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Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.
<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4><p>There are two datasets in this data release:</p><p>1. <strong>Model training dataset</strong>. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. </p><p>2. <strong>Model output dataset</strong>. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. </p><p>All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (<i>Ritchie et al., 2018</i>). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following <i>Over et al., 2021</i>) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). </p><p>The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):</p><p>1. vegetation / other (0)</p><p>2. water (1)</p><p>3. sediment (2)</p><p>4. large wood (3)</p><h3>1. Model training dataset.</h3><p>Imagery was labeled using a combination of the open-source software Doodler (<i>Buscombe et al., 2021</i>; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (<i>Buscombe and Goldstein, 2022</i>). </p><p>Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.</p><p>Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.</p><h4>Files:</h4><p>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs</p><p>2. Model_<i>training_</i> images1of4.zip, 1.5 GB, imagery tiffs</p><p>3. Model_<i>training_</i> images2of4.zip, 1.5 GB, imagery tiffs</p><p>4. Model_<i>training_</i> images3of4.zip, 1.7 GB, imagery tiffs</p><p>5. Model_<i>training_</i> images4of4.zip, 1.6 GB, imagery tiffs</p><h3>2. Model output dataset.</h3><p>Imagery was labeled using a deep-learning based semantic segmentation model (<i>Buscombe, 2023</i>) trained specifically for the task using the Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) modeling suite. We use the software package Segmentation Gym (<i>Buscombe and Goldstein, 2022</i>) to fine-tune a Segformer (<i>Xie et al., 2021</i>) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of <i>Xie et al. (2021)</i>, itself fine-tuned on ADE20k dataset (<i>Zhou et al., 2019</i>) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.</p><p>The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.</p><p>Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. </p><h4>Files:</h4><p>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)</p><p>2. Elwha<i>MR_ imagery_ part1_ of</i>_<i> </i>2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha<i>MR_ imagery_ part2_ of</i>_<i> </i>2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)</p><p>4. ElwhaL<i>R_ imagery_ part1_ of</i>_<i> </i>2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)</p><p>5. ElwhaL<i>R_ imagery_ part2_ of</i>_<i> </i>2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)<br> </p><p>This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. </p><h3>References</h3><p>• Buscombe, D. (2023). <strong>Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0)</strong> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a></p><p>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021).<strong> Human-in-the-loop segmentation of Earth surface imagery</strong>. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a></p><p>• Buscombe, D., & Goldstein, E. B. (2022). <strong>A reproducible and reusable pipeline for segmentation of geoscientific imagery.</strong> Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p><p>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, <strong>Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation</strong>: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.</p><p>• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, <strong>Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals</strong>: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.</p><p>• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. <strong>SegFormer: Simple and efficient design for semantic segmentation with transformers</strong>. Advances in Neural Information Processing Systems, 34, pp.12077-12090.</p><p>• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. <strong>Semantic understanding of scenes through the ade20k dataset</strong>. International Journal of Computer Vision, 127, pp.302-321.</p><p><br> </p>
Vegetation surveys in the riparian (bosque) corridor of the Middle Rio Grande valley, NM
This dataset contains vegetation cover information from 34 long-term Bosque Ecosystem Monitoring Program (BEMP) sites from 2000 – 2021. Data were collected along ten 30-m transects at each site at the centimeter scale each year in August-early October as funding and site access allowed. At the fullest extent, sites spanned 520 km of the riparian forest along the Rio Grande. The purpose of this dataset is to track plant species at sites along the Rio Grande in New Mexico. From this dataset, changes in plant species abundance, richness, and species diversity can be tracked and analyzed with ecosystem drivers such as flooding, fire, species removal/fuel reduction projects, and climate change. Species are coded using USDA Plant Database codes, allowing species information to be added to each species, including origin (native or nonnative), duration (e.g., annual, biennial, perennial), and plant type (e.g., grass, forb, vine, shrub, tree). This dataset has allowed the tracking of the ascendance of nonnatives in some sites, the recovery of natives in other sites, success or lack of success following restoration projects, and records of new species occurring in various counties and the state of New Mexico.
Meteorological and ground observations in the Qinghai-Tibet Engineering Corridor
<p>Observation of meteorological factors was conducted at two permanent meteorological stations (Golmud and Wudaoliang) and one field meteorological station (Xidatan) with daily meteorological records. All three meteorological stations contain ground observations.</p>
Data for "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations"
<p>Processed data used for the manuscript "Detection of large-scale cloud microphysical changes within a major shipping corridor after implementation of the IMO 2020 fuel sulfur regulations".</p> <p>Includes input data for kriging algorithm as "SSF1deg_shipkrige_Terra.nc" and output data files as "Data_Terra_[VAR]_[YEAR]_C_M[MONTH].nc" for [VAR] Acld (overcast albedo) or cer (cloud droplet effective radius), [YEAR] the starting year of a three-year period starting with 2002 and ending at 2020 or "clim" for the 2002-2019 climatology, and [MONTH] 1to12 (annual mean) or 9to11 (austral spring).</p> <p>For the output data, "Obs" is the original data, "Est" is the mean counterfactual field obtained via kriging, "lowEst" and "highEst" are the 2.5th and 97.5th percentiles of the kriged fields for each grid box, "krSims" stores the results of the 5,000 simulated kriged fields, "Semivariance" is the binned empirical variogram values, "pVal" is the raw field significance (not adjusted for multiple testing), "nOut" is the number of individually significant grid boxes, "tran" is the transform applied (none for cer, logit for Acld), "iniPhi" and "iniSigma2" are the initial values for the fitted variogram, "Phi" and "Sigma2" are the fitted values using weighted least squares, and "parSel" is the list of selected regressors for the mean function that minimize the Bayesian information criterion.</p>
Inventory and description of thermokarst features observed along the Umiat Corridor in July 2009.
Using a combination of aerial imagery and ~1m resolution airborne lidar (collected July, 2009), we use manual visual inspection of the two datasets to identify point locations of over 7000 thermal erosion features (thermokarst) of varying maturity. For each feature we report its x,y position, the facing direction of the feature, the local topographic setting, the geologic unit it occurs on, the relative age of the feature and the specific type of thermal erosion feature.
Wildlife along the Salt River corridor of the greater Phoenix, Arizona, USA metropolitan area: results of a camera-trapping project (2020-2021)
The goal of this research project was to evaluate how wildlife populations responded to the gradient of urbanization, water, and vegetation. We deployed 43 wildlife cameras across the gradient of urbanization January 2021 to January 2022. We documented a suite of wildlife species, from small mammals and birds to large mammals. Data present whether a species was detected at a site during this time period.
UAV RGB and TIR images in the Qinghai-Tibet Engineering Corridor
<p>Two permafrost slopes were conducted four flight experiments with UAV-mounted RGB and TIR sensors in 2016 and 2017.</p>
Automatic detection of clefts and corridors within the sandstone plateau - Szczeliniec Wielki & Szczeliniec Mały mesas, Poland
<p>This dataset presents the method of automatic clefts and corridors detection within areas of highly dissected relief. This methodical approach was developed for a geomorphological study on Szczeliniec Wielki and Szczeliniec Mały sandstone mesas (Stołowe Mts., SW Poland) (Migoń et al. 2023)</p> <p>Contents:</p> <ol> <li>CleftHunter_manual_v1_0.pdf - short method description</li> <li>kernel_examples.zip - set of exemplary kernel files </li> <li>Szczeliniec_Wielki_clefts_threshold_depth_2m.zip - raster dataset of automatically detected clefts within the plateau of Szczeliniec Wielki (.tif)</li> <li>Szczeliniec_Maly_clefts_threshold_depth_2m.zip - raster dataset of automatically detected clefts within the plateau of Szczeliniec Mały (.tif)</li> <li>Szczeliniec_Wielki_mesa_caprock_base.zip - Szczeliniec Wielki caprock zone (.shp, polygon)</li> <li>Szczeliniec_Maly_mesa_caprock_base.zip - Szczeliniec Mały caprock zone (.shp, polygon)</li> <li>Szczeliniec_Wielki_mesa_caprock_hillshade.zip - shaded relief of the Szczeliniec Wielki plateau (.tif)</li> <li>Szczeliniec_Maly_mesa_caprock_hillshade.zip - shaded relief of the Szczeliniec Mały plateau (.tif)</li> </ol> <p>Preferred citation:</p> <p>Migoń P., Duszyński F., Jancewicz K., Kotowska M., Porębna W. (2023), Surface-subsurface connectivity in the morphological evolution of sandstone-capped tabular hills – how much analogy to karst?. Geomorphology vol. 440, Id 108884, 1–22<br>DOI: 10.1016/j.geomorph.2023.108884</p> <p> </p> <p>This research was funded by National Science Centre, Poland, research project no. 2020/39/D/ST10/00861.</p>
Fig. 14. – Croton lasiopyrus Baill. A in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 14. – Croton lasiopyrus Baill. A. Habit, with patent leaves; B. Cross-section of woody stem (diam. c. 4 cm), showing reddish sap at cambial layer; C. Young twig cut to show the reddish sap that is exuded; D. Leaves. Note the atypical serrate leaf margin of this specimen (van Ee et al. 2215), which may be introgressed with C. enigmaticus; E. Shoot apex with anisophyllous leaves; F. Close up of shoot showing the reddish, wooly pubescence. Note the acropetiolar gland at the lower side of the blade; G. Inflorescence with old staminate flowers and bud. Note the divergent pedicels; H. Pistillate flower; I. A nearly mature capsule and the remaining calyx and columella of an already dehisced capsule. [A, C-E, G, I: van Ee et al. 2197; B, F: van Ee et al. 978; H: van Ee et al. 2215] [Photos: A, C-E, G-I: P. Berry; B, F: B. van Ee]
Fig. 13. – Croton hypochalibaeus Baill. A in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 13. – Croton hypochalibaeus Baill. A. Habit; B. Stem (diam. c. 2.5 cm) with cauliflorous flowers; C. Close-up of the underside of a leaf; D. Staminate flower; E-F. Pistillate flowers; G-H. Fruits. [Photos: P. Berry]
Fig. 10. – Croton plurispicatus P.E in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 10. – Croton plurispicatus P.E. Berry, Kainul. & B.W. van Ee. A. Top of felled tree with dense ascending branches; B. Cross-section of woody stem, showing reddish sap at cambial layer; C. Young twig cut to show reddish latex that is exuded; D. Twig with flattened and angled shoots; E. Flowering branch showing the multiple thyrses along a single stem, some of them entirely staminate; F. Inflorescence with staminate flowers and buds. Note also the basilaminar glands (lower right); G. Pistillate flower at anthesis showing the lepidote inner surface of the sepals and the intricately divided, patent styles; H. Inflorescence with three basal pistillate flowers at varying stages of fruit development; I. Inflorescence with a basal fruit and a staminate flower above it. [Photos: P. Berry].
Fig. 8 in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 8. – Croton ferricretus Kainul., B.W. van Ee & P.E. Berry. A. Typical habitat on ultramafic crust, with C. ferricretus dominating the understory; B. Habit; C. Underside of branch showing the silvery-lepidote and ferrugineous-punctate leaves; D. Inflorescence with staminate flowers and buds; E. Inflorescence with pistillate flower and bud; F. Infructescence with immature fruits; G. Mature capsule. [Photos: C-D, F: P.Berry; A-B, E, G: K. Kainulainen]
Fig. 9 in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 9. – Croton indrisilvae Kainul., B.W. van Ee & P.E. Berry. A. Habit; B. Stem showing smooth, tan bark; C. Branch with nearly mature capsule; note the rounded sepals that are green and subglabrous adaxially; D. Underside of branch showing whorled leaves; E. Detail of nearly mature capsule; note stiff, brown-stellate trichomes and triangular outline; F. Staminate flower, rehydrated; G. Pistillate flower, rehydrated. [F: van Ee et al. 2176; G: Service Forestier 715] [Photos: A-E: P. Berry]
Fig. 7 in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 7. – Croton ferricretus Kainul., B.W. van Ee & P.E. Berry (A-B), C. plurispicatus P.E. Berry, Kainul. & B.W. van Ee (C-D), C. hypochalibaeus Baill. (E-F), C. indrisilvae Kainul., B.W. van Ee & P.E. Berry (G-H), and C. enigmaticus P.E. Berry & B.W. van Ee (I-J). A, C, E, G, I. Capsule; B, D, F, H, J. Seed. [A-B: van Ee et al. 2436; C: van Ee et al. 2198; D: Ranaivojaona & Razanatsima 1173; E-F: van Ee et al. 2472; G-H: van Ee et al. 2176; I-J: van Ee et al. 2214]
Fig. 6. – Croton enigmaticus P.E. Berry & B.W in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 6. – Croton enigmaticus P.E. Berry & B.W. van Ee. A. Climbing habit, here together with a climbing bamboo; B. Adventitious roots emerging from the stems; C. Typical trichotomous branching; D. Leaves; E. Close-up of the densely hirsute newly emergent leaves. Note the falcate stipules and stipitate acropetiolar glands; F. Inflorescence with the pedicels of the staminate flowers perpendicular to the axis of the rachis; G. Pistillate flower. Note the somewhat unequal, foliaceous sepals and thrice bifurcating stigmas; H. Inflorescence showing an immature fruit, foliaceous bracts along the rachis, and staminate pedicels of varying lengths. [Photos: P. Berry
Fig. 4 in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 4. – Croton droguetioides Kainul. & Radcl.-Sm. A-B. Staminate flower; C. Staminate flower with stamens removed to show the nectaries; D-E. Pistillate flower; F. Pistillate flower with ovary removed to show the nectaries. Note the glandular filaments in the petal position. [A-C: van Ee et al. 2243; D-F: van Ee et al. 2247]
Fig. 2. – Croton ankeranae Kainul. A in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 2. – Croton ankeranae Kainul. A. Flowering branch with two staminate flowers; B. Staminate flower; C. Pistillate flower. Note bifurcate stigmas and greenish sepals; D. Branch with nearly mature capsule; E. Staminate flower; F. Staminate flower with stamens removed to show the five nectaries; G. Pistillate flower, showing bifurcate stigmas and filamentous structure in the position of petals; H. Pistillate flower with ovary and two sepals removed to show the five nectaries. Note the glandular filament in the petal position. [A, C-H:Antilahimena 7554; B: Ravelonarivo & Edmond 4082] [Photos: A, C-D: P. Antilahimena; B: D. Ravelonarivo]
Fig. 3 in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 3. – Croton droguetioides Kainul. & Radcl.-Sm. A. Habit and habitat in rainforest; B. Flowering branch with staminate flowers; C. Underside of branch showing the stipitate glands, the sparsely stellate-pubescent leaves and the villous petioles and stem; D. Inflorescence with staminate flowers and buds; E. Inflorescence with pistillate flower; F. Immature fruit. [Photos: A: K. Kainulainen; B-F: P. Berry]
Fig. 6. – Croton enigmaticus P.E. Berry & B.W in New species and species reports of Croton L. (Euphorbiaceae) from the eastern forest corridor of Madagascar
Fig. 6. – Croton enigmaticus P.E. Berry & B.W. van Ee. A. Climbing habit, here together with a climbing bamboo; B. Adventitious roots emerging from the stems; C. Typical trichotomous branching; D. Leaves; E. Close-up of the densely hirsute newly emergent leaves. Note the falcate stipules and stipitate acropetiolar glands; F. Inflorescence with the pedicels of the staminate flowers perpendicular to the axis of the rachis; G. Pistillate flower. Note the somewhat unequal, foliaceous sepals and thrice bifurcating stigmas; H. Inflorescence showing an immature fruit, foliaceous bracts along the rachis, and staminate pedicels of varying lengths. [Photos: P. Berry]
An Updated Inventory of Retrogressive Thaw Slumps Along the Vulnerable Qinghai-Tibet Engineering Corridor
<p>An inventory of 875 retrogressive thaw slumps over a landscape of 54000 km<sup>2</sup>, along the Qinghai-Tibet Engineering Corridor underlain by permafrost, was compiled using remote sensing and DeepLabv3+, a kind of deep learning model. The file in the format of Geopackage/GPKG contains the boundary of each retrogressive thaw slump as vectors in the Coordinate Reference System of EPSG:32646 - WGS 84. The associated attribute table includes probability, time of the satellite images, source of the satellite images, the near roads labels, year of initiation, longitude and latitude, area (units: m<sup>2</sup>), Deep Learning model. The corresponding names for the table fields are ‘Probability’, ‘Year-month’, ‘Source Image’, ‘Near roads’, ‘Initial year’, ‘Longitude’, ‘Latitude’, ‘Area’, ‘Deep Learning model’. The ‘Probability’, having values of ‘High’, ‘Medium’ and ‘Low’, measures how much we are sure about the mapped RTSs.</p>
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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.