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9,204 results for “tree”
Outputs of the Jupyter Notebook - Tree crown detection using DeepForest
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Matt Allen (reviewer), Department of Geography - University of Cambridge, <a href="https://github.com/mja2106">@mja2106</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Ben Weinstein (maintainer & developer), University of Florida, <a href="https://github.com/bw4sz">@bw4sz</a></p> </li> <li> <p>Henry Senyondo (support maintainer), University of Florida, <a href="https://github.com/henrykironde">@henrykironde</a></p> </li> <li> <p>Ethan White (PI and author), University of Florida, <a href="https://github.com/ethanwhite">@weecology</a></p> </li> <li> <p>Other contributors are listed in the <a href="https://github.com/weecology/DeepForest/graphs/contributors">GitHub repo</a></p> </li> </ul> <p><em>Modelling publications</em></p> <ul> <li> <p>Ben G Weinstein, Sergio Marconi, Mélaine Aubry-Kientz, Gregoire Vincent, Henry Senyondo, and Ethan P White. Deepforest: a python package for rgb deep learning tree crown delineation. <em>Methods in Ecology and Evolution</em>, 11:1743–1751, 2020. URL: <a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472">https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472</a>, <a href="https://doi.org/https://doi.org/10.1111/2041-210X.13472">doi:https://doi.org/10.1111/2041-210X.13472</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, and Ethan White. Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. <em>Remote Sensing</em>, 2019. URL: <a href="https://www.mdpi.com/2072-4292/11/11/1309">https://www.mdpi.com/2072-4292/11/11/1309</a>, <a href="https://doi.org/10.3390/rs11111309">doi:10.3390/rs11111309</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie A Bohlman, Alina Zare, and Ethan P White. Cross-site learning in deep learning rgb tree crown detection. <em>Ecological Informatics</em>, 56:101061, 2020. URL: <a href="https://www.sciencedirect.com/science/article/pii/S157495412030011X">https://www.sciencedirect.com/science/article/pii/S157495412030011X</a>, <a href="https://doi.org/https://doi.org/10.1016/j.ecoinf.2020.101061">doi:https://doi.org/10.1016/j.ecoinf.2020.101061</a>.</p> </li> </ul>
Data and code for Bauer et al. (2023) Trees
<p>Data and code for:</p> <p>Bauer M, Krause M, Heizinger V & Kollmann J (2023) <strong>Increased brick ratio in urban substrates has a marginal effect on tree saplings.</strong> – <em>Trees</em>. <a href="https://doi.org/10.1007/s00468-023-02391-8">DOI: 10.1007/s00468-023-02391-8</a></p>
Outputs of the Jupyter Notebook - Tree crown delineation using detectreeRGB
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge, <a href="https://github.com/shmh40">@shmh40</a></li> <li>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></li> </ul> <p><em>Modelling codebase</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge <a href="https://github.com/shmh40">@shmh40</a></li> <li>James G. C. Ball (contributor), University of Cambridge <a href="https://github.com/PatBall1">@PatBall1</a></li> <li>David A. Coomes (contributor), University of Cambridge</li> <li>Toby Jackson (contributor), University of Cambridge</li> </ul>
Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"
<p>Artifact for the paper "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"</p> <p>The package contains:</p> <ul> <li>example files for all static and dynamic fault tree models</li> <li>installation instructions for the three tools</li> <li>scripts to perform the benchmarking</li> <li>detailed result tables</li> </ul>
Data from: Vegetative phenologies of lianas and trees in two Neotropical forests with contrasting rainfall regimes
<ol> <li>Among tropical forests, lianas are predicted to have a growth advantage over trees during seasonal drought, with substantial implications for tree and forest dynamics. We tested the hypotheses that lianas maintain higher water status than trees during seasonal drought and that lianas maximize leaf cover to match high, dry-season light conditions while trees are more limited by moisture availability during the dry season.</li> <li>We monitored the seasonal dynamics of predawn and midday leaf water potentials and leaf phenology for branches of 16 liana and 16 tree species in the canopy of two lowland tropical forests with contrasting rainfall regimes in Panama.</li> <li>In a wet, weakly seasonal forest, lianas maintained higher water balance than trees and maximized their leaf cover during dry-season conditions, when light availability was high, while trees experienced drought stress. In a drier, strongly seasonal forest, lianas and trees displayed similar dry season reductions in leaf cover following strong decreases in soil water availability.</li> <li>Greater soil moisture availability and a higher capacity to maintain water status allow lianas to maintain the turgor potentials critical for plant growth in a wet and weakly seasonal forest but not in a dry and strongly seasonal forest.</li> </ol>
Phylogenetic tree of the Kho-Bwa languages
<p>This is a phylogenetic tree of the Kho-Bwa languages spoken in Western Arunachal Pradesh, India. The map has been prepared using the data and methodology described in Wu, Bodt and Tresoldi (accepted). The map has also been used in Bodt (accepted).</p> <p>Wu, Mei-Shin, Timotheus A. Bodt & Tiago Tresoldi. accepted. Bayesian phylogenetics illuminate shallower relationships among Trans-Himalayan languages in the Tibet-Arunachal area. <em>Linguistics of the Tibeto-Burman Area.</em></p> <p>Bodt, Timotheus Adrianus. accepted. <em>Proto-Western Kho-Bwa: Reconstructing the past of a small indigenous community.</em> Academia Sinica Language and Linguistics monograph series.</p> <p> </p>
Presence-Absence Points for Tree Species Distribution Modelling for Europe
<p>The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by <a href="https://zenodo.org/record/5524611">Heisig and Hengl (2020)</a> and absence data from the <a href="https://ec.europa.eu/eurostat/web/lucas">LUCAS</a> (in-situ source) dataset.</p> <p>A set of <strong>50</strong> different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by <a href="https://zenodo.org/record/4725429">Parente et al. (2021)</a>. We overlaid the points with the probability maps for the classes:</p> <ul> <li>311: Broad-leaved forest,</li> <li>312: Coniferous forest,</li> <li>313: Mixed forest,</li> <li>323: Sclerophyllous forest,</li> <li>324: Transitional woodland-shrub,</li> <li>333: Sparsely vegetated area.</li> </ul> <p>Points were included in the dataset only if the probability value extracted for at least one of the above classes was <strong>≥ 50%</strong> for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets.</p> <p>The final dataset contains <strong>4,359,999</strong> observations for and a total of <strong>630 </strong>columns. <br> <br> The first <strong>8 </strong>columns of the dataset contain metadata information used to uniquely identify the points:</p> <ul> <li><strong>id</strong>: unique point identifier,</li> <li><strong>year</strong>: year of observation,</li> <li><strong>postprocess</strong>: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,</li> <li><strong>Tile_ID</strong>: contains the tile id from the eu_tiling_system (30 km grid),</li> <li><strong>easting</strong>: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>northing</strong>: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>Atlas_class</strong>: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,</li> <li><strong>lc1</strong>: contains original LUCAS land cover class or NULL if it's a presence point.</li> </ul> <p>The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of <strong>16 target species </strong>for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (<em>i.e.</em> <a href="http://link.springer.com/article/10.1023/A:1010933404324">Random Forest</a>, <a href="http://dl.acm.org/doi/abs/10.1145/2939672.2939785">XGBoost</a>, <a href="https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2344614">GLM</a>), which served as input to train a linear meta-model (<em>i.e.</em> <a href="http://papers.nips.cc/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">Logistic regression classifier</a>), responsible for predicting the final probability of presence for each species.</p> <p>The <em>RDS </em>file is created from a data.table object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p>We provide <em>RDS </em>files for a 30km tile as an example containing raster stacks at 30m resolution of all the covariates included in the regression matrix. You can find the specific geographical location of the tile in Europe using the attached <em>GeoPackage </em>("eu_tiling_system_30km"): open it in QGIS and filter by "ID".</p> <p>In our approach we considered both static and dynamic covariates: dynamic covariates are calculated as averages of a 4 years time window (example: 2004 contains averages from 2002 to 2006). To get the predictions for a specific year, covariates contained in the <em>static</em> RDS file need to be bound with the respective year.</p> <p>To access our predictions (probabilities and uncertainties) produced for the target species access:</p> <ul> <li><strong>Open Data Science Europe viewer: <a href="https://maps.opendatascience.eu">https://maps.opendatascience.eu</a></strong></li> <li>Check the <strong>Related identifiers </strong>section of this repository to access each species individually</li> </ul> <p>If you instead would like to know more about the creation of this dataset and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS
<p>Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.</p>
Potential and realized distribution at 30m for Olive tree (Olea europaea) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the olive tree (<em>Olea europaea, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_olea.europaea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>olea.europaea</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_olea.europaea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_olea.europaea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_olea.europaea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_olea.europaea_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Xylem conductances of 24 tree species of Europe
<p>The dataset contains the xylem conductances (<em>k</em><sub>s</sub>) in kg m<sup>-2</sup>MPa<sup>-1</sup>s<sup>-1,</sup> and mm<sub>H2O </sub>mm<sup>-1</sup>s<sup>-1</sup> as the Community Land Model 5.0 (CLM5) requires. In addition, the data set has individual records of 24 tree species describing the plant functional types broadleaf deciduous (BDT), broadleaf evergreen (BET), and needleleaf evergreen (NET) trees. The data comes from 21 references from European experiments.</p> <p>The tree species selected for this data set are:</p> <p>BDT: <em>Acer pseudoplatanus, Betula occidentalis, Carpinus betulus, Fagus sylvatica, Fraxinus excelsior,Quercus alba, Quercus cerris, Quercus petraea, Quercus pubescens, Quercus robur, Quercus rubra, Tilia cordata, Carpinus orientalis, Quercus frainetto.</em></p> <p>BET: <em>Quercus ilex, Quercus suber, Arbutus unedo.</em></p> <p>NET: <em>Abies bornmulleriana, Picea abies, Pinus pinaster, Pinus sylvestris, Tsuga heterophylla, Pinus nigra, Pseudotsuga menziesii.</em></p> <p>Special consideration was taken to <em>Pseudotsuga menziesii</em>, which was included in the data set by selecting only the European records. The species was included in a European data set because of its importance as an introduced commercial tree species for the European continent.</p> <p>The individual xylem conductances were retrieved directly from tables, manuscript text, or figures. The online tool WebPlotDigitizer (https://automeris.io/WebPlotDigitizer) was used to retrieve individual records.</p>
Mastic_Tree_09/29/22
Documentation material from the Mastic pilot of the Mingei project
Virtual Delivery Trees Evaluation Results
<p>The artifacts represent evaluation results of real world networks having more than 40 nodes from <a href="http://www.topology-zoo.org/">Network Topology Zoo</a>. The applied topologies are listed in following table, sorted in descending order by diameter (d) and nodal degree fluctuation (σ^2)).</p> <table> <thead> <tr> <th scope="col">Topology</th> <th scope="col">|V|</th> <th scope="col">|E|</th> <th scope="col"><k></th> <th scope="col">σ^2</th> <th scope="col">d</th> </tr> </thead> <tbody> <tr> <td>Chinanet</td> <td>42</td> <td>66</td> <td>1.5</td> <td>10.52</td> <td>4</td> </tr> <tr> <td>Litnet</td> <td>43</td> <td>43</td> <td>0.98</td> <td>5.04</td> <td>4</td> </tr> <tr> <td>Cernet</td> <td>41</td> <td>58</td> <td>1.32</td> <td>5.6 |</td> <td>5 |</td> </tr> <tr> <td>Ntt</td> <td>32</td> <td>65</td> <td>1.48</td> <td>7.07</td> <td>6</td> </tr> <tr> <td>Cesnet200706</td> <td>44</td> <td>51</td> <td>1.16</td> <td>6.27</td> <td>6</td> </tr> <tr> <td>Carnet</td> <td>44</td> <td>43</td> <td>0.98</td> <td>5.48</td> <td>6</td> </tr> <tr> <td>Dfn</td> <td>50</td> <td>78</td> <td>1.77</td> <td>5.31</td> <td>6</td> </tr> <tr> <td>Telcove</td> <td>71</td> <td>70</td> <td>1.59</td> <td>9.13</td> <td>7</td> </tr> <tr> <td>Forthnet</td> <td>62</td> <td>62</td> <td>1.41</td> <td>7.72</td> <td>7</td> </tr> <tr> <td>Bellsouth</td> <td>51</td> <td>66</td> <td>1.5 |</td> <td>7.55 |</td> <td>7 |</td> </tr> <tr> <td>Garr200902</td> <td>54</td> <td>68</td> <td>1.55</td> <td>5.13</td> <td>7</td> </tr> <tr> <td>Arnes</td> <td>41</td> <td>57</td> <td>1.3 |</td> <td>4.53 |</td> <td>7 |</td> </tr> <tr> <td>BeyondTheNetwork</td> <td>53</td> <td>65</td> <td>1.48</td> <td>3.98</td> <td>7</td> </tr> <tr> <td>Uunet</td> <td>49</td> <td>84</td> <td>1.91</td> <td>7.38</td> <td>8</td> </tr> <tr> <td>Tw</td> <td>71</td> <td>115</td> <td>2.61</td> <td>| 5.58</td> <td>| 8</td> </tr> <tr> <td>Uninett</td> <td>71</td> <td>97</td> <td>2.2</td> <td>3.12</td> <td>9</td> </tr> <tr> <td>Renater2010</td> <td>43</td> <td>56</td> <td>1.27</td> <td>3.08</td> <td>9</td> </tr> <tr> <td>Surfnet</td> <td>50</td> <td>68</td> <td>1.55</td> <td>3.36</td> <td>11</td> </tr> <tr> <td>Iris</td> <td>51</td> <td>64</td> <td>1.45</td> <td>2.16</td> <td>11</td> </tr> <tr> <td>Palmetto</td> <td>45</td> <td>64</td> <td>1.45</td> <td>2.57</td> <td>12</td> </tr> <tr> <td>BtLatinAmerica</td> <td>45</td> <td>50</td> <td>1.14</td> <td>1.87</td> <td>12</td> </tr> <tr> <td>Bellcanada</td> <td>48</td> <td>64</td> <td>1.45</td> <td>2.59</td> <td>13</td> </tr> <tr> <td>Sanet</td> <td>43</td> <td>45</td> <td>1.02</td> <td>1.66</td> <td>13</td> </tr> <tr> <td>LambdaNet</td> <td>42</td> <td>46</td> <td>1.05</td> <td>1.57</td> <td>13</td> </tr> <tr> <td>HiberniaGlobal</td> <td>55</td> <td>81</td> <td>1.84</td> <td>2.72</td> <td>16</td> </tr> <tr> <td>Ntelos</td> <td>47</td> <td>58</td> <td>1.32</td> <td>1.92</td> <td>17</td> </tr> <tr> <td>RedBestel</td> <td>84</td> <td>93</td> <td>2.11</td> <td>0.85</td> <td>28</td> </tr> <tr> <td>VtlWavenet2008</td> <td>88</td> <td>92</td> <td>2.09</td> <td>0.11</td> <td>31</td> </tr> </tbody> </table> <p>The evaluation results consist of three major parts:</p> <ol> <li><em>Raw Data</em>: Configuration and results of all simulation experiments as CSV files.</li> <li><em>Strategy Results</em>: Visualization of the test results for each topology.</li> <li><em>Best Strategies</em>: Highlighting of the best strategies across all topologies.</li> </ol> <p>Therein, the "Raw Data<em>"</em> comprise the configuration of or simulation experiments and the simulation results. Each line stands for a single simulation run.</p> <p><em>"</em>Strategy Results<em>" </em>and "Best Strategies" accompany the results presented in the paper. Result plots in the paper are excerpts from the plots in this repository. See below for further details.</p> <p><strong>Raw Data</strong></p> <p>Both, the configuration of a run and its results correspond to one line within a CSV file in subfolder <code>./raw</code>. Each file comprises the results of a replication.<br> </p> <pre><code>raw ├── results_0.csv ├── results_1.csv ├── ... └── results_9.csv</code></pre> <p>The raw data of a CSV file is structured as follows.</p> <table> <thead> <tr> <th scope="col">Column</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>topo</td> <td>Topology name.</td> </tr> <tr> <td>peers</td> <td>Number of nodes.</td> </tr> <tr> <td>edges</td> <td>Number of links.</td> </tr> <tr> <td>p_publishers</td> <td>Proportion of nodes acting as publisher (15% - 45%).</td> </tr> <tr> <td>p_subscriber</td> <td>Proportion of nodes acting as subscriber (15% - 45%).</td> </tr> <tr> <td>n_rules</td> <td>Number of allowed rules per switch.</td> </tr> <tr> <td>distances</td> <td>Flag for consideration of geographical distances (currently not used).</td> </tr> <tr> <td>strategy</td> <td>Applied virtual tree strategy.</td> </tr> <tr> <td>distribution</td> <td>Distribution method for client (uniform, distant, nearby)</td> </tr> <tr> <td>n_cluster</td> <td>Number of simulated clusters within the topology.</td> </tr> <tr> <td>p_change</td> <td>Churn rate of clients (0% - 100%).</td> </tr> <tr> <td>pub_change</td> <td>Flag for publisher migration (currently not used).</td> </tr> <tr> <td>tree_count</td> <td>Number of virtual trees installed in the network.</td> </tr> <tr> <td>selected_subscribers</td> <td>Avg. number of subscribers addressed by a publisher</td> </tr> <tr> <td>init_cost</td> <td>Avg. number of entries of a non-optimized distribution tree (per notification)</td> </tr> <tr> <td>trees</td> <td>Avg. proportion of tree entries per notification.</td> </tr> <tr> <td>stops</td> <td>Avg. proportion of stop entries per notification.</td> </tr> <tr> <td>hops</td> <td>Avg. proportion of hop entries per notification.</td> </tr> <tr> <td>final_cost</td> <td>Aggregated proportions (trees + stops + hops).</td> </tr> <tr> <td>datetime</td> <td>Timestamp of the simulation run.</td> </tr> </tbody> </table> <p><strong>Result Charts</strong></p> <p>The simulation results are visualized in <code>plots.md</code> or <code>plots.html</code>, ordered according above topology table.</p> <p>Each topology accompanys following:<br> - Topology figures with the computed <em>Clusters</em> therein.<br> - Line charts outlining the behavior of the strategies over changing <em>Number of Flow Rules</em>.<br> - Bar charts outlining the strategies' performance for different <em>Migration Scenarios</em>.</p> <p>Details of the figures and diagrams are described next.</p> <ul> <li>Clusters:<br> Visualization of exemplary groups within the topology, computed by `clusters` and `partitions` strategy. The clusters strategy assigns 60% of a network's nodes to cluster groups; the partition strategy, in contrast, assigns all nodes to groups. Both strategies are described in Sec. III.<br> </li> <li>Number of Flow Rules:<br> Results for varying number of rules (from 5 to 40) per switch, as described in Sec. IV. The charts are organized in a 3 x 3 matrix. A row of the matrix corresponds to different proportions of subscribers per publisher (15%, 30%, and 45%); a column corresponds to different distributions of clients (uniform, nearby and distant).<br> </li> <li>Migration Scenarios:<br> Results for different migration scenarios with a fixed number of rules (40 rules per switch), as described in Sec. V. Each bar group stands for a strategy and reflects the results of different migration rates (0%, 30%, 50%, 70%, 100%).</li> </ul> <p><strong>Best Strategies</strong></p> <p>Scatter plots in subfolder <code>./fluctuation</code> visualize the most efficient strategies for different migration scenarios by considering different proportions of subscribers per publisher (15%, 30%, and 45%). The plots show the results for a fixed number of subscribers (30% per publisher) and a churn rate of 100%. The strategies therein require the fewest labels in the header stack to encode a notification distribution tree, represented by the strategy's dot size</p>
PIOP_MASTIC_TREE_09/30/22
Documentation material from the Mastic pilot of the Mingei project
Individual tree data of the temporary test plot Neusorgefeld 5138 - VERMOS project
<p>This dataset is an artificial dataset of the dataset of the temporary trial plot in Neusorgefeld 5138 from the VERMOS project. The original dataset is significantly larger, the adaptation was made for a planned publication by Chris Wudel and was also carried out by him.</p>
Dataset of the paper "Vermicomposting as a sustainable option for the management of the biomass of the invasive tree Acacia dealbata Link."
<p>Data generated during an experiment of vermicomposting of <em>Acacia dealbata</em> fresh biomass. Four files are included: "<strong>vermicompost_and_earthworm_data.csv</strong>" and "<strong>readme.csv</strong>" are the raw data of different parameters measured in vermicompost samples during the vermicomposting of <em>Acacia dealbata</em> by the earthworm <em>Eisenia andrei</em> and an explanation of each parameter and the unit in which the parameter is expressed. </p> <p>"<strong>germination test.csv</strong>" and "<strong>radicle_length.csv</strong>" are the results of an ecotoxicological test on the effect of <em>A. dealbata</em> biomass and vermicompost on the germination and radicle elongation in <em>Lepidium sativum</em>.</p>
Thimble-like object with shrine and tree
<p>Thimble-like object cast in a copper alloy; on one side a rectangular panel showing a sacred tree, shrine, altar and standard; pierced. Probably from central India. British Museum number 1995,1018.1</p>
Wind data (2007-2017) in florentine and chianti areas to support tree's damages reporting.
<p>Wind data of several weather station to support tree damages investigations.</p> <p><strong>Firenze Peretola</strong> Areoporto LIRQ ENAV LAT 43.809722 LON 11.203 ELEV 44</p> <p><strong>Sesto Polo Scientifico</strong> LAMMA-CNR LAT 43.8189 LON 11.2021 ELEV 40</p> <p><strong>Sesto Case Passerini</strong> Codice CFR TOS01001225 LAT 43.82 LON 11.17 ELEV 33</p> <p><strong>Scandicci San Giusto</strong> CFR TOS01001215 LAT 43.76 LON 11.19 ELEV 42</p> <p><strong>Tavarnelle</strong> CFR TOS11000021 LAT 43.57 LON 11.16 ELEV 374</p> <p><strong>Greve in Chianti</strong> CFR TOS11000073 LAT 43.61 LON 11.30 ELEV 254</p> <p>Data sets gives annual and seasonal windplot roses. Wind data summaries by sectors of wind provenience ( Mean, Max,Median and Quantile95). Futher the 500th maximum records of gust are also extracted. Data are provided to support tree damages reporting.</p>
Data from : Tree inventory data from permanent plots in French forest reserves
<p>We present a dataset resulting from the first round of a national monitoring program of forest reserves. It contains 9538 permanent plots, distributed across 111 study sites in mainland France (including Corsica). Notably focusing on dead wood measurement, this protocol has primarily been applied in strict forest reserves and special nature reserves (sensu Bollmann et Braunisch 2013), with 68% (6494) of the plots being currently located in strict forest reserves (unmanaged) and 24,7% (2363 plots) in forests unmanaged for at least 50 years. Sites cover a large variety of ecological conditions, from lowland to subalpine forests, but with an underrepresentation of Mediterranean forests (Table 1). The protocol assesses all the stages of a tree's life cycle, from seedling to decomposed lying dead wood. On each plot, a combination of three sampling techniques was used: (i) fixed area inventory for regeneration, standing dead trees, living trees and coarse woody debris (CWD) with diameter over 30 cm, (ii) transect lines for CWD with diameter < 30 cm, and (iii) fixed angle plot method for living trees with a diameter at breast height (DBH) > 30 cm (using a relascopic angle of 3%). Measurements include: exact tree location (azimuth, distance), species, diameter(s), tree-related microhabitats, decay stage and bark cover, seedling cover. With the ongoing climate change, the program network can also provide important information to monitor changes in forest ecosystems. It can also be used as forest management monitoring or conservation status assessment.</p>
The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life. Data file underpinning Figure 2A and Figure 2B
<div>These datasheets accompany the article "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" in Frontiers in Science</div> <div>This file contains data processed from Catalog of Life on 31 December 2023. The catalog was downloaded and post-processed to</div> <div>remove prokaryotic taxa</div> <div>remove extinct and fossil taxa</div> <div>remove taxon names that were listed as junior synonyms</div> <div>remove taxon names listed as "invalid"</div> <div>Total living, valid eukaryotic genera 167,085</div> <div>The taxa were sorted by the nomenclatorial Code under which they were declared (to avoid namespace clashes)</div> <div>International Code for Algae, Fungi and Plants https://www.iapt-taxon.org/nomen/main.php</div> <div>Algal, Fungal, Plant code genera 31,076</div> <div>International Code of Zoological Nomenclature https://www.iczn.org/the-code/the-code-online/</div> <div>Zoological code genera 136,009</div> <div>The Code-sorted taxa were aggregated by the generic portion of their names, and two plots were generated:</div> <div>a plot aggregating the cumulative number of species in genera sorted by species number (Figure 2A)</div> <div>a plot illustrating the distribution of the size of genera (Figure 2B)</div> <div>This data file gives access to these processed data for</div> <div>Figure 2 A Data</div> <div>Figure 2 B Data</div> <div>The original data including the intermediate calculations of values, and the plotted graphs, are available as a GoogleDoc at https://docs.google.com/spreadsheets/d/1V-bTtWjIRasC3AgID0jGlyToKqI-H9h1aPeSxUpNrjk/edit?usp=sharing</div>
A spatio-temporal dataset for ecophysiological monitoring of urban trees
<p>A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured <em>in situ</em> and in the laboratory; (ii) plant area density measured <em>in situ</em> under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.</p> <p>The repository comprised 3 files : </p> <ul> <li><strong>DATASET_PART1.csv</strong> : This file contains leaf trait measurements</li> <li><strong>DATASET_PART2.csv</strong> : This file contains plant area density measurements </li> <li><strong>DATASET_PART3.gpkg</strong> : This file contains two spatial vector layers: (1) <em>CROWN_EXTENT </em>that is<em> </em>a polygon layer describing tree crowns and (2) <em>TRUNK_LOCATION</em> that is a point layer describing tree location.</li> </ul> <p>More details on the study site, protocols and data can be found in the following reference:</p> <p>Théo Le Saint, Jean Nabucet, Cécile Sulmon, Julien Pellen, Karine Adeline, Laurence Hubert-Moy, A spatio-temporal dataset for ecophysiological monitoring of urban trees, Data in Brief, Volume 57, 2024, 111010, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.111010.</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.