first published January 14, 2020 <a href="https://doi.org/10.1016/j.celrep.2019.12.078">https://doi.org/10.1016/j.celrep.2019.12.078</a></p>
Dataset for "Supramolecular aggregation control in polyoxometalates covalently functionalized with oligoaromatic groups"
<p>Spectroscopic datasets for "Supramolecular aggregation control in polyoxometalates covalently functionalized with oligoaromatic groups"</p> <p>Acknowledgement and Funding</p> <p>The authors gratefully acknowledge the Deutsche Forschungsgemeinschaft DFG for financial support (projects TRR 234 “CataLight”, project no: 364549901; SPP2102, “Light-controlled reactivity of metal complexes”, project no: 359737763, 494988281). S.K. gratefully acknowledges a LGFG PhD fellowship by the State of Baden-Württemberg.</p>
Supporting data for "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics"
<p>Numerical settings, routines and post-processed data used to generate the figures presented in "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics", manuscript submitted to Journal of Advances in Modeling Earth Systems. This version is an update after accounting for comments of three reviewers to the submitted manuscript.</p>
Simulated above ground biomass of forests (larch) aggregated over the vicinity of the Ilirney lake system region, Chukotka, Russia
<p>The model LAVESI (Kruse et al. 2016) was updated (Kruse 2023) and forced with historical and future climate forcing for 3 simulation repeats. The data set contains simulated larch above ground biomass (AGB, in kg m<sup>-2</sup>) for the three climate forcings RCP 2.6, 4.5 and 8.5 and each complemented with a hypothetical cooling scenario from year 2300 CE onwards. The data provided is from years 1800, 1860, 1900, 1990, 2000 and in 5-year steps until 3000 CE and presents the mean over the three repeats of the sum of AGB of the whole study region: extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax).</p> <p>This data set is related to the data set of Kruse (2023).</p> <p>Format: csv, with headers 1-year, Year in CE, 2-average, mean AGB, in kg m<sup>-2</sup> for the study region, 3-upper and 4-lower, is the minimum and maximum value of the three simulations, 5-RCP, is the RCP scenario, 6-Cooling, contains in case of the cooling scenario the string “Cooling”.</p>
Aggregated PM10 data for Europe
<p>JSON-formatted and z-standard compressed data on PM10 emissions in the EU since 2013.</p>
DATASET - On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code
<p>Study package containing raw and analyzed data from the work entitled "On the Investigation of Empirical Contradictions - Aggregated Results of Local Studies on Readability and Comprehensibility of Source Code".</p> <p>The package comprises: 1) a summary of the information extracted from all papers mentioned in the Background; 2) the source code snippets used in the three studies; 3) the consent and characterization forms distributed to the participants; 4) the raw data, the aggregated data and other material generated with the collected data.</p>
Data for: Fungal parasitism on diatoms alters formation and bio–physical properties of sinking aggregates: Particle analyses
<p>Phytoplankton forms the base of aquatic food webs and element cycling in diverse aquatic systems. The fate of phytoplankton-derived organic matter, however, often remains unresolved as it is controlled by complex, interlinked remineralization and sedimentation processes. We here investigate a rarely considered control mechanism on sinking organic matter fluxes: fungal parasites infecting phytoplankton. We demonstrate that bacterial colonization was promoted 3.5-fold on fungal-infected phytoplankton cells in comparison to non-infected cells in a cultured model pathosystem (diatom <em>Synedra</em>, fungal microparasite <em>Zygophlyctis</em>, and co-growing bacteria), and even ≥17-fold in field-sampled populations (<em>Planktothrix</em>, <em>Synedra</em>, and <em>Fragilaria</em>). The <em>Synedra</em>–<em>Zygophlyctis</em> model system further revealed that fungal infections reduced the formation of aggregates. Moreover, carbon respiration was 2-fold higher and settling velocities 11–48% lower for similar-sized fungal-infected <em>vs</em> non-infected aggregates. Our data imply that parasites can effectively control the fate of phytoplankton-derived organic matter on a single-cell to single-aggregate scale, potentially enhancing remineralization and reducing sedimentation in freshwater and coastal systems.</p>
Dataset for Cell aggregation is associated with enzyme secretion strategies in marine polysaccharide-degrading bacteria
<p>Dataset includes: </p> <p>1. Analysed source data for figures</p> <p>2. Raw time-lapse images and tracking data that were used to generate the analysis</p>
Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p> - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy. </p> <p><br> Figure_3:</p> <p>Panel A:<br> <br> - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br> - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br> - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br> - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p> - Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br> - Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br> - Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p> - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p> - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br> - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br> - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p> - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br> - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br> - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br> - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br> </p> <p>Figure_5:<br> <br> Panel_C: </p> <p> - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br> - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p> - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br> - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p> - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br> - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br> - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p> - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br> - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br> - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br> - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br> - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br> - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p> - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br> - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br> - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br> - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p> - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system. </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br> </p>
FTD-tau S320F mutation stabilizes local structure and allosterically promotes amyloid motif-dependent aggregation
<p>Amyloid deposition of the microtubule-associated protein tau is a unifying theme in a multitude of neurodegenerative diseases. Disease-associated missense mutations in tau are associated with frontotemporal dementia (FTD) and enhance tau aggregation propensity. However, the molecular mechanism of how mutations in tau promote tau assembly into amyloids remains obscure. There is a need to understand how tau folds into pathogenic conformations to cause disease. Here we describe the structural mechanism for how an FTD-tau S320F mutation drives spontaneous aggregation. We use recombinant protein and synthetic peptide systems, computational modeling, cross-linking mass spectrometry, and cell models to investigate the mechanism of spontaneous aggregation of the S320F FTD-tau mutant. We discover that the S320F mutation drives the stabilization of a local hydrophobic cluster which allosterically exposes the <sup>306</sup>VQIVYK<sup>311</sup> amyloid motif. We identify a suppressor mutation that reverses the S320F aggregation phenotype through the reduction of S320F-based hydrophobic clustering <em>in vitro</em> and in cells. Finally, we use structure-based computational design to engineer rapidly aggregating tau sequences by optimizing nonpolar clusters in proximity to the S320 site revealing a new principle governing the regulation of tau aggregation. We uncover a mechanism for regulating aggregation that balances transient nonpolar contacts within local protective structures or in longer-range interactions that sequester amyloid motifs. The introduction of a pathogenic mutation redistributes these transient interactions to drive spontaneous aggregation. We anticipate that more profound knowledge of this process will permit control of tau aggregation into discrete structural polymorphs to aid the design of reagents that can detect disease-specific tau conformations.</p>
Aggregator portfolio generator
<p>Matlab script for generating a set of units for an aggregator..</p>
Data set of simulated rimed aggregates for "A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation"
<p><strong>Simulated rimed aggregates</strong> generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming".</p> <p>Aggregates were built from between 10 to 700 monomer crystals of <strong>columns, dendrites, needles, plates or rosettes</strong> with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes.</p> <p>The data set contains folders with <strong>evolution (evol) and shape files for each monomer crystal type</strong>. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. <strong>For further documentation see readme.</strong></p>
Risk factors that modifies the familial aggregation of the cervix-uterine cancer in affected individuals
<p>Base de datos</p>
Data from: Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events
<p>This data accompanies the paper entitled <em>Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events</em></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: the first one with 139 <em>Aβ16-22 </em><em>peptides, the second one with 106 peptides.</em><em> </em>Each system has been simulated at 300 K. Starting configurations of the peptides are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the peptides every 100 ps are provided for all the systems in XTC gromacs format. For system 1 we also provide XTC trajectories for all the replicas of the REST2 simulation.</p>
Data and Software for "Numerical diffusion and Turbulent mixing in convective self-aggregation"
<p>This folder contain the python scripts and the data necessary for reproducing figures and results reported in the paper " Numerical diffusion and turbulent mixing in convective self-aggregation" (in preparation for submission for the Journal of Advances in Modeling Earth System)</p>
Response to primary chemoradiotherapy of locally advanced oropharyngeal carcinoma is determined by the degree of cytotoxic T cell infiltration within tumor cell aggregates
<p><strong><span>Background</span></strong><span>: Effective anti-tumor immune responses are mediated by T cells and require organized, spatially coordinated interactions within the tumor microenvironment (TME). Understanding coordinated T-cell behavior and deciphering mechanisms of radiotherapy resistance mediated by tumor stem cells will advance risk stratification of oropharyngeal cancer (OPSCC) patients treated with primary chemoradiotherapy (RCTx). </span></p> <p><span><strong>Methods</strong>:</span> <span>To determine the role of CD8 T cells (CTL) and tumor stem cells in response to RCTx, we employed multiplex immunofluorescence stains on pre-treatment biopsy specimens from 86 advanced OPSCC patients and correlated these quantitative data with clinical parameters. Multiplex stains were analyzed at the single-cell level using QuPath and spatial coordination of immune cells within the TME was explored using the R-package Spatstat. </span></p> <p><span><strong>Results</strong>:</span><span> Our observations demonstrate that a strong CTL-infiltration into the epithelial tumor compartment (HR for overall survival, OS: 0.35; p<0.001) and the expression of PD-L1 on CTL (HR: 0.36; p<0.001) were both associated with a significantly better response and survival upon RCTx. As expected, p16 expression was a strong predictor of improved OS (HR: 0.38; p=0.002) and correlated with overall CTL infiltration (</span><span>r: 0.358, p<0.001). By contrast, tumor cell proliferative activity, expression of the tumor stem cell marker CD271 and overall CTL infiltration, regardless of the affected compartment, were not associated with response or survival. </span></p> <p><span><strong>Conclusion</strong>: </span><span>In this study, we could demonstrate the clinical relevance of the spatial organization and the phenotype of CD8 T cells within the TME. In particular, we found that the infiltration of CD8 T cells specifically into the tumor cell compartment was an independent predictive marker for response to chemoradiotherapy, which was strongly associated with p16 expression. Meanwhile, tumor cell proliferation and the expression of stem cell markers showed no independent predictive effect in response to RCTx and require further study.</span></p>
Data from: Controlled molecular arrangement of easily aggregated deoxycholate with layered double hydroxide
<p><span>Deoxycholate (DA) is a natural emulsifying agent involved in the absorption of dietary lipids. Due to the facial distribution of hydrophobic-hydrophilic region, DA easily aggregates under ambient conditions, and this property hinders the practical application of DA in clinical application. In this study, we found that the molecular arrangement of DA molecules could be controlled by utilizing layered double hydroxide (LDH) under a specific reaction condition. The effect of reaction methods such as co-precipitation, ion exchange, and reconstruction on the molecular arrangement of DA was investigated by X-ray diffraction, Fourier-transform infrared spectroscopy, high-resolution transmission electron microscopy, and differential scanning calorimetry. It was demonstrated that the self-aggregation of DA molecules could be suppressed by the oriented arrangement of DA between the gallery space of LDH. The DA moiety was well stabilized in the LDH layers due to the electrostatic interaction between DA molecules and LDH layers. The most ordered arrangement of DA molecules was observed when DA was incorporated into LDH via a reconstruction method. The DA molecules arranged in LDH via reconstruction did not show significant exothermic nor endothermic behavior up to 400</span><span>℃</span><span>, showing that the DA moiety lost its intermolecular attraction in between LDH layers.</span></p>
A Deep Learning Tool for the Assessment of Pavement Smoothness and Aggregate Segregation during Construction
<p>Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) and 129 pavement images collected from three construction sites a few days after paving. These images were randomly divided into 70%, 15%, and 15% for the training, testing, and validation phases, respectively. The roughness model achieved 93.8% and 92.6% accuracy in the training and validation stages; respectively, and predicted the International Roughness Index (IRI) values with a coefficient of determination R<sup>2</sup> of 0.98 and a Root-Mean Square Error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved adequate accuracy. Furthermore, the developed segregation detection procedure adequately described the relationship between mix density and segregation.</p>
Gridded and aggregated tropical cyclone disaster datasets in China (1990-2015)
<p><strong>Gridded_Datasets:</strong> The geotransformation from image coordinate space to georeferenced coordinate space for each tiff file is (97.0, 0.1, 0.0, 54.0, 0.0, -0.1).</p> <p><strong>Aggregated_Datasets: </strong></p> <p>Including variables aggregated to TC-event scale and province scale:</p> <ul> <li>year (year of the tropical cyclone)</li> <li>cnid (Chinese tropical cyclone ID)</li> <li>DEL (Direct Economic Loss, CNY)</li> <li>W (maximum of gridded sustained wind speed, m/s)</li> <li>P (maximum of gridded daily precipitation, mm)</li> <li>K (asset value exposure, CNY)</li> <li>I (GDP per capita, CNY)</li> <li>H (proportion of nonsteel-concrete residential buildings)</li> </ul> <p> </p>
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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.