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15,560 results for “How long”
Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018)
<p><strong>Aim:</strong> Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. However, global-scale and long-term (>30 years) studies on the cumulative<strong> </strong>effect of drought are still lacking. Thus, combining a new satellite based gross primary productivity (GPP) and multi-timescale Standardized Precipitation Evapotranspiration Index datasets, we investigated the cumulative effect of drought on global vegetation GPP.</p> <p><strong>Location: </strong>Global.</p> <p><strong>Time period: </strong>1982 – 2018 (37 years).</p> <p><strong>Major taxa studied: </strong>Forests and grasslands.</p> <p><strong>Method: </strong>Based on correlation analysis framework, we investigated the cumulative effect duration of drought on global vegetation GPP. Meanwhile, the variability of this cumulative effect across different elevation gradients and climatic zones was analyzed using variance analysis.</p>
Dataset for "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest"
<p>The final dataset used in manuscript "Long-term fluxes of carbonyl sulfide and their seasonality and interannual variability in a boreal forest" by Vesala et al. (2022). The dataset contains carbonyl sulfide (COS) and carbon dioxide (CO2) eddy covariance flux data and in-situ meteorological data measured at Hyytiälä forest in Juupajoki, Southern Finland, as well as meteorological drivers for SiB4 simulations and SiB4 simulated COS flux at the Hyytiälä grid cell from January 2013 to December 2017. Raw data are available upon request from the author.</p>
Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations
<p>MD simulations used in the studies of the publication "<strong>Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations</strong>"</p>
Model-informed target product profiles of long-acting- injectables for use as seasonal malaria prevention: code and simulation data
<p>This simulation data set and code reproduces the Figures and analysis of PLOS Global Public Health peer-reviewed article </p> <p><strong>Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention</strong></p> <p>Authors:</p> <p>Lydia Burgert<sup>1, 2</sup>, Theresa Reiker<sup>1, 2</sup>, Monica Golumbeanu<sup>1,2</sup>, Jörg J. Möhrle<sup>1, 2, 3</sup>, Melissa A. Penny*<sup>1, 2</sup></p> <p> </p> <p><sup>1</sup> Swiss Tropical and Public Health Institute, Basel, Switzerland</p> <p><sup>2</sup> University of Basel, Basel, Switzerland</p> <p><sup>3 </sup>Medicines for Malaria Venture, Geneva, Switzerland</p> <p>*Corresponding author: <a href="mailto:melissa.penny@unibas.ch">melissa.penny@unibas.ch</a></p>
Enhanced Protein Isoform Characterization Through Long-Read Proteogenomics - Workflow Results
<pre> </pre> <p>The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.</p> <p>Companion Repositories:</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> <p>Companion Datasets</p> <ol> <li><a href="https://zenodo.org/deposit/5703754">Long-Read-Proteogenomics Workflow Sample and Reference Data</a></li> <li><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></li> </ol> <p>This Repository contains the complete output from the execution of the <a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow</a>, using the input from <a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a>. </p> <p>The file <em>jurkat.flnc.bam </em>was 6.5 GB had to be split into 13 separate files and for use should be rejoined -- here are the steps that were used to split the file up. </p> <p>1. Convert <em>jurkat.flnc.bam</em> (binary format) to sam file (text format) without header: <em>samtools view jurkat.flnc.bam > jurkat.flnc.sam</em></p> <p>2. Capture the header: <em>samtools view -H jurkat.flnc.bam > jurkat.flnc.header.sam</em></p> <p>3. Split <em>jurkat.flnc.sam</em> into smaller files (aim to get final size under 2GB): <em>split -l 400000 jurkat.flnc.sam jurkat.flnc.chunk.</em></p> <p>4. Convert each of these files back to bam for uploading: <em>samtools view -b jurkat.flnc.chunk.a* -o jurkat.flnc.chunk.a*.bam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>After downloading, reverse this process including using the header file which is found in the LRPG-Manuscript-Results-results-results-jurkat-isoseq3-companion-files.tar.gz file></p> <p>1. Convert the bam files back to sam files: <em>samtools view jurkat.flnc.chunk.a*.bam > jurkat.flnc.chunk.a*.sam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>2. Combine the header together with the sam files: <em>cat jurkat.flnc.chunk.a*sam > jurkcat.flnc.sam (</em>verified the same number of lines of the sam files is identical to the number of lines of the original without header: 4,956,761. Header file is 13 lines.</p> <p>3. Convert to bam files if desired: <em>samtools view -b jurkat.flnc.sam -o jurkat.flnc.bam</em></p> <p>4. Rehead with the header file: <em>samtools reheader -P -i jurkat.flnc.header.sam jurkat.flnc.bam</em></p>
Data for Horowitz et al. (2022). The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy
<p>Data repository for Horowitz, et al. 2022, "The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy"</p> <p>All data shown in the paper is included here. Data includes results from:</p> <p>1. U.S. LTS GCAM scenarios (labeled "GCAM")<br> 2. U.S. LTS NEMS scenarios (labeled "NEMS")<br> 3. Princeton's Net-Zero America scenarios (labeled "NZA")<sup>a</sup><br> 4. Williams et al. (2020) scenarios (labeled "Williams")<sup>b</sup></p> <p><br> Results are included in the following files:</p> <p>1. waterfall.csv - Figure 1. Emissions decomposition by scenario. GCAM only.<br> 2. clean_fuels.csv - Figure 2. Clean fuel (bioliquids, biogas, and blue/green hydrogen) consumption. All models.<br> 3. coal.csv - Figure 2. Primary energy consumption of coal without CCS. All models.<br> 4. ZEV_stock.csv - Figure 2. Zero-emission vehicle percentage of light-duty vehicle stock. All models.<br> 5. ghg_by_type.csv - Figure 3. Greenhouse gas emissions by gas/source. GCAM only.</p> <p> </p> <p>Reference:</p> <p>a) Larson, E., Greig, C., Jenkins, J.,Mayfield, E., Pascale, A., Zhang, C., Drossman, J., Williams, R., Pacala, S., Socolow, R., et al. (2021). Net-zero America: Potential pathways, infrastructure, and impacts. (Princeton University).</p> <p>b) Williams, J. H., Jones, R. A., Haley, B., Kwok, G., Hargreaves, J., Farbes, J., & Torn, M. S. (2021). Carbon-Neutral Pathways for the United States. AGU Advances, 2, e2020AV000284. <a href="https://doi.org/https://doi.org/10.1029/2020AV000284">https://doi.org/https://doi.org/10.1029/2020AV000284</a>.</p>
Data from: Long-term and year-to-year stability and its drivers in a Mediterranean grassland
<p><span>Understanding the mechanisms underlying community stability has become an urgent need in order to protect ecosystems from global change and resulting biodiversity loss. While community stability can be influenced by richness, synchrony in annual fluctuations of species, species stability and functional traits, the relative contributions of these drivers to stability are still unclear. In semi-natural grasslands, land-use changes such as fertilization might affect stability by decreasing richness and influencing year-to-year fluctuations. In addition, they can promote long-term directional trends, shifting community composition and influencing grassland maintenance. Thus, it is important to consider how species and community stability vary year-to-year but also in the long term. </span></p> <p><span>Using a 14-year vegetation time series of a species-rich semi-natural Mediterranean grassland, we studied the relative importance of richness, synchrony, species stability and functional traits on community stability. To assess land-use change effects on stability, we applied a fertilization treatment. To distinguish stability patterns produced by year-to-year fluctuations from those caused by long-term trends, we compared the results obtained using a detrending approach from those without detrending. </span></p> <p><span>Stability is influenced by richness, synchrony and functional traits. Fertilization decreases species and community stability by promoting long-term trends in species composition, favouring competitive species and decreasing richness. Studying stability at the community and species level, and accounting for the effect of trends is essential to understand stability and its drivers more comprehensively.</span></p>
Supplementary material for: Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift
<p>Raw data and input files for phylogenetic and biogeographic analysis of the article "<strong>Phylogeny and biogeography of the ancient spider family Filistatidae (Araneae) is consistent both with long-distance dispersal and vicariance following continental drift</strong>".</p> <p><strong>Supplementary material S1. </strong>Matrix of phenotypic characters in .ss format.</p> <p><strong>Supplementary material S2. </strong>Alignment of COI sequences in fasta format..</p> <p><strong>Supplementary material S3. </strong>Alignment of H3 sequences in fasta format.</p> <p><strong>Supplementary material S4. </strong>Alignment of 16S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S5. </strong>Alignment of 28S sequences in fasta format before trimming with gblocks.</p> <p><strong>Supplementary material S6. </strong>Input for running parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S7. </strong>Input for running Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S8. </strong>Input for running parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S9. </strong>Input for running Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S10. </strong>Input for running parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S11. </strong>Input for running Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S12. </strong>Input for running parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S13. </strong>Input for running Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S14. </strong>Input for running Bayesian inference using MrBayes (total evidence) and estimating node ages using tip-dating.</p> <p><strong>Supplementary material S15. </strong>Input for running Bayesian inference using Beast (sequence data only) and estimating node ages using node-dating.</p> <p><strong>Supplementary material S16. </strong>Raw geographic distances among areas in each time slice and dispersal probability matrices for each biogeographic model.</p> <p><strong>Supplementary material S17. </strong>Inputs for estimating ancestral ranges and performing biogeographic stochastic maps for our dataset.</p> <p><strong>Supplementary material S18. </strong>Consensus tree found with parsimony analysis using TNT (phenotypic data only).</p> <p><strong>Supplementary material S19. </strong>Consensus tree found with Bayesian inference using MrBayes (phenotypic data only).</p> <p><strong>Supplementary material S20. </strong>Consensus tree found with parsimony analysis using TNT (sequence data only).</p> <p><strong>Supplementary material S21. </strong>Consensus tree found with Bayesian inference using MrBayes (sequence data only).</p> <p><strong>Supplementary material S22. </strong>Consensus tree found with parsimony analysis using TNT (total evidence).</p> <p><strong>Supplementary material S23. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence).</p> <p><strong>Supplementary material S24. </strong>Consensus tree found with parsimony analysis using TNT (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S25. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence, dataset with reduced number of terminals).</p> <p><strong>Supplementary material S26. </strong>Consensus tree found with Bayesian inference using MrBayes (total evidence) and with node ages estimated using tip-dating.</p> <p><strong>Supplementary material S27. </strong>Maximum clade credibility tree found with Bayesian inference using Beast (sequence data only) and with node ages estimated using node-dating.</p>
Observing emergent hydrodynamics in a long-range quantum magnet
<p>Here lies the experimental data and the analysis codes for the manuscript titled "Observing emergent hydrodynamics in a long-range quantum magnet" to be published in Science.</p>
Long-term simulation of snow cover and its potential impacts on seasonal frost dynamics in croplands across southern Canada
<p><em>In northern climes, accurate simulation of thermal and hydrological budgets for farmlands during overwintering conditions is crucial to both an accurate prediction of spring flooding and the successful management of nutrient losses. As snow cover influences soil freezing dynamics, it has been hypothesized that reduced snow cover due to warmer winters might increase the depth and duration of frozen soil conditions. Nonetheless, such impacts remain poorly understood and, given the difficulty in measuring the depth of frozen soil, no long-term field experiment has documented these potential effects. The present study was designed to test this hypothesis. Drawing upon observed snow depth and soil temperature data collected from six research farms across Southern Canada over various time spans from 1989 to 2020, the Root Zone Water Quality Model, integrated with the Simultaneous Heat and Water model, was calibrated and validated. The potential influence of warmer winter on shifts in soil frost dynamics was evaluated by estimating the depth and duration of frozen soil for each farmland site under various RCP temperature scenarios using the RZ-SHAW model. Soil frozen depth in Eastern site increased with the increase of RCP temperature scenarios in some years, but decreased under the highest RCP temperature scenario. The monthly relationship between snow depth and soil frozen depth was determined through partial correlation analysis. Snow was most effective in alleviating soil freezing in the months of January and February, a period when snow cover depth was least affected by warming air temperatures. This paper suggests that Global warming induced-snow cover reduction would be site-specific and is </em>more likely to occur in <em>regions where energy lost through reduced snow cover would outweigh the energy gained through warmer air temperature.</em></p>
Food web rewiring drives long-term compositional differences and late-disturbance interactions at the community level
<p><strong>Abstract</strong></p> <p>Ecological communities are constantly exposed to multiple natural and anthropogenic disturbances. Multivariate composition (if recovered) has been found to need significantly more time to be regained after pulsed disturbance compared to univariate diversity metrics and functional endpoints. However, the mechanisms driving the different recovery times of communities to single and multiple disturbances remain unexplored. Here, we apply for the first time quantitative ecological network analyses to try to elucidate the mechanisms driving long-term community composition dissimilarity and late-stage disturbance interactions at the community level. For this, we evaluate the effects of two pesticides, nutrients enrichment and their interactions in outdoor mesocosms containing a complex freshwater community. We found changes in interactions strength to be strongly related to compositional changes and identified post-disturbance interaction strength rewiring to be responsible for most of the observed compositional changes. Additionally, we found pesticides interactions to be significant in the long term only when both interactions strength and food web architecture are reshaped by the disturbances. We suggest that quantitative network analysis has the potential to unveil ecological processes that prevent long-term community recovery.</p> <p><strong>Significance Statement</strong></p> <p>Multiple anthropogenic disturbances affect the structure and functioning of communities. Recent evidence highlighted that, after pulse disturbance, the functioning a community performs may be recovered fast due to functional redundancy, whereas community multivariate composition needs longer time. Yet, the mechanisms that drive the different community recovery times have not been quantified empirically. We use quantitative food web analysis to assess the influence of species interactions on community recovery. We found species interactions strength to be the main mechanism driving differences between structural and functional recovery. Additionally, we show that interactions between multiple disturbances appear in the long term only when both species interaction strength and food web architecture change significantly.</p> <p>Please see the "readme" sheet in the datafile for a description of the file structure and treatments abreviations.</p>
The long and short of it: converting between maximum and minimum tarsus measurements in passerine birds
<p>Data and R scripts relating to:</p> <p>Caravaggi A, Bayley S, Facey RJ, de la Hera I, Shewring M, Smith JA. (2022) The long and short of it: converting between maximum and minimum tarsus measurements in passerine birds. Ringing & Migration. doi: <a href="https://doi.org/10.1080/03078698.2022.2050937">10.1080/03078698.2022.2050937</a></p>
Dataset: Long-term Change in Metabolism Phenology in North Temperate Lakes
<p>Scripts, model configurations and outputs to process the data and recreate the figures from Ladwig, R., Appling, A., Delany, A., Dugan, H.A., Gao, L., Lottig, N., Stachelek, J., Hanson, P.C.: Long-term Change in Metabolism Phenology in North-Temperate Lakes.</p> <p>This repository includes the setup and output from the metabolism model ran on the lakes Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout. Scripts to run the model are located under /src (odem_min_indexed_sed.stan, run_odem_chtc_sed.R, stan_utility.R, stan_utility_chtc.R) together with the scripts to create the driver data, 0_Model_Input.R, and the processed results for the discussion of the paper, 1_Postprocessing.R. Addtionally, all scripts to recreate the figures from the manuscript are also included in /src.</p> <p>The figures are located under /Figures and processed output under /Processed_Output.</p>
No evidence that grooming is exchanged for coalitionary support in the short- or long-term via direct or generalized reciprocity in unrelated rhesus macaques
<p>Reciprocity is a prominent explanation for cooperation between non-kin. Studies seeking to demonstrate reciprocity often focus on direct reciprocity in the timescale of minutes to hours, whereas alternative mechanisms like generalised reciprocity and the possibility of reciprocation over longer timescales of months and years are less often explored. Using a playback experiment, we tested for evidence of direct and generalised reciprocity, across short and longer timescales. We examined the exchange of grooming for coalitionary support between female rhesus macaques in a population with a complete genetic pedigree. Females that received grooming were not more responsive to calls for coalitionary support from female groupmates compared to control females that received agonism or no interaction – even when the call belonged to a females' most recent grooming partner. Similarly, females were not more responsive to calls for support from their most frequent grooming partner of the last two years, nor if they received large amounts of grooming from all other females in their group. We interpret these results as an absence of evidence for direct or generalised reciprocity on any timescale in the exchange of grooming for coalitionary support in rhesus macaques. If grooming is exchanged for support in this population, it is with an intensity below our ability to detect or over a longer timescale than we examined. We propose by-product explanations may be responsible and highlight the importance of investigating multiple mechanisms when testing apparently cooperative behaviours.</p>
Data from: Long-term cattle grazing shifts the ecological state of forest soils
<p><span>Cattle grazing profoundly affects abiotic and biotic characteristics of ecosystems. While most research has been performed on grasslands, the effect of large managed ungulates on forest ecosystems has largely been neglected.</span></p> <p><span>Compared to a baseline semi-natural state, we investigated how long-term cattle grazing of birch forest patches affected the abiotic state and the ecological community (microbes and invertebrates) of the soil subsystem.</span></p> <p><span>Grazing strongly modified the soil abiotic environment by increasing phosphorus content, pH and bulk density, while reducing the C:N ratio. The reduced C:N-ratio was strongly associated with a lower microbial biomass, mainly caused by a reduction of fungal biomass. This was linked to a decrease in fungivorous nematode abundance and the nematode channel index, indicating </span><span>a relative </span><span>uplift in the importance of the bacterial energy-channel in the nematode assemblages. </span></p> <p><span>Cattle grazing highly modified invertebrate community composition producing distinct assemblages from the semi-natural situation. Richness and abundance of microarthropods was consistently reduced by grazing (excepting collembolan richness) and grazing-associated changes in soil pH, Olsen P and reduced soil pore volume (bulk density) limiting niche space and refuge from physical disturbance. Anecic earthworm species predominated in grazed patches, but were absent from ungrazed forest, and may benefit from manure inputs, while their deep vertical burrowing behaviour protects them from physical disturbance.</span></p> <p><span>Perturbation of birch forest habitat by long-term ungulate grazing profoundly modified soil biodiversity, either </span><span>directly through increased physical disturbance and manure input or indirectly by modifying soil abiotic conditions.</span><span> Comparative analyses revealed the ecosystem engineering potential of large ungulate grazers in forest systems through major shifts in the composition and structure of microbial and invertebrate assemblages, including the potential for reduced energy flow through the fungal decomposition pathway. The precise consequences for species trophic interactions and biodiversity-ecosystem function relationships remains to be established, however. </span></p>
Fig. 1. The potential distribution map for B in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 1. The potential distribution map for B. bombina under contemporary climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig 3 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig 3. Response curve showing how the logistic prediction changes as the environmental variable Bio2 (Mean diurnal temperature range, oC, X-axis) is varied, keeping all other environmental variables at their average sample value. The curve shows the mean response of the 10 replicate Maxent runs (red) and and the mean +/– one standard deviation (blue).
Fig. 1 in Food Habits Of The Endemic Long Legged Wood Frog, Rana Pseudodalmatina (Amphibia, Ranidae) In Northern Iran
Fig. 1. Map showing the localities of Rana pseudodlmatina (Eiselt & Schmidtler, 1971) samples from Iran. For identification localities numbers, refer to table 1.
MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)
<p>The dataset of the abstract "MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)" for ISMRM 2022, London, UK. The data processing code with instructions could be found <a href="https://github.com/BRAIN-TO/girfISMRM2022">here</a>.</p> <p> </p> <p>Meas1.zip and Meas2.zip contain the first and the second measurements of the raw T2* decay signal acquired with the phantom-based method. Note that the coil dimension has been averaged to save data volume for demonstration purposes. This will lead to a lower SNR of the calculated output gradient and GIRF.</p> <p> </p> <p>CalculatedGIRF.zip provides the author's pre-calculated GIRFs using the data without coil averaging. This data is used for all the postprocessing (e.g. SNR and stability analysis, etc.) in the published abstract with the source code provided in the same Github repository.</p> <p> </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.