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1,454 results for “large scale”
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)
<p><strong>This page only provides the ground-level image dataset. </strong></p> <ul> <li><strong>For the drone-view image dataset, please visit <a href="https://zenodo.org/records/13955516" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)</em></a>.</strong></li> <li><strong>For the point clouds, please visit <em><a href="https://zenodo.org/records/14228467" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)</a>.</em></strong></li> </ul> <p>The dataset contains ground-level RGB images, depth maps, semantic segmentation labels, and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a <code>color_palette.xlsx</code> file, which contains the RGB_id and corresponding RGB values.</p> <p>All files follow the naming convention: <code>{central_tree_id}_{timestamp}</code>, where <code>{central_tree_id}</code> represents the ID of the tree centered in the image, which is typically in a prominent position, and <code>timestamp</code> indicates the time when the data was collected.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>rgb</strong>: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: <code>Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36</code>.</p> </li> <li> <p><strong>depth_pfm</strong>: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.</p> </li> <li> <p><strong>semantic_segmentation</strong>: This folder contains grayscale images representing semantic segmentation labels, where 1 indicates tree trunks and 0 represents other elements.</p> </li> <li> <p><strong>instance_segmentation</strong>: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps <code>tree_id</code> to <code>RGB_id</code>. The <code>tree_id</code> can be used to look up detailed information about each tree in <code>obj_info_final.xlsx</code>, while the <code>RGB_id</code> can be matched to the corresponding RGB values in <code>color_palette.xlsx</code>. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.</p> </li> <li> <p><strong>obj_info_final.xlsx</strong>: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).</p> </li> <li> <p><strong>landscape_info.txt</strong>: This file contains the ground location information within the scene, sampled every 0.5 meters.</p> </li> </ul> <p>For forest datasets: birch_forest, broadleaf_forest, burned_forest, rainforest and redwood_forest, there's an additional folder called <strong>coco_annotation</strong> where we generated the COCO-format annotation files (.json) for each image.</p> <p>⚠️: 7z files that begin with "<strong>!</strong>" indicate that the RGB values in the images within the <code>instance_segmentation</code> folder cannot be found in <code>color_palette.xlsx</code>. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in <a href="https://github.com/microsoft/AirSim/issues/3423" target="_blank" rel="noopener">link1</a> and <a href="https://github.com/microsoft/AirSim/issues/1852" target="_blank" rel="noopener">link2</a>.</p>
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)
<p><strong>This page only provides point clouds. </strong></p> <ul> <li><strong>For the ground-level image dataset, please visit <a href="https://zenodo.org/records/13570934" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)</em></a>.</strong></li> <li><strong>For the drone-view image dataset, please visit <a href="https://zenodo.org/records/13955516" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)</em></a>.</strong></li> </ul> <p>This dataset contains point clouds collected from different virtual forest scenes. Data from each scene is stored in a separate .7z file, along with a <code>point_cloud_color_palette.txt</code>file, which contains the Tree_id and corresponding RGB values.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>tree</strong>: This folder contains the point cloud data of every single tree within the forest scene. Each tree is stored separately in a <code>.ply</code> file including both location and color infomation. For performance reasons, the maximum number of point clouds for each tree is limited to 10,000.</p> </li> <li> <p><strong>ground</strong>: This folder contains a <code>landscape.ply</code> describing the ground information. The color of the point cloud is set to [0,0,0].</p> </li> </ul> <p><strong>The unit of the point cloud is meters (m).</strong></p>
Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona
<p>These are supplementary data for the paper "Forecasting of the Geomagnetic Activity for the Next 3 Days Utilizing Neural Networks Based on Parameters Related to Large-scale Structures of the Solar Corona". They are:</p> <ul> <li>Python code to forecast Kp index</li> <li><span>Code to construct a nerual network model</span></li> </ul>
CodePori: Large Scale System for Autonomous Software Development by Using Multi-Agents
<p>This dataset accompanies the paper <strong>"CodePori: A Large-Scale System for Autonomous Software Development Using Multi-Agents."</strong> The dataset is recorded in an MS Excel file, which contains the following sheets, with a brief description of each provided below:</p> <ol> <li> <p><strong>Selected Projects:</strong> Contains the descriptions of the 20 selected projects along with the GitHub URL for each.</p> </li> <li> <p><strong>Modifications:</strong> Contains details of the modifications made to the projects to ensure successful execution.</p> </li> <li> <p><strong>Outputs:</strong> Contains the output for each project.</p> </li> <li> <p><strong>Failed Projects:</strong> Contains data on the projects that failed.</p> </li> </ol>
Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia
<p><b>Aim:</b> Environmental DNA (eDNA) metabarcoding has demonstrated its applicability as a highly sensitive biomonitoring tool across small spatial and temporal scales in marine ecosystems. However, it has rarely been tested across large spatial scales, or biogeographical barriers. Here, we scale up marine eDNA metabarcoding, test its ability to detect a major marine biogeographic break, and evaluate its use as a regional biomonitoring tool in Australia.</p> <p><b>Location:</b> North-western Australia (NWA)</p> <p><b>Methods:</b> We applied metabarcoding assays targeting the mitochondrial 16S rRNA and CO1 genes to 284 surface seawater eDNA samples collected from 71 mid-shelf, inshore, coastal and nearshore estuarine sites over 700 km of the NWA coastline.</p> <p><b>Results:</b> Metabarcoding detected a wide range of bony fish (404 taxa), elasmobranchs (44) and aquatic reptiles (5). We detected bioregional and depth differentiation within inshore bony fish communities. These findings support the presence of a marine biogeographic break, which is purported to occur in the vicinity of Cape Leveque, demarcating the border between the Kimberley and Canning bioregions. Inshore bony fish and elasmobranch communities, as well as coastal bony assemblages, were additionally found to differ between the South and North Kimberley regions <span><span>suggesting</span></span> previously unrecognised subregional differentiation among these taxa. The overall compositional data has been used to update distribution information for a number of endangered, elusive and data-deficient taxa, including sawfish (family: Pristidae), northern river shark (<i>Glyphis garricki</i>) and wedgefish (genus: <i>Rhynchobatus</i>).</p> <p><b>Main conclusions</b>: eDNA metabarcoding demonstrated a high level of sensitivity that was able to discern fine-scale patterns across the large-scale, remote and oceanographically complex region of North-western Australia. Importantly, this study highlights the potential of integrating broad-scale eDNA metabarcoding alongside other baseline surveys and long-term monitoring approaches, which are crucial for the sustainable management and conservation of marine biodiversity in this unique marine region.</p>
Specimens from Brand et al. 2021, "Large-scale phylogenomics of the genus Macrostomum (Platyhelminthes) reveals cryptic diversity and novel sexual traits"
<p>The deposited folders contain image and video material of free-living flatworm specimens that were documented in vivo. These data support the following publication:</p> <p>Jeremias N. Brand, Gudrun Viktorin, R. Axel W. Wiberg, Christian Beisel, Lukas Schärer.<br> Large-scale phylogenomics of the genus<em> Macrostomum</em> (Platyhelminthes) reveals cryptic diversity and novel sexual traits.<br> Molecular Phylogenetics and Evolution. Volume 166. 2022. <a href="https://doi.org/10.1016/j.ympev.2021.107296">https://doi.org/10.1016/j.ympev.2021.107296</a></p> <p>For more information about these specimens see also http://macrostomorpha.info.</p>
COBALTO data set at large at CIEM large scale wave flume
<p>The experiments carried out are framed within the COBLTO project (CTM2017-88036-R) and<br> the doctoral thesis of Carlos Astudillo. A surrogate Posidonia meadow has been used to study<br> the wave attenuation, velocity affections and changes in sediment transport due to the<br> comparison of experiments with the meadow and the benchmark cases where the meadow<br> was absent on the flume experiments. Two different wave conditions, high energy waves and<br> low energy waves, have been considered. The case with higher energy was also repeated with<br> a third configuration in which a meadow length of 5 m was also studied.</p>
EnGRaiN : A Supervised Ensemble Learning Method for Recovery of Large-scale Gene Regulatory Networks
<p>EnGRaiN is a supervised machine learning method to construct ensemble networks. To benefit from the typical accuracy advantages of supervised learning methods while taking into account the impossibility of knowing true networks for training, we devised a method that uses small training datasets of true positives and true negatives among gene pairs.</p> <p>The datasets used to evaluate the performance of EnGaiN include (i) simulated datasets generated from Yeast networks and (ii) A. thaliana gene expression datasets.</p>
Large-scale conformational changes of FhaC provide insights into the two-partner secretion mechanism
<p>This dataset contains raw and processed data and analyses related to the manuscript "Large-scale conformational changes of FhaC provide insights into the two-partner secretion mechanism" by the authors. A preprint of this manuscript is available on bioRxiv (https://doi.org/10.1101/2021.11.09.467682).</p>
Large-scale temporal graph datasets
<p>Large scale datasets used for evaluation in the article: `Optimizing the Interval-centric Distributed<br> Computing Model for Temporal Graph Algorithms` to appear in `EuroSys 2022`.</p>
Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry
<p>Example data and scripts for the paper: Team maturity and reorganization during a very large-scale agile transformation in the automotive industry. </p>
Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks
<p>We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.</p>
Large-scale fungal strain sequencing unravels the molecular diversity in mating loci maintained by long-term balancing selection
<p><span>Balancing selection, an evolutionary force that retains genetic diversity, has been detected in multiple genes and organisms, such as the sexual mating loci in fungi. However, to quantify the strength of balancing selection and define the mating-related genes require a large number of strains. In tetrapolar basidiomycete fungi, sexual type is determined by two unlinked loci, <em>MATA </em>and <em>MATB</em>. Genes in both loci define mating type identity, control successful mating and completion of the life cycle. These loci are usually highly diverse. Previous studies have speculated, based on culture crosses, that species of the non-model genus <em>Trichaptum </em>(Hymenochaetales, Basidiomycota) possess a tetrapolar mating system, with multiple alleles. Here, we sequenced a hundred and eighty strains of three <em>Trichaptum </em>species. We characterized the chromosomal location of <em>MATA </em>and <em>MATB</em>, the molecular structure of <em>MAT </em>regions and their allelic richness. The sequencing effort was sufficient to molecularly characterize multiple <em>MAT </em>alleles segregating before the speciation event of <em>Trichaptum </em>species. Analyses suggested that long-term balancing selection has generated trans-species polymorphisms. Mating sequences were classified in different allelic classes based on an amino acid identity (AAI) threshold supported by phylogenetics. 17,550 mating types were predicted based on the allelic classes. <em>In vitro </em>crosses allowed us to support the degree of allelic divergence needed for successful mating. Even with the high amount of divergence, key amino acids in functional domains are conserved. We conclude that the genetic diversity of mating loci in <em>Trichaptum </em>is due to long-term balancing selection, with limited recombination and duplication activity. The large number of sequenced strains highlighted the importance of sequencing multiple individuals from different species to detect the mating-related genes, the mechanisms generating diversity and the evolutionary forces maintaining them.</span></p>
Large Scale Volcanism and the heat-death of terrestrial worlds
<p>The files herein are used to generate the main and SI figures in the GRL paper:</p> <p>Title: Large Scale Volcanism and the heat-death of terrestrial worlds<br> Authors: M.J. Way, Richard E. Ernst, Jeffrey D. Scargle</p> <p>The data are derived from an Excel formatted table: WAY2021-TableS1.xlsx</p>
FirmWire - Large Scale Analysis Dataset
<p>This dataset holds the cellular baseband modem firmware analysed in the paper "FirmWire: Transparent Dynamic Analysis for Cellular Baseband Firmware" (NDSS'22).</p> <p>It contains 229 firmware images, spanning 9 different phone models. The images were contained by crawling the collection of firmware update files for these phone models available at https://www.sammobile.com/.</p>
Large scale across-breed genome-wide association study reveals a variant in HMGA2 associated with inguinal cryptorchidism risk in dogs
<p class="MsoNormal"><span>Cryptorchidism is the most common congenital sex development disorder in dogs. Despite this, little progress has been made in understanding its genetic background. Extensive genetic testing of dogs through consumer and veterinary channels using a high-density SNP genotyping microarray coupled with links to clinical records presents the opportunity for a large-scale genome-wide association study to elucidate the molecular risk factors associated with cryptorchidism in dogs. Using an inter-breed genome-wide association study approach, a significant statistical association on canine chromosome 10 was identified, with the top SNP pinpointing a variant of <em>HMGA2 </em>previously associated with adult weight variance. In further analysis we show that incidence of cryptorchidism is skewed towards smaller dogs in concordance with the identified variant's previous association with adult weight. This study represents the first putative variant to be associated with cryptorchidism in dogs.</span></p>
A large-scale assessment of ant diversity across the Brazilian Amazon Basin: integrating geographic, ecological, and morphological drivers of sampling bias
<p>Tropical ecosystems are often biodiversity hotspots, and invertebrates represent the main underrepresented component of diversity in large-scale analyses. This problem is partly related to the scarcity of data widely available to conduct these studies and the lack of systematic organization of knowledge about invertebrates' distributions in biodiversity hotspots. Here, we introduce and analyze a comprehensive data compilation of Amazonian ant diversity. Using records from 1817 to 2020 from both published and unpublished sources, we describe the diversity and distribution of ant species in the Brazilian Amazon Basin. Further, using high-definition images and data from taxonomic publications, we build a comprehensive database of morphological traits for the ant species that occur in the region. In total, we recorded 1,067 nominal species in the Brazilian Amazon Basin, with sampling locations strongly biased by access routes, urban centers, research institutions, and major infrastructure projects. Large areas where ant sampling is non-existent represent about 52% of the basin and are concentrated mainly in the North, Southeastern, and Western Brazilian Amazon. We found that distance to roads is the main driver of ant sampling in the Amazon. Contrary to our expectations, morphological traits had lower predictive power in predicting sample bias than purely geographic variables. However, when geographic predictors were controlled, habitat stratum and traits contribute to explain the remaining variance. More species were recorded in better-sampled areas, but species richness estimation models suggest that areas in South Amazonian edge forests are associated with especially high species richness. Our results represent the first trait-based, large-scale study for insects in Amazonian forests and a starting point for macroecological studies focusing on insect diversity in the Amazon Basin.</p>
Bed topography of the large-scale meandering rivers at dynamic equilibrium state
<p>The simulation results for the bed topography of the 42 large-scale testbed rivers at dynamic equilibrium (along with instantaneous bed shear stress distributions) are presented.</p>
Mean bed shear stress of large-scale meandering rivers
<p>The source files (in Tecplot, and readable by ParaView) are the mean bed shear stress distribution of the 42 testbed rivers.</p>
First large-scale quantification study of DNA preservation in insects from natural history collections using genome-wide sequencing
<p>Insect declines are a global issue with significant ecological and economic ramifications. Yet we have a poor understanding of the genomic impact these losses can have. Genome-wide data from historical specimens has the potential to provide baselines of population genetic measures to study population change, with natural history collections representing large repositories of such specimens. However, an initial challenge in conducting historical DNA data analyses, is to understand how molecular preservation varies between specimens. Here, we highlight how Next Generation Sequencing methods developed for studying archaeological samples can be applied to determine DNA preservation from only a single leg taken from entomological museum specimens, some of which are more than a century old. An analysis of genome-wide data from a set of 113 red-tailed bumblebee (Bombus lapidarius) specimens, from five British museum collections, was used to quantify DNA preservation over time. Additionally, to improve our analysis and further enable future research we generated a novel assembly of the red-tailed bumblebee genome. Our approach shows that museum entomological specimens are comprised of short DNA fragments with mean lengths below 100 base pairs (BP), suggesting a rapid and large-scale post-mortem reduction in DNA fragment size. After this initial decline, however, we find a relatively consistent rate of DNA decay in our dataset, and estimate a mean reduction in fragment length of 1.9bp per decade. The proportion of quality filtered reads mapping our assembled reference genome was around 50 %, and decreased by 1.1 % per decade. We demonstrate that historical insects have significant potential to act as sources of DNA to create valuable genetic baselines. The relatively consistent rate of DNA degradation, both across collections and through time, mean that population level analyses - for example for conservation or evolutionary studies - are entirely feasible, as long as the degraded nature of DNA is accounted for. </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.