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Data from Yellow Sigatoka monitoring methods in the subtropical climate of southern Brazil
<h2>Description of the data and file structure</h2> <p>In this study four methods of disease monitoring were tested under field conditions: Biological Pre Warning (BPW); Stage of Evolution (SE); youngest Leaf Spotted (YLS); Infection Index (II). The BPW system evaluates the youngest leaves (2, 3, and 4), assigning a value for each type of lesion present, as well as for intensity of the lesion on the leaves (BUREAU et al., 1992). In the dataset is cited as the variable gross sum (points).</p> <p>The SE evaluates more leaves (1, 2, 3, 4, and 5) and scores only the most advanced symptoms of leaf disease, but without considering lesion intensity (GANRY et al., 2008). The SE calculation also corrects the gross sum of the disease according to leaf emission. The leaf emission rate was calculated using the Brun scale, which evaluates cigar leaf growth in decimals from 0.0 to 0.8. In the dataset is cited as the variable corrected gross sum (points).</p> <p>YLS is evaluated as the first leaf that has 10 spots with gray centers (CARLIER et al., 2003). In the dataset is cited as the variable YLS, which means the leaf position counted from the top to the botton of the plant (leaf number 3, leaf number 4...).</p> <p>Sigatoka Infection Index is quantified by assessing the severity of banana leaf disease using the Stover scale, with indexes from 0 to 50%, by means of the following formula: Infection Index =% (IF): [Σn × b / (N- 1) × T] × 100, in which: n = the number of leaves at each Stover scale level; b = degree according to the scale; N = the number of degrees employed in the scale (6); T = the total number of leaves evaluated (CARLIER et al., 2003). In the dataset is cited as the variable Infection index that should be understood like the severity of this leaf disease.</p> <p>In the second phase of the study, two monitoring methods were applied in commercial orchards in order to compare the standard model (Biological Pre-Warning – BPW) with the alternative method selected in the experimental phase (Youngest Leaf Spotted – YLS). The methods were applied, as described before in three sites in Criciúma (site 1) and Siderópolis (sites 2 e 3), municipalities in the southern coast of the state of Santa Catarina, from March 2016 to November 2018. During this period, 37 disease evaluations were performed at each location.</p> <p>Disease data of the experimental area were submitted to descriptive analysis and Pearson correlation at 5% probability of error. Disease progress curves were also plotted. The disease development data in commercial orchards were analyzed by plotting disease progress curves for BPW and by frequency distribution (%) for the YLS variable during all period of the experiment.</p>
Geochemistry of soils and eroded suspended sediments from two large rural catchments in southern Brazil for studies on Suspended Sediment Fingerprinting
<p> <strong>1. Introduction</strong></p> <p>This dataset comes from a research project entitled "Water and pollutants, from cropfields to cities: evaluation and improved of soil management technologies in a catchment network " supported by the Foundation for Research Support of the State of Rio Grande do Sul (FAPERGS) and National Council for Scientific and Technological Development (CNPq) (process n°10/0034-0). The project was carried out between 2010 and 2014 under the coordination of José Miguel Reichert and Danilo Rheinheimer dos Santos, professors at the Federal University of Santa Maria. One of the aims of this project was to understand the main pollutant transfer process from hillslopes to fluvial systems in large rural catchments representative of the agricultural production system in Southern Brazil. In this context, the Suspended Sediment Fingerprinting (SSF) was extremely useful for quantifying the origin of the sediment yield monitored at the outlet of these catchments. Among the various works carried out in this project, we highlight Tales Tiecher's doctoral thesis (Tiecher, 2015) that explored the SSF in many catchments, including the Conceição and Guaporé river basins.</p> <p><strong>2. Material and Methods</strong></p> <p>The catchments represent the magnitude of erosive and hydrological processes representative of Southern Brazil. The Conceição catchment has a drainage area of 804 km<sup>2</sup> (28°27′22″S and 53°58′24″ W). According to Köppen, the climate is Cfa type, with an annual rainfall between 1,750 and 2,000 mm. Geology is riodacithe basalt, with a formation of deep and highly weathered soils (Oxisols, Ultisols, and Alfisols). The relief is characterized by gentle slopes (6–9 %) on top and hillside slopes and higher steepness (10–14%) near the drainage channels. Farming based on the production of soybeans (<em>Glycine max</em>) in summer and wheat (<em>Triticumspp.</em>), oats (<em>Avena strigosa</em>), and ryegrass (<em>Lolium multiflorum</em>) in winter. The Guaporé catchment has a drainage area of 1,980 km<sup>2</sup> (28°54′41″S and 51°57′10″W), it covers part of the meridional plateau border. The climate is classified as Cfa, with annual rainfall varies between 1,400 and 2,000 mm. Geology is characterized by volcanic lava flows, and topography is undulating to hilly. Due to variations in landscape, several classes of soils (Entisols, Luvisol, Cambisol, Oxisol, Ultisol, and Chernosol). The land use is highly heterogeneous. In the upper third of the catchment, there is a predominance of soybean cultivated under no-tillage soil management. In the other two-thirds (middle and lower parts), land use and soil management are very heterogeneous. The main land uses are tobacco (<em>Nicotiana tabacum</em>) and maize (<em>Zea mays</em>) crops, Eucalyptus (<em>Eucalyptus</em> spp.), as well as pastures for dairy cattle. The contribution of unpaved roads is relevant to the sediment yield in both catchments (Didoné et al., 2014). Composite samples of potential sediment sources (cropland, unpaved roads, and stream channel banks) were collected. Sediment source samples were taken from the surface soil layer (0–0.05 m) of cropland and unpaved roads and on exposed sites located along the river channel network. Each sample was composed of at least 10 subsamples. To obtain representative samples of suspended sediment transported in the catchment’s outlet were used three strategies: (1) to collect flood suspended sediments (FSS) through the manual sampling (USDH-48) at different periods during the rising and falling stages of floods; (2) to deploy time-integrated suspended sediment samplers (TISS), by installing the device developed by Phillips et al. (2000) at different sites within the catchments; to collect fine-bed sediment (FBS) with a suction stainless sampler limiting the loss of fine material at the bed river. Source and sediment samples were oven‐dried at 50 °C, gently disaggregated using a pestle and mortar, and then sieved to 62,5 μm. The geochemical tracers evaluated were total organic carbon estimated by wet oxidation (K<sub>2</sub>Cr<sub>2</sub>O<sub>7</sub> + H<sub>2</sub>SO<sub>4</sub>) and the total concentration of Al, Ba, Be, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, V, and Zn using inductively coupled plasma optical emission spectrometry after microwave‐assisted digestion with concentrated HCl and HNO<sub>3</sub> (ratio 3:1) for 9.5 min at 182 °C (Tiecher, 2015; Tiecher et al. 2017, 2018).</p> <p> <strong>3. Final remarks</strong></p> <p> The SSF results provided by this dataset (Tiecher, 2015) combined with sediment yield monitoring were very important for the assessment and modeling studies in these two catchments that took place after that (Didoné et al., 2015; 2017). In addition, other studies have explored the same sample bank, expanding upon the array of tracer properties and increasing our understanding about the mechanisms of sediment and pollutant transfer in these catchments (Le Gall et al. 2017; Zafar et al., 2017; Ramon et al., 2020).</p> <p> <strong>4. References</strong></p> <p> Didoné, E. J., Minella, J. P. G., Reichert, J. M., Merten G. H., Dalbianco, L., Barros, C. A. P., Ramon, R. (2014) Impact of no-tillage agricultural systems on sediment yield in two large catchments in southern Brazil. J Soils Sediments 14:1287–1297.</p> <p>Didoné, E.J., Minella, J.P.G., Evrard, O. (2017). Measuring and modelling soil erosion and sediment yields in a large cultivated catchment under no-till of Southern Brazil. Soil Tillage Res. 174, 24-33. https://doi.org/10.1016/j.still.2017.05.011</p> <p>Didoné, E. J.; Minela, J. P. G.; Merten, G. H. (2015). Quantifying soil erosion and sediment yield in a catchment in southern Brazil and implications for land conservation. J. Soils Sediments 11, 2334-2346. https://doi.org/10.1007/s11368-015-1160-0</p> <p>le Gall, M., Evrard, O., Dapoigny, A., Tiecher, T., Zafar, M., Minella, J. P. G., Laceby, J. P., & Ayrault, S. (2017). Tracing sediment sources in a subtropical agricultural catchment of southern Brazil cultivated with conventional and conservation farming practices. Land Degradation and Development, 28(4). https://doi.org/10.1002/ldr.2662</p> <p>Ramon, R., Evrard, O., Laceby, J. P., Caner, L., Inda, A. v., Barros, C. A. P., Minella, J. P. G., & Tiecher, T. (2020). Combining spectroscopy and magnetism with geochemical tracers to improve the discrimination of sediment sources in a homogeneous subtropical catchment. Catena, 195, 104800. https://doi.org/10.1016/j.catena.2020.104800</p> <p>Tiecher, T. (2015). Fingerprinting sediment sources in agricultural catchments in Southern Brazil. Doctoral Dissertation in Soil Science. Universidade Federal de Santa Maria, Santa Maria, RS.</p> <p>Tiecher, T., Minella, J. P. G., Caner, L., Evrard, O., Zafar, M., Capoane, V., le Gall, M., & Santos, D. R. D. (2017). Quantifying land use contributions to suspended sediment in a large cultivated catchment of Southern Brazil (Guaporé River, Rio Grande do Sul). Agriculture, Ecosystems and Environment, 237. https://doi.org/10.1016/j.agee.2016.12.004</p> <p>Tiecher, T., Minella, J. P. G., Evrard, O., Caner, L., Merten, G. H., Capoane, V., Didoné, E. J., & dos Santos, D. R. (2018). Fingerprinting sediment sources in a large agricultural catchment under no-tillage in Southern Brazil (Conceição River). Land Degradation and Development, 29(4). https://doi.org/10.1002/ldr.2917.</p> <p>Zafar, M., Tiecher, T., Capoane, V., Troian, A., dos Santos, D.R. (2017). Characteristics, lability and distribution of phosphorus in suspended sediment from a subtropical catchment under diverse anthropic pressure in Southern Brazil. Ecol. Eng. 100, 28–45.</p>
Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).
<p>This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III.</p> <p>All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude.</p> <p><strong>ww3-2.inp</strong> and <strong>Bathymetry2.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. <strong>gfs-2.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. <strong>ww3-2.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain.</p> <p><strong>ww3-3.inp</strong> and <strong>Bathymetry3.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. <strong>gfs-3.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. <strong>ww3-3.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain.</p> <p>The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).</p> <p> </p> <p><strong>Other data that complement this dataset:</strong></p> <p><strong><a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375"><em>Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</em></a></strong></p> <p><em><strong><a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a></strong></em></p> <p> </p> <p> </p>
Hydrographic gridded data set for the South Brazil Bight and Southern Brazilian Shelf
<p>This dataset includes climatological and seasonal maps, spanning data from 1972 to 2024, across 8 different depth levels: 5, 10, 25, 50, 100, 200, 500, and 1000 dBar, with a spatial resolution of 10 km. The maps were generated using the griddata function with triangulation-based natural neighbor interpolation. The variables included in this dataset are conservative temperature (°C), absolute salinity (g kg⁻¹), neutral density (kg m⁻³), dissolved oxygen (mL L⁻¹), total alkalinity (µmol kg⁻¹), total dissolved inorganic carbon (µmol kg⁻¹), pH (total scale), partial pressure of carbon dioxide (µatm), nitrate (µmol kg⁻¹), and phosphate (µmol kg⁻¹). The name description of each variable is provided in the readme_DatasetATLAS.txt. The dataset can be directly accessed using Ocean Data View (ODV) software. </p> <p> </p>
Figure 4 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 4. Parastacus pilicarpus sp. nov. A, habitus dorsal view (holotype); B, cephalon dorsal view (holotype); C, cephalon lateral view (holotype); D, female pleon, dorsal view (paratype 1); E, male first to third pleonal pleura (holotype); F, female first to third pleonal pleura (paratype 1); G, telson and uropods dorsal view (holotype). Scale bars: A, D, F – 1 cm; E - 5 mm; C, G – 3.33 mm; B – 2.5 mm.
Figure 8 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 8. Distribution of Parastacus buckupi sp. nov. (star) and P. pilicarpus sp. nov. (triangle) in the states of Rio Grande do Sul and Santa Catarina, southern Brazil.
Figure 7 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 7. Comparative board of the chelipeds of selected species of genus Parastacus Huxley, 1879 with pilous cutting edge of fingers. A – P. buckupi sp. nov. (holotype); B – P. pilicarpus sp. nov. (holotype); C – P. fluviatilis Ribeiro & Buckup in Ribeiro et al. (2016) (UFRGS 2704); D – P. pilimanus (von Martens, 1869) (UFRGS 2413, CL 38.74). Scale bars: 1 cm.
Figure 6 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 6. Parastacus pilicarpus sp. nov., habitat and living specimens. A, B, Typical habitat, a first order stream in the municipality of Morro Grande, state of Santa Catarina; C, living specimen. Photographs by Caio R. M. Feltrin. No available information of scale in photograph C.
Figure 1 in New endemic species of freshwater crayfish Parastacus Huxley, 1879 (Crustacea: Decapoda: Parastacidae) from the Atlantic forest in southern Brazil
Figure 1. Parastacus buckupi sp. nov. A, habitus dorsal view (holotype); B, cephalon dorsal view (holotype); C, cephalon lateral view (holotype); D, female pleon (paratype 4); E, male first to third pleonal pleura (holotype); F, female first to third pleonal pleura (paratype 4); G, tailfan dorsal view (holotype). Scale bars: A – 1 cm; B–D, G – 5 mm; E, F – 3.33mm.
Fig. 3 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus
Fig. 3. Heterophoxus shoemakeri sp. nov., holotype, ♀ (UERJ 433). A. Gnathopod 1. B. Gnathopod 2. C. Pereopod 3. D. Pereopod 4. Scale bars = 0.2 mm.
Fig. 2 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus
Fig. 2. Heterophoxus shoemakeri sp. nov., holotype, ♀ (UERJ 433). A. Head. B. Antenna 1. C. Antenna 2. D. Left mandible. E. Right mandible. F. Maxilliped. G. Maxilla 1. H. Maxilla 2. Scale bars: A = 0.5 mm; B–F = 0.2 mm; G–H = 0.1 mm.
Fig. 1 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus
Fig. 1. Heterophoxus shoemakeri sp. nov., habitus. A. Holotype, ♀ (UERJ 433). B. Paratype, ♂ (UERJ 434). Scale bars = 1.0 mm.
Fig. 2 in Movement and longitudinal distribution of a migratory fish (Salminus brasiliensis) in a small reservoir in southern Brazil
Fig. 2. Total time spent by dourados (Salminus brasiliensis) in the reservoir (Tin), total time spent in an unknown location (Tun) and total time spent outside of the reservoir in a stretch of the upstream Erechim river (Tout). ANOVA with permutation tested for differences among groups (F(2,54) = 87.17; p <0.0002). The numbers above the plots represent the number of individuals. Different letters above the plots represent significant differences according to Tukey's test. Circles represent outlier values; the heavy horizontal line crossing the box is the median; the bottom and top of the box are the lower and upper quartiles, respectively; and the whiskers are the minimum and maximum values.
Fig. 1 in Age and growth of Zapteryx brevirostris (Elasmobranchii: Rhinobatidae) in southern Brazil
Fig. 1. Distribution of length frequencies (1cm TL size classes) of the sample of Zapteryx brevirostris collected in the southern coast of Brazil and used in this study. M refers to males and F to females.
Figure 8 in Two new Geoplaninae species (Platyhelminthes: Continenticola) from Southern Brazil based on an integrative taxonomic approach
Figure 8. Obama maculipunctata sp. nov. in dorsal view. (A) Photograph of a live specimen (holotype MZUSP PL.1565). (B) Dorsal pattern of pigmentation of the holotype. (C) Eye pattern of a fixed specimen (paratype MZU PL.00197). (D) Photograph of monolobated eyes of a fixed specimen (paratype MZU PL.00197) in clove oil. (E) Photograph of trilobated eyes of a fixed specimen (paratype MZU PL.00197) in clove oil.
Figure 2 in Two new Geoplaninae species (Platyhelminthes: Continenticola) from Southern Brazil based on an integrative taxonomic approach
Figure 2. Cratera ochra sp. nov., holotype, in dorsal view. (A) Photograph of the live specimen. (B) Dorsal pattern of pigmentation of the preserved specimen.
Figure 1. Bayesian phylogenetic tree inferred from the 640 in Two new Geoplaninae species (Platyhelminthes: Continenticola) from Southern Brazil based on an integrative taxonomic approach
Figure 1. Bayesian phylogenetic tree inferred from the 640-bp of cytochrome c oxidase subunit I gene under GTR + I + G model of sequence evolution. The two new species are highlighted in light grey (Cratera ochra sp. nov.) and dark grey (Obama maculipunctata sp. nov.). Values indicate support for each node according to the maximum posterior probabilities>70% and bootstrap support values> 70%, respectively.
Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile–Peru wintering grounds and the South Georgia (Islas Georgias del Sur) feeding ground
<p></p><p>As species recover from exploitation, continued assessments of connectivity and population structure are warranted to provide information for conservation and management. This is particularly true in species with high dispersal capacity, such as migratory whales, where patterns of connectivity could change rapidly. Here we build on a previous long-term, large-scale collaboration on southern right whales (Eubalaena australis) to combine new (nnew) and published (npub) mitochondrial (mtDNA) and microsatellite genetic data from all major wintering grounds and, uniquely, the South Georgia (Islas Georgias del Sur: SG) feeding grounds. Specifically, we include data from Argentina (npub mtDNA/microsatellite = 208/46), Brazil (nnew mtDNA/microsatellite = 50/50), South Africa (nnew mtDNA/microsatellite = 66/77, npub mtDNA/microsatellite = 350/47), Chile–Peru (nnew mtDNA/microsatellite = 1/1), the Indo-Pacific (npub mtDNA/microsatellite = 769/126), and SG (npub mtDNA/microsatellite = 8/0, nnew mtDNA/microsatellite = 3/11) to investigate the position of previously unstudied habitats in the migratory network: Brazil, SG, and Chile–Peru. These new genetic data show connectivity between Brazil and Argentina, exemplified by weak genetic differentiation and the movement of 1 genetically identified individual between the South American grounds. The single sample from Chile–Peru had an mtDNA haplotype previously only observed in the Indo-Pacific and had a nuclear genotype that appeared admixed between the Indo-Pacific and South Atlantic, based on genetic clustering and assignment algorithms. The SG samples were clearly South Atlantic and were more similar to the South American than the South African wintering grounds. This study highlights how international collaborations are critical to provide context for emerging or recovering regions, like the SG feeding ground, as well as those that remain critically endangered, such as Chile–Peru.</p><p></p>
Fig. 5 in A new Silver Dollar species of Metynnis Cope, 1878 (Characiformes: Serrasalmidae) from Northwestern Brazil and Southern Venezuela
Fig. 5: Osteology of Metynnis melanogrammus, INPA 18457, 144.8 mm SL: a. hyopalatine and opercular series; b, infraorbitals; c. hyoid arch; d. left pectoral girdle, lateral view; e. left pectoral girdle, medial view. ach, anterior ceratohyal; apal, autopalatine; ant, antorbital; br, branchiostegal rays; cle, cleithrum; cor, coracoid; dh, dorsal hypohyal; ect, ectopterygoid; end, endopterigoid; exs, extrascapular; hyo, hyomandibular; io1-5, infraorbital 1-5; iop, interopercular; mes, mesocoracoid; met, metapterygoid; op, opercle; pch, posterior ceratohyal; pcle1-3, postcleithrum 1-3; pecR, pectoral-fin rays; pop, preopercle; ptem, posttemporal; qua, quadrate; sca, scapula; scle, supracleithrum; sop, subopercle; sym, symplectic; uh, urohyal; vh, ventral hypohyal. Scale bars: 10 mm.
Fig. 8 in A new Silver Dollar species of Metynnis Cope, 1878 (Characiformes: Serrasalmidae) from Northwestern Brazil and Southern Venezuela
Fig. 8. Map of northern South America, including northern Brazil and southern Venezuela, showing distribution of Metynnis melanogrammus. The red star represents the type locality, red circles represent the paratype localities, and the black circle represents the locality on the rio Surunduri at which the live photograph of the new species (Fig. 7) was taken.
ScienceDex guides
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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.