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1,552 results for “patches”
Data from: Mean landscape-scale incidence of species in discrete habitats is patch size dependent
<p>Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'.</p> <p>Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants). </p> <p>The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups. </p>
Identifying the proximal cue(s) for pupal color variation in the bordered patch butterfly, Chlosyne lacinia (Geyer 1837; Lepidoptera: Nymphalidae)
<p>Color is a multifaceted trait with many functions such as predator defense, thermoregulation, and immune response. We investigated pupal color variation in <em>Chlosyne lacinia</em> pupae, focusing on identifying the cue for increased melanization. Through laboratory experiments, we demonstrated pupae reared on black backgrounds exhibited significantly higher melanization compared to those on white backgrounds. Additionally, black pupae experienced longer developmental periods, suggesting a trade-off between defense and developmental time. Our findings support crypsis as a likely evolutionary driver for increased melanization in response to substrate color. We discuss potential implications for predator avoidance, immune response, and developmental costs associated with melanization. This study provides insights into the adaptive significance of pupal melanization in response to environmental cues, shedding light on the complex interplay between life history traits in butterflies.</p>
BETA-FOR_SPZ_Patches_2022/2023
<p>This dataset contains location information for all 234 BETA-FOR patches.</p>
Polar Cap Patches Detected with RISR-N
<h3>Database of Polar Cap Patches detected with the North face of the Resolute Bay Incoherent Scatter Radar (RISR-N)</h3> <p>This dataset was generated by applying two polar cap patch detection algorithm described to the full RISR-N database.</p> <p>The Ren2018 algorithm was first described in <a href="http://https//doi.org/10.1029/2018JA025621">Ren et al., 2018 (doi:10.1029/2018JA025621)</a>. It includes the time, peak, and prominence of all detected patches, asd well as the average electron and ion temperature and electron density within each patch.</p> <p>The Perry2018 algorithm was first described in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018RS006600">Perry & St. Maurice, 2018 (doi:10.1029/2018RS006600)</a>. Instead of a list of patches, this algorithm generates a patch index for each time in the database which corresponds to the percentage of the F-region FoV filled with patches.</p> <p>The script that generates this database in available on GitHub: <a href="https://github.com/Polar-Cap-Scintillation/polar_cap_patch_detection">Polar-Cap-Scintillation/polar_cap_patch_detection</a></p> <p>Version 4 of this dataset includes RISR-N data through 2024 and has an error corrected that may cause some patches to be counted multiple times in the earlier versions.</p>
Contrasting parasite-mediated reductions in fitness within vs. between patches of a nematode host
<p>Host and parasites interact across spatial scales, but parasite-mediated fitness effects are often measured at local scales only. Recent work suggests that parasites can reduce host fitness during dispersal between patches, highlighting the potential for both within- and between-patch effects to contribute to the net fitness consequences of parasitism. Building on this work, we measured the contribution of the dispersal phase to parasite-mediated reductions in host fitness. We used the nematode <em>Caenorhabditis elegans</em> and its natural microsporidian parasite <em>Nematocida parisii</em> to quantify the fitness consequences of parasitism at the individual, population, and metapopulation level. <em>N. parisii</em> reduced individual fecundity and population growth but had its greatest fitness impact at the dispersal stage: parasitism reduced the fitness of dispersing larvae by 63 – 100%. These results indicate that the cost of parasitism in this system is greatly underestimated if the metapopulation level is not taken into account. We also found that the effects of <em>N. parisii</em> vary with host genotype, and the relative advantage of the most resistant genotype increases with inclusion of the dispersal stage. Taken together, our findings demonstrate that host-parasite interactions at the dispersal stage magnify selection for parasite resistance.</p>
Fig.ç3.D isasterina akajimaensis sp. nov., holotype (NSMT E-6758). A, Anal pore and a patch; B, madreporite; C, proximal part of arm, abactinal view; D, oral plate pair and interradial uncalci ed area, some oral spines have been lost (see also Fig. 6 for oral plate pair and interradial uncalci ed area at another interradius); E, proximal part of ambulacral furrow (oral plates seen at the lower-le corner are drawn in Fig. 6); F, inferomarginal spinelets, abactinal view. Abbreviations: als, actinolateral spine; apo, anal pore; fs, furrow spine; imp, inferomarginal plate; ims, inferomarginal spine; ir, interradial; iua, interradial uncalci ed area; md, madreporite; op, oral plate; os, oral spine; r, radial; rp, rigid patch; sas, subambulacral spine. in A New Asterinid Sea Star, Disasterina akajimaensis (Echinodermata: Asteroidea) from the Ryukyu Islands, Japan, with Notes on the Genus Disasterina
Fig.ç3.D isasterina akajimaensis sp. nov., holotype (NSMT E-6758). A, Anal pore and a patch; B, madreporite; C, proximal part of arm, abactinal view; D, oral plate pair and interradial uncalci ed area, some oral spines have been lost (see also Fig. 6 for oral plate pair and interradial uncalci ed area at another interradius); E, proximal part of ambulacral furrow (oral plates seen at the lower-le corner are drawn in Fig. 6); F, inferomarginal spinelets, abactinal view. Abbreviations: als, actinolateral spine; apo, anal pore; fs, furrow spine; imp, inferomarginal plate; ims, inferomarginal spine; ir, interradial; iua, interradial uncalci ed area; md, madreporite; op, oral plate; os, oral spine; r, radial; rp, rigid patch; sas, subambulacral spine.
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
<p>Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally demanding and requires careful hyperparameter tuning. To overcome these issues, we propose ImageNet-Patch, a dataset to benchmark machine-learning models against adversarial patches. It consists of a set of patches optimized to generalize across different models and applied to ImageNet data after preprocessing them with affine transformations. This process enables an approximate yet faster robustness evaluation, leveraging the transferability of adversarial perturbations.</p> <p>We release our dataset as a set of folders indicating the patch target label (e.g., `banana`), each containing 1000 subfolders as the ImageNet output classes.</p> <p>An example showing how to use the dataset is shown below.</p> <pre><code class="language-python"># code for testing robustness of a model import os.path from torchvision import datasets, transforms, models import torch.utils.data class ImageFolderWithEmptyDirs(datasets.ImageFolder): """ This is required for handling empty folders from the ImageFolder Class. """ def find_classes(self, directory): classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir()) if not classes: raise FileNotFoundError(f"Couldn't find any class folder in {directory}.") class_to_idx = {cls_name: i for i, cls_name in enumerate(classes) if len(os.listdir(os.path.join(directory, cls_name))) > 0} return classes, class_to_idx # extract and unzip the dataset, then write top folder here dataset_folder = 'data/ImageNet-Patch' available_labels = { 487: 'cellular telephone', 513: 'cornet', 546: 'electric guitar', 585: 'hair spray', 804: 'soap dispenser', 806: 'sock', 878: 'typewriter keyboard', 923: 'plate', 954: 'banana', 968: 'cup' } # select folder with specific target target_label = 954 dataset_folder = os.path.join(dataset_folder, str(target_label)) normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) transforms = transforms.Compose([ transforms.ToTensor(), normalizer ]) dataset = ImageFolderWithEmptyDirs(dataset_folder, transform=transforms) model = models.resnet50(pretrained=True) loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=5) model.eval() batches = 10 correct, attack_success, total = 0, 0, 0 for batch_idx, (images, labels) in enumerate(loader): if batch_idx == batches: break pred = model(images).argmax(dim=1) correct += (pred == labels).sum() attack_success += sum(pred == target_label) total += pred.shape[0] accuracy = correct / total attack_sr = attack_success / total print("Robust Accuracy: ", accuracy) print("Attack Success: ", attack_sr) </code></pre> <p> </p>
Patch quality and genotype-by-environment interactions shape dispersal and post-settlement survival in a butterfly metapopulation
Active dispersal is driven by extrinsic and intrinsic factors at the three stages of departure, transfer, and settlement. Most empirical studies capture only one stage of this complex process, and knowledge of how much can be generalized from one stage to another remains unknown. Here we use genetic assignment tests to reconstruct dispersal across five years and 232 patches of a butterfly metapopulation. We link individual dispersal events to weather, landscape structure, size and quality of patches, and individual genotype to identify the factors that influence the three stages of dispersal and post-settlement survival. We found that nearly all tested factors strongly affected departure probabilities, but that the same factors explained very little variation in realized dispersal distances. Surprisingly, we found no effect of dispersal distance on post-settlement survival. Rather, survival was influenced by weather conditions, carry-over effects of natal patch quality, and a strong interaction between genotype and occupancy status of the settled patch, with more mobile genotypes having higher survival as colonists rather than as immigrants. Our work highlights the multicausality of dispersal and that some dispersal costs can only be understood by considering extrinsic and intrinsic factors and their interaction across the entire dispersal process.
Dataset for tumor infiltrating lymphocyte classification (304,097 image patches from TCGA)
<p>This is a dataset of images with or without tumor-infiltrating lymphocytes (TILs). The original images are from Abousamra et al. (2022) and Saltz et al. (2018), and the original whole slide images are from TCGA. This dataset is a subset of the data presented in Abousamra et al. (2022) (with new data partitions).</p> <p>If you use this dataset, please cite the following papers, as well as this Zenodo page.</p> <p>Abousamra, S., Gupta, M. D., Hou, L., Batiste, R., Zhao, T., Shankar, A., Rao, A., Chen, C., Samaras, D., Kurc, T., & Saltz, J. (2022). Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. <em>Frontiers in Oncology</em>, 5971. https://doi.org/10.3389/fonc.2021.806603</p> <p>Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., & Danilova, L. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. <em>Cell Reports</em>, <em>23</em>(1), 181-193.</p> <p> </p> <p>The acknowledgements from the <em>Frontiers in Oncology</em> and <em>Cell Reports</em> papers are included below:</p> <blockquote> <p>This work was supported by the National Institutes of Health (NIH) and National Cancer Institute (NCI) grants UH3-CA22502103, U24-CA21510904, 1U24CA180924-01A1, 3U24CA215109-02, and 1UG3CA225021-01 as well as generous private support from Bob Beals and Betsy Barton. AR and AS were partially supported by NCI grant R37-CA214955 (to AR), the University of Michigan (U-M) institutional research funds and also supported by ACS grant RSG-16-005-01 (to AR). AS was supported by the Biomedical Informatics & Data Science Training Grant (T32GM141746). This work was enabled by computational resources supported by National Science Foundation grant number ACI-1548562, providing access to the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center, and also a DOE INCITE award joint with the MENNDL team at the Oak Ridge National Laboratory, providing access to Summit high performance computing system. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.</p> </blockquote> <p> </p> <blockquote> <p>We are grateful to all the patients and families who contributed to this study. Funding from the Cancer Research Institute is gratefully acknowledged, as is support from National Cancer Institute (NCI) through U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845,U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, P30 CA016672, U24CA180924, U24CA210950, U24CA215109, NCI Contract HHSN261201400007C, and Leidos Biomedical Contract 14X138. A.U.K.R. and P.S were supported by CCSG Bioinformatics Shared Resource P30 CA01667, ITCR U24 Supplement 1U24CA199461-01, a gift from Agilent technologies, CPRIT RP150578, and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation XSEDE Science Gateways program under grant ACI-1548562 allocation TG-ASC130023. The authors would like to thank Stony Brook Research Computing and Cyberinfrastructure and the Institute for Advanced Computational Science at Stony Brook University for access to the high-performance LIred and SeaWulf computing systems, the latter of which was supported by National Science Foundation grant (#1531492).</p> </blockquote> <p>------------------------------------</p> <p>This dataset includes 304,097 image patches. All images are 100 x 100 pixels at 0.5 micrometers per pixel. An image is TIL-positive if there are at least two TILs present.</p> <p>Refer to `images-tcga-tils-metadata.csv` for information about each image. That spreadsheet has the following columns:</p> <pre><code>partition,study,barcode,label,path,md5</code></pre> <p>Partition specifies which partition the image is part of (train, val, test). Study is the TCGA study the image is part of (e.g., acc for TCGA-ACC). Barcode is the TCGA participant barcode. This is used during partitioning, to ensure that images from the same participant are not present in different data partitions. Label is either til-negative or til-positive. An image is til-positive if there are at least two TILs in the image. Path is the path to the PNG image. All images are stored as PNG. Md5 is the md5 hash of the image. This can be used to ensure there are no duplicate images and to verify the integrity of images.</p> <p>There are study-specific directories in the directory `images-tcga-tils`, and there is a directory named `pancancer` that includes images from all the included TCGA studies. That directory uses symlinks to avoid storing duplicate data.</p> <p> </p>
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
<p>Dataset for the paper "Predicting Patch Correctness Based on the Similarity of Failing Test Cases"</p> <p>Tool name: BATS</p>
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
<p>Change the absolute path of the files in 'experiment/config' of the github repository to refer to the two downloaded files below.</p> <p>1. ASE2022withTextUnique.zip: the original dataset with patches text and commit messages text.</p> <p>2. ASE_features2_bert.pickle: the feature from Tian et al.'s ASE2020 paper.</p>
Stability of patch-turnover relationships under equilibrium and nonequilibrium metapopulation dynamics driven by biogeography
<p>Two controversial tenets of metapopulation biology are whether patch quality and the surrounding matrix are more important to turnover (colonization and extinction) than biogeography (patch area and isolation) and whether factors governing turnover during equilibrium also dominate nonequilibrium dynamics. We tested both tenets using 18 years of surveys for two secretive wetland birds, black and Virginia rails, during (1) a period of equilibrium with stable occupancy and (2) after drought and arrival of West Nile Virus (WNV), which resulted in WNV infections in rails, increased extinction and decreased colonization probabilities modified by WNV, nonequilibrium dynamics for both species, and occupancy decline for black rails. Area (primarily) and isolation (secondarily) drove turnover during both stable and unstable metapopulation dynamics, greatly exceeding the effects of patch quality and matrix conditions. Moreover, slopes between turnover and patch characteristics changed little between equilibrium and nonequilibrium, confirming the overriding influences of biogeographic factors on turnover.</p>
Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use
<p>This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species <em>Avicennia germinans, Conocarpus erectus, Laguncularia racemosa,</em> and <em>Rhizophora</em> <em>mangle</em>. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').</p>
Text-fig. 19. Turritella shell from White Patch Fossil Site 1. a: stereo lateral view; b: obverse view to show the spiral ridges. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 19. Turritella shell from White Patch Fossil Site 1. a: stereo lateral view; b: obverse view to show the spiral ridges.
Text-fig. 21. Femur head from White Patch Bone Site belonging to a large mammal approximately the size of a pygmy hippopotamus, probably an embrithopod. View of ligamentary fossa. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 21. Femur head from White Patch Bone Site belonging to a large mammal approximately the size of a pygmy hippopotamus, probably an embrithopod. View of ligamentary fossa.
Text-fig. 20. Proximal right ulna of an embrithopod from White Patch Bone Site. a: lateral view; b: proximal view (anterior to the left); c: stereo view of the articular surface for the humerus. Note the damaged medial and lateral sides of the articular surface (dotted lines) which makes the distal part of the articular surface look narrower than it would have been in life. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 20. Proximal right ulna of an embrithopod from White Patch Bone Site. a: lateral view; b: proximal view (anterior to the left); c: stereo view of the articular surface for the humerus. Note the damaged medial and lateral sides of the articular surface (dotted lines) which makes the distal part of the articular surface look narrower than it would have been in life.
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 17. White Patch fossil sites (18°56′10.9″S: 34°38′41.0″E) Gorongosa National Park, south of the 4×4 vehicle track from Urema to Muanza. 1 – Marine molluscs, 2 – Bones, 3 – Bones, 4 – Marine snails (these sites were subsequently named GPL 12 and GPL 12b by d'Oliveira Coelho et al. 2021). Image modified from Google Earth.
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 7. Geology of the Muaredzi-Muanza sector of the Cheringoma Plateau showing the location of fossil occurrences. White stars – fossiliferous localities mapped by Pickford (2012, 2013), Black stars – fossil sites mapped by Habermann et al. (2019) and d'Oliveira Coelho et al. (2021) (GPL 12 and GPL 12b correspond to the White Patch sites). TTI – Cheringoma Formation, TTs1 – Mazamba Formation, TTs1a – Palaeopan facies, TTs2 – Inhaminga Formation, Qc – Quaternary sediments. The base map is modified from Google Earth.
Text-fig. 14. Photomicrographs of thin sections of specimen BP/16/1732 Palmoxylon dutoitii from Mhengere Hill, Gorongosa, Mozambique. a: transverse section (TS) with four fibre vascular bundles (fvb) and poorly preserved parenchyma between; b: diagram of one of the fvbs in (a) of the reniform type (f – fibres, mx – metaxylum, p – phloem, px – protoxylum); c: close up of the vascular part of a fvb with 2 metaxylem elements and the collapsed cells to the lower left represents the phloem; d: fvb with 2 metaxylem elements, fibrous part to the left and parencymarous ground tissue to the right; e: lower magnification of fvb in (c); f: fvb with three metaxylem elements and phloem patch below; g: longitudinal section (LS) showing the vascular sections alternating with the fibrous sections; h: LS showing the horizontal thickening on the walls of the metaxylem vessels and a patch of parenchyma to the right (darker cells); i: spheroid echinate phytoliths that are typical of Hyphaene and Borassus. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 14. Photomicrographs of thin sections of specimen BP/16/1732 Palmoxylon dutoitii from Mhengere Hill, Gorongosa, Mozambique. a: transverse section (TS) with four fibre vascular bundles (fvb) and poorly preserved parenchyma between; b: diagram of one of the fvbs in (a) of the reniform type (f – fibres, mx – metaxylum, p – phloem, px – protoxylum); c: close up of the vascular part of a fvb with 2 metaxylem elements and the collapsed cells to the lower left represents the phloem; d: fvb with 2 metaxylem elements, fibrous part to the left and parencymarous ground tissue to the right; e: lower magnification of fvb in (c); f: fvb with three metaxylem elements and phloem patch below; g: longitudinal section (LS) showing the vascular sections alternating with the fibrous sections; h: LS showing the horizontal thickening on the walls of the metaxylem vessels and a patch of parenchyma to the right (darker cells); i: spheroid echinate phytoliths that are typical of Hyphaene and Borassus.
Text-fig. 5. Plant fragments from Govone with evidence of preserved cuticle. a: Decussate pair of leaves of "Thuja" saviana (C.T.GAUDIN) C.T.GAUDIN with a window (arrow) opened in the brownish cuticle, showing the yellowish mesophyll cells and some possible resin canals (dark), MGPT-PU141094. b: Angiosperm leaf fragment (from sample MGPT-PU141017) under the stereomicroscope, showing the blackish compressed mesophyll on the right and patches of cleaned, yellowish cuticle at the top (arrow). Scale bar 1 mm. in Remains Of A Subtropical Humid Forest In A Messinian Evaporitebearing Succession At Govone, Northwestern Italy - Preliminary Results
Text-fig. 5. Plant fragments from Govone with evidence of preserved cuticle. a: Decussate pair of leaves of "Thuja" saviana (C.T.GAUDIN) C.T.GAUDIN with a window (arrow) opened in the brownish cuticle, showing the yellowish mesophyll cells and some possible resin canals (dark), MGPT-PU141094. b: Angiosperm leaf fragment (from sample MGPT-PU141017) under the stereomicroscope, showing the blackish compressed mesophyll on the right and patches of cleaned, yellowish cuticle at the top (arrow). Scale bar 1 mm.
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.