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Data from: Genotyping-in-Thousands by Sequencing panel development and application for high-resolution monitoring of introgressive hybridization within sockeye salmon
<p>Stocking programs have been widely implemented to re-establish extirpated fish species to their historical ranges; when employed in species with complex life histories, such management activities should include careful consideration of resulting hybridization dynamics with resident stocks and corresponding outcomes on recovery initiatives. Genetic monitoring can be instrumental for quantifying the extent of introgression over time, however, conventional markers typically have limited power for the identification of advanced hybrid classes, especially at the intra-specific level. Here, we demonstrate a workflow for developing, evaluating, and deploying a Genotyping-in-Thousands by Sequencing (GT-seq) SNP panel with the power to detect advanced hybrid classes to assess the extent and trajectory of intra-specific hybridization, using the sockeye salmon (<em>Oncorhynchus nerka)</em> stocking program in Skaha Lake, British Columbia, as a case study. Previous analyses detected significant levels of hybridization between the anadromous (sockeye) and freshwater resident (kokanee) forms of <em>O. nerka</em>, but were restricted to assigning individuals to pure-stock or "hybrid". Simulation analyses indicated our GT-seq panel had high accuracy, efficiency and power (> 94.5%) of assignment to pure-stock sockeye salmon/kokanee, F<sub>1</sub>, F<sub>2</sub>, and B<sub>2</sub> backcross-sockeye/kokanee. Re-analysis of 2016/2017 spawners previously analyzed using TaqMan<span> </span>assays and otolith microchemistry revealed shifts in assignment of some hybrids to adjacent pure-stock or B<sub>2</sub>-backcross classes, while new assignment of 2019 spawners revealed hybrids comprised 31% of the population, ~74% of which were B<sub>2</sub>-backcross or F<sub>2</sub>. Overall, the GT-seq panel development workflow presented here could be applied to virtually any system where genetic stock identification and intra-specific hybridization are important management parameters.</p>
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
<p><strong>Project Page</strong></p> <p><a href="https://open-earth-map.org/">https://open-earth-map.org/</a></p> <p><strong>Paper</strong></p> <p><a href="https://arxiv.org/abs/2210.10732">https://arxiv.org/abs/2210.10732</a></p> <p><strong>Overview</strong></p> <p>OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.</p> <p><strong>Reference</strong></p> <pre><code>@inproceedings{xia_2023_openearthmap, title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping}, author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6254-6264} }</code></pre> <p><strong>License</strong></p> <p>Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data <a href="https://open-earth-map.org/attribution.html">here</a>. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0</a> International License.</p> <p><strong>Note for xBD data</strong></p> <p>The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from <a href="https://xview2.org/dataset">https://xview2.org/dataset</a> and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.</p> <p><strong>Code</strong></p> <p>Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available <a href="https://github.com/bao18/open_earth_map">here</a>.</p> <p><strong>Leaderboard</strong></p> <p>Performance on the test set can be evaluated on the <a href="https://codalab.lisn.upsaclay.fr/competitions/9121">Codalab webpage</a>.</p>
Figure 1 from: Huntenburg J, Wagstyl K, Steele C, Funck T, Bethlehem R, Foubet O, Larrat B, Borrell V, Bazin P (2017) Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. Research Ideas and Outcomes 3: e12346. https://doi.org/10.3897/rio.3.e12346
Figure 1 - Laminar python pipeline, demonstrated using high-resolution MR data of a ferret brain. a) Binary images demarcating inner (grey-white matter interface, top) and outer (pial surface, bottom) boundaries of the cortex. b) Levelset representations of the same surfaces, where positive values are assigned to voxels outside of the volume deliminated by the surface, and negative values to voxels inside, each increasing in value with euclidean distance from the surface. c) Continuous equivolumetric intracortical depth, which models the positions of laminae relative to cortical morphology. d) Discrete representations of equivolumetric depth levels. e) T2 values, sampled at the six equivolumetric intracortical depths. Note that the equivolumetric laminae do not represent architectonic layers, but provide an anatomically meaningful coordinate system of cortical depth.
Figure 6 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 6. Direct sequence results of two genotypes of the Trp-2027-Cys (G> C) mutation in Rottboellia cochinchinensis samples. (A) Wild type and (B) mutant type.
Figure 3 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 3. High-resolution melting analysis (HRMA) for detection of mutation Trp-2027-Cys in the Rottboellia cochinchinensis carboxyl-transferase domain of the acetyl-coenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. Representative profiles of the melting curves (derivative melt curves) (A), normalized melt curves (B) and differential curves using susceptible (wild type) as reference genotype (C) for resistant (TGC, in red) and susceptible plants (wild type, TGG in blue at x-axis level).
Data for "Integrating high-resolution Sr/Ca and ultrastructural analyses of the Tridacna squamosa shell to reconstruct sub-daily seawater temperature variation"
<p>This repository contains all data generated for the publication "Integrating high-resolution Sr/Ca and ultrastructural analyses of the Tridacna squamosa shell to reconstruct sub-daily seawater temperature variation" currently under review</p>
Figure 18 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 18. Field photographs of Early Triassic bedding surfaces with structures of possible microbial origin. (a) Flower-shaped structure in the pale-brown micritic matrix containing filaments, bivalve shell fragments, ammonoids and high-spired gastropods. (b) Kidney-shaped "twin" morphology characterized by an irregular, partly concentric structure, where micrite and sparite alternate. (c) Structures of different size, some of which grow on the margin of a previous generation. (d) Dark-grey, ovoidal, lenticular sparry calcite structure, filled by an argillaceous micrite, in pale-brown micritic matrix containing abundant filaments (probably sponge spicule remains).
Figure 14 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 14. Succession of conodont species and zones at Baghuk Mountain; combination of Baghuk Mountain sections 1 (up to the extinction horizon) and Baghuk Mountain section A (above the extinction horizon). ChZ – Clarkina hauschkei Zone. (after Farshid et al., 2016).
Figure 7 in Baghuk Mountain (Central Iran): high-resolution stratigraphy of a continuous Central Tethyan Permian-Triassic boundary section
Figure 7. Slab of a marly limestone within the uppermost part of the Hambast Formation with many small ammonoids of the genus Arasella. Baghuk Mountain C section, lower view of the bedding plane at −0.05 m. Scale bar units = 10 mm.
High-resolution continuum source graphite furnace molecular absorption spectrometry for the monitoring of Sr isotopes via SrF formation: a case study
<p>This dataset contains the raw data corresponding to the figures of the publication <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D2JA00245K">https://doi.org/10.1039/D2JA00245K</a></p>
A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM): Datasets
Open the record for dataset details and reuse information.
Glacial sediment-rich meltwater plume investigation using a high-resolution multispectral sensor embedded in an Unmanned Aerial Vehicle
<p>Methodology video</p>
Fig. 28 in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study
Fig. 28. Scheme of the crystal structure in a lorica surface in Eutintinnus angustatus. The primitive unit cell of the crystal is rhombohedral, while three of them form a hexagonal pattern (see white lines). In the simplified model of the crystal structure, the basic units consist of triangles with a diameter of ~ 18 nm, which are interconnected on each side by channels with a diameter of ~ 7 nm. The empty space between the triangles and channels appear as dark areas, having the shape of a cloverleaf, and are ~ 7 nm long. The rim of the triangle and the interconnecting channels visible as bright lines in the image consists of a protein wall, which is ~ 2.5 nm thick. Within the triangles no clear or regular structures could be observed.
Figs 1–9 in On the Nature of Tintinnid Loricae (Ciliophora: Spirotricha: Tintinnina): a Histochemical, Enzymatic, EDX, and High-resolution TEM Study
Figs 1–9. Loricae after mercuric bromophenol blue (1–6) and alcian blue stain (7–9). 1 – Codonella aspera, the staining is restricted to the lorica matrix; 2 – Eutintinnus brandti, the lorica is uniformly stained; 3, 4 – Climacocylis spec., the alveolar texture of the wall is well recognizable; 5, 6 – Rhabdonella spiralis, the alveolar texture, the minute openings, and the spiralled surface ridges are recognizable; 7–9 – Stenosemella ventricosa, lateral (7, 8) and oblique top (9) views. The staining is restricted to the bowl matrix. Scale bars: 50 µm (1, 7–9), 200 µm (2, 3), 40 µm (4), 100 µm (5), and 20 µm (6).
Fig. 2. A-B in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 2. A-B: (A) Derivative melt curves of T. copemani, T. noyesi G8, T. vegrandis G7; (B) Derivative melt curves of T. microti, T. cruzi and T. rangeli.
Fig. 6. A-D in Species-level identification of trypanosomes infecting Australian wildlife by High-Resolution Melting - Real Time Quantitative Polymerase Chain Reaction (HRM-qPCR)
Fig. 6. A-D: (A) Derivative melt curves of wildlife samples containing T. copemani and T. vegrandis G7 infections from woylie tissue and the (B) the respective normalised melt domains; (C) Derivative melt curves of wildlife samples containing mixed infections with T. copemani and T. noyesi G8 in woylie blood; (D) Derivative melt curves showing wildlife samples containing mixed infections of T. copemani, T. noyesi G8 and T. vegrandis G7 in woylie tissue.
Spatiotemporal high-resolution (daily, 1-km) atmospheric CO2 reconstruction data across China
<p>We employed an enhanced regression-based machine learning model to reconstruct full-coverage daily atmospheric CO2 concentrations in China from 2015 to 2020 at a 0.01° spatial resolution. Utilizing spatiotemporal high-resolution column-averaged dry-air mole fraction of CO2 (XCO2) data from the Orbiting Carbon Observatory 2 (OCO-2) as the dependent variable and multi-source environmental factors as independent variables, we achieved overall, spatial, and temporal cross-validation R2 [RMSE] results of 0.98 [0.74 ppm], 0.95 [1.15 ppm], and 0.93 [1.44 ppm], respectively. </p> <p>The daily XCO2 data are archieved in NetCDF format. If you want to use this dataset, please cite the following publication. If you want annual or monthly data, please go to <a href="../records/10022905">10.5281/zenodo.10022905</a>.</p> <p>--He, Q., Ye, T., Chen, X., Dong, H., Wang, W., Liang, Y., & Li, Y. (2023). Full-coverage mapping high-resolution atmospheric CO2 concentrations in China from 2015 to 2020: Spatiotemporal variations and coupled trends with particulate pollution. <em>Journal of Cleaner Production</em>, 139290. [<a href="https://doi.org/10.1016/j.jclepro.2023.139290">url</a>]</p> <p> </p> <p>We also share other reconstruction datasets of atmopsheric parameters:</p> <p>For full-coverage, daily, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p> <p>For full-covereage, daily, 1-km, PM2.5 data in China, please go to <a href="../doi/10.5281/zenodo.8437234">10.5281/zenodo.8437234 or </a><a href="../record/8347128">10.5281/zenodo.8347128.</a></p> <p>For full-coverage, daily, 1-km ozone data in China, please go to <a href="13623698">10.5281/zenodo.13623698</a>.</p>
A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics
<p><strong>Motivation</strong></p> <p>Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections. </p> <p><strong>Data Record</strong></p> <p><strong>CMIP6</strong></p> <p>We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal.</p> <p>Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2] approach respectively.</p> <p>The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target.</p> <p>The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where:</p> <ul> <li> <p>"VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively),</p> </li> <li> <p>"GCM" denotes the CMIP6 GCM name,</p> </li> <li> <p>"X" indicates the warming target compared to the preindustrial period,</p> </li> <li> <p>"yyyy" represents the year index (0001-0027) of the 27-year sample</p> </li> </ul> <p><strong>Table 1 </strong>CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT.</p> <div> <table> <tbody> <tr> <td> <p>GCM</p> </td> <td> <p>Realization</p> </td> <td> <p>GCM grid spacing</p> </td> <td> <p>Ta</p> </td> <td> <p>Tw</p> </td> <td> <p>WBGT</p> </td> </tr> <tr> <td> <p>ACCESS-CM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>BCC-CSM2-MR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.1ox1.125o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CanESM5</p> </td> <td> <p>r1i1p2f1</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-CM2-SR5</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CMCC-ESM2</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.94ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>CNRM-CM6-1</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> </td> </tr> <tr> <td> <p>EC-Earth3</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.7ox0.7o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>GFDL-ESM4</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.0ox1.25o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-LL</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>HadGEM3-GC31-MM</p> </td> <td> <p>r1i1p1f3</p> </td> <td> <p>0.55ox0.83o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KACE-1-0-G</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.25ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>KIOST-ESM</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.9ox1.9o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC-ES2L</p> </td> <td> <p>r1i1p1f2</p> </td> <td> <p>2.8ox2.8o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MIROC6</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.4ox1.4o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-HR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>0.93ox0.93o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> <tr> <td> <p>MPI-ESM1-2-LR</p> </td> <td> <p>r1i1p1f1</p> </td> <td> <p>1.85ox1.875o</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> <td> <p>✓</p> </td> </tr> </tbody> </table> </div> <p><strong>ERA5</strong></p> <p>We also provide hourly Tw and WBGT derived from ERA5 reanalysis during 1950-2023 to enable analyses of heat stress changes from historical period to a warmer climate.</p> <p><strong> </strong></p> <p><strong>Data Access</strong></p> <p>An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus. The bias-corrected CMIP6 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?origin_id=6538f53a-1ea7-4c13-a0cf-10478190b901&origin_path=%2F">Globus Link1</a>, and the ERA5 dataset is available at <a href="https://transfer.rcac.purdue.edu/file-manager?destination_id=63242aea-d3e0-4aa4-9372-0e19dd0c6539&destination_path=%2F">Globus Link2</a>. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID)—"6538f53a-1ea7-4c13-a0cf-10478190b901" for CMIP6, and “63242aea-d3e0-4aa4-9372-0e19dd0c6539” for ERA5 dataset—in Globus.</p> <p><strong>Dataset Validation</strong></p> <p>We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts.</p> <p>For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.</p> <p><strong><br><br><br></strong></p>
Data supporting publication: MiFoDB, a workflow for microbial food metagenomic characterization, enables high-resolution analysis of fermented food microbial dynamics
<p>MiFoDB (Microbial Foods Database) is a workflow and primary reference database which includes 675 assembled MAGs and RefSeq bacterial, yeast, fungal, and substrate genomes from fermented foods.</p>
NDUI+: A fused DMSP-VIIRS based multidecadal, high-resolution global normalized difference urban index (NDUI) dataset
<p>M. Singh and S. Ghosh are equal contributors to this work and are designated as co-first authors</p>
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.