Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
181
datasets available to search
ShareScore release 0.9.0
Dataset results
181 results for “woody plant”
Leaf and Flower Phenology of Woody Plant Species at Harvard Forest and Southern Quebec 2015
Accurate predictions of spring plant phenology with climate change are critical for projections of growing seasons, plant communities and a number of ecosystem services, including carbon storage. Progress towards prediction, however, has been slow because the major cues known to drive phenology – temperature (including winter chilling and spring forcing) and photoperiod – generally covary in nature and may interact, making accurate predictions of plant responses to climate change complex and nonlinear. Alternatively, recent work suggests many species may be dominated by one cue, which would make predictions much simpler. Here, we manipulated all three cues across 28 woody species from two North American forests. Study sites were Harvard Forest and St. Hipplolyte, Quebec. Species were selected for this study based on their prevalence at the study sites; 28 species are included in this study. At each site, multiple cuttings of six or more representative individuals were collected. In total, we tracked the phenology of 2,137 cuttings from 275 individual source plants. All species responded to all cues examined. Chilling exerted a strong effect, especially on budburst (-15.8 d), with responses to forcing and photoperiod greatest for leafout (-19.1 and -11.2 d, respectively). Interactions between chilling and forcing suggest that each cue may compensate somewhat for the other. Cues varied across species, leading to staggered leafout within each community and supporting the idea that phenology is a critical aspect of species’ temporal niches. Our results suggest that predicting the spring phenology of communities will be difficult, as all species we studied could have complex, nonlinear responses to future warming.
2019-2020 woody plants pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous woody plants pollen collected in Lithuania. Data is sorted by pollen type.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
GIS70 Konza Prairie Woody Plant Mapping in Core Watersheds (1D, 20B, and 4B) in 2019
This dataset contains the point and polygon boundaries of shrubs and trees mapped in watersheds 1D, 4B, and 20B from May to August 2019. Datatype one (GIS700) defines the point locations of all trees mapped in these watersheds. Datatype two (GIS701) defines the point locations of all shrubs less than one meter wide in these watersheds. Datatype three (GIS702) defines the boundaries of select shrub species greater than one meter wide in these watersheds. These data are available to download as zipped shapefiles (.zip), and compressed Google Earth KML layers (.kmz).
WPE01 Assessing the value added of NEON for using machine learning to quantify vegetation mosaics and woody plant encroachment at Konza Prairie
Woody encroachment, or invasion of woody plants, is rapidly shifting tallgrass prairie into shrub and evergreen dominated ecosystems, mainly due to exclusion of fire. Tracking the pace and extent of woody encroachment is difficult because shrubs and small trees are much smaller than the coarse resolution (>10m2) of common remote sensed images. However, the US government has been investing in finer resolution (<2m2) remote sensing through USDA NAIP and the National Ecological Observatory Network (NEON), both of which cost multi-million dollars each year and contain different remote sensed products. We compared two methods of classification (random forests and support vector machines) with these two freely available remotely sensed aerial images to determine if and how much NEON adds to classification accuracy and determine which method of machine learning was more accurate. All models have very high overall classification accuracy (>91%), with the NEON image a few percent more accurate than NAIP. The NEON image significantly relies on canopy height (LiDAR) to make classifications, but the importance of bands is more evenly distributed during NAIP classification. Lastly, accuracy for Eastern Red Cedar specifically is high with NEON (78-84%), compared to the relatively low classification accuracy using NAIP imagery (55-61%).
Inventory data of woody plants surveyed and measured in North Senegal (Ferlo) in 2015-2017
<p>This dataset gathers measurements from field inventory on woody vegetation carried in the sylvo-pastoral zone of Ferlo (Senegalese Sahel) in 2015-2016-2017. The data consist of dendrometric measurements, location and species for 3215 woody individuals (trees, bushes, and shrubs) belonging to 25 species and 11 families.</p> <p><strong>Sites </strong></p> <p>The study sites are located in the Northern Sandy Pastoral Region of Senegal around the deep wells of Widou Thiengoly (15.99° N, 15.32° W) and Tessékéré (15.85° N, 15.06° W). The vegetation formation is an open savanna, with a relatively low woody cover.</p> <p>For the <strong>field work of 2015</strong>, we applied a stratified sampling according to the topography and the distance to the studied deep wells. We inventoried 139 plots of 0.25 ha each. The center of each plot was marked by a gps point. The plots were located at increasing distances from the boreholes: 2, 3.5, 5, 7.5, 10, 12.5 and 15 km (20 plots per distance). For each distance, we randomly selected at least six plots in depressions (43 plots in total), the other plots being located on slopes or on hilltops, with a total vertical drop of several meters (96 plots in total). Whenever possible, the plots in each category of topography were distributed between the two soil types. In total, 86 plots were allocated to the ferruginous soils and 53 plots to the sub-arid brown red soils. </p> <p>In<strong> 2016, </strong>additional woody plants were surveyed within 10 circular plots with a variable radius between 27 m and 52 m, so that at least 10 individuals were counted for each plot. <strong>In 2017, </strong>woody plants were surveyed within 30 square plots of 0.25 ha each. Woody individuals were all geotagged in 2016 and 2017. </p> <p><strong>Field measurements</strong></p> <p>Adults woody plants (with a circumference superior to 10 cm at ground level) were inventoried within square plots (2015, 2017) or circular (2016). Species name was identified for all individuals and recorded following the taxonomic referential of the African Plant Database (version 3.4.0). Three types of dendrometric measurements were performed on woody plants: (i) circumference, measured at 30 cm from ground level, except for shrubs for which circumference was measured at ground level; (ii) tree height, measured by an ultrasonic hypsometer Vertex IV (Haglof Inc.) (iii) two perpendicular crown diameters. GPS points were taken (GPSMAP 62, Garmin Inc.) at the center of each plot (for 2015) and, in some cases for each individual (2016-2017).</p> <p><strong>Data structure and metadata</strong></p> <p>Data are encoded in a single file, using comma-delimited format and UTF-8 encoding. Each row describes one individual woody plant with its corresponding measurements. The following table presents the variables (columns) contained in the dataset.</p> <p>Shapefile format is also available (same data as the .csv).</p> <table> <tbody> <tr> <td> <p><strong>Variable name</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Definition</strong></p> </td> </tr> <tr> <td> <p>Tree_id</p> </td> <td> <p>-</p> </td> <td> <p>Unique identifier of the individual, with a 13 characters length. The 4 characters following the “y” indicate the year of the inventory. For instance, “tr.y2015.0034” is referring to the woody plant number 34 inventoried in 2015.</p> </td> </tr> <tr> <td> <p>Plot_id</p> </td> <td> <p>-</p> </td> <td> <p>Plot identifier</p> </td> </tr> <tr> <td> <p>Plot_area_ha</p> </td> <td> <p>ha</p> </td> <td> <p>Area of the inventoried plot</p> </td> </tr> <tr> <td> <p>Species</p> </td> <td> <p>-</p> </td> <td> <p>Genus, species, subspecies names and botanical authors</p> </td> </tr> <tr> <td> <p>Family</p> </td> <td> <p>-</p> </td> <td> <p>Family name</p> </td> </tr> <tr> <td> <p>Growth_form</p> </td> <td> <p>-</p> </td> <td> <p>Shrub, bush or tree</p> <p>Growth form expresses the extent of growth and the potential branching of the main-shoot axis. In this work, we refer to three types of growth form: shrub, bush and tree. A shrub refers to a small woody plant with a height below 2 meters and multi-stemmed. A tree designates a woody plant taller than 5 to 6 meters, generally presenting a single trunk. A bush, or a dwarf tree as in Pérez-Harguindeguy et al. (2013), is the intermediary between a shrub and a tree. Its height is usually between 2 to 6 meters and it is often multi-stemmed. Because of intra-specific traits variation, the mentioned growth form is valid for our study area.</p> </td> </tr> <tr> <td> <p>Circ30_m</p> </td> <td> <p>m</p> </td> <td> <p>Circumference measured at 30 cm from ground level. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Height_m</p> </td> <td> <p>m</p> </td> <td> <p>Tree height. “NA” indicates missing data (for the individuals measured in 2017).</p> </td> </tr> <tr> <td> <p>Dcrown1_m</p> </td> <td> <p>m</p> </td> <td> <p>First diameter of the crown</p> </td> </tr> <tr> <td> <p>Dcrown2_m</p> </td> <td> <p>m</p> </td> <td> <p>Second diameter of the crown (perpendicular to the first diameter)</p> </td> </tr> <tr> <td> <p>Geoloc_method</p> </td> <td> <p>-</p> </td> <td> <p>Geolocation method; indicates if it is the center of the plot which was geolocated (“geoloc.plot”, for individuals in 2015) or the woody plant (“geoloc.tree”, for 2016-2017).</p> </td> </tr> <tr> <td> <p>Lat_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>North latitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geoloc.tree”.</p> </td> </tr> <tr> <td> <p>Long_dd</p> </td> <td> <p>Decimal degrees</p> </td> <td> <p>West longitude of the plot if the geoloc_method == “geoloc.plot” and of the woody plant if the geoloc_method == “geloc.tree”.</p> </td> </tr> <tr> <td> <p>Topography</p> </td> <td> <p>-</p> </td> <td> <p>Local topography of the plot. Indicates if the plot is located within a depression (lowland) or on a hilltop.</p> </td> </tr> <tr> <td> <p>Date</p> </td> <td> <p>-</p> </td> <td> <p>Date of the survey: dd-mm-yy</p> </td> </tr> </tbody> </table> <p> </p>
Topoedaphic constraints on woody plant cover in a semi-arid grassland
<p>Provided is an excel spreadsheet which contains data used to estimate maximum potential shrub cover across a semi-arid grassland in Southern Arizona. Data was obtained using a classified shrub cover (mesquite) map of Las Cienegas National Conservation Area in Southeastern Arizona which was derived using 2017 NAIP imagery which is free available on EarthExplorer. Classified shrub cover map was created using an unsupervised ISO classification technique within ArcGIS. This shrub cover map was upscaled to 100m and a number of topoedaphic spatial layers were overlaid onto this shrub cover layer and their layers extracted per pixel. This data was then analyized within R using a segmented quantile regression approach to identify maximum shrub cover by topoedaphic characteristics at the 95th percent quantile. For sample of quantile code please contact the corresponding author.</p> <p>Topoedaphic variables analyzed in this data set are: <br> Shrub Cover (%)<br> Elevation (m)<br> Slope Inclination (°)<br> Slope Aspect (Cardinal Direction)<br> Value 2 = North<br> Value 3 = East<br> Value 4 = South<br> Value 5 = West<br> Percent Clay between 0 to 5cm (%)<br> Depth to bedrock (cm)<br> Topographic Wetness index (TWI) (unitless with higher values representing more run-on/wetter conditions)</p> <p>Shrub cover was analyzed at the study site level and at the ecological site level. </p>
Structure and composition and carbon Stocks of woody plant community in assisted and unassisted ecological succession in a Tamaulipan thornscrub, Mexico
<p>In November of 2017, the structure and composition of woody plant communities were investigated through a floristic composition and diversity evaluation on three areas: a control area, an assisted ecological succession area and an unassisted ecological succession area.</p>
New woody plant functional types and parameters for the SAVANNA ecosystem model
<p>New woody plant functional types (PFTs) are defined and parameterised for use in the SAVANNA ecosystem model (Coughenour, 1992, 1993). Supplementary material used in creating the PFTs and parameters are included. Details of the methods are available from the authors on request. The new woody PFTS are defined in terms of growth form, leaf size and defences in relation to large mammal herbivores.</p> <p>1. shrub types are <4 m (Zizka et al., 2014),</p> <p>2. fine-leaf types have bipinnate leaves with leptophyllous- or nanophyllous-sized leaflets (<225 mm2) according to Raunkaier’s leaf size classes (Fuller and Bakke, 1918) given that leaflets of compound leaves are separate morphological units analogous to simple leaves (Milla, 2012; Mo et al., 2022),</p> <p>3. high chemical defence investment (CDI) types have either nitrogen:acid detergent fibre (N:ADF) <0.10 (Wallis et al., 2012) or condensed tannin (CT) >5% (Cooper and Owen-Smith, 1985) when expressed in sorghum tannin or leucocyanidin equivalents as determined by the acid-butanol assay,</p> <p>4. all types, except fine_highcdi and fine_lowcdi, have the square-root of Charles-Dominique et al.'s (2017) "investment in structural defence" (ISD) < 13.</p>
Murchison Falls National Park Uganda Woody Plant and Palm Inventory Plots 2022
Murchison Falls National Park (MFNP) is a protected area in northern Uganda along the border with the Democratic Republic of the Congo and straddling the Victoria Nile. This project was designed to assess the accuracy of woody cover maps developed in (Nagelkirk & Dahlin, 2020). We determined an area of interest and then identified 40 plots that we expected would range from zero to nearly 100% woody cover. Due to restrictions related to the COVID-19 pandemic, we could only spend six days in the field, and so our sampling time was limited. We were able to collect 36 30x30 m square plots (four plots were not measured due to safety or accessibility issues). In each, we collected data describing woody plant species, when possible, diameter at breast height (DBH) or basal diameter depending on the size of the plant, and two crown diameter measurements: one at the widest width and another approximately perpendicular to the first. Together these measurements allow us to estimate woody plant canopy cover and basal area, along with species diversity both by count and by basal area. With additional information, aboveground biomass, functional diversity, and phylogenetic diversity could also be estimated in the future. Although this project was limited in scope, since eastern African savannas are underrepresented in global databases of woody cover and aboveground biomass, this data set will contribute to our overall understanding of vegetation patterns and processes.
Data from "Grassland woody plant management rapidly changes woody vegetation persistence and abiotic habitat conditions but not herbaceous community composition"
These files contain microhabitat, soil, vegetation structure, and woody plant species data used in the paper "Grassland woody plant management rapidly changes woody vegetation persistence and abiotic habitat conditions but not herbaceous community composition". The project was conducted at seven publicly accessible remnant (i.e., unplowed or old-growth) tallgrass prairie within 100 miles of Madison, Wisconsin, United States starting in the 2020 growing season and commencing following the 2022 growing season. The goal was to assess the initial effects of different management interventions on woody vegetation persistence, abiotic habitat conditions, and herbaceous community composition, including physical and chemical management interventions and their combination.
Large-scale woody plant removal outcomes in southern New Mexico, USA, 2007-2024
This data package contains datasets from a large-scale woody plant-specific herbicide experiment in southern New Mexico, USA. The study monitored vegetation cover in 43 pairs of plots representing treated and untreated conditions of the same plant community and environmental setting, including baseline and records at 5, 10, and in some cases 15 years post treatment. We also evaluated environmental factors that may control variation in treatment outcomes. This package supports the manuscript “Large-scale experimental evaluation of woody plant removal outcomes in desert grassland: restoration, novelty, or degradation?” by Bestelmeyer, Burkett, James, Gamon, and Schooley.
Drought and herbivory effects on woody plant seedling establishment and grass competition in the Jornada Basin, 2016-2018
This dataset contains observations of seedling establishment and grass competition under precipitation manipulations, and herbivory and granivory exclosure treatments, in the Chihuahuan desert of southern New Mexico, USA. The experiment took place at the Jornada Basin LTER site. We used a rainfall manipulation system and various herbivore exclosures in a factorial design, to test hypotheses about how precipitation (PPT), competition between grasses and shrub seedlings, and predation affect the germination and first-year survival of Mesquite (Prosopis glandulosa), a shrub that has encroached in Southern Great Plains and Chihuahuan Desert grasslands. Data collected in these files include seedling counts in each treatment over observation years 2016 to 2018. This data supports the related publication in Ecological Applications (Weber-Grullon et al. in press). The dataset is complete.
Dataset of Invasion risks and social interest of non-native woody plants in urban parks of Spain
<p>Full datasets for the research entitled "Invasion risks and social interest of non-native woody plants in urban parks of Spain"</p>
Foliar stoichiometry of woody plants worldwide
<p>This data includes foliar N, P, K % in DW of mature leaves in woody plants worldwide. It also contains the georeferenced information and specie. It gathers data from 230 published articles, TRY database (<a href="http://www.try-db.org/TryWeb/dp.php),">http://www.try-db.org/TryWeb/dp.php),</a> ICP forest database (<a href="http://icp-forests.net/page/data-requests),">http://icp-forests.net/page/data-requests),</a> Tundra Trait Team and the Catalan Forest Inventory (Gracia et al., 2004).</p>
Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients
<p><em>Background:</em></p> <p>Understanding the mechanisms by which genes control and regulate complex quantitative traits during periods of fluctuating resources remains a challenging and uncertain task in photosynthesis studies. Most studies have focused on the structure of photosynthesis, the photosynthetic response under stress, or the genetic mechanisms involved in photosynthetic effects and neglected the interactive genetic mechanism that governs various traits through significant quantitative trait loci (QTLs). Results In this study, we have developed a differential dynamic system that enables the identification of QTLs based on the photosynthetic phenotypic and genotypic data under varying levels of light intensity gradients. The framework not only allows for the assessment of the direct effects of QTLs on phenotypes but also captures how they influence interactions among phenotypes as light intensities change. We have analyzed the genetic effects and genetic variance, visualized the genetic network associated with photosynthesis interactions, and validated the effectiveness and stability of the DDS framework. Pivotal QTLs were identified individually to uncover the process and pattern of interaction. Through functional annotation, we made an intriguing discovery that seemingly unimportant QTLs can still have significant genetic effects on phenotypic changes through their regulation with other QTLs. Conclusions This finding emphasizes the significance of considering the interactive genetic architecture when seeking to understand the genetic interaction mechanism of photosynthesis in natural populations of woody plants. Moreover, our research provides a novel framework that can be extended to explore the interactive genetic architecture among organisms, contributing to a deeper understanding of stress resistance mechanisms in woody plants.</p>
Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Image of Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Image of Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Image of Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Image of Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
Image of Acer negundo (Aceraceae) - woody angiosperms - fruit - as borne on the plant
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.