Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
100
datasets available to search
ShareScore release 0.9.0
Dataset results
100 results for “soil fungi”
Global patterns in endemicity and vulnerability of soil fungi
<p>This repository contains the data associated with the paper Tedersoo et al. (2022) <em>Global patterns in endemicity and vulnerability of soil fungi</em> // <strong>Global Change Biology</strong>. DOI:10.1111/gcb.16398</p> <p>Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general.</p> <p>This repository contains the following data associated with the publication:</p> <ul> <li>Supplementary tables S1 - S6 (`<strong>Tables_S1-S6.xlsx</strong>`):</li> </ul> <p>- Table S1. Definition of ecoregions and assignment of samples to ecoregions<br> - Table S2. GSMc dataset used for endemicity analyses<br> - Table S3. Dataset used for modeling endemicity values<br> - Table S4. Dataset used for calculating and mapping vulnerability scores<br> - Table S5. Dataset used for calculating and mapping conservation value<br> - Table S6. Additional funding sources by authors</p> <ul> <li>OTU distribution by samples and ecoregions (`<strong>Data_taxon_assignment_to ecoregions.xlsx</strong>`)</li> </ul> <p>Gridded maps:</p> <ul> <li>Conservation priorities for all fungi and fungal groups</li> </ul> <p>- ConservationPriority_AllFungi.tif<br> - ConservationPriority_AM.tif<br> - ConservationPriority_EcM.tif<br> - ConservationPriority_Moulds.tif<br> - ConservationPriority_NonEcMAgaricomycetes.tif<br> - ConservationPriority_OHPs.tif<br> - ConservationPriority_Pathogens.tif<br> - ConservationPriority_Unicellular.tif<br> - ConservationPriority_Yeasts.tif</p> <ul> <li>The average vulnerability of all fungi and fungal groups and the model uncertainty estimates</li> </ul> <p>- AverageVulnerability_AllFungi.tif<br> - AverageVulnerability_AM.tif<br> - AverageVulnerability_EcM.tif<br> - AverageVulnerability_Moulds.tif<br> - AverageVulnerability_NonEcMAgaricomycetes.tif<br> - AverageVulnerability_OHPs.tif<br> - AverageVulnerability_Pathogens.tif<br> - AverageVulnerabilityUncertainty_AllFungi.tif<br> - AverageVulnerabilityUncertainty_AM.tif<br> - AverageVulnerabilityUncertainty_EcM.tif<br> - AverageVulnerabilityUncertainty_Moulds.tif<br> - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif<br> - AverageVulnerabilityUncertainty_OHPs.tif<br> - AverageVulnerabilityUncertainty_Pathogens.tif<br> - AverageVulnerabilityUncertainty_Unicellular.tif<br> - AverageVulnerabilityUncertainty_Yeasts.tif<br> - AverageVulnerability_Unicellular.tif<br> - AverageVulnerability_Yeasts.tif</p> <ul> <li>The relative importance of predicted vulnerability of all fungi</li> </ul> <p>- RelativeImportanceOfVulnerability_AllFungi.tif</p> <ul> <li>Vulnerability to drought, heat, and land cover change for all fungi</li> </ul> <p>- Vulnerability_AllFungi_Heat-Drought-LandCoverChange.tif<br> - VulnerabilityUncertainty_AllFungi_Heat-Drought-LandCoverChange.tif</p> <ul> <li> Human footprint index based on the Land-Use Harmonisation (LUH2; Hurtt et al., 2020, doi:10.5194/gmd-13-5425-2020) - `<strong>LandCoverChange_1960-2015.tif</strong>`</li> <li> MD5 checksums for all files (`<strong>MD5.md5</strong>`)</li> </ul> <p>Fungal groups:<br> - <strong>AM</strong>, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes)<br> - <strong>EcM</strong>, ectomycorrhizal fungi (excluding dubious lineages)<br> - <strong>NonEcMAgaricomycetes</strong>, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies)<br> - <strong>Moulds</strong> (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales)<br> - Putative <strong>pathogens</strong> (including plant, animal and fungal pathogens as primary or secondary lifestyles)<br> - <strong>OHPs</strong>, opportunistic human parasites (excluding Mortierellales)<br> - <strong>Yeasts</strong> (excluding dimorphic yeasts)<br> - <strong>Unicellular</strong>, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages)</p> <p>Detailed processing steps can be found here:<br> <a href="https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability">https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability</a></p>
Soil extracellular enzyme activities in plots dominated by trees that associate with arbuscular mycorrhizal or ectomycorrhizal fungi in the N fertilized and reference watershed at the Fernow Experimental Forest, WV.
Our objective was to detect possible differences in N fertilization responses of soil extracellular enzymes in plots dominated by trees that associate with arbuscular mycorrhizal fungi (AM) or ectomycorrhizal fungi (ECM). To do this, we established a plot network of 6 AM and 6 ECM dominated 10 x 10 m plots in both the reference and N fertilized watersheds (N=24 plots) at the Fernow Experimental Forest, Parsons, WV. We assayed the potential activity of hydrolytic enzymes that release N (N-acetylglucosaminidase; NAG), phosphorus (acid phosphatase; AP), and simple carbon (ß-glucosidase; BG). In addition, we measured microbial allocation to complex C degrading oxidative enzymes phenol oxidase and peroxidase. The activities of these enzymes were measured separately in bulk mineral, rhizosphere, and organic horizon soils during the growing season in 2017.
Climate Change Across Seasons Experiment (CCASE) at the Hubbard Brook Experimental Forest: growth and enzyme activity traits of soil fungi isolated from CCASE in July 2017, grown under a common garden experiment in the laboratory that mimicked CCASE soil temperature treatments
Projections for the northeastern U.S. indicate that mean air temperatures will rise and snowfall will become less frequent, causing more frequent soil freezing. To test fungal responses to these combined chronic and extreme soil temperature changes, we conducted a laboratory-based common garden experiment with soil fungi that had been subjected to different combinations of growing season soil warming, winter soil freeze/thaw cycles, and ambient conditions for four years in the field. We found that fungi originating from field plots experiencing a combination of growing season warming and winter freeze/thaw cycles had inherently lower activity of acid phosphatase, but higher cellulase activity, that could not be reversed in the lab. In addition, fungi quickly adjusted their physiology to freeze/thaw cycles in the laboratory, reducing growth rate and potentially reducing their carbon use efficiency. Our findings suggest that less than four years of new soil temperature conditions in the field can lead to physiological shifts by some soil fungi, as well as irreversible loss or acquisition of extracellular enzyme activity traits by other fungi. These findings could explain field observations of shifting soil carbon and nutrient cycling under simulated climate change. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Effects of Altered Precipitation on Biological Soil Crusts, Fungi, and Soil Nitrogen Availability at the Monsoon Rainfall Manipulation Experiment (MRME) in the Sevilleta National Wildlife Refuge, New Mexico (2016)
Microbial activity in drylands is mediated by the magnitude and frequency of growing season rain events that will shift as climate change progresses. Nitrogen is often co-limiting with water availability to dryland plants, and thus we investigated how microbes important to the nitrogen (N) cycle and soil N availability varied temporally and spatially in the context of a long-term rainfall variability experiment in the northern Chihuahuan Desert. Specifically, we assessed biological soil crust (biocrust) chlorophyll content, fungal abundance, and inorganic N in soils adjacent to individuals of the grassland foundation species, Bouteloua eriopoda, and in the unvegetated interspace at multiple time points associated with an experimental monsoon rain treatment. Treatments included small weekly (5 mm) or large monthly (20 mm) rain events, which had been applied during the summer monsoon for nine years prior to our sampling. Additionally, we evaluated target plant C:N ratios and added 15 N-glutamate to biocrusts to determine potential for nutrient transport to B. eriopoda. Biocrust chlorophyll was up to 67% higher in the small weekly or large monthly rainfall regimes compared to ambient controls. Fungal biomass was 57% lower in soil interspaces than adjacent to plants but did not respond to rainfall regime treatments. Ammonium and nitrate concentrations near plants declined through the sampling period but varied little in soil interspaces. There was limited movement of 15 N from interspace biocrusts to leaves but high 15 N retention in the soils even after additional ambient and experimental rain events. Plant C:N ratio was unaffected by rainfall treatments. The long-term alteration in rainfall regime in this experiment did not change how short-term microbial abundance or N availability responded to the magnitude or frequency of events, suggesting a limited response of N availability to future climate change.
Data from: Ectomycorrhizal fungi are more sensitive to high soil nitrogen levels in forests exposed to nitrogen deposition
<p>Ectomycorrhizal fungi are essential for nitrogen cycling in many temperate forests and responsive to anthropogenic nitrogen addition, which generally, decreases host carbon allocation to the fungi. In the boreal region, however, ectomycorrhizal fungal biomass has been found to correlate positively with soil nitrogen availability. Still, responses to anthropogenic input, for instance through atmospheric deposition, are commonly negative.</p> <p>To elucidate whether variation in nitrogen supply affects ectomycorrhizal fungi differently depending on geographical context, we investigated ectomycorrhizal fungal communities along two fertility gradients across nemo-boreal forests with similar ranges in soil N/C ratios and inorganic nitrogen availability but located in regions with contrasting rates of nitrogen deposition.</p> <p>Ectomycorrhizal biomass and community composition remained relatively stable across the nitrogen-gradient with low atmospheric nitrogen deposition, but biomass decreased, and the community changed more drastically, with increasing nitrogen availability in the gradient subjected to higher rates of nitrogen deposition. Moreover, potential activities of enzymes involved in ectomycorrhizal mobilisation of organic nitrogen decreased as N/C ratios increased.</p> <p>In forests with low external input, we propose that stabilising feedbacks in tree-fungal interactions maintain ectomycorrhizal fungal biomass and communities even in highly fertile soils. In contrast, anthropogenic nitrogen input seems to impair ectomycorrhizal functions.</p>
Figure 1 in Amazonian soil fungi are efficient degraders of glyphosate herbicide; novel isolates of Penicillium, Aspergillus, and Trichoderma
Figure 1. Mass spectrum resulting from the HPLC-MS of the isolated Penicillium 4A21 filtered. The filtrate presents possible peaks of glyphosate (170.07), AMPA (112.13) and sarcosine (89).
APPENDIX 3. — Maximum likelihood phylogram inferred from 47 taxa and 3314 in Mucoralean fungi in Thailand: novel species of Absidia from tropical forest soil
APPENDIX 3. — Maximum likelihood phylogram inferred from 47 taxa and 3314 characters based on LSU, SSU and ACT-1 matrix using GTR+G model. ML bootstrap support (≥ 70%) are indicated above the branches or near the nodes. Tree is artificially rooted using Cunninghamella homothallica (CBS 168.53), C. phaeospora (CBS 692.68), and C. bainieri (FSU319). The new species are in black bold and the type species in the dataset are indicated using T. (-) represent bootstrap support lower than 70%. (*) indicates unrecovered branching.
FIG. 7 in Mucoralean fungi in Thailand: novel species of Absidia from tropical forest soil
FIG. 7. — Mycelial growth of A. edaphica V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov.(MFLUCC 20-0088, ex-type) and A. soli V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov. (MFLUCC 20-0086, ex-type) in various media at room temperature (around 26°C to 27°C) after two days: A-D, colonies on MEA; E-H, colonies on PDA; I-L, colonies on CMA; M-P, colonies on YMA. The first two rows represent colonies of of A. edaphica sp. nov. and the bottom two rows A. soli sp. nov (obverse (first and third rows) and reverse (second and fourth rows).
FIG. 6 in Mucoralean fungi in Thailand: novel species of Absidia from tropical forest soil
FIG. 6. — Mycelial growth of Absidia edaphica V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov. and Absidia soli V.GHurdeal., E.Gentekaki., H.B.Lee & K.D.Hyde, sp. nov. in different media at 25°C.
Figure 3 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn
Figure 3. Dynamics of the soil arbuscular mycorrhizal fungal spore density within Desmodium triflorum coverage levels and seasons.
Figure 6 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn
Figure 6. Conceptual framework demonstrating possible mechanisms of soil arbuscular mycorrhizal fungi (AMF) during the spreading process of Desmodium triflorum in the Zoysia tenuifolia lawn. Numbers 1, 2, 3, and 4 indicate different spreading stages of the invasive plant D. triflorum. Corresponding mycorrhizal structures were shown as the four microscopic views. Light-green and medium-yellow circles indicate AM fungal spores predominantly produced by the root mycorrhizal structures of Z. tenuifolia and D. triflorum, respectively. Medium-green and dark-yellow lines indicate the life cycle of spores in Z. tenuifolia plants and in D. triflorum plants, respectively. The AM fungi might influence the spread of D. triflorum by the following steps: (1) the early stage of the lawn's development with only Z. tenuifolia growing but without D. triflorum present. This occurs at the very beginning of the lawn establishment, and the AM fungal spores that previously existed in the lawn soil first infected the fine roots of Z. tenuifolia and completed the life cycle on their own. (2) The early spreading stage of D. triflorum (level 1). The roots of the two plants come into contact with each other, inducing the external hyphae that originally grow closely on the Z. tenuifolia roots to infect the roots of D. triflorum. The difference between the mycorrhizal infections of the two host plants contributes to higher root mycorrhizal colonizations of D. triflorum compared with Z.tenuifolia. However, at this stage,D. triflorum is not as competitive as Z. tenuifolia in the lawn, although it has advantages in terms of mycorrhizal infections. Therefore, the soil AM fungal spores are still predominantly produced by the mycorrhizal structures of the AMF-infected Z. tenuifolia roots. (3) The intermediate spreading stage of D. triflorum (levels 2 and 3). Desmodium triflorum continues to spread in the lawn. The contact of the two plants becomes more frequent and further induces a much closer relationship between the AM infections of the two plants. The increased D. triflorum plants in the lawn and the advantage of D. triflorum in root mycorrhizal infections facilitate the contribution of the mycorrhizal structures of the D. triflorum roots to sporulation. Thus, in this stage, the soil AM fungal spores were produced by the mycorrhizal structures of both plants, thereby inducing insignificant correlations between the spore densities and the root colonizations of either Z. tenuifolia or D. triflorum. (4) The late spreading stage of D. triflorum (levels 4 and 5). Desmodium triflorum is dominant in the lawn.The large numbers of D. triflorum plants and the AM infection advantage of D. triflorum facilitate AMF sporulation in the soil, thereby inducing significant correlations between the spore densities and the root colonizations of D. triflorum. At the different spreading stages of D. triflorum, the soil AM fungal communities also change as a result of the changed contributions of the AMF-infected host plants to the sporulation.
Figure 5 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn
Figure 5. The relative abundance and community composition at the family (A) and species levels (B) of arbuscular mycorrhizal fungi (AMF) in soils of different Desmodium triflorum coverage levels.
Figure 2 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn
Figure 2. Dynamics of the total, hyphal, and vesicular colonizations of Zoysia tenuifolia and Desmodium triflorum among different D. triflorum coverage levels and seasons. "Season," "Coverage," and "Species" indicate ANOVA results of each indicator among seasons and D. triflorum coverage levels and between the two plants, respectively.
Figure 4 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn
Figure 4. Correlations among the root mycorrhizal colonizations, arbuscular mycorrhizal fungal spore densities ("AMF spore density"), and soil properties in different coverage levels of Desmodium triflorum. ZTC, ZHC, and ZVC in light-green circles indicate the total colonization (TC), hyphal colonization (HC), and vesicular colonization (VC) of Zoysia tenuifolia, respectively. DTC, DHC, and DVC in light-red circles indicate the TC, HC, and VC of D. triflorum, respectively. Green lines and green-colored numbers indicate significant correlations between the colonization indicators of Z. tenuifolia and corresponding correlation coefficients, respectively. Red lines and red-colored numbers indicate significant correlations between the colonization indicators of Z. tenuifolia and corresponding correlation coefficients, respectively. Dark-green double arrows and dark-green numbers indicate the correlations between the colonizations of Z. tenuifolia and those of D. triflorum and corresponding correlation coefficients, respectively. Light-blue double arrows and light-blue numbers indicate the correlations between the spore densities and soil properties/root colonizations and corresponding correlation coefficients,respectively. Darkyellow double arrows and dark-yellow numbers indicate the correlations between the soil properties and root colonizations and corresponding correlation coefficients, respectively. Correlation is significant at: *P <0.05; **P <0.01; ***P <0.001. The minus sign indicates a negative correlation. Insignificant correlations are not shown.
Figure 1 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn
Figure 1. Dynamics of the soil physiochemical properties (average ± SE, n = 5) within different Desmodium triflorum coverage levels and seasons. "Season" and "Coverage" indicate ANOVA results of each indicator among seasons and D. triflorum coverage levels, respectively. Level 1, level 2, level 3, level 4, and level 5 indicate the coverage levels of D. triflorum in the Zoysia tenuifolia lawn, respectively, in this and all following figures.
Figure 1 in Diversity of entomopathogenic fungi from soils of eucalyptus and soybean crops and natural forest areas
Figure 1. Entomopathogenic fungi of the genera Aspergillus (A), Beauveria (B), Cordyceps (C), Fusarium (D), Metarhizium (E), Penicillium (F) and Purpureocillium (G) cultivated in Petri dishes with PDA medium.
Figure 4 in New insights on the impact of earthworm extract on the growth of beneficial soil fungi: species-specific alteration of the nematophagous fungal growth and limitation of an entomopathogenic fungus
Figure 4. Growth of Purpureocillium lilacinum after 20 days postexposer to two different earthworm based media: fresh earthworms (FE) (four concentration C1, C2, C3, and C4), and earthworms devoid of gut contents (EDG) (four concentration C1, C2, C3, and C4), C1 = 40 g/L, C2 = 20 g/L, C3 = 10 g/L, C4 = 5 g/L, and two rich media: potato dextrose agar (PDA), and brain heart infusion (BHI).
Figure 5 in New insights on the impact of earthworm extract on the growth of beneficial soil fungi: species-specific alteration of the nematophagous fungal growth and limitation of an entomopathogenic fungus
Figure 5. Evaluation of conidial germination of the fungus Beauveria bassiana exposed to two different earthworm extracts: fresh earthworms (FE) and earthworms without gut contents, EDG, and two conventional media: potato dextrose agar (PDA), and brain heart infusion agar (BHI). A. Percentage germination on conventional and earthworm-based media. B. Percent germination as a function of concentration and earthworm-based medium. Concentrations are equivalent to C1 = 40 g/L, C2 = 20 g/L, C3 = 10 g/L, and C4 = 5 g/L. Results of one-way ANOVA (A) or two-way ANOVA (B), and differences are significant according to Tukey's test (HSD) and groups "a", "b" and "c".
Figure 3 in New insights on the impact of earthworm extract on the growth of beneficial soil fungi: species-specific alteration of the nematophagous fungal growth and limitation of an entomopathogenic fungus
Figure 3. Evaluation of vegetative growth, conidial production and germination in the fungus Purpureocillium lilacinum exposed to two earthworm extracts: fresh earthworm (FE), earthworms devoid of intestinal contents (EDG) and two conventional media: potato dextrose agar (PDA), and brain heart infusion agar (BHI). A. Cumulative growth from 3 to 18 days according to conventional and earthworm-based media. B. Cumulative growth as a function of concentration and earthworm-based medium. C. Conidia production (×10⁵ conidia/mL) according to conventional and earthworm-based media. D. Conidia production (×10⁵ conidia/mL) according to concentration and earthworm-based medium. E. Percent germination on conventional and earthworm-based media. F. Percent germination as a function of concentration and earthworm-based medium. Concentrations are equivalent to C1 = 40 g/L, C2 = 20 g/L, C3 = 10 g/L, and C4 = 5 g/L. Results of one-way ANOVA (A, C, E) or two-way ANOVA (B, D, F), and differences are significant at Tukey's test (HSD) and groups "a", "b" and "c".
Figure 1 in New insights on the impact of earthworm extract on the growth of beneficial soil fungi: species-specific alteration of the nematophagous fungal growth and limitation of an entomopathogenic fungus
Figure 1. Evaluation of vegetative growth, conidial production and germination in the fungus Arthrobotris musiformis exposed to two earthworms' extracts: fresh earthworm (FE), earthworms devoid of intestinal contents (EDG) and two conventional media: potato dextrose agar (PDA), and brain heart infusion agar (BHI). A. Cumulative growth from 3 to 18 days according to conventional and earthworm-based media. B. Cumulative growth as a function of concentration and earthworm-based medium. C. Conidia production (×10⁵ conidia/mL) according to conventional and earthworm-based media. D. Conidia production (×10⁵ conidia/mL) according to concentration and earthworm-based medium. E. Percent germination on conventional and earthworm-based media. F. Percent germination as a function of concentration and earthworm-based medium. Concentrations are equivalent to C1 = 40 g/L, C2 = 20 g/L, C3 = 10 g/L, and C4 = 5 g/L. Results of one-way ANOVA (A, C, E) or two-way ANOVA (B, D, F), and differences are significant at Tukey's test (HSD) and groups "a", "b" and "c".
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.