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1,037 results for “large-scale”
Monitoring of large-scale archaeological excavations using photogrammetric techniques - Nea Paphos case study
<p>Photogrammetric data (images) for five contexts from a single archaeological trench from the Paphos Agora Project (Excavations conducted in Nea Paphos Archaeological Site by Jagiellonian Unversity in Krakow - under the direction of Prof. Ewdoksia Papuci-Władyka). Images for photogrammetric modelling of all contexts were taken in 2015 with the NIKON D7100 camera (24 Mpx resolution), with the number of images per model varying from 25 to 41. For photogrammetric processing, the Agisoft PhotoScan and Reality Capture were used. </p><p>The data (images) were uploaded together with project files from Agisoft Metashape and RealityCapture, exported results (3D models, orthoimages and DEMs) and reference data - point clouds from terrestrial laser scanner measurements are available as supplementary material for the article in Journal of Archaeological Science: Reports. Also, data for one layer below and above, which do not include reference point clouds, have been added.</p>
Large-scale facilitative effects of Vachellia caven
<p>The importance of nurse plants structuring plant communities is well appreciated at local scales, yet the effect of a single nurse on large scales has been neglected in analyses. So far, studies only use environmental gradients within one type of ecosystem and tend to generalize the nurse effects. To assess how the effect of a single nurse species is modulated by different environmental settings, interactions between the shrub <em>Vachellia caven</em> and the surrounding plant communities were evaluated at 481 paired plots (outside vs underneath the plant crown), in 39 sites across two distribution ranges, the Mediterranean west and the mostly subtropical east of the Andes mountains (covering ca. 2x10 6 km<sup>2</sup>). Cover, abundance, and richness of perennial plants underneath and outside <em>V. caven </em>were used as response variables to estimate an index indicative of plant interactions (RII) and tested how this was affected by the rainfall gradient and distribution range.</p> <p>Overall, RII responses to rainfall gradients had low conditional R<sup>2</sup> (~0.25) at this large-scale of analysis, but were significantly different between ranges: the RII followed a quadratic trend across the rainfall gradient in the western range, while this relationship was positive and close to linear at the eastern range. Then, by projecting the RII models (i.e., for abundance, cover, and richness) spatially through a consensus map, we show that most positive effects of <em>V. caven</em> are geographically found in dissimilar areas: the central part of Chile (western range) and across the Paraná river (eastern range). When local fine-scale predictors (i.e., annual herbs cover and height, and herbivores feces cover), were used to model each response variable at the plot level (underneath or outside <em>V. caven</em>) similar trends were kept as when considering only large-scale predictors.</p> <p>Synthesis: Here, we show that the effect of the same nurse species on neighbouring plant communities can be very different depending on ranges of distribution, stressing that its ecological function cannot be generalized and not only depends on local factors but also is large-scale context-dependent. </p>
CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection
<p>In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.</p> <p>If you use our data, please cite our work published in Scientific Data.</p> <p>Xu, Y., Ouyang, C., Xu, Q. <em>et al.</em> CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection. <em>Sci Data</em> <strong>11</strong>, 12 (2024). https://doi.org/10.1038/s41597-023-02847-z</p>
ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models
<p>This repository contains data and models used in the following paper.</p> <p> </p> <p>Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.</p> <p> </p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/admet_ai">ADMET-AI</a> code, which runs the ADMET-AI web server at <a href="https://admet.ai.greenstonebio.com/">admet.ai.greenstonebio.com</a>.</p> <p> </p> <p>The data.zip file has the following structure.</p> <p>data</p> <p> drugbank: Contains files with drugs from the <a href="https://go.drugbank.com/">DrugBank</a> that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.</p> <p> tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the <a href="https://tdcommons.ai/">Therapeutics Data Commons</a> (TDC).</p> <p> tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.</p> <p> tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.</p> <p> tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.</p> <p> tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.</p> <p> </p> <p>The models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below.</p> <p>models</p> <p> tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.</p> <p> tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).</p> <p> tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.</p>
CoUDlabs_WP8_T831_AaU_001 Application of Large-Scale Particle Image Velocimetry (LSPIV) technique in Aalborg retention pond
<p>This dataset includes the data obtained during the installation of a camera system to determine the surface velocities in a retention pond monitored by Aalborg University (AAU). The dataset consists of raw and processed images and surface velocity maps obtained during preliminary experiments when the retention tank was being filled and emptied. The objective is to assess the feasibility and usefulness of this type of measurement for the calibration of CFD models to optimize the operation of the installation. The data presented may help to improve and assess imaging techniques in real conditions in urban environments.</p><p>The dataset is a result from the Joint Research Activity 3 (WP8, Improving Resilience and Sustainability in Urban Drainage solutions), Task 8.3.1. (Hydrodynamic design for stormwater detention ponds optimized for cost-efficient maintenance) within Co-UDlabs project, funded under the European Union's Horizon 2020 research and innovation program under grant agreement No 101008626. </p>
Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics
<p>IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). </p> <p><em>We thank the institutes who maintain the IMAGE Magnetometer Array (<a href="https://space.fmi.fi/image/">https://space.fmi.fi/image/</a>): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.</em></p>
Contrasting responses of fine root biomass and traits to large-scale nitrogen and phosphorus addition in tropical forests in the Guiana shield
<p>Fine roots mediate plant nutrient acquisition and growth. Depending on soil nutrient availability, plants can regulate fine root biomass and morphological traits to optimise nutrient acquisition. Little is known, however, about the importance of these parameters influencing forest functioning. In this study, we measured root responses to nutrient additions to gain a mechanistic understanding of plant adaptations to nutrient limitation in two tropical forests in French Guiana, differing two-fold in their soil nutrient statuses. We analysed the responses of root biomass, mean root diameter (RD), specific root length (SRL), specific root area (SRA), root tissue density (RTD) and carbon (C), nitrogen (N) and phosphorus (P) concentrations in roots down to 15 cm soil depth after three years of N and P additions. At the lower-fertility site Paracou, no changes in root biomass or morphological traits were detected with either N or P addition, although P concentrations in roots increased with P addition. In the higher fertility site, Nouragues, root biomass and P concentrations in roots increased with P addition, with no changes in morphological traits. In contrast, N addition shifted root traits from acquisitive to more conservative by increasing RTD. A significant interaction between N and P in Nouragues pointed to stronger responses to P addition in the absence of N. Our results suggest that the magnitude and direction of root biomass and trait expression were regulated by soil fertility, corroborated by the response to N or P additions. At low fertility sites, we found lower plasticity in root trait expression compared to more fertile conditions, where N and P additions caused stronger and antagonistic responses. Identifying the exact role of mechanisms affecting root nutrient uptake in Amazon forests growing in different soils will be crucial to foresee if and how rapid global changes can affect their carbon allocation.</p>
Soil properties shape the heterogeneity of denitrification and N2O emissions across large-scale flooded paddy soils
<p>The dataset's contents include the relative abundance of different denitrifier types at the genus level across eight different flooded paddy soils. This dataset categorizes denitrifier types in its first column labeled 'Type.' The second column, 'Genus,' associates each type with various species at the genus level. Subsequent columns present the relative abundance of these species in diverse sampling locations. Researchers can leverage this dataset to derive more patterns regarding the differences within specific dominant denitrifier species. No ethical considerations are pertinent to the utilization of this dataset.</p>
Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics -- Reproducibility Files
<p>This repository contains the files needed to reproduce the data, analysis and visualizations from the paper 'Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics'.</p> <h3>Data collection</h3> <p>For the data collection, we make use of the IOHexperimenter library to access the BBOB functions. In addion, we rely on the set of libraries discussed in the paper. For the full set of libraries used during the benchmarking experiment, please refer to reqs_benchmark.txt (in the Benchmarking folder).</p> <p>The scripts used throughout the data collection are located in the 'benchmarking' directory. We have 1 file for each set of algorithms (expect mealpy and NIAPY, which are combined in the 'bestiary' file). Running these scripts generates a large amount of IOHprofiler-compatible files, which are availalbe in the 'Raw.zip' (Note: some of the libraries have their raw data split between multiple folders, if you want to run the full analysis, please merge these folders into one before processing). When running these scripts, please make sure to set the relevant FOLDER-variables at the top of the script!</p> <h3>Data processing</h3> <p>The raw data can be processed both for anytime performance (AOCC) and fixed-budget performance using the respective 'processing_METHOD.py' scripts. This generates several large csv-files, which are available in 'processed_data.zip'</p> <h3>Data analyis and visualization</h3> <p>The full set of analysis and corresponding visualizations can be reproduced using the 'Vizualization.ipynb' notebook. This notebook can use the processed performance data from the previous step, or the next level of processing with resulted in the remaining csv-files in this repository (this step is explained in the notebook itself).</p>
Data from: Large-scale bioacoustic monitoring to elucidate the distribution of a non-native katydid
<p>For animals that produce species-specific audible sounds, environmental recordings combined with automated acoustic monitoring software (passive acoustic monitoring, PAM) may be an effective monitoring tool because it allows audio data from many, widely distributed autonomous recording units (ARUs) to be processed in a relatively short period of time. Males of many insect species produce loud, species-specific mating songs, yet acoustic insects have received less attention from PAM relative to vertebrates.</p> <p>We evaluated the use of PAM to monitor, <em>Roeseliana roeselii</em> (Orthoptera, Tettigoniidae), an acoustic insect that has expanded its range to Alberta, Canada far outside its naturalized North American range. We analyzed environmental recordings from ARUs: 1) at two control sites known to be occupied by <em>R. roeselii</em>, and 2) across Alberta established by the Alberta Biodiversity Monitoring Institute (ABMI) to search for new populations.</p> <p>PAM successfully detected <em>R. roeselii</em> at the two control sites, but not at any of the 74 ABMI sites that we analyzed. Despite the failure to detect new locations of <em>R. roeselii</em>, our analysis of ABMI environmental recordings detected several other species of acoustic insects, including <em>Orchelimum gladiator</em>, <em>Gryllus</em> sp. and <em>Allonemobius</em> spp.</p> <p>Our results add to the growing body of work showing the feasibility of using PAM for acoustic insects. We make suggestions for how to maximize the effectiveness of this monitoring tool for the conservation and management of singing insects in North America.</p>
Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy"
<p>Source code and data for manuscript "Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy", <em>Nat Methods</em> (2024), https://doi.org/10.1038/s41592-024-02275-w.</p>
Data from: Stable isotope evidence for long-term stability of large-scale hydroclimate in the Neogene North American Great Plains
<p>The Great Plains of North America host a stark climatic gradient, separating the humid and well-watered eastern US from the semi-arid and arid western US, and this gradient shapes the region's water availability, its ecosystems, and its economies. This climatic boundary is largely set by the influence of two competing atmospheric circulation systems that meet over the Great Plains—the wintertime westerlies bring dominantly dry air that gives way to moist, southerly air transported by the Great Plains Low-Level Jet in the warmer months. Climate model simulations suggest that, as CO<sub>2</sub> rises, this low- level jet will strengthen, leading to greater precipitation in the spring, but less in the summer and, thus, no change in mean annual precipitation. Combined with rising temperatures that will increase potential evapotranspiration, semi-arid conditions will shift eastward, with potentially large consequences for the ecosystems and inhabitants of the Great Plains. We examine how hydroclimate in the Great Plains varied in the past in response to warmer global climate by studying the paleoclimate record within the Ogallala Formation, which underlies nearly the entire Great Plains and provides a spatially resolved record of hydroclimate during the globally warmer late Miocene. We use the stable isotopes of oxygen (δ<sup>18</sup>O) as preserved in authigenic carbonates hosted within the abundant paleosol and fluvial successions that comprise the Ogallala Formation as a record of past hydroclimate. Today, and coincident with the modern aridity gradient, there is a sharp meteoric water δ<sup>18</sup>O gradient with high (−6 to 0‰) δ<sup>18</sup>O in the southern Great Plains and low (−12 to −18‰) δ<sup>18</sup>O in the northern Plains. We find that the spatial pattern of reconstructed late Miocene precipitation δ<sup>18</sup>O is indistinguishable from the spatial pattern of modern meteoric water δ<sup>18</sup>O. We use a recently developed vapor transport model to demonstrate that this δ<sup>18</sup>O spatial pattern requires air mass mixing over the Great Plains between dry westerly and moist southerly air masses in the late Miocene—consistent with today. Our results suggest that the spatial extent of these two atmospheric circulation systems have been largely unchanged since the late Miocene and any strengthening of the Great Plains Low-Level Jet in response to warming has been isotopically masked by proportional increases in westerly moisture delivery. Our results hold implications for the sensitivity of Great Plains climate to changes in global temperature and CO<sub>2</sub> and also for our understanding of the processes that drove Ogallala Formation deposition in the late Miocene.</p>
Data from: Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
<p>The everlasting pursuit for a more powerful artificial general intelligence (AGI) continuously demands for higher computing performance. Breaking the restrictions from the Moore's Law, integrated photonic neural networks have shown great potential in terms of superior processing speed and high energy-efficiency. However, the capacity and scalability are restricted by the unavoidable and time-varying on-chip errors that only simple tasks and shallow models (e.g., 10-category digits classification with thousand-parameter-level networks) can be on-chip realized optically. To support modern AGIs with optical computing, we innovate Taichi, large-scale photonic chiplets owning millions-of-neuron on-chip computing capability with 160 TOPS/W energy efficiency. For the first time, Taichi experimentally realized on-chip optical neural networks (ONNs) with over 13.96M neurons for thousand-category-level classification (tested 91.89% of accuracy for 1623-category Omniglot dataset) and high-fidelity artificial intelligence-generated content (AIGC) with state-of-the-art performances, while achieving up to 2 orders of magnitude improvement in computing efficiency than state-of-the-art AI chips. Based on an integrated diffractive-interference-hybrid design and a general distributed computing architecture, Taichi paves its own way for large-scale on-chip ONN models and advanced tasks, further exploiting the flexibility and potential of optical devices, which will lead to a giant leap in scalability, accuracy and efficiency to support modern AGI.</p>
Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy (J. Roy Soc INTERFACE, to be published, march 2024)
<p>data and matlab code from paper "Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy" (paper submitted to J. Roy Soc INTERFACE, to be published, march 2024)</p> <p><strong><span>Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy</span></strong></p> <p><span> </span><span>J.P. Rieu</span><sup><span>1, *</span></sup><span>, H. Delanoë-Ayari</span><sup><span>1</span></sup><span>, C. Barentin</span><sup><span>1</span></sup><span>, T. Nakagaki</span><sup><span>2</span></sup><span> and S. Kuroda</span><sup><span>3</span></sup><sup><span> ,*</span></sup></p> <p><sup><span>1</span></sup><span> </span><span>Institut Lumière Matière, University of Lyon, Université Claude Bernard Lyon 1, CNRS, F-69622, Villeurbanne, France</span></p> <p><sup><span>2</span></sup><span> Research Institute for Electronic Science, Hokkaido University, N20W10 Kita-ku, Sapporo Hokkaido 001-0020, Japan</span></p> <p><sup><span>3 </span></sup><span>Faculty of Software and Information Technology, Aomori University, Koubata 2-3-1, Aomori, 030-0943, Japan</span></p> <p><span>* Authors for correspondence:<span> </span>Jean-Paul Rieu (e-mail: jean-paul.rieu@univ-lyon1.fr) and Shigeru Kuroda (shigeru-kuroda@aomori-u.ac.jp)</span></p> <p> </p> <p><strong><span>Abstract</span></strong></p> <p><span>We present a novel approach to traction force microscopy (TFM) for studying the locomotion of 10cm-long walking centipedes on soft substrates.</span><span> Leveraging the </span><span>remarkable</span><span> elasticity and ductility of kudzu starch gels, we utilize them as a deformable gel substrate, providing resilience against the centipedes' sharp leg tips. </span><span>Through optimizing fiducial marker size and density and fine-tuning imaging conditions, we enhance measurement accuracy. Our TFM investigation reveals traction forces along the centipede's longitudinal axis that effectively counterbalance inertial forces within the 0-10mN range, providing the first report of non-vanishing inertia forces in TFM studies. Interestingly, we observe waves of forces propagating from the head to the tail of the centipede, corresponding to its locomotion speed. Furthermore, we discover a characteristic cycle of leg clusters engaging with the substrate: forward force (friction) upon leg tip contact, backward force (traction) as the leg pulls the substrate while stationary, and subsequent forward force as the leg tip detaches to reposition itself in the anterior direction. </span><span>This work </span><span>opens perspectives for TFM applications in ethology, tribology, and robotics</span><span>.</span></p>
Supplementary Data for A large-scale transcontinental river system crossed West Antarctica during the Eocene
<p>The file contains supplementary figures to the paper "A large-scale transcontinental river crossed West Antarctica during the Eocene", by Max Zundel, Cornelia Spiegel, Chris Mark, Ian Millar, David Chew, Johann Klages, Karsten Gohl, CD Hillenbrand, Yani Najman, Ulrich Salzmann, Werner Ehrmann, Jürgen Titschack, Thorsten Bauersachs, Gabi Uenzelmann-Neben, Torsten Bickert, Jule Müller, Frank Lisker, Steve Bohaty, Gerd Kuhn, and the Science Team of Polarstern Expedition PS104, published in Science Advances</p> <p><a name="_Hlk153790947"></a></p>
Data for: Salivary metabolomics in the family environment: A large-scale study investigating oral metabolomes in children and their parental caregivers
<p>Human metabolism is complex and dynamic and is impacted by genetics, diet, health, and countless inputs from the environment. Beyond the genetics shared by family members, cohabitation leads to shared microbial and environmental exposures. Furthermore, metabolism is affected by factors such as inflammation, antibiotic potential, environmental tobacco smoke (ETS) exposure, metabolic regulation, and environmental exposure to heavy metals within the home. Metabolomics represents a useful analytical method to assay the metabolism of individuals to find potential biomarkers for metabolic conditions that may not be phenotypically obvious or represent unknown physiological processes. As such, we applied untargeted LC-MS metabolomics to archived saliva samples from a racially diverse group of elementary school-aged children and their caregivers collected during the "90-month" assessment of the Family Life Project. We assayed a total of 1,425 saliva samples of which 1,344 were paired into 672 caregiver/child dyads. We compared the metabolomes of children (N = 719) and caregivers (N = 706) within and between homes, performed population-wide "metabotype" analyses, and measured associations between metabolites and salivary biomeasures of inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Dyadic analyses revealed that children and their caregivers have largely similar salivary metabolomes. Although there were differences between the dyads at the individual levels of analysis, dyads explained most (62%) of the metabolome variation. At a population level of analysis, our data clustered into two large groups, indicating that people likely share most of their metabolomes, but that there are distinct "metabotypes" across large sample sets. Lastly, individual differences in several metabolites – which were putative oxidative damage-associated or pathological markers – were significantly correlated with salivary measures indexing inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Implications of the effects of family environment on metabolomic variation at the population, dyadic, and individual levels of analyses for health and human development are discussed.</p>
BDFoodSent: A Large-Scale Sentiment-Labeled Restaurant Review Dataset from Bangladesh
<p>BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.</p> <p> </p> <h3>Dataset Statistics</h3> <p>Total Reviews: 334,119</p> <p>Features/Columns: 19</p> <p> </p> <h3>Potential Applications</h3> <p>Sentiment Analysis</p> <p>Restaurant Review Classification</p> <p>Customer Satisfaction Analysis</p> <p>Opinion Mining</p> <p>Natural Language Processing Research</p> <p>Food Service Industry Analysis</p>
Tree census data associated to 'Large-scale informative priors to better predict the local occurrence rate of a rare tree-related microhabitat'
<p>This repository contains two excel spreadsheets providing data about the trees studied in Cottais et al. study (doi : 10.1101/2024.11.28.625900):</p> <ul> <li>df_TreM.xlsx has 1462 lines (headers not included); each line corresponds to one oak tree sampled in 2021 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id : the id of the sampling plot where the tree is located</li> <li>Tree_Id : a unique id of the tree used in the BloBiForM project</li> <li>Species_Latin_Name : the latin name of the tree species ('Quercus_sp' for all trees given than only oaks are reported here)</li> <li>DBH_cm : the diameter at breast height of the tree in cm</li> <li>TreM : whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Life_Stage : whether the tree is a living tree or a snag</li> <li>Last_Logging_Year : the year of the last logging event in the stand where the tree is located</li> <li>conv : whether the last logging event occured less that 120 years before 2024 (conv=0; the stand is now classic high forest) or is more ancient (conv=1, the stand is being converted to high forest through sprout thinning)</li> </ul> </li> <li>DMH_2022.xlsx has 1190 lines (headers not included); each line corresponds to one tree sampled in 2022 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id: the id of the sampling plot where the tree is located</li> <li>Species_Latin_Name: the latin name of the tree species, obtained as a translation from french using the dictionnary in page 2; oaks are not the only species reported, but only oaks are used in Cottais et al. study;</li> <li>Species_French_Name : the french name of the tree species;</li> <li>DBH_cm: the diameter at breast height of the tree in cm</li> <li>TreM: whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Tree_Id: when the tree belongs to the cohort of the BloBiForM project, the tree id is reported; left empty otherwise</li> <li>Nb_brin: number of stems on the tree stump;</li> <li>Commentaire: any comments during fieldwork</li> </ul> </li> </ul>
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>
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.