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13,064 results for “predictions”
Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
<p>The 'StatAnalysis.zip' contains the data and model files. We used it for the four analyses below.</p> <p>First, we estimated population densities, the mean body mass and camera-trap capture rates of five main bushmeat targets in a rainforest of southeast Cameroon: Peters's duikers (<em>Cephalophus callipygus</em>), bay duikers (<em>C. dorsalis</em>), blue duikers (<em>Philantomba monticola</em>), brush-tailed porcupines (<em>Atherurus africanus</em>) and Emin's pouched rats (<em>Cricetomys emini</em>). Second, on the basis of the density and body mass estimates, we estimated bushmeat biomass—the total biomass of the five bushmeat species—and its spatial variation. Third, we calculated six bushmeat indicators based on the capture rate estimates. Lastly, we examined the correlation between bushmeat biomass and the indicators.</p> <p>The ZIP file consists of 16 R script files, three CSV files (in the 'data' subfolder) and 135 stan files (in the 'stan' subfolders). It also has two empty folders, 'figure' and 'res', where the figures and R objects of model results will be stored following the analyses. Please see the document 'README.txt' before performing the analysis. This text file gives the ZIP file structure and brief descriptions of the files.</p>
The predator activity landscape predicts the anti‐predator behavior and distribution of prey in a tundra community
<p>Predation shapes communities through consumptive and non-consumptive effects. In the latter case, prey respond to perceived predation risk through proactive or reactive risk management strategies occurring at different spatial and temporal scales. The predator-prey space race and landscape of fear concepts are useful to better understand how predation risk affects prey behavioral decisions and distribution. We assessed predation-risk effects in a terrestrial Arctic community, where the arctic fox is the main predator of ground-nesting birds. Using high frequency GPS data, we estimated a predator activity landscape corresponding to fox space use patterns, and validated with an artificial prey experiment that this predator activity landscape correlated with the predation risk landscape. We then investigated the effects of the fox activity landscape on multiple prey species, by assessing the anti-predator behavior of a main prey (snow goose) actively searched for by foxes, and the nest distribution of several incidental prey species. We first found that snow geese showed a stronger level of nest defense in areas highly used by foxes, possibly responding with a reactive strategy to variation in predation risk. Then, nests of incidental prey reproducing in habitats easily accessed by foxes had a lower probability of occurrence in areas highly used by foxes, suggesting these birds may use a proactive risk management strategy by shifting their distribution away from risky areas. For incidental prey species nesting in microhabitat refuges difficult to access by foxes, probability of nest occurrence was independent of predation risk in the surrounding area, as they avoid risk at a finer spatial scale. By tracking all individuals of the dominant predator species in our study area, we demonstrated the value of using predator space use patterns to infer spatial variation in predation risk. Overall, we highlight the diversity of risk management strategies in prey sharing a common predator, hence refining our understanding of the mechanisms driving species distribution and community structure.</p>
Dataset for research paper "An Impact-Driven Approach to Predict User Stories Instability"
<p>Dump of all the data from an SQL server containing all the data used for the research in the research paper "An Impact-Driven Approach to Predict User Stories Instability", which is currently under review for Requirements Engineering journal. </p>
MetalNet: Prediction of metal binding sites in protein
<p>We have developed a machine learning method named “MetalNet” to predict metal binding sites in proteins.</p> <p>This dataset contains the result of prediction with high confidence to bind metal ions. The table includes the Uniprot id, gene name and a short description of specific predicted protein and potential metal-binding residues predicted by MetalNet. </p>
Comparative Accuracies of Models for Drag Prediction During Geomagnetically Disturbed Periods: A First Principles Model versus Empirical Models
<p>This dataset contains observational and TIEGCM simulation data used for a manuscript that is being submitted to the <em>Space Weather</em> journal. The abstract for the study follows: We examine the accuracy of density prediction by the first principals model Thermosphere Ionsosphere Electrodynamics General Circulation Model (TIEGCM) developed by the National Center for Atmospheric Research and compare it to the accuracy of three empirical models: Jacchia 71, the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Extended 2000 (NRLMSIS), Jacchia 1971 and Jacchia-Bowman 2008. Comparisons are made for three large storms: the October 2003 storm, the March 2013 storm, and the March 2015 storm. To evaluate the accuracy of these models we use tracking data for nine space objects in low earth orbit (three for each storm). Additionally, and evaluate the accuracy of the TIEGCM and NRLMSIS with data from high precision accelerometers on the Challenging Minisatellite Payload (CHAMP) and Gravity field and Circulation Explorer (GOCE) satellites. The goal is to assess the use of a first principles model as a potential tool for forecasting satellite drag during large magnetic storms. We find that the TIEGCM accuracy is substantially better than for the Jacchia 71 and NRLMSIS models. The accuracies of the TIEGCM and JB2008 models are similar, but overall the TIEGCM is more accurate. We found smaller mean percentage differences for TIEGCM versus CHAMP than for NRLMIS for the Halloween Storm and smaller differences than results published for JB2008 and the assimilative model HASDM. The empirical models are at present more practical for operational purposes, but the first principles TIEGCM was developed as a research model and with a greater focus on operational use offers the potential for improved utility during stressing conditions.</p>
Host phylogeny and elevation predict infection by avian haemosporidians in a diverse New Guinean bird community (R script for analyses, figures, and supplemental figures )
<p>This script performs the glmm analysis of the elevation and infection prevalence data as well as the script required to generate the figures in the article.</p>
Grooming time predicts survival in American kestrels
<p>Animals have evolved a variety of adaptations to care for the body surface, such as grooming behavior, which keeps the integument clean, parasite-free, and properly arranged. Despite extensive research on the grooming of mammals, birds, and arthropods, the survival value of grooming has never been measured directly in natural populations. We monitored grooming and survival in a population of marked American kestrels (<em>Falco sparverius</em>) on San Salvador Island, Bahamas. We found a strong association between time spent grooming and survival over a two-year period. The quadratic relationship we show is consistent with stabilizing natural selection on grooming time. To our knowledge, this is the first evidence for a correlation between grooming time and survival in a natural population. Grooming time may predict the survival of many animal taxa, but additional studies are needed to determine the shape and strength of the relationship among birds, mammals and arthropods.</p>
Action Prediction Error: a value-free dopaminergic teaching signal that drives stable learning - Behavioral dataset
<p>Behavioral data to reproduce figures of this paper: https://doi.org/10.1101/2022.09.12.507572</p> <p>See Github repository: https://github.com/HernandoMV/APE_paper</p>
A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications
<p>## GDSC dataset</p> <p>**GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p> <p>## PMID17185464 (Bortezomib) dataset</p> <p>**PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p>**PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).</p>
Local timing of rainfall predicts the timing of moult within a single locality and the progress of moult among localities that vary in the onset of the wet season in a year-round breeding tropical songbird
<p>Rainfall seasonality is likely an important cue for timing key annual cycle events like moult in birds living in seasonally arid environments, but its precise effect is difficult to establish because seasonal rainfall may affect other covarying annual events such as breeding in the same way. In central Nigeria, however, Common Bulbuls <em>Pycnonotus barbatus</em> moult in the wet season but only show weak breeding seasonality. This suggests that moult is more sensitive to rainfall than breeding, but a similar outcome is possible if moult is simply periodic. We tested the relationship between rainfall and moult in Common Bulbuls at a single location over 18 years: on average moult started 5th May (± 41 days: 25th March–15th June), being on average later than the onset of the rains which is usually mid-April. The likelihood of finding a moulting Common bulbul was best predicted by rainfall 9–15 weeks before moult was scored. We then tested the generality of this across populations: the progress of moult should, therefore, correlate with the average timing of the wet season along a spatial environmental gradient where the rains start at different times each year south-to-north of Nigeria. To test this, we modelled moult progress just before the rains across 15 localities 6°–13° N as a function of the onset of the wet season among localities. As predicted, moult progressed further in localities with earlier wet seasons, confirming that the onset of moult is timed to the onset of the wet season in each locality despite weak breeding seasonality in the Common Bulbul. This strategy may evolve to maintain optimal annual cycle routine in seasonal environments where breeding is prone to unpredictable local perturbations like nest predation. It may, however, be less obvious in temperate systems where all annual cycle stages are seasonally constrained, but it may help with explaining the high frequency of breeding–moult overlaps in tropical birds.</p>
Geomorphology variables predict fish assemblages for forested and endorheic rivers
<p>This dataset contains data from field collections described in the paper: "Shields, R., Pyron, M., Arsenault, E., Thorp, J., Minder, M., Artz, C., Costello, J., Otgonganbat, A., Mendsaikhan, B., Maasri., A. (2022) Geomorphology variables predict fish assemblages for forested and endorheic rivers. Ecology and Evolution. ECE-2021-08-01367". </p> <p>Stream fishes are restricted to specific environments with appropriate habitats for feeding and reproduction. Interactions between streams and surrounding landscapes influence the availability and type of fish habitat, nutrient concentrations, suspended solids, and substrate composition. Valley width and gradient are geomorphological variables that influence the frequency and intensity that a stream interacts with the surrounding landscape. For example, in constrained valleys, canyon walls are steeply-sloped and valleys are narrow, limiting the movement of water into riparian zones. Wide valleys have long, flat floodplains that are inundated with high discharge. We tested for differences in fish assemblages with geomorphology variation among streams in US and Mongolia montane forested and endorheic ecoregions. Montane rivers of Mongolia are pristine and have few invasive species, compared to montane rivers of the western US. Sites where we collected were defined as geomorphologically unique river segments (i.e., functional process zones; FPZs) using an automated ArcGIS-based tool. This tool extracts geomorphic variables at the valley and catchment scales and uses them to cluster stream segments based on their similarity. We collected a representative fish sample from replicates of FPZs. Then, we used constrained ordinations to determine if river geomorphology could predict fish assemblage variation. Our constrained ordination approach using geomorphology to predict fish assemblages resulted in significance using fish taxonomy and traits in several watersheds. The watersheds where constrained ordinations were not successful were next analyzed with unconstrained ordinations to examine patterns among fish taxonomy and traits with geomorphology variables. US and Mongolia montane river fish assemblages varied with geomorphology variables including river elevation, gradient, valley width, channel sinuosity, valley slope, and annual precipitation. These results provide evidence that fish assemblages respond similarly and strongly to geomorphic variables on two continents. We recommend increased conservation of river ecosystems of the US and Mongolia, to prevent further degradation.</p>
A machine learning based prediction model for life expectancy
<p>The social and financial systems of many nations throughout the world are significantly impacted by life expectancy (LE) models. Numerous studies have pointed out the crucial effects that life expectancy projections will have on societal issues and the administration of the global healthcare system. The computation of life expectancy has primarily entailed building an ordinary life table. However, the life table is limited by its long duration, the assumption of homogeneity of cohorts and censoring. As a result, a robust and more accurate approach is inevitable. In this study, a supervised machine learning model for estimating life expectancy rates is developed. The model takes into consideration health, socioeconomic, and behavioral characteristics by using the eXtreme Gradient Boosting (XGBoost) algorithm to data from 193 UN member states. The effectiveness of the model's prediction is compared to that of the Random Forest (RF) and Artificial Neural Network (ANN) regressors utilized in earlier research. XGBoost attains an MAE and an RMSE of 1.554 and 2.402, respectively outperforming the RF and ANN models that achieved MAE and RMSE values of 7.938 and 11.304, and 3.86 and 5.002, respectively. The overall results of this study support XGBoost as a reliable and efficient model for estimating life expectancy.</p>
Living on the edge: Predicting invertebrate richness and rarity in disturbance-prone aquatic–terrestrial ecosystems
<p>1. Temporal fluctuations in water levels cause the spatial extent of wet and dry habitats to vary in aquatic–terrestrial riverine ecosystems, complicating their biomonitoring. As such, biomonitoring efforts may fail to characterise the species that inhabit such habitats, hampering assessments of their biodiversity and implementation of evidence-informed management strategies.</p> <p>2. Relationships between the dynamic characteristics of aquatic-terrestrial habitats and their communities are well known. Thus, habitat characteristics may enable estimation of faunal assemblage characteristics such as taxonomic richness, regardless of in-channel conditions.</p> <p>3. We investigated whether indicators summarising habitat survey data can predict two metrics representing terrestrial invertebrate assemblages (e.g. taxa richness) in two aquatic–terrestrial habitats: exposed riverine sediments and dry temporary streams. We also compared the performance of unimetric and multimetric habitat indicators in making predictions.</p> <p>4. In exposed riverine sediments, >88% of predictions were correlated with observed taxa richness and an index of conservation status. Values predicted by exposed riverine sediment samples were correlated with those observed in temporary stream channels with comparable riparian (i.e. largely agricultural) land use, but not those observed in channels with contrasting (i.e. more urban) land use.</p> <p>5. Unimetric habitat indicators performed similarly to more complex multimetric indicators, with each explaining ≤6% of the variability in taxa richness and the index of conservation status. The different spatial scales at which invertebrates respond to habitat conditions and at which indicators record habitat conditions, and a more comprehensive training dataset that incorporates a full range of habitat conditions (i.e. land use), may improve future predictions.</p> <p>6. We demonstrate that invertebrate assemblage characteristics can be predicted regardless of in-channel conditions. Agreement between exposed riverine sediment predictions and temporary stream observations suggests that these predictions are transferable among a range of aquatic–terrestrial habitat types, and could thus be widely applied to aid conservation of riverine biodiversity in dynamic aquatic–terrestrial ecosystems.</p>
Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
<p>Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and the total number of individuals harvested within an integrated band-recovery framework to estimate abundance at large scales.</p> <p>A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM.</p> <p>We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (<em>Meleagris gallapavo</em>) to estimate local and statewide abundance in Maine, USA.</p> <p>We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some sub-regions. Consistent with simulation results, this led to over-estimation of both local and statewide abundance.</p> <p>Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large-scale band recovery datasets and has applicability for the estimation of population parameters in other ecological models as well.</p>
Preprocessed EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains intermediate preprocessing files for the above-named paper. Preprocessing scripts are stored at: <a href="http://osf.io/fndk5/">https://osf.io/fndk5/</a></p> <p>For the raw EEG data, please see <a href="http://zenodo.org/record/6992085">https://zenodo.org/record/6992085</a>.</p> <p>The intermediate preprocessing files are *_ica.rds (R datasets saved after running ICA), and *_prepro.rds (R datasets saved after individual preprocessing complete, before combination with other participants). To use the data with the preprocessing scripts, download and save it in a folder called prepro_EEG_data as explained in the preprocessing script.</p>
Predictive simulation of sit-to-stand based on reflexive-controllers
<p>Data used in the analyses of the paper. There is a file for each single parameter analyzed (hip_angle, ankle _angle , knee _angle , SOL_EMG, TA _EMG, RF_EMG , BF_EMG , VAS_EMG , GAS_EMG , GLU_EMG , seat reaction force, and feet reaction force) and per type (experimental, simulations for 4-phases, and simulations for 2-phases controller). All EMGs are pre-processed and presented as envelopes. Experimental files have 13 subjects, 4-phases controller files have 4 successful STS simulations, and 2-phases controller files have 8 successful STS simulations. All files are length-normalized to 100 samples.</p>
Predictive modelling of brain metastasis risk and non-invasive biomarker detection using DNA methylation signatures
<p>Methylated cell-free DNA was sequenced for 123 BM plasma and compared to plasma methylomes from 107 gliomas, central nervous system (CNS) lymphomas (CNSL), and non-CNS tumor controls. Plasma methylome-based classifiers of BM from other entities were built in fifty 80% discovery set iterations of 92/123 BM samples. External publicly-available tissue methylation data on 442 LUAD, 85 BM, and 146 glioma/CNSL/control samples were acquired for validation and the remaining 31/123 BM plasma samples were used for additional validation.</p>
Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance
<p>Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive presence-only datasets. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using presence-absence and presence-only data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km<sup>2</sup> resolution across the island. Models' predictions were associated to spatial estimates of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.</p>
Spatiotemporal predictions of the alternative prey hypothesis: Predator habitat use during decreasing prey abundance
<p>The alternative prey hypothesis supposes that predators supported by a primary prey species will shift to consume alternative prey during a decrease in primary prey abundance. The hypothesis implies that during declines of one prey species, a predator modifies their behavior to exploit a secondary, or alternative, species. Despite occurring in many systems, the behavioral mechanisms (e.g., habitat selection) allowing predators to shift toward alternative prey during declines in the abundance of their primary prey are poorly understood. We evaluated habitat selection and use by a generalist predator with respect to two prey species during a dramatic decrease in the abundance of primary prey. Further, we evaluated how spatial variation in access to primary prey affected habitat selection and assessed similarity and overlap between habitats used by each prey species. Coyotes (<em>Canis</em> <em>latrans</em>) exhibited decreasing selection for cottontail rabbits (<em>Sylvilagus</em> spp.; primary prey) during population decreases but did not shift habitat selection toward neonate mule deer (<em>Odocoileus</em> <em>hemionus</em>; alternative prey). Use of rabbit habitat remained high even during historically low rabbit abundance, while mule deer habitat was used in proportion to its availability. Coyotes seemingly do not make large shifts in habitat selection toward alternative prey following spatial and temporal decreases in the abundance of primary prey, but instead, take advantage of habitat overlap to facilitate prey-switching behavior. Our work extends previous research conducted under the alternative prey hypothesis by explicitly evaluating the influence of habitat overlap between prey species and variation in access to prey habitat as factors affecting prey-switching behaviors in predators.</p>
Body mass and triglycerides predict departure of free-living nomadic pine siskins
<p>1. Facultative migrations are observed across vertebrate taxa, include irruptive and nomadic movements, and occur in response to ephemeral and unpredictably variable resources. While the physiology underlying seasonal, obligate migrations is thoroughly studied, much less is known about the physiological mechanisms of facultative movements. We test two hypotheses in a free-living, nomadic bird, the pine siskin (Spinus pinus).</p> <p>2. The Prepare Hypothesis predicts that, like obligate migrants, siskins increase fuel stores to prepare for migratory movements and elevations of baseline corticosterone (CORT) support departure. The Escape Hypothesis predicts that siskins do not prepare for departure, body condition declines as food availability declines, and stress-related levels of CORT induce escape from resource-poor areas.</p> <p>3. Under the controlled lab conditions of previous studies, food restriction induces declines in body condition and increases in CORT and locomotor activity, supporting the Escape Hypothesis. This study evaluates the ecological relevance of these captive findings by testing the Prepare and Escape Hypotheses in the field for the first time.</p> <p>4. During two fall field seasons, we radio-tagged siskins and tracked their local movements using handheld and automated telemetry. We assess how body condition and CORT relate to feeding behavior (estimated via plasma triglycerides (TRIG)), space use, and departure.</p> <p>5. We do not find support for either the Prepare or Escape Hypothesis, but rather observe an intermediate pattern. Birds with higher TRIG, and therefore greater food intake, are more likely to depart. Birds in poor condition stay longer near the field site; however, above a threshold body mass, body condition does not predict departure.</p> <p>6. These findings suggest moderate energy stores are necessary for departure but movement decisions depend on other factors among birds with sufficient fuel. Siskin movements are physiologically distinct from both obligate and fugitive movements, and we discuss how food availability and body condition interact to drive different types of movement.</p>
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.