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zenodo44/100

UAV outputs and associated field measurement of the herbaceous and tree of the Senegalese savanna across Senegal

<p>This dataset contains UAV outputs (mosaic, surface and terrain model) and field measurement of vegetation that were made in northern and Eastern Senegal.</p> <p>Sites</p> <p>National gradient measurements</p> <p>For the national gradients, the measurements were made on 45 different plots in two different field campaign. One in the Northern part at the end of September 2020 and the other in South eastern part of Senegal in middle of October. The selection of the site was a combination of accessibility (not far from the road) and diversity of vegetation. The average rainfall for the period 1981-2018 was ranging from 221 mm.y-1 to 468 mm. y-1 for the Northern Part and ranging 759 mm.y-1 to 1246 mm y-1 for the south eastern part.</p> <p>UAV flight plan</p> <p>We used a low-cost UAV with an RGB (Red Green Blue) captor integrated in the UAV.&nbsp; The UAV was an Anafi of Parrot with PIX4D capture application using the double gird flight plan in a square generally of 100m*100m; The height of the flight was 80m with an overlap of 80% at low speed with 80&deg; angle &deg;. &nbsp;The flights were made at any time during the day.</p> <p>Field measurement.</p> <p>Herbaceous Biomass.</p> <p>3 squares of 1 m&sup2; were sampled. All the aboveground biomass was cut and weighted in fresh. A composite sample was made for each site and weighted dry to evaluated the dry matter content and so the dry matter of each sample.</p> <p>The height of 5 herbaceous individuals selected randomly were measured. We recorded the species composition with percentage of cover of each species. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team. The positions of the squared was mark with a wood triangle painted on the ground.</p> <p>Tree measurement.</p> <p>Four trees were measured on the field. It was the four woody individuals the closest to the first square of herbaceous measurements were made in each direction (Northwest, North east, South West, South East).</p> <p>The distance to the first square of each tree were measured using a telemeter. The height was also measured with a laser telemeter. The circumference at 0.30cm and 1.3 cm were measured. The diameter of the tree crown in the north-south direction and in the west-east direction were measured to the crow area calculated assuming that the crown was a circle.</p> <p>The species were recorded. We collected an herbarium sample each time we had a new species. The sample were used to identified the species by the IFAN herbarium team.</p> <p>Image analysis.</p> <p>The images taken during each flight were processed using a PiX4D mapper (Pix4D SA, Lausanne, Switzerland). 3D mapping is the basic parameter proposed in the software. For each plot, an orthophotograph, a digital surface model, and a digital elevation model were computed and exported in GeoTIFF format.</p> <p>Data organization</p> <p>The data are organized in two separated folders for each dataset.</p> <p>Each dataset folders contains four folders:</p> <ul> <li>DSM that contains the surface model in tiff</li> <li>DTM that contains the terrain model in tiff</li> <li>Mosaic that the orthomosaic in tiff.</li> <li>Data that contains the shapefile with the position and table with the field measurements.</li> </ul> <p>The shapefile&rdquo; national-shape.shp&quot; contains the positions of both tree and herbaceous samples. In some case it was hard to position the squared or the tree. The position and the shape of the object are not well defined.</p> <p>The file &ldquo;tree-national.xlsx&rdquo; contains the information on the tree measurement. The ID that contains the site and the positions of the trees, the distance from the squared in m that indicate the distance of the tree to the biomass square. The height H (in m), the trunk circumference at 1.30m (TC1.3) and at 0.3m(TC0.3) in cmand the area of crown (Area). The species is also described.</p> <p>The file &ldquo; herbacous_national.xlsx&rdquo; contains the information on the herbaceous layer.</p> <p>For each square, the height of the herbaceous layer (H), Fresh mass (FM), Dry matter content (DMC) and dry Mass (DM) are presented; The last columns of the file are the different species with the percentage of cover in each case.</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2021View details →
zenodo44/100

Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks

<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes&#39; contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p>&nbsp;</p> <p>Abbreviation for column:</p> <p>X&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Longitude<br> Y&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Latitude<br> LAImax&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Leaf area index max reach<br> Nha&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Density per hectar<br> Vha&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Volume per hectar<br> NEE&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Net ecosystem exchange<br> NPP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;net primary production<br> Reco&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Respiration ecosystem<br> GPP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Gross primary production<br> Etveg&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Evapotranspiration canopy<br> Etsol&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Evapotranspiration sol<br> TR&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;tree transpiration<br> ETP&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;evapotranspiration potentiel<br> BiomassOfReserves&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Biomass of reserve<br> rw&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;ring width<br> dbh&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;diameter at breast heast<br> height&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;height<br> BBday&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Budburst date<br> rFD&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of frost<br> CRIM_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum combined risk index of mortality reach<br> rNSC&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of carbon starvation<br> rPLC&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;risk of embolism<br> rPLC_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum risk of embolism reach<br> CRIM&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;combined risk index of mortality<br> Climate&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Climatic model<br> rNSC_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;maximum risk of carbon starvation reach<br> rFD_max&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Maximum risk of frost&nbsp; reach<br> Scenario_Sylvicol&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;null means no silvulcture simulated<br> species&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;species<br> Country&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Country<br> alt_watch&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;altitude of climate simulated<br> grid_watch&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;number of the pixel point of WATCH<br> grid_eurocordex&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;number of the pixel point of Eurocordex<br> Pinus_sylvestris&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Fagus_sylvatica&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Quercus_petraea&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Picea_abies&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence<br> Pinus_pinaster&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;0 abscence&nbsp;; 1 presence</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2020View details →
zenodo44/100

tree architectural traits in adult trees subjected to stemflow

<p>Dataset containing key tree architectural traits from tree individuals of Maple and Ash subjected to stemflow and stemflow-supression conditions</p>

opencc-by-4.0Aug 2021View details →
zenodo44/100

ForTrunkDet - Image dataset of visible and thermal annotated images for forest tree trunk detection

<p>Forest dataset composed by&nbsp;visible and thermal images with&nbsp;trunk&nbsp;annotations. The images were acquired in three different portuguese forests and were captured by four different cameras:</p> <ul> <li>GoPro Hero6</li> <li>Allied Mako G-125</li> <li>FLIR M232</li> <li>ZED Stereo</li> </ul> <p>The images and annotations are stored in two zip files:</p> <ul> <li>forest_dataset_original.zip -&nbsp;original dataset</li> <li>forest_dataset_augmented - augmented dataset</li> </ul> <p>Also, the subsets that were used to train, validate and test some deep learning models are available in three .TXT files (train.txt, val.txt and test.txt), where each file line corresponds to an image name.</p> <p>&nbsp;</p>

opencc-by-4.0Aug 2021View details →
zenodo44/100

A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects

<p>Unified, inferred tree sequences built from&nbsp;the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects. Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included).&nbsp;Tree sequences were inferred using&nbsp;<a href="https://tsinfer.readthedocs.io/">tsinfer</a>&nbsp;version 0.2.1,&nbsp;dated using&nbsp;<a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4&nbsp;and compressed using&nbsp;<a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on&nbsp;<a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can&nbsp; be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using&nbsp;<a href="https://tskit.readthedocs.io/">tskit</a>.&nbsp;</p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with nodes contain&nbsp;the mean and variance of tsdate&#39;s posterior distribution on node time. To access these values, we can use:</p> <pre><code>import json node = ts.node(10000) metadata_dict = json.loads(node.metadata) print("The mean of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["mn"])) print("The variance of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["vr"]))</code></pre> <p>Age estimates for&nbsp;each variant site can be derived from the mean of the age estimates of the&nbsp;upper and lower bounding nodes of the oldest mutation associated with a site. tsdate includes <a href="https://tsdate.readthedocs.io/en/latest/python-api.html?highlight=sites_time_from_ts#tsdate.sites_time_from_ts">a function to find the age estimates of all sites in the tree sequence</a>:</p> <pre><code>import tsdate site_times = tsdate.sites_time_from_ts(ts, node_selection='arithmetic')</code></pre> <p>This returns a numpy array which has a length equal to the number of sites.</p> <p>Accessing variant sites in the tree sequence provides&nbsp;the position and id of variants:</p> <pre><code>site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>)&nbsp;and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain&nbsp;all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the&nbsp;ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>

opencc-by-4.0Sep 2021View details →
zenodo44/100

Data: The Role of Urban Trees in Reducing Land Surface Temperatures in European Cities

<p>Data on the LST differences between urban fabric, urban trees and urban green spaces for each city and the LST differences between urban fabric, rural forests and rural pastures (for hot days and JJA (June, July and August) average). In addition, estimates of the evapotranspiration of forests and pastures of each city and albedo estimates of urban fabric and forests are provided.</p> <p>The description of the column names is provided in the readme file.</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2020View details →
zenodo44/100

Potential tree species distributions from the Last Glacial Maximum in North America

<p>Modern tree distributions modeled under current climate and predicted to past climate.</p> <p>Values of &#39;2&#39; represent presence.</p> <p>The column mark is current presence, while _20000 is 20 ka, _14000 is 14 ka, _13000 is 13 ka, etc.</p> <p>For quick download, the .dbf for each species can be joined to the shapefile (us_can_ecosub). Alternatively, download and use the zipped folder of shapefiles (glac_shapes)..</p>

opencc-by-4.0Nov 2022View details →
zenodo44/100

Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves

<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format).&nbsp;</p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>

opencc-by-4.0Nov 2022View details →
zenodo44/100

Reconstitution of August SPEI3 drought index based on the δ18O in tree-ring cellulose for the Eastern Carpathian, for the period 1331-2012CE

<p>Here we report the reconstruction of the summer (June to August) Standardized Precipitation-Evapotranspiration Index (SPEI3), for the period 1331-2012CE, for eastern Europe, based on annually-resolved stable oxygen isotope ratios (&delta;<sup>18</sup>O) from Pinus cembra L. tree-ring cellulose from the Călimani Mountains, Romania. Variations of the &delta;18O values capture the August SPEI3 changes both at high and low frequencies (from interannual to multidecadal scales).<br> <br> The palaeoclimate potential of stable isotopes in Pinus cembra L. (Swiss stone pine) tree-ring cellulose from the Călimani Mountains has been demonstrated by Nagavciuc et al. 2019 (DOI: 10.1002/joc.6349), showing that &delta;18O variability allows high-resolution paleoclimatic reconstructions over the eastern part of Europe, where few such reconstructions are available.</p>

opencc-by-4.0Nov 2022View details →
zenodo44/100

Data for: The structure of evolutionary model space for proteins across the tree of life

<p>Supporting data for &quot;The structure of evolutionary model space for proteins across the tree of life,&quot;&nbsp;submitted by GE Scolaro&nbsp;and EL Braun. The data files correspond to three gzipped tarballs including protein multiple sequence alignments, PAML format models of protein evolution, and model fit data; see included README for details.</p>

opencc-by-4.0Dec 2022View details →
zenodo44/100

Semantic 3D Tree Model Dresden 2017

<p>The semantic 3D tree model contains reconstructed tree crowns within the City of Dresden (Germany). Area-wide availability of such models and their integration into semantic 3D city models facilitates enriched visualizations of urban areas as well as 3D spatial modeling that simulates the interaction of trees with buildings and the built environment.</p> <p>The individual tree crowns were modeled using geometric primitives and correspond to the CityGML Level of Detail (LoD) 2. Individual modeling parameters were determined for each tree aiming for a realistic volume replication. LiDAR data from a survey in the year 2017 were used to parameterize the tree crowns. Tree crowns were modeled via ellipsoids fitted to crown extent, cylinders were used for trunk representation. The framework for segmenting individual trees in the LiDAR point cloud and for modeling individual tree crowns via geometric primitives is described in <a href="https://doi.org/10.1016/j.ufug.2022.127637">this article</a>.</p> <p>The tree models are available as CityGML files in the coordinate system ETRS89/UTM zone 33 (EPSG: 25833). The dataset was divided into tiles. The tile number results from the coordinate of the lower left corner in the coordinate reference system.</p> <p>The source data used was made freely available by the &ldquo;Landesamt f&uuml;r Geobasisinformation Sachsen&rdquo; (GeoSN) under the license &quot;Data license Germany - attribution - Version 2.0&quot; and can be downloaded under the following links:<br> LiDAR: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html</a><br> 3D Building Model: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html</a><br> Aerial Imagery: <a href="https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html">https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html</a></p>

opencc-by-4.0Jan 2023View details →
zenodo44/100

Parameterized Tree Positions Dresden 2017

<p>The parameterized tree positions represent the location of all trees detected on the basis of a laser scan survey from 2017 within the City of Dresden (Germany). Geometric attributes such as tree height and crown diameter were derived for each tree. For this purpose, the urban forest was classified in the point cloud and then individual tree crowns were segmented. Based on these segments, geometric attributes were determined from the LiDAR point cloud.</p> <p>In total, this automated inventory contains ~ 3 million tree positions. This is an overestimation of the actual number of trees which is due to inaccuracies in the segmentation of individual trees, especially in dense tree stands. Despite this overestimation, the tree volumes detected in the point cloud are realistically reproduced. The methodology is described in detail in <a href="https://doi.org/10.1016/j.ufug.2022.127637">this article</a>.</p> <p>Each tree has the following attributes:</p> <ul> <li>&quot;treeID_utm33_tiles&quot; - ID</li> <li>&quot;H_Tree&quot; &ndash; tree height (m)&nbsp;&nbsp; &nbsp;</li> <li>&quot;H_Crown&quot; &ndash; crown height (m)</li> <li>&quot;H_Trunk&quot; &ndash; trunk height (m)</li> <li>&quot;D_Crown&quot; &ndash; crown diameter (m)</li> <li>&quot;X&quot;, &quot;Y&quot;,&quot;Z&quot; &ndash; position</li> </ul> <p>The dataset is available either as a Shapefile or GeoJSON in the coordinate system ETRS89/UTM zone 33 (EPSG: 25833).<br> The dataset was divided into tiles. The tile number results from the coordinate of the lower left corner in the coordinate reference system.</p> <p>The source data used was made freely available by the &ldquo;Landesamt f&uuml;r Geobasisinformation Sachsen&rdquo; (GeoSN) under the license &quot;Data license Germany - attribution - Version 2.0&quot; and can be downloaded under the following links:<br> LiDAR: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html</a><br> 3D Building Model: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html</a><br> Aerial Imagery: <a href="https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html">https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html</a></p>

opencc-by-4.0Jan 2023View details →
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Vertebrate gene family trees

<p>This is a dataset of phylogenies for 118 vertebrate gene families, used in two papers published by James Cotton and Roderic Page in 2002. The folder named &quot;genes.zip&quot; contains FASTA and&nbsp;NEXUS sequence files, and Newick-format&nbsp;tree files for each gene family. There is a PDF file &quot;suppl.pdf&quot; listing the names of each family. The file &quot;final_dataset.gml&quot; contains a graph of the taxonomic overlap in the gene trees. All 118 gene trees have been combined into the file &quot;final_dataset.gtr&quot; which is a NEXUS file with custom blocks recognised by GeneTree.</p> <table> <tbody> <tr> <th>HOVERGEN FAMILY CODE</th> <th>GENE FAMILY NAME</th> </tr> <tr> <td>FAM000030A</td> <td>wnt 5</td> </tr> <tr> <td>FAM000030B</td> <td>wnt 7</td> </tr> <tr> <td>FAM000030C</td> <td>wnt 11</td> </tr> <tr> <td>FAM000030D</td> <td>wnt/int 1</td> </tr> <tr> <td>FAM000030E</td> <td>wnt 4</td> </tr> <tr> <td>FAM000030F</td> <td>wnt 10/12</td> </tr> <tr> <td>FAM000030G</td> <td>wnt 3</td> </tr> <tr> <td>FAM000030H</td> <td>wnt 2</td> </tr> <tr> <td>FAM000030I</td> <td>wnt 8</td> </tr> <tr> <td>FAM000105</td> <td>rhodopsin</td> </tr> <tr> <td>FAM000214</td> <td>beta-B globin</td> </tr> <tr> <td>FAM000033</td> <td>protamine 1</td> </tr> <tr> <td>FAM000370</td> <td>PrP a prion-protein</td> </tr> <tr> <td>FAM000014</td> <td>growth hormone</td> </tr> <tr> <td>FAM000556</td> <td>Rag-1 recombination activation gene</td> </tr> <tr> <td>FAM001493</td> <td>c-mos proto-oncogene</td> </tr> <tr> <td>FAM001462</td> <td>tyrosine kinase / yes / fyn / src / lck</td> </tr> <tr> <td>FAM001041</td> <td>metallothionein</td> </tr> <tr> <td>FAM000364</td> <td>Ldh-2 lactate dehydrogenase-B (EC</td> </tr> <tr> <td>FAM000215</td> <td>alpha globin</td> </tr> <tr> <td>FAM000016</td> <td>placental lactogen - prolactin</td> </tr> <tr> <td>FAM000008</td> <td>insulin</td> </tr> <tr> <td>FAM000824</td> <td>phosphoglycerate kinase</td> </tr> <tr> <td>FAM000550</td> <td>neurotrophin-4 (NT-4)</td> </tr> <tr> <td>FAM001478</td> <td>tyrosine kinase receptor, c-fms oncogene</td> </tr> <tr> <td>FAM000173A</td> <td>guanine nucleotide-binding protein</td> </tr> <tr> <td>FAM000173B</td> <td>transducin alpha</td> </tr> <tr> <td>FAM000502</td> <td>cytochrome P-450 aromatase</td> </tr> <tr> <td>FAM000192</td> <td>alpha-fetoprotein / serum albumin</td> </tr> <tr> <td>FAM000627</td> <td>neurone-specific enolase</td> </tr> <tr> <td>FAM001232</td> <td>preprotrypsin (ta)</td> </tr> <tr> <td>FAM000664</td> <td>complement component 3 (C3)</td> </tr> <tr> <td>FAM000175</td> <td>Ras</td> </tr> <tr> <td>FAM000248</td> <td>alpha B-crystallin</td> </tr> <tr> <td>FAM000006</td> <td>insulin-like growth factor II</td> </tr> <tr> <td>FAM000639</td> <td>transthyretin (prealbumin)</td> </tr> <tr> <td>FAM001303</td> <td>butylcholinesterase (BCHE)</td> </tr> <tr> <td>FAM000242</td> <td>connexin / gap junction protein</td> </tr> <tr> <td>FAM000058</td> <td>dopamine D1 receptor</td> </tr> <tr> <td>FAM000055</td> <td>beta-3-adrenergic receptor .</td> </tr> <tr> <td>FAM000131</td> <td>ATPase (Na+K+, H+K+)</td> </tr> <tr> <td>FAM003983</td> <td>preprogastrin</td> </tr> <tr> <td>FAM000353</td> <td>vasopressin</td> </tr> <tr> <td>FAM000152</td> <td>acetylcholine receptor</td> </tr> <tr> <td>FAM001327</td> <td>peripherin, desmin, vimentin, GFAP</td> </tr> <tr> <td>FAM003199</td> <td>(C57BL/6J)</td> </tr> <tr> <td>FAM002881</td> <td>cytochrome P-450, 17a-hydroxylase (CYP17)</td> </tr> <tr> <td>FAM002789</td> <td>(MUAHRB-1) Ah-receptor (Ah)</td> </tr> <tr> <td>FAM001461</td> <td>tropomyosin</td> </tr> <tr> <td>FAM000385</td> <td>Y3 peptide supply factor</td> </tr> <tr> <td>FAM000378</td> <td>pancreatic polypeptide, neuropeptide Y</td> </tr> <tr> <td>FAM001329</td> <td>cytokeratin</td> </tr> <tr> <td>FAM001619</td> <td>amelogenin (enamel-specific protein)</td> </tr> <tr> <td>FAM000286</td> <td>glucagon</td> </tr> <tr> <td>FAM000330</td> <td>tissue inhibitor of</td> </tr> <tr> <td>FAM000475</td> <td>lipophilin</td> </tr> <tr> <td>FAM001060</td> <td>ornithine carbamoyltransferase</td> </tr> <tr> <td>FAM001328</td> <td>neurofilament</td> </tr> <tr> <td>FAM001370</td> <td>peroxisome proliferator</td> </tr> <tr> <td>FAM000371</td> <td>somatostatin</td> </tr> <tr> <td>FAM001607</td> <td>Wilms tumor assocated protein (WT1)</td> </tr> <tr> <td>FAM000495</td> <td>aldolase A, B, C</td> </tr> <tr> <td>FAM001365</td> <td>liver receptor homologous protein (LRH-1)</td> </tr> <tr> <td>FAM000672</td> <td>ribosomal protein S4,</td> </tr> <tr> <td>FAM000135</td> <td>Na, K-ATPase beta-1 subunit</td> </tr> <tr> <td>FAM000271</td> <td>enkephalin : 1 2.</td> </tr> <tr> <td>FAM000799</td> <td>RING10</td> </tr> <tr> <td>FAM001664</td> <td>glutamate decarboxylase</td> </tr> <tr> <td>FAM000617</td> <td>creatine kinase</td> </tr> <tr> <td>FAM001337</td> <td>amyloid beta protein precursor</td> </tr> <tr> <td>FAM000274</td> <td>basic fibroblast growth factor (bFGF)</td> </tr> <tr> <td>FAM001108</td> <td>Sl-d mutant allele kit ligand (KL)</td> </tr> <tr> <td>FAM000504</td> <td>anion exchange protein 3</td> </tr> <tr> <td>FAM001239</td> <td>prothrombin</td> </tr> <tr> <td>FAM001360</td> <td>high mobility group proteins HMG1 and HMG2</td> </tr> <tr> <td>FAM000534</td> <td>glutamine synthetase</td> </tr> <tr> <td>FAM001053</td> <td>nucleoside diphosphate kinase</td> </tr> <tr> <td>FAM001390</td> <td>low density lipoprotein receptor LDLR</td> </tr> <tr> <td>FAM001464</td> <td>Cek6 receptor tyrosine kinase</td> </tr> <tr> <td>FAM000801</td> <td>manganese-containing superoxide</td> </tr> <tr> <td>FAM003946</td> <td>Six2 / Six1 mRNA</td> </tr> <tr> <td>FAM001606</td> <td>Ikaros binding protein (Ikaros)</td> </tr> <tr> <td>FAM000350</td> <td>SPARC protein</td> </tr> <tr> <td>FAM001339</td> <td>calcium-binding protein</td> </tr> <tr> <td>FAM000553</td> <td>pyruvate kinase</td> </tr> <tr> <td>FAM000604</td> <td>t complex polypeptide 1 (Tcp-1-a)</td> </tr> <tr> <td>FAM002988</td> <td>mSlo</td> </tr> <tr> <td>FAM001266</td> <td>transcription factor / hepatocyte nuclear factor</td> </tr> <tr> <td>FAM000453</td> <td>terminal deoxynucleotidyltransferase</td> </tr> <tr> <td>FAM001642</td> <td>transformation associated protein p53</td> </tr> <tr> <td>FAM000300A</td> <td>glucose-regulated protein 78 / HSP70 PART A</td> </tr> <tr> <td>FAM000300B</td> <td>glucose-regulated protein 78 / HSP70 PART B</td> </tr> <tr> <td>FAM000492A</td> <td>alpha actin etc</td> </tr> <tr> <td>FAM000492B</td> <td>beta actin etc</td> </tr> <tr> <td>FAM000649</td> <td>Myelin Basic Protein</td> </tr> <tr> <td>FAM000843</td> <td>ribosomal protein S4</td> </tr> <tr> <td>FAM000170</td> <td>atrial natriuretic protein</td> </tr> <tr> <td>FAM000871A</td> <td>tyrosinase</td> </tr> <tr> <td>FAM000871B</td> <td>tyrosinase related protein 1</td> </tr> <tr> <td>FAM001605</td> <td>ZFX put. transcription activator</td> </tr> <tr> <td>FAM001479</td> <td>fibroblast growth factor</td> </tr> <tr> <td>FAM000160</td> <td>pro-opiomelanocortin (POMC)</td> </tr> <tr> <td>FAM001644</td> <td>fibrinogen alpha subunit</td> </tr> <tr> <td>FAM000564</td> <td>SNAP-25</td> </tr> <tr> <td>FAM000266</td> <td>nitric oxide synthase</td> </tr> <tr> <td>FAM004159</td> <td>chondroitin-6 sulfotransferase</td> </tr> <tr> <td>FAM000800</td> <td>Lmp-2 (LMPq) proteasome subunit</td> </tr> <tr> <td>FAM001595</td> <td>factor B</td> </tr> <tr> <td>FAM001134</td> <td>zona pellucida (ZP)</td> </tr> <tr> <td>FAM001632</td> <td>stromelysin-3</td> </tr> <tr> <td>FAM001366</td> <td>steroid receptor (TR2-9)</td> </tr> <tr> <td>FAM000526</td> <td>c-ski protein</td> </tr> <tr> <td>FAM002463</td> <td>thymosin beta 4 peptide</td> </tr> <tr> <td>FAM000904</td> <td>sequence-specific DNA-binding protein (AP-2)</td> </tr> <tr> <td>FAM001465</td> <td>T-cell specific tyrosine kinase (ltk)</td> </tr> <tr> <td>FAM000567</td> <td>triosephosphate isomerase</td> </tr> <tr> <td>FAM006113</td> <td>DNA-dependent RNA polymerase III, large subunit</td> </tr> <tr> <td>FAM001733</td> <td>DNA-dependent RNA polymerase II</td> </tr> </tbody> </table>

opencc-by-4.0Jan 2023View details →
zenodo44/100

Dijkstra's Algorithm using a Fibonacci Heap, Binary Heap and Self-balancing Binary Tree

<p>Efficient C++ implementation of Dijkstra&#39;s algorithm using&nbsp;Fibonacci Heaps, Binary Heaps and Self-balancing Binary Trees. Also contains two .csv data sets from expeiments using directed planar graphs and random graphs of varying densities.</p> <p>Paper is published at&nbsp;</p> <p>Lewis, R. (2023), &quot;A&nbsp;Comparison of Dijkstra&#39;s Algorithm Using Fibonacci Heaps, Binary Heaps, and Self-Balancing Binary Trees&quot;,&nbsp;<a href="https://arxiv.org/abs/2303.10034">arXiv:2303.10034</a>,&nbsp;<a href="https://doi.org/10.48550/arXiv.2303.10034">https://doi.org/10.48550/arXiv.2303.10034</a></p>

opencc-by-4.0Mar 2023View details →
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African Savanna grasses outperform trees across the full spectrum of soil moisture availability

<p>Summary</p> <ul> <li>Models of tree-grass coexistence in savannas make different assumptions about the relative performance of trees and grasses under wet vs. dry conditions. We quantified transpiration and drought tolerance traits in 26 tree and 19 grass species from the African savanna biome across a gradient of soil water potentials to test for a tradeoff between water use under wet conditions and drought tolerance.</li> <li>We measured whole-plant hourly transpiration in a growth chamber and quantified drought tolerance using leaf osmotic potential (&Psi;<sub>osm</sub>). We also quantified whole-plant water use efficiency (WUE) and relative growth rate (RGR) under well-watered conditions.</li> <li>Grasses transpired twice as much as trees on a leaf-mass basis across all soil water potentials. Grasses also had a lower &Psi;<sub>osm</sub> than trees, indicating higher drought tolerance in the former. Higher grass transpiration and WUE combined to largely explain the threefold RGR advantage in grasses.</li> <li>Our results suggest that grasses outperform trees under a wide range of conditions, and that there is no evidence for a trade-off in water use patterns in wet vs. dry soils. This work will help inform mechanistic models of water use in savanna ecosystems, providing much-needed whole-plant parameter estimates for African species.</li> </ul>

opencc-by-4.0Mar 2023View details →
zenodo44/100

GlobalUsefulNativeTrees: useful tree species

<p>The GlobalUsefulNativeTrees database (GlobUNT; <a href="https://worldagroforestry.org/output/globalusefulnativetrees">https://worldagroforestry.org/output/globalusefulnativetrees</a>) was developed after a first step of combining native distribution data across 242 countries and territories from <strong>GlobalTreeSearch</strong> (accessed 8<sup>th</sup> May 2022; Beech et al. <a href="https://doi.org/10.1080/10549811.2017.1310049">2017</a>; BGCI <a href="https://tools.bgci.org/global_tree_search.php">2022</a>) with information on ten categories of human usage documented in the <strong>World Checklist of Useful Plant Species </strong>(WCUPS; Diazgranados et al. <a href="https://knb.ecoinformatics.org/view/doi:10.5063/F1CV4G34">2020</a>). GlobUNT was described in more detail in the following publication: Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) <strong>GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration</strong>. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a>. Version v.2023.01 of the database includes 14,014 useful tree species, representing roughly a quarter of the known tree species (as listed by GlobalTreeSearch) and a third of the plant species from WCUPS. The data set included here provides the taxonomic names for all tree species included in GlobUNT together with details on the process of standardization via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>). This taxonomic standardization process was completed <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">during the preparation of the third major release</a> of the <a href="https://apps.worldagroforestry.org/products/switchboard">Agroforestry Species Switchboard</a> (as a consequence, all species listed in GlobUNT are included among the 230,000+ species from the <em>Switchboard</em>).</p> <p>The development of GlobUNT was supported by the Darwin Initiative to project DAREX001 of <a href="https://www.darwininitiative.org.uk/project/DAREX001/"><em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em></a> and by Norway&rsquo;s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the <a href="https://www.worldagroforestry.org/project/provision-adequate-tree-seed-portfolio-ethiopia"><em>Provision of Adequate Tree Seed Portfolio</em></a> project in Ethiopia. When using the GlobUNT species list in your work, please cite the publication (Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) provided above) as well as this repository using the DOI (<a href="https://zenodo.org/record/7994433">https://zenodo.org/record/7994433</a>).</p>

opencc-by-4.0May 2023View details →
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Data_Schönauer et al. (2023)_Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest

<p>Data used in</p> <p>Sch&ouml;nauer, M., Hietz, P., Schuldt, B., and Rewald, B. (2023). Root and branch hydraulic functioning and trait coordination across organs in drought-deciduous and evergreen tree species of a subtropical highland forest. Frontiers in plant science 14, 1127292. doi: 10.3389/fpls.2023.1127292</p>

opencc-by-4.0May 2023View details →
zenodo44/100

Data for: Regularized sequence-context mutational trees capture variation in mutation rates across the human genome

<p>Additional data on output models from Bayer as reported in:</p> <p>Regularized sequence-context mutational trees capture variation in mutation rates across the human genome</p> <p>Adams CJ, Conery M, Auerbach BJ, Jensen ST, Mathieson I, Voight BF. BioRxiv&nbsp;https://doi.org/10.1101/2022.10.14.512160</p> <p>Accepted, PLoS Genetics.&nbsp;</p> <p>Code Available at:&nbsp;https://github.com/bvoightlab/Baymer</p>

opencc-by-4.0Jun 2023View details →
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DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America

<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America.&nbsp;The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled &quot;Assessing the impact of past and ongoing deforestation on rainfall patterns in South America&quot;. When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong>&nbsp;contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover.&nbsp;</p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc:&nbsp;</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards.&nbsp;</p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982&nbsp;onwards.</p>

opencc-by-4.0Jun 2023View details →
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Genomic insight into domestication of rubber tree

<p>we upgraded H. brasiliensis genome assembly (contig N50 of 11.21 megabases), presented a map of genome variations by resequencing 335 accessions and revealed domestication-related molecular signals and a major domestication trait, the higher number of laticifer rings. HbPSK5, encoding the small-peptide hormone phytosulfokine (PSK), was a key domestication gene. It was closely associated with the major domestication trait by positively controlling laticifer differentiation from vascular cambia. The transcriptional activation of HbPSK5 by MYC members linked JA signaling to PSK signaling in enhancing laticifer formation.</p> <p>cite:https://www.nature.com/articles/s41467-023-40304-y</p>

opencc-by-4.0Sep 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record