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464 results for “model structure”
Figure 1: Asymmetric representation for the ¯rst four generations, in its elec- trical equivalent-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>For example, the average of the radius ratio<br> changes from 2¡0:1713 = 0:8881 to 0:8923 when only the ¯rst 16 generations are<br> taken into account, respectively to 0:8783 for the alveoli (generations 17-24)<br> [5]. This implies that the homothety factor changes, depending on the spatial<br> location within the tree. On the other hand, if we analyze the radius ratio from<br> generations 1 to 24 in steps of 4, we obtain an average of 0:8535, whereas if we<br> use steps of 2, we obtain an average homothety factor of 0:8623. These changes<br> might not seem signi¯cant, but one should recall that they are originated by<br> the symmetric geometry of the respiratory tree. However, when asymmetry<br> is considered, one deals with several homothety factors, i.e. as schematically<br> drawn in figure 1.</p>
Figure 1: Asymmetric representation for the ¯rst four generations, in its elec- trical equivalent-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>These changes<br> might not seem signi¯cant, but one should recall that they are originated by<br> the symmetric geometry of the respiratory tree. However, when asymmetry<br> is considered, one deals with several homothety factors, i.e. as schematically<br> drawn in ¯gure 1.</p>
Figure 5. A chromosome structure in case we have 2 visible states and 3 invisible states-Neuroevolution Mechanism for Hidden Markov Model
<p>Generating a population of size n of HMMs at random can be performed with some<br> restrictions:<br> - The weights representing the input layer in the chromosome should be always negligible as<br> initial values.<br> - The weights which are involved in summation of 1.0 in the hidden layer part of the<br> chromosome should be exactly 1.0.<br> Let us assume the following case<br> Visible states are 2 and invisible states (observations) are 3, , then we shall have a<br> chromosome as shown in Figure 5.</p>
Figure 4. A chromosome structure for HMM shown in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>The chromosome which represents the HMM can be extracted from its corresponding neural<br> network. The general structure of the chromosome is divided into two sections, input layer and<br> hidden layer. Each section contains many slots, and each slot represents a weight from one node in<br> that layer to a node in the next layer (from input to hidden and from hidden to output). The number<br> of slots in the input layer is the same number of input nodes in the neural network. In the hidden<br> layer, number of slots is equal to nodes in the output layer multiplied by the nodes in the hidden<br> layer.</p>
Figure 2. The general structure of the proposed approach-Genetic Algorithms Principles Towards Hidden Markov Model
<p>The chromosome contains 8 genes, each is represented by the relation between two states<br> accompanied with a probability value. The genes should be formed in this way because this is<br> important in the crossover operation as to be explained later. The most important thing is that each<br> two genes has the probability summation of 1.0. For example Med-Med:02 and Med-High:08 have<br> the summation of 1.0. Similarly High-High:0.6 and High-Med:0.4 have the summation of 1.0. Each<br> two genes with summation of 1.0 should be neighbors.</p>
A Pelagic Size Structure database (PSSdb) to support biogeochemical modeling: third update to first release of PSSdb-bulk
<p>This dataset represents the third update to the first release of the Pelagic Size Structure database (PSSdb, <a href="https://pssdb.net">https://pssdb.net</a>) scientific project, investigating the global particle size distributions measured from multiple pelagicǂ imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and <a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>). Links to the PSSdb code and documentation are available on the PSSdb webpage (<a href="https://pssdb.net">https://pssdb.net</a>). </p> <p>This <em>updated version</em> includes the following changes: </p> <p>● Duplicate data entries and NaN values have been removed.<br>● Data products now include Normalized Biomass Size Spectra (NBSS), and Particle Size Distribution (PSD), two widely used methods to represent plankton and particles size distribution in marine ecology and biogeochemistry (Jonasz and Fournier 1996, San Martin et al. 2006).<br>● Linear regressions are now performed with log10 transformations of the normalized biovolume/abundance and the size classes.<br>● Inclusion of UVP6 and other benchtop plankton Scanner datasets from net tows, which expand the temporal and spatial coverage of the data products.<br>● Unbiased portion of the size spectra is selected by a new thresholding method that accounts for both uncertainties on particle sizes, limited by the camera resolution, and particle count (Schartau et al. 2010), so that only size classes with less than 20% uncertainty are retained, in addition to gaps in the size spectra.</p> <p>Added in this version (March, 2024):<br>● An error in the thresholding function (scripts/funcs_NBS.py) was corrected.<br>● A quality control function was implemented (scripts/funcs_NBS.py) to flag size spectra calculations in products 1a and 1b.</p> <p>Added in this version (April, 2024):</p> <p><span><span>●<span> </span></span></span><span>An error in the size classes defined in the <a href="https://github.com/jessluo/PSSdb/blob/main/ancillary/ecopart_size_bins.tsv"><span>ecopart_size_bins.tsv</span></a> used by the size binning function (<a href="https://github.com/jessluo/PSSdb/commit/9e3c52179b2d3333971028a4a023a06e48444283#diff-28c0193fe5dad62dccba0020363d6cc496a8921fccb723a5e4bf3608d3dd0879"><span>scripts/funcs_NBS.py</span></a>) was corrected, the size ratio between consecutive size bins is the same across all the size ranges now.</span></p> <p> </p> <p>This PSSdb dataset is composed of two products, specific to each imaging device: </p> <ul> <li><strong>Product </strong>1a includes the size distribution , computed from normalized biovolume, for NBSS, and normalized abundance, for PSD, of plankton and particles within a set of pre-defined size classes (expressed in both biovolume and equivalent circular diameter), averaged by year and month, and in 1-degree longitude/latitude grid cells.</li> <li><strong>Product 1b</strong> includes the results of NBSS and PSD regression fit parameters, slopes, intercept, and coefficient of determination (R2), averaged by year and month, and in 1-degree longitude/latitude grid cells. The regression parameters are defined using ordinary least squares linear regressions applied to a log10 transformed normalized biovolume/normalized abundance and biovolume/ diameter size class values.</li> </ul> <p>Size spectra parameters were averaged over a maximum of 16 spatial and temporal subsets (0.5°x0.5°x1 week) to avoid over-representation of repeated sampling events (e.g., time-series datasets) within a grid cell. Linear regressions were performed on the linear portion of the log10-transformed NBSS and PSD estimates, between the size classes with a size measurement or particle count uncertainty greater than 20% (Schartau et al. 2010) , and where the maximum NB/PSD is observed and the largest size class before three empty consecutive size classes.</p> <p><em> <strong> For additional information, please see the PDF documentation available below ...</strong></em></p> <p> </p>
PSSdb-Taxa: A Pelagic Size Structure database taxa-specific product to support biogeochemical modeling: Update to first release of taxa-specific products
<p>This dataset represents the first update to the first release of taxa-specific products from the Pelagic Size Structure database (PSSdb,<a href="https://pssdb.net/"> </a><a href="https://pssdb.net">https://pssdb.net</a>), a scientific project investigating the global particle size distributions measured from multiple pelagic imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and<a href="https://ifcb-data.whoi.edu/dashboard"> </a><a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>).</p> <p>Taxa-specific products were generated after standardization of the automated or manual taxonomic annotations assigned to individual particles following the recent guidelines of Neeley et al. (2021). We used the World Register of Marine Species (WoRMS, <a href="https://www.marinespecies.org/">https://www.marinespecies.org/</a>) to assign each particle its final taxonomic annotation, and published group-specific relationships linking biovolume to carbon biomass or dry weight.</p> <p>The herein taxa-specific products include both taxonomic class-specific data, obtained after grouping all particles in a given taxonomic class<strong><sup>§</sup></strong>, and broad plankton functional type (PFT) data<strong><sup>Ɨ</sup></strong>. Detrital materials were also separated based on common categories (marine snow, aggregate, fecal pellet) and known biovolume-to-biomass conversion factors (Durkin et al. 2021). Links to the PSSdb code (including the taxonomic and allometric look-up tables) and documentation are available on the PSSdb webpage (<a href="https://pssdb.net">https://pssdb.net</a>).</p> <p> </p> <p>This <em>updated version</em> includes the following changes (modified in April, 2024):</p> <p>● An error in the size classes defined in the <a href="https://github.com/jessluo/PSSdb/blob/main/ancillary/ecopart_size_bins.tsv">ecopart_size_bins.tsv</a> used by the script generating taxa-specific products (<a href="https://github.com/jessluo/PSSdb/commit/b5725df2f32f0300ab64b0a630136c7f5a6857c7">scripts/5_compute_taxa_NBSS.py</a>) was corrected, the size ratio between consecutive size bins is the same across all the size range.</p> <p> </p> <p><em><strong> For additional information, please see the PDF documentation available below ...</strong></em></p>
DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model
<p>Here, we store the modeled structures generated by DiffModeler for its 4 benchmark datasets: CryoREAD dataset(0-5A resolution, protein-DNA/RNA complex), ModelAngelo dataset(0-5A resolution, most protein complexes, a few protein-RNA complex), intermediate resolution dataset (5-10A resolution, protein complex), low resolution dataset (10-20A resolution, protein complex). For all protein-DNA/RNA complex, the map will be modeled by CryoREAD+DiffModeler.</p> <p>For each dataset, we keep the modeled structures by DiffModeler, named as [EMD-ID]_DiffModeler.cif; and their corressponding native structures from RCSB are saved as [EMD-ID]_[PDB_ID]_native.cif.</p> <p>For CryoREAD dataset, it includes 61 targets. For ModelAngelo dataset, it includes 28 targets. For intermediate resolution dataset , it includes 71 targets. For low resolution dataset, it incldues 6 targets.</p> <p>For intermediate resolution dataset, many maps were run with inaccurate AF2 predicted single-chain structures. We also benchmarked DiffModeler's performance by using native single-chain structures as input. They are saved under "dataset_5_10A_nativechain" folder.</p> <p>Additionally, we have stored the traced backbone map of the intermediate resolution dataset in the "dataset_5_10A_diffusion_traced_backbone_map" folder. The traced maps are saved as [EMD-ID]_diffusion.mrc. The intermediate reverse diffusion maps of the intermediate resolution dataset are saved in the "dataset_5_10A_reverse_diffusion_maps" folder. Each sub-folder is named according to the corresponding map's [EMD-ID] and contains three intermediate reverse diffusion maps: 20percentile_reverse_diffusion.mrc, 50percentile_reverse_diffusion.mrc, and 80percentile_reverse_diffusion.mrc. A higher percentile indicates a map closer to the end of the reverse diffusion steps.</p> <p>If you used DiffModeler, please cite: "Wang, Xiao, Han Zhu, Genki Terashi, Manav Taluja, and Daisuke Kihara. "DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model." bioRxiv (2024): 2024-01.".</p> <p>If you used CryoREAD, please cite: "Xiao Wang, Genki Terashi & Daisuke Kihara. De novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods, 2023."</p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
<p>Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Unlike previous methods that mainly focused on protein sequence features, we use a pretrained large natural language model to understand the semantic meaning of protein labels. Specifically, we introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPM's flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation.</p>
PSSdb-Merged: A Pelagic Size Structure database merged product to support biogeochemical modeling: First release of Merged products
<p>This dataset represents the first release of <strong>merged products</strong> from the Pelagic Size Structure database (PSSdb,<a href="https://pssdb.net/"> </a><a href="https://pssdb.net/">https://pssdb.net</a>), a scientific project investigating the global particle size distributions measured from multiple pelagic imaging systems. These devices include the Imaging Flow Cytobot (Olson and Sosik 2007), benchtop scanners like the ZooScan (Gorsky et al. 2010), and the Underwater Vision Profiler (Picheral et al. 2010). The data sources originate from Ecotaxa (<a href="https://ecotaxa.obs-vlfr.fr/">https://ecotaxa.obs-vlfr.fr/</a>), Ecopart (<a href="https://ecopart.obs-vlfr.fr/">https://ecopart.obs-vlfr.fr/</a>), and Imaging FlowCytobot dashboards (<a href="https://ifcb.caloos.org/dashboard">https://ifcb.caloos.org/dashboard</a> and<a href="https://ifcb-data.whoi.edu/dashboard"> </a><a href="https://ifcb-data.whoi.edu/dashboard">https://ifcb-data.whoi.edu/dashboard</a>).</p> <p><em><strong>Merged products</strong></em> correspond to the most complete datasets in PSSdb, whereby all instrument-specific datasets are merged together to produce a single, quasi-continuous size spectrum where only optimal values in overlapping size classes are selected for each spatio-temporal bin. The resulting spectra are produced after generating the taxa-specific products following the recent approach of Soviadan et al. (2023). Briefly, this approach assumes that one device is best suited in detecting certain taxa (amongst the living functional groups, see documentation), and size classes (depending on the resolution of the camera). As a result, we consider the maximum observed normalized biovolume/biomass and abundance amongst a set of non-null values from different instruments as the optimal measure for detection.</p> <p>Links to the PSSdb code (including the taxonomic and allometric look-up tables) and documentation are available on the PSSdb webpage (<a href="https://pssdb.net/">https://pssdb.net</a>).</p> <p> </p> <p><em><strong> For additional information, please see the PDF documentation available below ...</strong></em></p>
Рис. 3. Изменение ΔΛины теΛа у Bufo sachalinensis с возрастом: A — самки; B — самцы Fig. 3. The von Bertalanffy growth models for Bufo sachalinensis: A — females; B — males in Age structure and sexual dimorphism of the Far Eastern toad, Bufo sachalinensis Nikolsky, 1905 in the Ussurisky Nature Reserve
Рис. 3. Изменение ΔΛины теΛа у Bufo sachalinensis с возрастом: A — самки; B — самцы Fig. 3. The von Bertalanffy growth models for Bufo sachalinensis: A — females; B — males
Supplementary material for publication "Multi-Echelon Inventory Optimization in Supply Chain Networks: Exploring Network Structures and Predictive Modeling"
<div> <div> <div> <p>This dataset collects different supply chain network structures generated artificially. We present four types of networks: Serial, Convergent, Divergent, and General, each type consisting of 20,000 individual instances. All 80,000 network instances generated are available to researchers and practitioners in Excel. The repository consists of separate files for each network instance consisting of each network inventory data, node connections, and a visual representation.</p> </div> </div> </div>
The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 4. The artefact during the initial structuring stage
<p>The IT developer elaborates the detailed structure of the artefact, while considering several aspects:</p> <p>— The resources that are necessary to the artefact in order to accomplish its mission;</p> <p>— The artefact’s resistance to the changes regarding the functional requirements;</p> <p>— The artefact’s resistance to the technological changes;</p> <p>— The reasonably priced integration of the artefact in the structure of the host system;</p> <p>— The assurance of a reasonable reusability coefficient of the artefact during the struc- turing process of other artefacts;</p> <p>— The flexibility of the relations that exist among the components of the artefact;</p> <p>— The flexibility of the artefact’s connections with the host system.</p> <p> </p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
Structural modelling results to accompany the paper "Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing: an update"
<p>This repository contains the results of modelling missense mutants in KRAS, NRAS and BRAF observed in our study in the corresponding protein structures. Modelling was performed using FoldX.</p>
Data sets for modeling double strand break susceptibility and interrogating structural variation in cancer
<p>This is data used and produced for the study of "Modeling double strand break susceptibility to interrogate structural variation in cancer". </p> <p><strong>Background: </strong>Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs).</p> <p><strong>Results:</strong> We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumours, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumours. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumours and are enriched for active promoters and enhancers.</p> <p><strong>Conclusions:</strong> We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumours.</p>
A structural model of the human serotonin transporter in an outward-occluded state: MD simulation data
<p>The uploads contain relevant data to supplement the study https://www.biorxiv.org/content/10.1101/637009v1, where the details of the methods are described.</p> <p>charmm_energy_minimization.inp is the input file that was used to run an energy minimization on structural models</p> <p>The two archives contain relevant MD simulation data in coordinate, parameter and trajectory files:</p> <p>hSERT_Ce.tar.gz outward-open X-ray structure PDB 5I71</p> <p>hSERT_Ceo.tar.gz outward-occluded structural model</p>
WASHTREET. Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.
<p><strong>WASHTREET</strong><strong> - </strong><strong>Application of Structure from Motion (SfM) photogrammetric technique to determine surface elevations in an urban drainage physical model.</strong></p> <p>This dataset contains raw data and surface elevations results from the application of the Structure from Motion (SfM) photogrammetric technique in a 36 m<sup>2</sup> full-scale urban drainage physical model, which is placed in the Hydraulic Laboratory of the Centre for Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain). This work is part of the <a href="https://zenodo.org/communities/washtreet">WASHTREET project</a>, where a series of high-resolution experiments were performed measuring urban surface wash-off and sediment transport through gully pots and pipes under laboratory-controlled conditions. The accurately measurement of the surface elevations is needed for a proper representation of surface flow, which is key in the detachment and transport of solids in the model surface. The dataset was used in the work developed in Naves et al. (2019) (DOI: <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a>)</p> <p>A detailed description of experimental procedure and data collected can be consulted in ‘<em>1_ExperimentalProcedure.pdf’</em>. Raw images taken as input for the SfM software are included in ‘<em>2_RawImages.zip’</em>. Then, the point cloud resulted is provided in ‘<em>3_SFM_RawPointCloud.ply</em>’. This point cloud was processed and the final elevation map with a resolution of 5 mm is included in ‘<em>4_SfM_ElevationMap(m).xyz</em>’.</p> <p>Further details of the physical model and hydraulic and sediment transport experiments can be consulted in the dataset <a href="http://doi.org/10.5281/zenodo.3233918"><em>WASHTREET - Hydraulic, wash-off and sediment transport experimental data</em></a>. In addition, raw data and runoff velocities results obtained using seeded and unseeded Particle Image Velocimetry (PIV) techniques are provided in the dataset <a href="http://www.doi.org/10.5281/zenodo.3239401">WASHTREET - PIV data</a>.</p> <p>The WASHTREET project is being developed in the scope of the PhD thesis of the first author, which is in receipt of a Spanish Ministry of Science, Innovation and Universities predoctoral grant [FPU14/01778]. The project also receive funding from the Spanish Ministry of Science, Innovation and Universities under POREDRAIN project RTI2018-094217-B-C33 (MINECO/FEDER-EU)</p> <p>Derived publications:</p> <ul> <li>Naves, J., Anta, J., Puertas, J., Regueiro-Picallo, M., & Suárez, J. (2019). Using a 2D shallow water model to assess Large-Scale Particle Image Velocimetry (LSPIV) and Structure from Motion (SfM) techniques in a street-scale urban drainage physical model. <em>Journal of Hydrology</em>, <em>575</em>, 54-65. <a href="https://doi.org/10.1016/j.jhydrol.2019.05.003">https://doi.org/10.1016/j.jhydrol.2019.05.003</a></li> <li>Naves, J., Anta, J., Suárez, J., & Puertas, J. (2020). Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model. <em>Scientific Data</em>, <em>7</em>(1), 1-13.<a href="http://doi.org/10.1038/s41597-020-0384-z"> https://doi.org/10.1038/s41597-020-0384-z</a></li> </ul>
Compilation of Moon internal structure models and seismic event locations presented in Garcia et al., Space Science Review, 2019 study
<p>Compilation of of previously published internal structure model of the Moon in "named discontinuities" seismological format + 3 new models associated to the above mentioned study + previously published Moon quake location estimates by various authors.</p> <p>The study, the internal structure models and quake locations compilation were performed by the ISSI international research team on Moon Seismology and internal structure described here: http://www.issibern.ch/teams/internstructmoon/</p>
UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA
<p>Data for:</p> <p>Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA<br>Sean Reilly 1, Matthew L. Clark 2, Lika Loechler 2, Jack Spillane 2, Melina Kozanitas 3, Paris Krause 4, David Ackerly 3, Lisa Patrick Bentley 4, and Imma Oliveras Menor 1,5</p> <p>1 Environmental Change Institute, University of Oxford, Oxford OX1 3QY, UK<br>2 Center for Interdisciplinary Geospatial Analysis, Department of Geography, Environment, and Planning, Sonoma State University, Rohnert Park, CA 94928, USA<br>3 Departments of Integrative Biology and Environmental Science, Policy, and Management, University of California, Berkeley, CA 94720, USA<br>4 Department of Biology, Sonoma State University, Rohnert Park, CA 94928, USA<br>5 AMAP (Botanique et Modélisation de l’Architecture des Plantes et des Végétations), CIRAD, CNRS, INRA, IRD, Université de Montpellier, Montpellier, France</p> <p>Study abstract:</p> <p>There is a pressing need for well-informed management to reduce wildfire hazard and restore fire’s beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.</p> <p>Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69 – 0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49 – 0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.</p> <p>These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs. </p> <p>Published in Remote Sensing of Environment</p> <p><br>Contents:</p> <p>This repository contains multispectral UAS-SfM data from four sites around California, USA:<br>jcksn: Jackson Demonstration State Forest<br>ltr: LaTour Demonstration State Forest<br>ppwd: Pepperwood Preserve<br>sdlmtn: Saddle Mountain Open Space Preserve</p> <p>Data were collected during a series of campaigns:<br>c1: Pepperwood, 2019-09-01 to 2019-10-15<br>c3: Jackson, 2020-06-15 to 2020-07-02<br>c4: LaTour, 2020-07-07 to 2020-07-17<br>c6: Saddle Mountain, 2020-08-04 to 2020-08-09<br>c9: Jackson, 2021-07-08 to 2021-07-12</p> <p>Data are included in three formats:<br>raw: Raw outputs from Pix4D (spectral and las)<br>reg_grnd, reg_cnpy: Las files with merged multispectral data and classified ground, registered to ALS using either ground points (grnd) or, in cases with insufficient ground points for registration, to the canopy (cnpy)<br>hnrm: Height normalized las files, normalization performed using ALS terrain model</p> <p>File naming structure:<br>site_campaign_flightzone_uas_processedstate</p> <p>See accompanying paper for methods on data collection and processing</p> <p>Data are grouped into zipped folder by product type</p> <p>Funding:</p> <p>Funding for this research was supported by CAL FIRE Forest Health and Forest Legacy (8GG18806) and California State University, Agricultural Research Institute (20-01-106) awards to L.P.B and M.L.C. S.R. was funded by the Rhodes Trust and through the University of Oxford Environmental Change Institute Small Grant Scheme. Pepperwood ground data collection was supported by funding from the Gordon and Betty Moore Foundation and National Science Foundation grants 1754475 and 1835086.</p> <p>Citation:</p> <div> <div>Reilly, S., Clark, M.L., Loechler, L., Spillane, J., Kozanitas, M., Krause, P., Ackerly, D., Bentley, L.P., Menor, I.O., 2024. Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA. Remote Sensing of Environment 312, 114310. <a href="https://doi.org/10.1016/j.rse.2024.114310">https://doi.org/10.1016/j.rse.2024.114310</a></div> </div> <p> </p> <p> </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.