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1,646 results for “plot”
Supplementary Figure S1 The scatter plots of the association between genetically predicted in the reverse MR analysis
<p>This figure described the scatter plots of the association between genetically predicted in the reverse MR analysis.</p>
Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations Confined Between Charged Electrodes With Various Surface Charges: Plots
<p>Plots of the data contained in the data sets</p> <ul> <li>Uncharged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13164944">https://doi.org/10.5281/zenodo.13164944</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165450">https://doi.org/10.5281/zenodo.13165450</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165725">https://doi.org/10.5281/zenodo.13165725</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13166024">https://doi.org/10.5281/zenodo.13166024</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> </ul> </li> <li>Charged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13166152">https://doi.org/10.5281/zenodo.13166152</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167128">https://doi.org/10.5281/zenodo.13167128</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167338">https://doi.org/10.5281/zenodo.13167338</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167551">https://doi.org/10.5281/zenodo.13167551</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167614">https://doi.org/10.5281/zenodo.13167614</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes With Various Surface Charges</li> </ul> </li> </ul>
Plot-level semantically labelled terrestrial laser scanning point clouds
<p><strong>Abstract</strong></p> <p>Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function. However, unlocking their full potential currently requires extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture, competition, space optimisation and physiology, and is a key step in common approaches to individual tree extraction. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture developed from PointNet++ and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, applied to diverse natural European forests. Our model combines meticulously labelled data with voxel-based sampling and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across an extensive dataset, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet++-based approach for leaf/wood segmentation on a high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We tested our model against others’ open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, finding consistently strong performance, showcasing the transferability of our model to a wide range of ecosystems and sensors. Our newly developed evaluation metric for assessing performance in the outer parts of the canopy, such as in twigs and small branches, found our model to clearly outperform the most widely used approach.</p> <p><strong>Methods</strong></p> <p>Within each country, we scanned a subset of the 30 m x 30 m FUNDIV plots using a Riegl VZ400i TLS instrument (RIEGL Gmbh, Horn, Austria), scanning at 600MHz and with an angular resolution of 0.04 mrad. All plots were scanned following a 10m grid system with a minimum of 16 upright and 16 tilt scans (following Wilkes et al. 2017), with additional scans to minimise occlusion in dense areas, and on the plot perimeter. To ensure high-quality data with minimal noise, scanning was paused when wind conditions rose above 5 m/s (measured with an anemometer on the ground) or when gusts were visually evident. In order to create our labelled dataset, we used a semi-automated approach informed by existing approaches followed by significant manual cleaning. Vicari et al. (2019) found anisotropy, verticality and linearity to be informative features for leaf-wood separation, so we created these geometric features at spatial scales of approx. 5 cm - 0.5 m (using CloudCompare, 2023). Alongside these, we used reflectance and xyz information for each point and labelled leaf-wood by thresholding these features. We followed this with intensive manual checking and cleaning to ensure high label quality, especially in the smaller branches and twigs. Our dense scanning and labelling strategy means that this dataset is an ideal candidate for training and testing the capabilities of processing algorithms. Our dataset comprises nine 10 m x 10 m blocks of x, y, z coordinates with corresponding reflectance values and labels, all at 1 cm resolution achieved through voxel downsampling. This data was used for training and validation. For comprehensive evaluation, we incorporated additional openly available datasets, which we cropped to reduce size and cleaned to rectify erroneous labels. The original, unmodified versions of these datasets can be accessed as follows:</p> <div> <p>*Mspace Lab (2024) ‘ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing’, Geo-spatial Information Science. Zenodo. <a href="https://doi.org/10.5281/zenodo.13285640">https://doi.org/10.5281/zenodo.13285640</a>. Distributed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International licence.</p> <p>Wang, Di; Takoudjou, Stéphane Momo; Casella, Eric (2021). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.np5hqbzp6">https://doi.org/10.5061/dryad.np5hqbzp6</a>. Distributed under a Creative Commons 0 1.0 Universal licence.</p> <p>Wan, Peng; Zhang, Wuming; Jin, Shuangna (2021). Plot-level wood-leaf separation for terrestrial laser scanning point clouds [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rfj6q5799">https://doi.org/10.5061/dryad.rfj6q5799</a>. Distributed under a Creative Commons CC0 1.0 Universal licence.</p> <p>Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", <a href="https://doi.org/10.11588/data/UUMEDI">https://doi.org/10.11588/data/UUMEDI</a>, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]. Distributed under a Creative Commons Attribution 4.0 International Deed.</p> </div> <p>*For licensing reasons these data re not included in this repository but can be downladed from the doi provided. </p> <p> </p> <p> </p>
Supplementary plots
<p>To comprehensively evaluate our proposed methodology for lightweight and effective compression ratio estimation, we present experimental results on multiple datasets. We consider three different lossy compressors: SZ3, ZFP, and SPERR, each with error bounds of 1e-3 and 1e-6.</p> <p>For the Hurricane, Miranda, and NYX datasets, we perform a 12-fold cross-validation using our proposed method. The median approximate percentage error (MedAPE) from each validation fold is used to create a distribution of MedAPE values, as depicted in the respective plots. These experiments exemplify the "in-sample prediction accuracy" mentioned in the main paper. The color-coded crosses in the plots represent the maximum MedAPE observed across all the fields for each of the compressors.</p> <p>Regarding the CESM datasets, we focus on the first 10 time snapshots, each containing multiple 2D and 3D fields. We specifically consider the following 3D fields with dimensions of 3600X1800X26 due to their spatial diversity: CLOUD, CONCLD, FICE, GCLDLWP, ICLDIWP, ICLDTWP, OMEGA, OMEGAT, RELHUM, T, U, UU, V, VT, VU, VV, Z3. Similar to the in-sample prediction experiments, we employ a 12-fold cross-validation and illustrate the distribution of MedAPE values obtained from each validation fold.</p> <p>Lastly, for the Qmcpack dataset, which consists of 280 time-points, each containing a 3D dataset with dimensions of 69X69X115, we assess the out-of-sample prediction accuracy. We train our model using datasets from time points 1:20, 40:60, 80:100, ..., 240:260, and then use the remaining fields as the test data. The MedAPE (%) is plotted on the y-axis, while the x-axis represents the corresponding time points as the test dataset.</p>
Simulations and plot scripts for Ringhals
<p>Simulations and plot scripts for Ringhals that were used to make the report "In search of lost storms: or can one infer the magnitude of extremely rare events from short observational records"</p>
Data from: Variation in three community features across habitat types and scales within a 15-ha subtropical evergreen-deciduous broadleaved mixed forest dynamics plot in China
Open the record for dataset details and reuse information.
GBM Quicklook Daily and Orbital Plots
Time histories of NaI, GBO, and GOES data, with flare and day/night markers. Orbital plots are on RHESSI orbit times.
HIRENASD Experimental Data, Individual Plots
The HIRENASD data produced by analyzing the experimental data is repeated on this website, for those who can not download the information in the zip format found on the primary Experimental Data page, or who wish to examine the plots of the data online.
LAT 4-day Quicklook Plots
LAT solar gamma-ray flux > 100 MeV, one point per solar exposure (i.e. average of the 20-40 minutes of solar observation every ~1.5 hours) calculated by two different methods.
GBM Quicklook Daily and Orbital Plots
Time histories of NaI, GBO, and GOES data, with flare and day/night markers. Orbital plots are on RHESSI orbit times.
LAT 4-day Quicklook Plots
LAT solar gamma-ray flux > 100 MeV, one point per solar exposure (i.e. average of the 20-40 minutes of solar observation every ~1.5 hours) calculated by two different methods.
HIRENASD Experimental Data, Static Cp Plots and Data files
Tecplot (ascii) and matlab files are posted here for the Static pressure coefficient data sets. To download all of the data in either tecplot format or matlab format, you can go to https://c3.nasa.gov/dashlink/resources/485/ Please consult the documentation found on this page under Support/Documentation for information regarding variable definition, data processing, etc.
To a better understanding of the impact of vine nitrogen status on volatile thiols from plot to transcriptome level
GEO Series GSE77895. Vitis vinifera. 24 samples. Type: Expression profiling by high throughput sequencing.
Fig. 6 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 6 Fusarium lumajangense (ex-type InaCC F993). a. Culture grown on PDA; b–c. sporodochia on carnation leaves; d–i. aerial conidiophores and phi- alides; j–m. aerial conidia; n–p. sporodochial conidiophores and phialides; q–s. sporodochial conidia. — Scale bars: b–d = 50 µm; e = 5 µm; f–s = 10 µm.
Fig. 1 Maximum likelihood tree inferred using the rpb2 in New endemic Fusarium species hitch-hiking with pathogenic Fusarium strains causing Panama disease in small-holder banana plots in Indonesia
Fig. 1 Maximum likelihood tree inferred using the rpb2 gene region of the Indonesian isolates in the Fusarium fujikuroi species complex (FFSC), Fusarium incarnatum-equiseti species complex (FIESC), Fusarium sambucinum species complex (FSSC), and Fusarium oxysporum species complex (FOSC) isolates from a previous study (Maryani et al. 2019). Bootstrap support values and Bayesian posterior probabilities are given at each node. The tree is rooted to Fusarium acuminatum (NRRL 54210) and Fusarium heterosporum (NRRL 20692).
Exploring Context-Free Languages via Planning: The Case for Automating Machine Learning -- Additional Plots
<p>A comparison to SOTA AutoML approach on various datasets</p>
Dataset for Stairway Plot 2
<p>File description: </p> <p>1. Fig 1A data.zip: blueprint files for producing Figure 1A.</p> <p>2. Fig 2 data.zip: blueprint files for producing Figure 2. </p> <p>3. sharpCEU.macs.ms.out.gz: simulated sequence data based on the sharpCEU model in the ms format. Used for comparison in Supplementary Figure 3.</p> <p>4. sharpYRI.macs.ms.out.gz: simulated sequence data based on the sharpYRI model in the ms format. Used for comparison in Supplementary Figure 4.</p> <p>5. stairway-plot-v2-src.zip: Java source codes of Stairway Plot 2.</p> <p>6. stairway_plot_v2.1.zip: executable package of Stairway Plot 2.</p> <p>7. Supplementary Fig 1 data.zip: blueprint files and demographic models used in Supplementary Figure 1.</p> <p>8. zig-zag-10Mx10.zip: simulated sequence data based on the zig-zag model in the ms format. Used for comparison in Figure 1B.</p>
Datasets and Plots used in CODARFE publication
<p>Al the 30 datasets used in the project are inside the folder Data.</p> <p>Every plot used in the paper has its own Google Colab extension document (ipynb), which is kept distinct in its own folder. </p>
Plotting Something?
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677514700.4161.jpg">https://4dcity.org/imgupload/1677514700.4161.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/3826/13536115795_c8d9b24965_m.jpg">https://live.staticflickr.com/3826/13536115795_c8d9b24965_m.jpg</a>
SHANNON__20240612__DATA_RawExp__1T2R_PCM_28nm_waveform_plots
<p>Plots of applied and measured waveforms.</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.