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6,175 results for “Composite”
Dataset of Water Hyacinth Composites
<p>Tensile test, impact test and density of water hyacinth composite</p>
Spore traits mediate disturbance effects on AM fungal community composition and mutualisms
<p>Trait-based approaches in ecology are powerful tools for understanding how organisms interact with their environment. These approaches show particular promise in disturbance and community ecology contexts for understanding how disturbances like prescribed fire and bison grazing influence interactions between mutualists like AM fungi and their plant hosts. In this work, we examined how disturbance effects on AM fungal spore community composition and mutualisms were mediated by selection for specific functional spore traits at both the species and community level. We tested these questions by analyzing AM fungal spore communities and traits from a frequently burned and grazed (bison) tallgrass prairie system and using these spores to inoculate a plant growth response experiment. Selection for darker, pigmented AM fungal spores, changes in the abundance and volume of individual AM fungal taxa, and altered sporulation, were indicators of fire and grazing effects on AM fungal community composition. Disturbance-associated changes in AM fungal community composition were then correlated with altered growth responses of <em>Schizachyrium scoparium</em> grass. Our work shows that utilization of trait-based approaches in ecology can clarify the mechanisms that underly belowground responses to disturbance, and provide a useful framework for understanding interactions between organisms and their environment.</p>
Viral Regulation of Bacterial Metabolism and Community Composition across Trophic Status in Marine Environments
<p>Table 1: Details and microbial abundances during the incubation at the three stations.</p> <p>Table 2: Details and parameters at the end of the incubation.</p>
Tree community composition stabilizes ecosystem functions in response to drought
In summer 2018, Central Europe was hit by an extreme drought event that widely impacted ecosystems and markedly increased tree mortality in forest ecosystems across the continent. As climate models predict an increase in frequency and severity of such events, there is an urgent need to adapt forests in order to maintain the diverse benefits they provide. Soil processes play an essential role in this context and are key for a plethora of terrestrial ecosystem functions but are strongly dependent on water availability. Here we investigated how tree species richness, composition, and identity in a 13-year-old temperate tree diversity experiment influenced selected ecosystem functions (as important representatives of different ecosystem processes) during the 2018 summer drought. We focused on the stability of soil microbial biomass and standard litter decomposition, as well as tree species-specific mortality rates. Contrary to our expectations, tree species richness did not generally increase the resistance of soil functions and decrease tree mortality rates. However, the resistance of these functions was determined by tree species identity and community composition. For the resistance of both soil functions (microbial biomass and litter decomposition), we found that tree species richness effects depended on the presence of certain tree species. Moreover, we found that the performance of a specific tree species in monoculture, Norway Spruce, was a poor predictor of its response to drought in tree species mixtures. Taken together, the results of our study demonstrate that the species composition of tree stands determines tree mortality and the resistance of soil functions under drought. This indicates that enhancing multiple ecosystem functions under environmental disturbance requires maintaining diverse forests.
Social structure and individuals' behavior shaped by group composition in long-term tracking of rats
<p>Trajectory data and behavioral metrics from long-term tracking of groups of rats. Experiments performed in 2012 at Eötvös Loránd University, Budapest, Hungary.</p> <p>The data here is used in the following publication. See the publication for information on experimental structure and notation.<br> Nagy, Máté, Jacob Davidson, Gábor Vásárhelyi, Dániel Ábel, Enikő Kubinyi, Ahmed El Hady, and Tamás Vicsek. 2023. “Social Structure and Individuals’ Behavior Shaped by Group Composition in Long-Term Tracking of Rats.” bioRxiv. <a href="https://doi.org/10.1101/2023.03.18.533183">https://doi.org/10.1101/2023.03.18.533183</a>.</p> <p>Github repository to reproduce results in the paper: <a href="https://github.com/jacobdavidson/ratsocialgroups">github.com/jacobdavidson/ratsocialgroups</a><br> Github repositories used for tracking and computation of behavioral metrics: <a href="https://github.com/vasarhelyi">github.com/vasarhelyi</a></p> <p>The behavioral metrics, approach-avoidance networks, individual labels and group membership information, and behavioral assay data needed to reproduce the results in the publication are included in the zip file <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/metrics-networks-labels.zip">metrics-networks-labels.zip</a>. This includes the following files in .csv format:</p> <p><strong>Naming/labels:</strong></p> <ul> <li><strong>uidlabels.csv</strong>: References the ranked-mass labels used in the paper to the 3-color-code labels that identify each rat from the tracking. "Group" is the membership during the original groups (Period 1), and "GroupG" is membership during the remixed groups (Period 3). <table> <tbody> <tr> <td> </td> <td> <p><strong>masslabel</strong></p> </td> <td> <p><strong>GroupMassRank-Exp7</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>GroupG</strong></p> </td> </tr> <tr> <td> <p><strong>GOB</strong></p> </td> <td> <p>a5</p> </td> <td> <p>5.0</p> </td> <td> <p>A1</p> </td> <td> <p>G3</p> </td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> </li> <li><strong>groupnames.csv</strong>: Group membership information for each rat, during each experimental period <table> <tbody> <tr> <td> <p><strong>Experiments</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1-7</strong></p> </td> <td> <p>A1</p> </td> <td> <p>A1</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p><strong>Behavioral metrics and approach-avoidance networks:</strong></p> <ul> <li><strong>metrics.csv: </strong>Behavioral metrics for each rat, calculated for each experiment. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>metric</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>AA</p> </td> <td> <p>0</p> </td> <td> <p>0.690964</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Period1+Period3.csv: </strong>Approach-avoidance interaction networks for the groups of 7 rats in Period 1 and Period 3. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>DayNight</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>index</strong></p> </td> <td> <p><strong>winner</strong></p> </td> <td> <p><strong>loser0</strong></p> </td> <td> <p><strong>loser1</strong></p> </td> <td> <p><strong>loser2</strong></p> </td> <td> <p><strong>loser3</strong></p> </td> <td> <p><strong>loser4</strong></p> </td> <td> <p><strong>loser5</strong></p> </td> <td> <p><strong>loser6</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>Day</p> </td> <td> <p>A1</p> </td> <td> <p>0</p> </td> <td> <p>GOB</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Experiment8.csv</strong> and <strong>Networks-Experiment9.csv: </strong>Approach-avoidance interaction networks for the larger combined groups in the experiments during Period 2. </li> </ul> <p> </p> <p><strong>Behavioral assays results:</strong></p> <ul> <li><strong>IndividualTests.csv</strong>: Individual behavioral assay test results for the black and white box, elevated plus-maze, and canopy tests. <table> <tbody> <tr> <td> <p><strong>ID</strong></p> </td> <td> <p><strong>BWB-All Frames</strong></p> </td> <td> <p><strong>BWB-Black</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>7500</p> </td> <td> <p>6860</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>PairedTests.csv: </strong>Pairwise social interaction behavioral assay test results for tests with unfamiliar and familiar rats <table> <tbody> <tr> <td> <p><strong>rat_ID</strong></p> </td> <td> <p><strong>rat_group_N</strong></p> </td> <td> <p><strong>freq_standup_unfam1</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>2</p> </td> <td> <p>28</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p> </p> <p><strong>Trajectory data</strong> is included as multiple text files for each experiment in TSV format. #t is frame number, Color_code_name identifies each rat, Real_recognition(1)_or_virtual(0) = 1 is a detection of the rat's color codes while 0 is an interpolated value, x,y, and Orientation are the position and orientation of the rat.</p> <table> <tbody> <tr> <td>#t</td> <td>Color_code_name</td> <td>Real_recognition(1)_or_virtual(0) </td> <td>x</td> <td>y</td> <td>Orientation</td> </tr> <tr> <td>0</td> <td>ORG</td> <td>0</td> <td>872.4000</td> <td>778.7000</td> <td>-79.5000</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p> <p><strong>Time-lapse video of the entire experiment:</strong> <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/ratlab_timelapse_entire_exp.avi?versionId=a58df78d-1291-4301-9763-86a97b9e146c">ratlab_timelapse_entire_exp.avi</a></p>
FIGURE 1 in Improved modelling of compositional heterogeneity reconciles phylogenomic conflicts among lacewings
FIGURE 1. Phylogram of Neuropterida relationships based on the CAT-GTR+G4 analysis of anchored hybrid enrichment (AHE) amino acid data. All branches have a support value of ≥ 0.93 Bayesian posterior probability (BPP) except those indicated by red dots (BPP <0.9). Abbreviations: Conio., Coniopterygoidea; Dilar., Dilaroidea; Ithon., Ithonoidea; Megal., Megaloptera; Psych., Psychopsoidea; Raphi., Raphidioptera. Superfamilial classification is adapted from Engel et al. (2018).
FIGURE 2 in Improved modelling of compositional heterogeneity reconciles phylogenomic conflicts among lacewings
FIGURE 2. Congruent interfamilial relationships of Neuropterida inferred from transcriptomic (left) and AHE (right) amino acid data respectively under the site-heterogeneous CAT-GTR+G4 model. All branches have a strong support (BPP> 0.93), except for the monophyly of Myrmeleontidae (only weakly supported in AHE nucleotide data under the CAT-GTR+G4 model). Note that the transcriptomic dataset has fewer and sparser familial sampling than the AHE data.
Supporting data for: Emissions background, climate, and season determine the impacts of past and future pandemic lockdowns on atmospheric composition and climate
<p>COVID-19 pandemic responses affected atmospheric composition and climate. These effects are historically contingent, depending on the background emissions, climate, and season in which they occur. We used the GISS ModelE Earth System Model to evaluate how atmospheric and climate impacts depend on the decade and season in which lockdowns occurred. Data underlying the figures and analysis are provided as Python numpy arrays as a courtesy for peer reviewers. These data are annual means of diagnostic variables from ModelE.</p>
Homogenised Constitutive Law Composite
<p>Homogenised Constitutive Law Composite </p>
Compilation of the chemical composition data of pelagic clay around Minamitorishima Island in the western North Pacific Ocean
<p>A compilation of bulk chemical composition data of pelagic clay around Minamitorishima Island in the western North Pacific Ocean. Original data were published in the cited articles (shown in Data source column). This is a supplementary dataset for "High-dimensional chemostratigraphy of pelagic clay in western North Pacific Ocean revealed <em>via</em> unsupervised clustering approach" by Yasukawa et al.</p>
High-resolution quasi-idealized experiments for future and present-day based upon composites from a decades-long set of recurving landfalling (RCL) North Atlantic ET cases
<p>We present a high-resolution quasi-idealized experiment based upon composites from a decades-long set of recurving landfalling (RCL) North Atlantic extratropical transition (ET) cases. Following Jung and Lackmann (2021), we apply the track-based classification method of Colbert and Soden (2012) to the historical database (1979 to 2018) of North Atlantic RCL ET events; recurving landfalling tropical cyclones (TCs) are defined as those which cross 70°W north of 25°N or cross 65W north of 40N and threaten the U.S. East Coast. We use the Atlantic Hurricane Database (HURDAT2) best-track data (Landsea and Franklin 2013); the track-based classification procedure yields 37 RCL ET cases over the 40-year period. To minimize the spread of the selected ET cases in terms of time to complete transition, we apply a threshold of 36 h as in Jung and Lackmann (2021). As HURDAT2 only specifies when transition is complete but does not provide the timeline of the transition, we supplement it with the cyclone phase space (CPS) method of Hart (2003) to diagnose onset and completion times of ET; ET events are first identified by HURDAT2 and then we compute the CPS parameters to confirm the ET timeline for each event using the ERA5 reanalysis dataset (Hersbach et al. 2020). This filtering process yields 21 RCL ET cases. This dataset is initialized by randomized 15-case composites selected from the 21 RCL ET cases as in Jung and Lackmann (2021). This dataset was used to produce figures and tables in the manuscript of Jung and Lackmann (2023, in review). </p> <p>Due to storage limitations, the full dataset is much too large to be published (~ 500GB). Instead, a subset consisting of 3-hourly atmospheric temperature at 17 vertical levels, zonal and meridional wind at 17 vertical levels, 3 hourly accumulated precipitation, mean sea level pressure, 10-m zonal and meridional wind, sea surface temperature, and relative humidity at 17 vertical levels is presented. If you wish to access the full dataset, please contact one of the authors. </p>
FIG. 3 in Dietary composition of four common chiropteran species in a bottomland hardwood forest
FIG. 3. Abundance of insect prey as assessed by black light traps (% frequency in total traps indicated by the black columns), compared with insect consumption (% frequency in pellets indicated by the white columns) by four chiropteran species as assessed using the Barcode of Life Database (BOLD): A) P. subflavus, B) N. humeralis, C) L. seminolus, D) L. borealis. Hardness index [1–5] is indicated for each taxon by the number after the taxon name. Frequencies with the same letter are not significantly different at P <0.05
FIG. 1 in Dietary composition of four common chiropteran species in a bottomland hardwood forest
FIG. 1. Location of Old Sabine Bottom Wildlife Management Area (OSBWMA) on the Sabine River in Smith County, Texas, with area used for bat and insect capture during summer 2013 indicated in dark gray
FIG. 2 in Dietary composition of four common chiropteran species in a bottomland hardwood forest
FIG. 2. Relative abundance (% of total biomass) for insect orders captured in black light traps at OSBWMA, Smith County, Texas during summer 2013. Note that the vertical scale is expanded on the lower panel to show relative abundance for less abundant orders that are difficult to visualize on the top panel where the scale goes up to 90% (and Coleoptera was the only order with > 10% of total biomass)
Data for: Cover crop functional types differentially alter the content and composition of soil organic carbon in particulate and mineral-associated fractions
<p>Cover crops (CCs) can increase soil organic carbon (SOC) sequestration by providing additional OC residues, recruiting beneficial soil microbiota, and improving soil aggregation and structure. The various CC species that belong to distinct plant functional types (PFTs) may differentially impact SOC formation and stabilization. Biogeochemical theory suggests that selection of PFTs with distinct litter quality (C:N ratio) should influence the pathways and magnitude of SOC sequestration. Yet, we lack knowledge on the effect of CCs from different PFTs on the quantity and composition of physiochemical pools of SOC. We sampled soils under monocultures of three CC PFTs (legume [crimson clover]; grass [triticale]; and brassica [canola]) and a mixture of these three species, from a long-term CC experiment in Pennsylvania, USA. We measured C content in bulk soil and C content and composition in contrasting physical fractions: particulate organic matter, POM; and mineral-associated organic matter, MAOM. The bulk SOC content was higher in all CC treatments compared to the fallow. Compared to the legume, monocultures of grass and brassica with lower litter quality (wider C:N) had higher proportion of plant-derived C in POM, indicating selective preservation of complex structural plant compounds. In contrast, soils under legumes had greater accumulation of microbial-derived C in MAOM. Our results for the first time, revealed that the mixture contributed to a higher concentration of plant-derived compounds in POM relative to the legume, and a greater accumulation of microbial-derived C in MAOM compared to monocultures of grass and brassica. Mixtures with all three PFTs can thus increase the short- and long-term SOC persistence balancing the contrasting effects on the chemistries in POM and MAOM imposed by monoculture CC PFTs. Thus, despite different cumulative C inputs in CC treatments from different PFTs, the total SOC stocks did not vary between CC PFTs, rather PFTs impacted whether C accumulated in POM or MAOM fractions. This highlights that CCs of different PFTs may shift the dominant SOC formation pathways (POM vs. MAOM), subsequently impacting short- and long-term SOC stabilization and stocks. Our work provides a strong applied field test of biogeochemical theory linking litter quality to pathways of C accrual in soil.</p>
Data for: Phenotypic outcomes of predator-prey coevolution are predicted by landscape variation in climate and community composition
<ol> <li>Landscape patterns of phenotypic coevolution are determined by variation in the outcome of predator-prey interactions. These outcomes may depend not only on the functional phenotypes that mediate species interactions but also on aspects of the environment that enable encounters between coevolutionary partners.</li> <li>Exploring the relationship between coevolutionary traits and the environment requires extensive sampling across the range of the interaction to determine the relationship between local ecological variation and coevolution.</li> <li>In this study, we synthesized >30 years of data on predator-prey interactions between toxic newts (<em>Taricha</em> <em>granulosa</em>) and their snake predators (<em>Thamnophis</em> <em>sirtalis</em>) to explore the environmental predictors of arms race escalation.</li> <li>We found that geographic variation in phenotypes at the interface of coevolution was best predicted by a combination of community and climatic variation. Coevolutionary phenotypes were greatest in environments with climate favorable for newt-snake overlap. We found prey toxicity was elevated in regions with more predator species, and predator resistance was higher in regions with more prey species.</li> <li>Our results suggest specific environmental conditions reinforce the process of coevolution, signifying the phenotypic outcomes of coevolutionary arms races are sensitive to local ecological contexts that vary across the landscape. </li> </ol>
Pateto charts for the article "Biomass composition of Nephroselmis sp. under different ex-perimental conditions of nitrogen, salinity, and light: optimiza-tion of lipids accumulation (including EPA)"
<p>Pareto charts for the statistical significance of the nitrogen concentration, salinity level and illuminance, regarding the derived equations for (a) proteins, (b) carbohydrates, (c) pigments, (d)fatty acids and (e) EPA production.</p>
Datsets and RF model for oxygen isotopic compositions at Chichijima
<p>The datasets in the uploaded file include quarterly data of observed ions and oxygen-17 excess as well as predicted oxygen-17 excess between 2001 abd 2011. On the other hand, the RF model is also uploaded.</p>
THE EFFECT OF BODY SIZE AND COMPOSITION ON LUMBAR SPINE TRABECULAR BONE SCORE IN MORPHOLOGICALLY DIVERSE SUBJECTS
<p><strong>Aim:</strong> The trabecular bone score (TBS) is a tool for assessing bone quality and health. Current TBS algorithm corrects for body mass index (BMI), as a proxy of regional tissue thickness. However, this approach fails to consider BMI inaccuracies due to individual differences in body stature, composition and somatotype. This study investigated the relationship between TBS and body size and composition in subjects with a normal BMI, but with large morphological diversity in body fatness and height.</p> <p><strong>Methods:</strong> Young male subjects (n=97; age 17.2±1.0 years), including ski jumpers (n=25), volleyball players (n=48) and non-athletes (controls n=39), were recruited. The TBS was determined from L1-L4 dual-energy X-ray absorptiometry (DXA) scans using TBSiNsight software.</p> <p><strong>Results:</strong> TBS correlated negatively with height and tissue thickness in the L1-L4 area in ski jumpers (r= -0.516 and r= -0.529), volleyball players (r= -0.525 and r= -0.436), and the total group (r=-0.559 and r=-0.463), respectively. Multiple regression analyses revealed that height, L1-L4 soft tissue thickness, fat mass and muscle mass were significant determinants of TBS (R<sup>2</sup>= 0.587, p<0.001). L1-L4 soft tissue thickness explained 27% and height 14% of the TBS variance.</p> <p><strong>Conclusion: </strong>The negative association of TBS and both features suggests that a very low L1-L4 tissue thickness may lead to overestimation of the TBS, while tall stature may have the opposite effect. It seems that the utility of the TBS as a skeletal assessment tool in lean and/or tall young male subjects could be improved if tissues thickness in the lumbar spine area and stature instead of BMI were considered in the algorithm.</p>
Data for: Coping with abrasive food – diverging composition of radular teeth in two Porifera-consuming nudibranch species (Mollusca, Gastropoda)
<p><span>Molluscs forage with their radula, a chitinous membrane with teeth. Adaptations to hard or abrasive ingesta were well studied in Polyplacophora and Patellogastropoda, but for other taxa, there are large gaps in knowledge. Here, we investigated the nudibranch gastropods <em>Felimare</em> <em>picta</em> and <em>Doris</em> <em>pseudoargus</em>, both of which feed on Porifera. Tooth morphologies were documented by scanning electron microscopy and mechanical properties were tested by nanoindentation. We found that these parameters are rather similar in both species, indicating that teeth are similar in their function. To study the composition, teeth were visualized using confocal laser scanning microscopy (CLSM), to determine the degree of tanning, and analysed with energy-dispersive X-ray spectroscopy, to test the elemental composition. The emitted autofluorescence signal and the inorganic content differed between the species. This was especially prominent when studying the inner and outer tooth surfaces (leading and trailing edges). In <em>F. picta</em>, we detected high proportions of Si, whereas teeth of <em>D. pseudoargus</em> contained high amounts of Ca, which influenced the autofluorescence signal in CLSM. Employing nanoindentation, we determined high Young's modulus and hardness values for the leading edges of teeth, which relate to the Si- and Ca-content. This highlights that teeth with a similar morphology and mechanical properties can be mechanically enhanced via different chemical pathways in Nudibranchia.</span></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.