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22,710 results for “Plant”
The dataset of photovoltaic power plant distribution in China by 2020
<p>Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil-fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established photovoltaic (PV) power plants. However, a comprehensive map regarding the PV power plants' locations and extent remain scarce on the country scale. This study developed a workflow combining machine learning and visual interpretation methods with big satellite data to map PV power plants across China. We applied a pixel-based Random Forest (RF) model to classify the PV power plants from composite images in 2020 with 30-meter spatial resolution on Google Earth Engine (GEE). The result classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km<sup>2</sup>. We found that most PV power plants were sited on cropland, followed by barren land and grassland based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants.</p>
Genome-wide identification of cell-surface and intracellular immune receptors in 350 plant species
<p>Here we identified cell-surface (LRR-RLKs, LRR-RLPs, LysM-RLKs and LysM-RLPs) and intracellular immune receptors (NB-ARCs) from the genomes of 350 plant species. </p> <p> </p> <p>Zip file contains:</p> <p>Folder 'Immune_receptor_sequences' - FASTA files of the identified LRR-RLPs, Lys-RLKs, LysM-RLPs and NB-ARCs.</p> <p>Folder 'RLK_sequences' - FASTA files of the identified LRR-RLKs (all and 20 individual subgroups).</p> <p>Folder 'RLK_trees' - Phylogenetic TREE files of the identified LRR-RLKs (all and 20 individual subgroups); classified according to their kinase domains.</p> <p>238.species - Phylogenetic tree of the 238 plant species used in the analyses (taken from <a href="https://doi.org/10.1093/jpe/rtv047">https://doi.org/10.1093/jpe/rtv047</a>).</p> <p>350.species - Phylogenetic tree of the 350 plant species used in the analyses.</p> <p>simple.to.original.ids- Translator file for the original ID of each gene. </p> <p> </p>
TiFe0.85Mn0.05 alloy produced at industrial level for a hydrogen storage plant
<p>Data type: XRD patterns; SEM and EDX results, hydrogen sorption data (pcT-curves, absorption/desoprtion curves). </p> <p>Data format: *.opj; *.tif.; *docx; *jpg</p> <p>Software needed: Origin.</p>
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>
Krefeld_Plants_Mingei
<p>Documentation material from the Silk pilot of the Mingei project</p>
Wastewater Treatment Plant Inflow Rate, Network Water Levels and Rain Rate for Koeln Weiden, Germany
<p>wtp_network_rain.csv contains time series data of measured wastewater treatment plant inflow rates, water levels from multiple locations within the wastewater sewer network and rain rate measured from a rain recorder located at the wastewater treatment plant.</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>
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Evolutionary dynamics of mycorrhizal symbiosis in land plant diversification - phylogenetic data
<p>This submission supplements the manuscript entitled <em>Evolutionary dynamics of mycorrhizal symbiosis in land plant diversification</em> by <strong>Frida A.A. Feijen, Rutger A. Vos, Jorinde Nuytinck & Vincent S.F.T. Merckx.</strong></p> <p>The contents of this submission are dating analysis results for rootings of the land plant topology. Contains the following files:</p> <ul> <li>*.log.gz BEAST logs</li> <li>*.trees.gz BEAST trees</li> <li>*.tiff screen dumps of tracer, showing the burn-in</li> <li>*.consensus.trees produced with treeannotator</li> </ul> <p><strong>For more information</strong>: https://github.com/naturalis/mycorrhiza/tree/v1.0.0</p>
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
Achnatherum occidentale ssp. occidentale (Poaceae) - whole plant - in flower - general view
Image of Achnatherum occidentale ssp. occidentale (Poaceae) - whole plant - in flower - general view
Wood density for 26 plant species collected from Northern Western Ghats
<p>This dataset contains wood density estimates for species collected from Sindhudurg district of Maharashtra. The data was collected for baseline data generation for the Sahyadri Restoration Program as part of CEROS Lab at Nature Conservation Foundation. The fieldwork was carried out in Feb-Mar 2024.</p> <p>Wood cores were collected using Increment Borer (Haglof 12inch, 3 thread, 5.15mm)</p> <p>Usage notes:</p> <p>readme_wood_density.txt contains the information for each columns and the values calculated</p> <p>Wood Density Maharashtra.csv contains the dataset</p> <p><strong>ACKNOWLEDGEMENTS:</strong></p> <p>I would like to thank GCPL CSR (Godrej Consumers Products) for funding this data collection as part of the Sahyadri Restoration project and S.P.K College Sawantwadi for providing the necessary lab support. Special thanks to Dr. Deelip Bharmal (Principal S.P.K College) and Dr. G.S Margaj (Professor Zoology Dept) for help with the lab analysis.</p>
Images of article "Sexy ways: the methodical approaches to study plant sex chromosomes"
<p><strong>Figure 1. </strong>Schematic diagram of sex chromosome evolution in dioecious plants. Species are shown according to their level of sex chromosome differentiation and Y chromosome asynapsis. In<em> S. oleracea, A. officinalis</em> and <em>C. papaya</em>, the sex chromosomes are mostly homomorphic with recently formed non-recombining regions (region with suppressed recombination). The non recombining region is largely extended almost to entire chromosomal length in species with heteromorphic sex chromosomes, namely in <em>S. latifolia, R. hastatulus</em> (XY cytotype), <em>R. acetosa, H. lupulus, H. japonicus </em>and<em> M. polymorpha</em>. The position of the centromere, the PAR length and the ratio between X and Y is illustrative. </p> <p><strong>Figure 2.</strong> Laser microdissection as a tool to reduce genome complexity. Sex chromosomes in metaphase are isolated from plant cells (mostly pollen mother cells or root tips) and subsequently spread on a special microscopic slide covered with the membrane. After microdissection, chromosomes are transferred into a tube and processed to other applications. In case of chromosome sorting, the chromosome suspension is stained with a DNA-specific dye and introduced into a flow chamber. Within this chamber, individual chromosomes interact with a laser beam, and the scattered light and emitted fluorescence are measured. Through this process, a histogram of fluorescence intensity (known as a flow karyotype) is generated. Sorting is accomplished by breaking the liquid stream into droplets and electrically charging the droplets containing the chromosomes of interest.</p> <p><strong>Figure 3.</strong> Cytogenetic tools to study sex chromosome origin and evolution. Cytogenetics nowadays combine genomic tools to study repeat fraction including TEs and satellites (a), design unique barcodes to distinguish particular chromosome or chromosomal domain using chromosome oligo-painting probe design (b), and bioinformatic tools to dissect single chromosomes or genome parts (c). The combination of above methods helps to understand sex chromosome evolution regarding their autosomal origin, chromosomal rearrangements, and Y(W) chromosome differentiation. Arrows represent evolutionary steps during sex chromosome divergence (d). The sex chromosome barcoding allows understanding of meiotic pairing which in turn supports chromosomal fusions and inversion/translocations. To chromosomes belong to species with references, from the top to the bottom as follows: <em>S. latifolia </em>Ogre retroelement (Kubat et al., 2014), <em>R. hastatulus</em> XY cytotype satellite Cl135 (Sacchi et al., 2023, Preprint), <em>S. latifolia</em> PAR oligo-painting probe with the subtelomeric satellite X43.1 and centromeric satellite STAR-C (Bačovský et al., 2020), and the same DNA probes on chromosomes in metaphase I in <em>S. latifolia </em>(Bernasconi et al., 2009; Bačovský et al., 2022). </p> <p><strong>Figure 4.</strong> Methodical strategies to assess the function of sex chromosomes in plants. Experimental assays with polyploids (alternatively aneuploids) represent the classical way to determine the role of individual sex chromosomes (a). These assays with plants of various ploidy levels were usually supported by analyses of deletion lines (plants carrying short-chromosomal <br>deletions or microdeletions) (b) that allowed researchers to identify sex-linked regions involved in sex determination and floral development. Modern assays using reverse genetics, such as CRISPR/Cas9, virus-induced gene silencing (VIGS) or peptide treatment of shoot apical meristem (c) provide direct evidence of the gene function and its contribution to the development <br>of reproductive organs. Parasite infected (d) or chemically induced (e) hermaphrodites from either female or male individuals, e.g. in <em>Silene</em> or kaki, let to the identification of key mechanisms and genes that regulate sexual phenotypes, and to understand the regulatory networks leading to separate sexes. </p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Dataset Natural plant disease suppressiveness in soils extends to insect pest control
<p>This dataset is related to the study "<strong>Natural plant disease suppressiveness in soils extends to insect pest control</strong>" (Harmsen et al., 2024) and contains the raw data described therein. </p> <p>Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive under the BioProject number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1075215/">PRJNA1075215</a>.</p> <p>The scripts used to analyze the data generated in the study are available at <a href="https://github.com/nhrmsn/SuppressSoil-Data">GitHub</a>. </p>
Farmers' fields network of oilseed rape intercropped with service plant in Western Switzerland.
<p>These data are associated with the publication of Bousselin et al. (2024) in which the experiment and the protocols are explained into details.</p> <p><span>Bousselin, X., Lorin, M., Valantin-Morison, M. </span><em>et al.</em><span> Determinants of oilseed rape-service plant intercropping performance variability across a farmers’ fields network in Western Switzerland. </span><em>Agron. Sustain. Dev.</em><span> </span><strong>44</strong><span>, 40 (2024). https://doi.org/10.1007/s13593-024-00972-6</span></p>
Supplementary Material for "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems"
<p>This Zenodo repository contains all data, scripts, and supplementary materials for the manuscript entitled, "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems".</p>
Taxonomy, occurrences, phylogeny, traits and uses of the entire plant genus Scleria (Cyperaceae)
<p>This resource includes several datasets:</p> <p>(1) Taxonomy (261 species): Updated taxonomy of the genus Scleria at the species level based on Bauters et al. 2016 and 2019.</p> <p>(2) Occurrences (latitude/longitude): data was compiled using observations from the Global Biodiversity Information Facility, Red List, research grade identifications from iNaturalist (accessed 13/08/2023) and records for collections from BR, K, GENT, L, MO, NY, P, US and WAG which were georeferenced using Google Earth. The dataset includes 22,759 observations from 248 species. Methodology follows Larridon et al. (2021).</p> <p>(3) Phylogeny of the genus based on three markers (ITS, ndhF, rps16) from Larridon et al. (2021) (136 species).</p> <p>(4) Traits. (i) We measured maximum height, maximum blade length, maximum blade width, stem width, nutlet length and nutlet width from 1,254 specimens of 209 species housed at Royal Botanic Gardens, Kew and the Muséum National d'Histoire Naturelle in Paris. (ii) We also compiled another dataset of 16 continuous and categorical traits for all 261 Scleria species derived from protologues and descriptions from regional floras. (iii) We measured nutlet weight for 141 species.</p> <p>(5) Uses & ecology: ethnobotany (mostly medicinal uses) and references to its ecology in several ecosystems (e.g., pollination, dispersal, ecological role). This data was gathered from several bibliographical sources, also provided.</p> <p>(6) Pictures of nutlets from 141 species.</p>
Dataset - Gravisensors in plant cells behave like an active granular liquid
<p>Dataset corresponding to data shown in article : https://hal.archives-ouvertes.fr/hal-01425298v2 (avalanches of statoliths pile in wheat coleoptile cells, avalanches of silica micro-particles in biomimetic cells, vertical fluctuations of statoliths in wheat coleoptile cells or extracted from their cells).</p> <p>Examples of python scripts that open and plot the data are also included.</p>
Plant Phenology Forecasts
<p>These are forecasts of plant phenology for 66 species of plants in North America. The forecasts models are made using data from the National Phenology Network, and climate drivers from the NOAA CFSv2 forecast model and the PRISM Climate Group. Maps from the data are also available at the site http://phenology.naturecast.org.</p> <p> </p> <p> </p>
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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.