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312 results for “change in distribution”
Wetland abundance and distribution changes in Sycamore Creek, Arizona, USA (2014-2019)
The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). This dataset was collected to understand: (1) the spatial pattern of wetland distribution and abundance; (2) the influence of multi-annual variability in hydrological regime on wetland distribution and abundance; and (3) the mechanism of the resilience of wetland to different disturbances in terms of hydrology (i.e., drying and flooding).
Current and future global distribution of potential biomes under climate change scenarios
<p>Probability and uncertainty maps showing the potential current and future natural vegetation on a global scale under three different climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) predicted using ensemble machine learning. Current (2022 - 2023) conditions are calculated on historical long term averages (1979 - 2013), while future projections cover two different epochs: 2040 - 2060 and 2061 - 2080.</p> <p>Files are named according to the following naming convention, e.g.:</p> <ul> <li>biomes_graminoid.and.forb.tundra.rcp85_p_1km_a_20610101_20801231_go_epsg.4326_v20230410</li> </ul> <p>with the following fields:</p> <ul> <li>generic theme: <strong>biomes</strong>,</li> <li>variable name: <strong>graminoid.and.forb.tundra.rcp85</strong>,</li> <li>variable type, e.g. probability ("<strong>p</strong>"), hard class ("<strong>c</strong>"), model deviation ("<strong>md</strong>")</li> <li>spatial resolution: <strong>1km</strong>,</li> <li>depth reference, e.g. below ("<strong>b</strong>"), above ("<strong>a</strong>") ground or at surface ("<strong>s</strong>"),</li> <li>begin time (YYYYMMDD): <strong>20610101</strong>,</li> <li>end time: <strong>20801231</strong>,</li> <li>bounding box, e.g. global land without Antarctica ("<strong>go</strong>"),</li> <li>EPSG code: <strong>epsg.4326</strong>,</li> <li>version code, e.g. creation date: <strong>v20230410</strong>.</li> </ul> <p>We provide probability and hard class layers using a revised classification system of the <a href="https://www.jstor.org/stable/2846196">BIOME 6000 project</a> explained in the work of <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a>. The 20 classes from this classification system have then been aggregated in 6 biome classes following the <a href="https://global-ecosystems.org/page/typology">IUCN Global Ecosystem Typology</a> classification system.</p> <p>For probability layers, the uncertainty (model deviation: <strong>md</strong>) is calculated as the standard deviation of the predicted values of the base learners of the ensemble model. The higher the standard deviation the more uncertain the model is regarding the right value to assign to the pixel.</p> <p>For hard class layers the uncertainty is calculated using the margin of victory (<a href="https://doi.org/10.1016/j.rse.2020.112148">Calderón-Loor et al., 2021</a>) defined as the difference between the first and the second highest class probability value in a given pixel. High values would be measures of low uncertainty, while low values would indicate a high uncertainty. It is highly recommended to use the <strong>md </strong>layers to properly interpret the results of the map.</p> <p>Styling files are provided in both <em><strong>.SLD</strong></em> and <em><strong>.QML</strong></em> format; two different styling files are provided for the uncertainty of the probability layers and the hard classes due to the different interpretation of the chosen uncertainty metrics.</p> <p>The R scripts and a tutorial will be uploaded to the <a href="https://github.com/Envirometrix/PNVmaps">PNVmaps Github repository</a>, where previous versions of the biomes maps from <a href="https://doi.org/10.7717/peerj.5457">Hengl et al. (2018)</a> is currently hosted. To cite the maps and the methodology, it is possible to refer to the scientific publication:</p> <p>Bonannella C, Hengl T, Parente L, de Bruin S. 2023. Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation. PeerJ 11:e15593 <a href="https://doi.org/10.7717/peerj.15593">https://doi.org/10.7717/peerj.15593</a></p>
Linking temporal changes in species composition and biomass in a globally distributed grassland experiment: The Nutrient Network
Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition, and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonization, and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained, or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e., local extinctions) were balanced by gains (i.e. colonization). Under fertilization, the decline in species richness resulted from increased species loss and from decreases in species gained. Biomass increase under fertilization resulted mostly from species that persist, and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition, and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained, or persisting.
Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective
<p>Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. <em>Functional Ecology, </em>2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.</p>
Raw Data - 3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer
<p>This Data set contains the raw data of the article:</p> <p>3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer, Small, 2021, 17, 2101233.</p> <p>C. Iffelsberger, C. W. Jellett, and M. Pumera*,</p> <p>https://doi.org/10.1002/smll.202101233</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming
<p>Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, we analyzed η values based on 3,013 plots and 26,337 plant-specific measurements representing eight sites across the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Four process-based biogeochemical models failed to simulate the observed changes in η, which highlights the importance of improved process understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems.</p>
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
<p>Climate change mitigation is a shared global challenge that involves the collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together toward a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer - sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters'" and the richest into a "greedy cluster''. Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.</p> <p>Vicens J, Bueno-Guerra N, Gutiérrez-Roig M, Gracia-Lázaro C, Gómez-Gardeñes J, Perelló J, et al. (2018) Resource heterogeneity leads to unjust effort distribution in climate change mitigation. PLoS ONE 13(10): e0204369. https://doi.org/10.1371/journal.pone.0204369</p>
Projected distribution of invasive plant species in the tropical Andes under climate change
<p>Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission.</p> <p>Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS. </p> <p>For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change</p> <p>List of modeled invasive plant species and their known impacts in the tropics.</p> <table> <tbody> <tr> <td> <p><strong>Species </strong></p> </td> <td> <p><strong>Biogeographic origin</strong></p> </td> <td> <p><strong>Impacts </strong></p> </td> <td> <p><strong>References</strong></p> </td> <td> <p><strong>GBIF data (DOIs)</strong></p> </td> </tr> <tr> <td> <p><em>Acacia decurrens </em></p> </td> <td> <p>Australian</p> </td> <td> <p>Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires.</p> </td> <td> <p> (Cárdenas López et al., 2017; Le Maitre et al., 2011)</p> </td> <td> <p>https://doi.org/10.15468/dl.mjyxhw</p> </td> </tr> <tr> <td> <p><em>Acacia melanoxylon</em></p> </td> <td> <p>Australian</p> </td> <td> <p>Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture.</p> </td> <td> <p>(Kumschick and Jansen, 2023; Le Maitre et al., 2011)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.4cugnk</p> </td> </tr> <tr> <td> <p><em>Arundo donax</em></p> <p><em> </em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter<em> </em>the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock.</p> </td> <td> <p>(Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010)</p> </td> <td> <p>https://doi.org/10.15468/dl.bfep4t</p> </td> </tr> <tr> <td> <p><em>Genista monspessulana</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans.</p> </td> <td> <p>(Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008)</p> </td> <td> <p>https://doi.org/10.15468/dl.gyhnxh</p> </td> </tr> <tr> <td> <p><em>Hedychium coronarium </em></p> </td> <td> <p>Indo-Malesian</p> </td> <td> <p>Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops.</p> </td> <td> <p>(Cárdenas López et al., 2017; Costa et al., 2019)</p> </td> <td> <p>https://doi.org/10.15468/dl.6z2jgb</p> </td> </tr> <tr> <td> <p><em>Melinis minutiflora</em></p> </td> <td> <p>African</p> </td> <td> <p>Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.fsqwsv</p> </td> </tr> <tr> <td> <p><em>Pteridium aquilinum</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p> (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.sp4uuv</p> </td> </tr> <tr> <td> <p><em>Ricinus communis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.dhbphb</p> </td> </tr> <tr> <td> <p><em>Senecio madagascariensis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans. </p> </td> <td> <p>(Wijayabandara et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.7e8eyx</p> </td> </tr> <tr> <td> <p><em>Thunbergia alata</em></p> </td> <td> <p>African</p> </td> <td> <p>Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems.</p> </td> <td> <p>(Cárdenas López et al., 2017; Quijano-Abril et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.g9zybc</p> </td> </tr> <tr> <td> <p><em>Ulex europeaus</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands.</p> </td> <td> <p>(Anderson and Anderson, 2009; Cárdenas López et al., 2017)</p> </td> <td> <p>https://doi.org/10.15468/dl.6642q9</p> </td> </tr> </tbody> </table>
Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"
<p>DATA FILES from the study below:</p> <p><strong><a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a></strong></p> <p>Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth</p> <p>bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a></p> <p>*.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a></p> <p>Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning</p>
Figure 5. Changes from 1987 in Environmental influences on movements and distribution of a wild horse (Equus caballus) population in western Nevada, USA: a 25-year study
Figure 5. Changes from 1987 to 2007 in patterns of the geographic distribution of wild horses in the Montgomery Pass Wild Horse Territory (MPWHT). The central, lightly shaded area is the key summer range (KSR). The black mark within it is the highest elevation in the Territory (elevation decreases 360 degrees around it throughout). Dark shaded areas indicate regular horse use and represent> 90% of the population. Many wild horse populations in the intermountain west accommodate to seasonal conditions, spending winter at lower elevations and summer at higher elevations. The pattern across years in the MPWHT changed from summer horse concentration in the KSR to decreasing return to KSR from winter range. In addition to establishment of decreased KSR use and increased year-round use of historical winter-range areas, expansion of the geographic use areas occurred in the latter, including seasonal use beyond MPWHT map boundaries.
Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic - Estimates for Local Authority Districts
<p><strong>Overview:</strong></p> <p>Population estimates from the publication: <em>Population disruption: estimating changes in population distribution in the UK during the COVID-19 pandemic.</em> </p> <p>Population estimates were aggregated to Local Authority Districts (LADs). </p> <p><strong>Methodology: </strong></p> <p>Population estimates were extracted from Bing Tiles (Zoom Level 12) to 2019 LADs by assigning tiles to LADs by their percent areal overlap. This method assumes constant population distribution across a single Bing Tile.</p> <p>2019 LAD boundaries are available from the <a href="https://geoportal.statistics.gov.uk/datasets/local-authority-districts-december-2019-boundaries-uk-bfc/explore">UK Government Open Geography Portal</a>.</p> <p> </p>
Рис. 2. Изменение виΑового богатства и таксономической структуры зонаΛьных фаун прямокрыΛых северо-востока Русской равнины. Fig. 2. Changes in species richness and taxonomic structure of Orthoptera fauna of the Russian Plain. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 2. Изменение виΑового богатства и таксономической структуры зонаΛьных фаун прямокрыΛых северо-востока Русской равнины. Fig. 2. Changes in species richness and taxonomic structure of Orthoptera fauna of the Russian Plain.
Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects
<p><strong>Premise </strong></p> <p>Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO<sub>2</sub> exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO<sub>2</sub> exchange rates of these poikilohydric organisms change through time as a function of weather conditions.</p> <p><strong>Methods</strong></p> <p>PoiCarb simulates diel fluctuations of CO<sub>2</sub> exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO<sub>2</sub>-exchange responses to light, temperature, atmospheric CO<sub>2</sub> concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO<sub>2</sub> exchange to such courses from field measurements on the tropical lichen <em>Crocodia aurata</em>. To demonstrate the model's usefulness, we simulated potential climate-change effects.</p> <p><strong>Results </strong></p> <p>Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration.</p> <p><strong>Conclusions</strong></p> <p>Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.</p>
Geographic distribution change and climatic niche change of Odonates in Great Britain
<p>Species are largely thought to maintain broadly static niches over time, an assumption underpinning much theoretical ecology including the implementation of ecological models to project species' current and future distributions. Here, we assess niche conservatism in odonates in Great Britain over the past six decades by simultaneously quantifying changes in species geographic distribution and evaluating temporal trends in species realised climatic niche.</p>
Fig. 3 in Distribution Range Extensions of Parapercis bicoloripes and P. diplospilus (Perciformes: Pinguipedidae) in the South China Sea and the Adjacent Waters, with Notes on Ontogenetic Changes in P. bicoloripes
Fig. 3. Ontogenetic changes in relationship of snout length (circles) and fleshy orbit diameter (triangles) as percentage of standard length to standard length (mm) in Parapercis bicoloripes.
Fig. 2 in Distribution Range Extensions of Parapercis bicoloripes and P. diplospilus (Perciformes: Pinguipedidae) in the South China Sea and the Adjacent Waters, with Notes on Ontogenetic Changes in P. bicoloripes
Fig. 2. Distribution of Parapercis bicoloripes (triangles) and P. diplospilus (circles). Closed and open symbols indicate previously known and new records, respectively.
Fig. 1 in Distribution Range Extensions of Parapercis bicoloripes and P. diplospilus (Perciformes: Pinguipedidae) in the South China Sea and the Adjacent Waters, with Notes on Ontogenetic Changes in P. bicoloripes
Fig. 1. Fresh specimens of Parapercis bicoloripes from Malaysia (A–B) and the Philippines (C). A, KAUM–I. 79754, 136.0 mm SL, off Kuala Terengganu; B, KAUM–I. 16935, 120.4 mm SL, off Kuala Terengganu; C, KAUM–I. 69435, 66.1 mm SL, off Miagao, Iloilo, Panay Island.
Fig. 3 in Acoustic Discrimination Of Pipistrellus Kuhlii And Pipistrellus Nathusii (Chiroptera: Vespertilionidae) And Its Application To Assess Changes In Species Distribution
Fig. 3. Bar graph about the number of settlements where P. kuhlii and P. nathusii occurred or were absent in case of the two studied areas, from North and South Hungary
Fig. 2 in Acoustic Discrimination Of Pipistrellus Kuhlii And Pipistrellus Nathusii (Chiroptera: Vespertilionidae) And Its Application To Assess Changes In Species Distribution
Fig. 2. Occurrences of the two species in the two studied areas from North and South Hungary. (open circle = none of the species found, black square = P. nathusii, black triangle = P. kuhlii, black circle =
Fig. 1 in Acoustic Discrimination Of Pipistrellus Kuhlii And Pipistrellus Nathusii (Chiroptera: Vespertilionidae) And Its Application To Assess Changes In Species Distribution
Fig. 1. The distribution of the canonical scores between P. kuhlii and P. nathusii resulted from the discriminant function analysis based on 5 call parameters
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.