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Figure 2 in A segmented and clawed male foreleg in a newly described genus and species of eumaeine butterfly (Lepidoptera: Lycaenidae)
Figure 2. Male foretarsus of Grishinata penny showing five tarsal segments and pretarsal claws (yellow arrows). Dorsal (top) and lateral aspects.
Figures 28–35. Abdominal segment 7 in New species and new combinations in Afrotropical Eucosmocydia Diakonoff, 1988 (Lepidoptera: Tortricidae: Olethreutinae)
Figures 28–35. Abdominal segment 7 of males of Eucosmocydia. 28) E. pappeana, USNM slide 143,426. 29) E. ugandensis, NHMO slide 3762. 30) E. deinbolliana, USNM slide 153,700. 31) E. lecaniodiscana, USNM slide 153,704. 32) E. nigeriana, USNM slide 153,740. 33) E. pancoviana, USNM slide 153,695. 34) E. chlorobathra, USNM slide 153,691. 35) E. kirimiriana, USNM slide 143,441.
Figure 1 in A segmented and clawed male foreleg in a newly described genus and species of eumaeine butterfly (Lepidoptera: Lycaenidae)
Figure 1. Adult Grishinata penny. Male holotype dorsal and ventral wings (top). Female dorsal and ventral wings (bottom). Scale 1 cm.
Figure 3 in A segmented and clawed male foreleg in a newly described genus and species of eumaeine butterfly (Lepidoptera: Lycaenidae)
Figure 3. Male genitalia of Grishinata penny (left) and Theclopsis gargara (Hewitson, 1868) (right). Lateral view of genital capsule (top). Lateral view of penis (middle). Ventral view (bottom). Posterior of butterfly to the right. Scale 0.5 mm.
Jewelry segmentation masks for the 11k Hands dataset
<p>We provide an additional set of segmentation masks for jewelry in the 11K Hands dataset [1]. We filtered out a total of 3179 hands <br> with jewelry and were manually annotated using CVAT. For ease of use, the maks have the same size and filename as the original images and are exported in png format. The pixel value represents whether jewelry exists, being 0 background and 1 jewelry.</p> <p>The 11k Hands [1] dataset is a collection of 11,076 hand photos (1600 × 1200 pixels) from 190 people aged 18 to 75 years old. Each hand was shot from both the dorsal and palmar sides, on a uniform white background, at roughly the same distance from the camera. Each image has a metadata record that includes the following information: the subject ID, gender, age, skin color, and a set of information about the captured hand, such as right- or left-hand, hand side (dorsal or palmar), and logical indicators indicating whether the hand image contains accessories, nail polish, or irregularities. You can download <a href="https://drive.google.com/open?id=1KcMYcNJgtK1zZvfl_9sTqnyBUTri2aP2">here</a> the original 11K Hands dataset and the <a href="https://drive.google.com/file/d/1RC86-rVOR8c93XAfM9b9R45L7C2B0FdA/view?usp=sharing">metadata</a>.</p> <p>In the future, we will add our paper if accepted. In the meantime, if you use the masks provided on this webpage, please cite our DOI: <em>10.5281/zenodo.6541286</em> and the original 11K Hands paper.</p> <p>[1] Mahmoud Afifi, "11K Hands: Gender recognition and biometric identification using a large dataset of hand images." Multimedia Tools and Applications, 2019.</p>
Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation
<p>This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings, using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (<a href="https://doi.org/10.1016/j.autcon.2022.104430">https://doi.org/10.1016/j.autcon.2022.104430</a>)</p>
Dataset of very-high-resolution satellite RGB images to train deep learning models to detect and segment high-mountain juniper shrubs in Sierra Nevada (Spain)
<p>This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.</p>
A Dataset of Synthetic Images of Outdoor Scenes Taken from Sidewalks, for Temporal Semantic Segmentation Applications
<p>This dataset has been generated using the CARLA simulator (release 0.9.11), an open-source 3D simulator for experiments in autonomous vehicle, based on the Unreal Engine game engine. It comes with pre-made city environment maps. CARLA is distributed with several integrated maps as well as parameters to increase the variety in the dataset. In the release that we have used, there are 13 semantic segmentation classes: None, Building, Fence, Other, Pedestrian, Pole, Lane-marking, Road, Sidewalk, Vegetation, Vehicle, Wall, and Traffic sign. The "None" category corresponds to textures that are not part of an object, such as lawns which are not part of "Vegetation", or sky. In the “Other” category are found objects that are not included in the other classes like plant and flower pots. For smart mobility applications, the “Sidewalks” and “Road” classes are of particular importance to find the way forward, as well as “Buildings” and “Poles” for obstacle avoidance. Sequences are made of 4 images. The dataset is composed of 46436 frames (11609 sequences) partitioned in 41024 frames (10256 sequences) for train, 2696 frames (674 sequences) for validation, and 2716 for test (679 sequences). The size of the images is 800 x 600 (resp. width x height).</p> <p>Additionaly, we have generated another smaller dataset with images taken from 2 different viewpoints: one located on the road and the other located on the sidewalk. The number of frames for train/validation/test is respectively 7288 (1822 sequences) partitioned in 6344 (1687 sequences) for train, 416 frames (104 sequences) for validation, and 424 for test (106 sequences). This smaller dataset is aimed at showing the importance of the viewpoint in the result of semantic segmentation. This can be done by cross-validation: learning on images taken from a viewpoint located on the road and test on images with a viewpoint located on the sidewalk, and vice versa.</p>
A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer
<p>Dataset access for the paper: A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer</p>
Data from: imageseg: An R package for deep learning-based image segmentation
<p>1. Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can for example be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists.</p> <p>2. Here, we present R package imageseg which implements a CNN-based workflow for general-purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understory vegetation images.</p> <p>3. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes.</p> <p>4. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on color or grayscale images, e.g. for applications in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.</p>
visuAAL Skin Segmentation Dataset
<p>The visuAAL Skin Segmentation Dataset contains 46,775 high quality images divided into a training set with 45,623 images, and a validation set with 1,152 images. Skin areas have been obtained automatically from the <a href="https://fashionpedia.github.io/home/index.html">FashionPedia garment dataset</a>. The process to extract the skin areas is explained in detail in the paper <a href="https://doi.org/10.1007/978-3-031-13321-3_6">'From Garment to Skin: The visuAAL Skin Segmentation Dataset'</a>.</p> <p>If you use the visuAAL Skin Segmentation Dataset, please, cite:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.6973396">https://doi.org/10.5281/zenodo.6973396</a></li> <li><a href="https://doi.org/10.1007/978-3-031-13321-3_6">https://doi.org/10.1007/978-3-031-13321-3_6</a></li> </ul> <p>How to use:</p> <ol> <li>Download the FashionPedia dataset from <a href="https://fashionpedia.github.io/home/Fashionpedia_download.html">https://fashionpedia.github.io/home/Fashionpedia_download.html</a></li> <li>Download the visuAAL Skin Segmentation Dataset. The dataset consists of two folders, namely train_masks and val_masks. Each folder corresponds to the training and validation sets in the original FashionPedia dataset.</li> <li>After extracting the images from FashionPedia, for each image existing in the visuAAL skin segmentation dataset, the original image can be found with the same name (file_name in the annotations file).</li> </ol> <p>A sample of image data in the FashionPedia dataset is:</p> <p>{'id': 12305,</p> <p> 'width': 680,</p> <p> 'height': 1024,</p> <p> 'file_name': '064c8022b32931e787260d81ed5aafe8.jpg',</p> <p> 'license': 4,</p> <p> 'time_captured': 'March-August, 2018',</p> <p> 'original_url': 'https://farm2.staticflickr.com/1936/8607950470_9d9d76ced7_o.jpg',</p> <p> 'isstatic': 1,</p> <p> 'kaggle_id': '064c8022b32931e787260d81ed5aafe8'}</p> <p>NOTE: Not all the images in the FashionPedia dataset have the correponding skin mask in the visuAAL Skin Segmentation Dataset, as there are images in which only garment parts and not people are present in them. These images were removed when creating the visuAAL Skin Segmentation Dataset. However, all the instances in the visuAAL skin segmentation dataset have their corresponding match in the FashionPedia dataset.</p> <p> </p>
Micro-CT Imaging Dataset on ex-vivo Ovine Functional Spinal Segments as Healthy, Injured and Treated with Cement Discoplasty
<p>General information:</p> <p>- This dataset contains micro-CT images and mechanical test data from ovine functional spinal units (FSU). <br> - The micro-CT data was produced using a Bruker SkyScan 1172. The settings for the scans are given in the '.log' files in each folder. <br> - The compression testing was conducted on an MTS 858 Mini Bionix T/II. The settings for each test can be found in test '.txt' files.<br> - In short, every FSU was mechanically tested in compression under different conditions. Before and after every test, the FSUs were scanned to ensure there was no damage<br> to the sample. More information can be found in the related publication: <br> - The mechanical testing data is arranged in folders with consecutive cycles. It is highly recommended to use the last three cycles for analysis. </p> <p>Data set notation:</p> <p>- All the datasets are noted by Sheep number. Sh7 = Sheep 7; Sh8 = Sheep 8; Sh9 = Sheep 9. In the publication, the numbers were switched to 1,2,3 respectively.<br> - files denoted with '_rec' contain the reconstruction of the projection images. <br> - 'Tested' or 'After test' files refers to the scan after mechanical testing. </p>
Assessing and Addressing Deficiencies in the HCM Weaving Segment Analyses- Phase II- Project J5
<p>Traffic operation and geometric data from Ramp and Major weaves. These data were used to develop the speed and capacity models under the STRIDE J3 project.</p>
Python scripts / Jupyter Notebooks and data for training segmentation models on slide scans of diatom preparations from river Menne
<p>This archive contains the Jupyter Notebooks and data used for the deep learning experiments published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>The notebooks are numbered according to the order in which they are to execute. Please refer to the comments and documentation within the notebooks as well as to the manuscript for details. The data (image data, mask data & segmentation ground truth in COCO format for several different tiling strategies) is stored in separate subfolders corresponding with data usage (model training, validation, test) and tiling strategy. Please refer to the "readme" files for detailed information.</p> <p> </p> <p> </p>
Segmentation models and experimental results on segmenting slide scans of diatom preparations from river Menne
<p>This archive contains the models and the results of the deep learning experiments published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>The folders contain models and results of each of the 24 training runs, the files “metrics.experiments.pt[prediction score threshold].csv contain segmentation score thresholds the models obtained on unknown evaluation data.</p> <p>The data pertaining to each model is stored in a separate folder. Its name follows the convention “experiment.[model architecture].[tiling method].[dataset size].[timestamp]”. Please note that the naming of the tiling method differs from the manuscript; “fixed” refers to fixed-stride tiling, “objectcentred” object-based positioning, and “objectcentred_with_cropped” to object-based positioning + object integrity constraint. Dataset size “10p” refers to a 10% subsample of the complete training data set, “25p” to a 25% subsample and so on.</p> <p>Each folder contains the best performing model of the corresponding training run as pth (Mask R-CNN, PyTorch) or h5 (U-Net, Tensorflow/Keras) file, along with a files describing setup and conduction of the training. Subfolders “test_images_segmented*” contain the segmentation predicted by the models on the evaluation data. These are supplied as 1.) mask image either with the intensity value representing the prediction score (score 0.0 – 1.0 = intensities 0 – 255) or thresholded by the prediction score threshold given in the folder name; 2.) input image overlayed with ground truth (red) and segmentation mask (green), resulting in TP marked in yellow; 3.) a CSV file giving info on the filenames and the segmentation performance metrics.</p> <p>Please refer to the manuscript for further details.</p>
Re-Training Extension of the Benchmark for Automatic Glottis Segmentation (BAGLS-RT)
<p>BAGLS-RT is an extension of the BAGLS dataset (DOI 10.5281/zenodo.3762320) intended for (re-)training glottis segmentation models.</p>
Text-fig. 6. Scanning electron micrographs of seeds of ericalean affinity (a–d) and seeds of uncertain affinity (e–i) from Zliv-Řídká Blana locality. a: Protovisnea sp. 1, rounded seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3177; b: Protovisnea sp. 2, angular seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3179; c, d: Eurya crassitesta, one seed split into two parts, no. NM-F 3211, c – surface cells of the seed coat are palisade, d – cross-section of the seed; e: Nympheaceae sp. 1, seed, no. NM-F 3636; f: Nympheaceae sp. 2, seed, no. NM-F 4634; g: Klikovispermum sp.1, seeds with irregular outline and smooth outer surface, no. NM-F 3203; h: Klikovispermum malechii, seed with an orange-segment shape, no. NM-F 3299; i: Taxon 35, seed, no. NM-F 3236. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 6. Scanning electron micrographs of seeds of ericalean affinity (a–d) and seeds of uncertain affinity (e–i) from Zliv-Řídká Blana locality. a: Protovisnea sp. 1, rounded seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3177; b: Protovisnea sp. 2, angular seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3179; c, d: Eurya crassitesta, one seed split into two parts, no. NM-F 3211, c – surface cells of the seed coat are palisade, d – cross-section of the seed; e: Nympheaceae sp. 1, seed, no. NM-F 3636; f: Nympheaceae sp. 2, seed, no. NM-F 4634; g: Klikovispermum sp.1, seeds with irregular outline and smooth outer surface, no. NM-F 3203; h: Klikovispermum malechii, seed with an orange-segment shape, no. NM-F 3299; i: Taxon 35, seed, no. NM-F 3236.
Text-fig. 17. Aeduellidae. Scale bars 5 mm. a: the scales of oblong shape on the lateral side of the body, locality Otovice "Stěnava", DP 4307, whitened; b: drawing of the scales with fine denticles on their posterior edge, locality Otovice "Stěnava", DP 4307; c, d: drawing and photograph (whitened) of the scales of lateral side of the body, two times large scales occur in the rows 14, 16, 17, 19 (they are marked with arrows), locality Otovice "Černý potok", NM-M 4912; e: lepidotrichia of the anal fin with sigmoidal sutures between the segments (marked by arrows), locality Otovice, NM-M 4931, whitened; f: anterior edge of the dorsal fin and lepidotrichia with sigmoidal sutures between the segments, locality Otovice "Stěnava", DP 4307, whitened; g: anterior edge of the ventral lobe of the caudal fin, locality Otovice, NM-M 4931, whitened; h: the caudal peduncle with begin of bifurcation of the dorsal and ventral lobes of the caudal fin, locality Otovice, NM-M 4931. in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)
Text-fig. 17. Aeduellidae. Scale bars 5 mm. a: the scales of oblong shape on the lateral side of the body, locality Otovice "Stěnava", DP 4307, whitened; b: drawing of the scales with fine denticles on their posterior edge, locality Otovice "Stěnava", DP 4307; c, d: drawing and photograph (whitened) of the scales of lateral side of the body, two times large scales occur in the rows 14, 16, 17, 19 (they are marked with arrows), locality Otovice "Černý potok", NM-M 4912; e: lepidotrichia of the anal fin with sigmoidal sutures between the segments (marked by arrows), locality Otovice, NM-M 4931, whitened; f: anterior edge of the dorsal fin and lepidotrichia with sigmoidal sutures between the segments, locality Otovice "Stěnava", DP 4307, whitened; g: anterior edge of the ventral lobe of the caudal fin, locality Otovice, NM-M 4931, whitened; h: the caudal peduncle with begin of bifurcation of the dorsal and ventral lobes of the caudal fin, locality Otovice, NM-M 4931.
Text-fig. 10. Progyrolepis heyleri POPLIN, 1999. a: dorsal lobe of the caudal fin with the fulcral scales along the dorsal edge of the lobe, GMC 55, whitened, scale bar 5 mm; b: basal fulcral scales from the dorsal edge of the caudal peduncle, G 123, whitened, scale bar 5 mm; c: fragment of the body of juvenile specimen with dorsal and anal fins, GMC 11, whitened, scale bar 5 mm; d: isolated scales from lateral side of the body, G 123, whitened, scale bar 5 mm; e: ridges on the scale surface, the frame delineates the area illustrated in (f) at higher magnification, G 123, scale bar 500 µm; f: details of the surface with microtubercles, scale bar 50 µm; g: isolated lepidotrichium of an adult specimen with very short and wide segments and with unsegmented basal part, GMC 101, whitened, scale bar 5 mm; h: large conical teeth from the internal row of the maxilla, G 123, scale bar 2 mm; i: microsculpture formed by elliptical proximo-distally elongated protuberances on the large conical tooth, G 123, scale bar 100 µm; j: large conical tooth from the internal row of the maxilla, G 123, scale bar 2 mm; k: microsculpture formed by elliptical proximo-distally elongated protuberances on the large conical tooth, G 123, scale bar 100 µm. in New Actinopterygians From The Permian Of The Brive Basin, And The Ichthyofaunas Of The French Massif Central
Text-fig. 10. Progyrolepis heyleri POPLIN, 1999. a: dorsal lobe of the caudal fin with the fulcral scales along the dorsal edge of the lobe, GMC 55, whitened, scale bar 5 mm; b: basal fulcral scales from the dorsal edge of the caudal peduncle, G 123, whitened, scale bar 5 mm; c: fragment of the body of juvenile specimen with dorsal and anal fins, GMC 11, whitened, scale bar 5 mm; d: isolated scales from lateral side of the body, G 123, whitened, scale bar 5 mm; e: ridges on the scale surface, the frame delineates the area illustrated in (f) at higher magnification, G 123, scale bar 500 µm; f: details of the surface with microtubercles, scale bar 50 µm; g: isolated lepidotrichium of an adult specimen with very short and wide segments and with unsegmented basal part, GMC 101, whitened, scale bar 5 mm; h: large conical teeth from the internal row of the maxilla, G 123, scale bar 2 mm; i: microsculpture formed by elliptical proximo-distally elongated protuberances on the large conical tooth, G 123, scale bar 100 µm; j: large conical tooth from the internal row of the maxilla, G 123, scale bar 2 mm; k: microsculpture formed by elliptical proximo-distally elongated protuberances on the large conical tooth, G 123, scale bar 100 µm.
Text-fig. 4. Briveichthys chantepieorum gen. et sp. nov. a, b: photograph and drawing of the parasphenoid in dorsal view, GMC 15, whitened, scale bars 5 mm; c: maxillary plate in medial view with horizontal lamina along the ventral edge of the bone, segment of the lower jaw with assembly of large slender teeth of the inner row and coronoids with small teeth, GMC 15, whitened, scale bar 5 mm; d: detail of the sculpture on the maxilla and dentalosplenial, GMC 126, whitened, scale bar 5 mm; e: detail of the teeth of the inner and outer row and coronoids on the lower jaw, the frame delineates the area illustrated in (f) at higher magnification, GMC 15, scale bar 2 mm; f: microsculpture formed by elliptical proximo-distally elongated protuberances on the large conical teeth, GMC 15, scale bar 100 µm; g: small fringing fulcra tightly attached to the anterior edge of a lepidotrichium, individual fulcral scales are indicated by arrows, GMC 18, scale bar 2 mm. Abbreviation: bhf – bucco-hypophysial foramen, Cor – coronoids, cp – corpus parasphenoidis, De – dentalosplenial, hl – horizontal lamina, mc – pores of the mandibular sensory canal, Mx – maxilla, paa – processus ascendens anterior, pap – processus ascendens posterior. in New Actinopterygians From The Permian Of The Brive Basin, And The Ichthyofaunas Of The French Massif Central
Text-fig. 4. Briveichthys chantepieorum gen. et sp. nov. a, b: photograph and drawing of the parasphenoid in dorsal view, GMC 15, whitened, scale bars 5 mm; c: maxillary plate in medial view with horizontal lamina along the ventral edge of the bone, segment of the lower jaw with assembly of large slender teeth of the inner row and coronoids with small teeth, GMC 15, whitened, scale bar 5 mm; d: detail of the sculpture on the maxilla and dentalosplenial, GMC 126, whitened, scale bar 5 mm; e: detail of the teeth of the inner and outer row and coronoids on the lower jaw, the frame delineates the area illustrated in (f) at higher magnification, GMC 15, scale bar 2 mm; f: microsculpture formed by elliptical proximo-distally elongated protuberances on the large conical teeth, GMC 15, scale bar 100 µm; g: small fringing fulcra tightly attached to the anterior edge of a lepidotrichium, individual fulcral scales are indicated by arrows, GMC 18, scale bar 2 mm. Abbreviation: bhf – bucco-hypophysial foramen, Cor – coronoids, cp – corpus parasphenoidis, De – dentalosplenial, hl – horizontal lamina, mc – pores of the mandibular sensory canal, Mx – maxilla, paa – processus ascendens anterior, pap – processus ascendens posterior.
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.