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22,710 results for “Plants for planting”
Fig. 5 in Vascular plant diversity of the Gogunsan Archipelago in the Korean Peninsula
Fig. 5. Dendrograms showing the degree of Sørensen similarity based on native flora data (except invasive alien plants, ruderal plants) of the Gogunsan Archipelago.
Fig. 5 in New distribution record of northern lineage plant of Stellaria filicaulis (Caryophyllaceae) from South Korea
Fig. 5. The neighbor-joining phylogenetic tree of the Korean Stellaria species based on nuclear ribosomal internal transcribed spacer (rITS) sequences. The bootstrap values for each node are indicated in the tree. Voucher numbers corresponding to each species are shown next to scientific names.
Fig. 1 in New distribution record of northern lineage plant of Stellaria filicaulis (Caryophyllaceae) from South Korea
Fig. 1. Type specimen of Stellaria filicaulis Makino in National Museum of Nature and Science (TNS), Japan.
Fig. 2 in New distribution record of northern lineage plant of Stellaria filicaulis (Caryophyllaceae) from South Korea
Fig. 2. Voucher specimen of Stellaria filicaulis Makino in National Institute of Biological Resources (KB), South Korea.
Ecological and Forest Inventory of Catalonia: Complete Plant Trait Dataset for Imputation Assessment
<p>Plant trait and forest data were retrieved from the Ecological and Forest Inventory of Catalonia (IEFC), carried out between 1988 and 1998 (Gracia et al. 2000‒2004). The subset of the IEFC was limited to 13 study species. Forest structure, lithology and sampling information for each plot were retrieved from the IEFC database. Climate data were obtained from the Climatic Digital Atlas of Catalonia, with a spatial resolution of 180 m (Ninyerola et al. 2000).</p> <p>We selected five plant traits (leaf mass per area, LMA, mg cm<sup>-2</sup>; leaf nitrogen per unit mass, <em>N</em><sub><em>mass</em></sub>, %mass; maximum tree height, <em>H</em><sub><em>max</em></sub><em>,</em><sub><em> </em></sub>m; wood density, WD, gm cm<sup>-3</sup>; leaf biomass to sapwood area ratio, <em>B</em><sub><em>L</em></sub><em>:A</em><sub><em>S</em></sub>, t m<sup>-2</sup>) that are used to describe major plant functional strategies. The auxiliary variables we considered were species identity, a set of climatic variables (mean annual temperature, annual thermal amplitude, both in °C), a set of forest structure variables (total aboveground biomass [T ha<sup>-1</sup>] and stem density [stems ha<sup>-1</sup>]), a set of topographical variables (county, elevation [m.a.s.l.], slope [°] and aspect), lithology (calcareous, non-calcareous or undetermined) and sampling month.</p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 1. Intelligence of different simple living creature (accessed 01.11.2017). 1.1. A carnivorous plants catching an insect (https://phys.org/news/2016-05-colombia-peace-reveal-jungle-species.html); 1.2. A colony of ants solving a very complex task (https://mappingignorance.org/2016/05/27/rafting-ants); 1.3. The collective behaviour of a school of fish (https://simple.wikipedia.org/wiki/Shoaling_and_schooling)
<p>The biological intelligence of different life forms, ranging from very simple (such as plants) to very complex (such as humans) is the subject of many studies and a large amount of research. Frequent studies related to different kind of biological intelligence include: the intelligence of horses (Krueger, & Heinze, 2008; Krueger, Farmer, & Heinze, 2014; Schuetz, Farmer, & Krueger, 2016), intelligence of pigs (Broom, Sena, & Moynihan, 2009), intelligence of dogs (Coren, 1995), intelligence of primates (Reader, Hager, & Laland, 2011) and so one. Figures 1, 2, and 3 present some biological life forms that are frequently considered intelligent. Trewavas (2002; 2005) considered that plants intelligence should be based on principles such as their ability to adjust their morphology, and phenotype accordingly to ensure self- preservation and reproduction. Figure 1.1 presents an intelligent plant (carnivorous) that uses a strategy for catching very fast flying insects. In order to eat the insect, it makes a movement. Figure 1.1 presents the catching of an insect by a carnivorous plant. The intelligence of colonies of ants, termites and other insects that live in large colonies is considered at the colony level (Brady, Fisher, Schultz, & Ward, 2014; Johnson, Borowiec, Chiu, Lee, Atallah, & Ward, 2013). Figure 1.2 presents the coherent intelligent surviving behaviour of a colony of a species of ants. The ants make a structural reorganization in order to move on the surface of the water. Figure 1.3 presents a very large school of fish with an intelligent coherent collective feeding and self-protecting behaviour. Each individual fish has a very simple behavior. Based on this it cannot be considered intelligent. The intelligence in large schools of fish emerges at the collective level (Shaw, 1978; Parrish, Viscedo, & Grunbaum, 2002).</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
Data from: Rhizosphere bacterial community composition depends on plant species identity and soil legacy effects
<p>This record contains supplementary information for the article "Rhizosphere bacterial community composition depends on plant diversity legacy in soil and plant species identity".</p> <p><strong>Supplemental Table S1.</strong> The table contains the annotation for all the samples sequenced and analyzed.</p> <p><strong>Supplemental Table S2. </strong>The table contains all primer sequences used in the study.</p> <p><strong>Supplemental Table S3.</strong> The zip-file contains a table with the taxonomic annotation of the operational taxonomic units (OTUs) identified in the study.</p> <p><strong>Supplemental Table S4. </strong> The zip-file contains a table with sequence counts of the operational taxonomic units (OTUs) identified in the study.</p> <p><strong>Supplemental Table S5. </strong>The workbook contains a sheet with the number of operational taxonomic units (OTUs) exhibiting differential abundance in any of the contrasts tested in this study. Note that “down/up” indicates whether the OTU was less (“down”) or more (“up”) abundant in the first group of the contrast. For example, given the contrast “PH_mix_vs_mon_”, “down” corresponds to higher abundance in the pots from the monoculture plant history. Conversely, “up” refers to higher abundance in the pots from the mixed culture plant history. In addition, the workbook contains one sheet per contrast with the logBaseMean (log2 of the average normalized abundance across all samples), the logFC (log2 of the fold-change), the <em>P</em>-value, and the adjusted <em>P</em>-value (FDR). Only OTUs with a <em>P</em>-value <= 0.05 or an adjusted <em>P</em>-value (FDR) <= 0.1 are given.</p> <p><strong>Supplemental Table S6. </strong>The table contains the number of bacterial OTUs annotated with a given bacterial phylum.</p> <p><strong>Supplemental Table S7. </strong>The table contains all phyla tested for enrichment/depletion in the set of OTUs with an increased abundance in monoculture and mixed culture soils respectively. “Total counts (all OTUs)” corresponds to the total number of all OTUs annotated with a given phyla (reference set). “Observed” corresponds to the number of OTUs annotated with a given phyla in the set OTUs with increased abundance in monoculture/mixed culture soils (test set). "Expected" gives the number of OTUs which would be expected to be annotated with a given phyla if the test set were randomly sampled from the reference set.</p> <p><strong>Supplemental File S1.</strong> The zip-file contains a fasta file with the 10'205 OTU sequences identified in the study.</p> <p> </p> <p> </p>
Agriculture - General: Natural Resources and Environment, Plant Production and Protection
<p>Original data comes from a project which takes or took place as part of the DFG priority program “Exploratories for large-scale and long-term functional biodiversity research”. The data is stored together with descriptive metadata, in combination called a dataset, in the project repository (<a href="https://www.bexis.uni-jena.de">https://www.bexis.uni-jena.de</a>). Species information was extracted from that original dataset. The second paragraph is part of the metadata of the original <a href="<a href="http://dataset.in/">http://dataset.in/</a>">dataset.In this project we investigate seed bank and bryophyte propagule content in top soil in grasslands. Müller J (2016). Seed bank grassland. Biodiversity Exploratories. Occurrence dataset <a href="https://doi.org/10.15468/jax26w">https://doi.org/10.15468/jax26w</a> accessed via GBIF.org</p>
K-mer databases of plant virus sequences for use with the Kodoja workflow
<p><strong>Details</strong></p> <p>This is a gzipped tar file that includes the plant virus database files required to run the Kodoja workflow (https://github.com/abaizan/kodoja)[1]. Kodoja is a workflow for the detection of plant virus sequences in RNA-seq data files that uses two previoulsy published tools Kraken[2] and Kaiju[3].</p> <p>This file contains databases for Kraken [2] and Kaiju [3]. The file includes the kraken database files: database.idx, database.kdb, nodes.dmp, names.dmp and the kaiju database file kaij_library.fmi.</p> <p>These k-mer databases are based on virus sequences in RefSeq [4] (ttps://www.ncbi.nlm.nih.gov/refseq/) with plant hosts as defined in the Virus-Host Database [5] (https://www.genome.jp/virushostdb/).</p> <p><strong>Version</strong><strong> 1.0</strong></p> <p>kodojaDB_v1.0 is based on RefSeq v89 and the Virus-Host Database (accessed 03/09/2018 which is based on RefSeq 89 and Genbank 226.0). The viral partition of RefSeq v89 genome comprises 7946 viruses (ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/viral/assembly_summary.txt).</p> <p>kodojaDB_v1.0 was created using kodoja_retrieve.py which is part of the kodoja workflow (v0.05) (https://github.com/abaizan/kodoja).</p> <p><strong>References</strong></p> <p>[1] Baizan-Edge, A, Cock, P, MacFarlane, S, McGavin, W, Torrance, T, Jones, S. Kodoja: A workflow for virus detection in plants using k-mer analysis of RNA-sequencing data (under review Nucleic Acids Research). </p> <p>[2] Wood,D.E. and Salzberg,S.L. (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments. <em>Genome Biol.</em>, <strong>15</strong>, R46</p> <p>[3] Menzel,P., Ng,K.L. and Krogh,A. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. <em>Nat. Commun.</em>, <strong>7</strong>, 1–9.</p> <p>[4] O’Leary,N.A., Wright,M.W., Brister,J.R., Ciufo,S., Haddad,D., McVeigh,R., Rajput,B., Robbertse,B., Smith-White,B., Ako-Adjei,D., <em>et al.</em> (2016) Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. <em>Nucleic Acids Res.</em>, <strong>44</strong>, D733–D745.</p> <p>[5] Mihara,T., Nishimura,Y., Shimizu,Y., Nishiyama,H., Yoshikawa,G., Uehara,H., Hingamp,P., Goto,S. and Ogata,H. (2016) Linking virus genomes with host taxonomy. <em>Viruses</em>, <strong>8</strong>, 10–15</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks
<p>This data set contains confocal stacks from <em>Arabidopsis thaliana </em><em>35S::GFP-MBD</em> light grown hypocotyl as well as propidium iodide stained cotyledon pavement cells and shoot apical meristem. This is the test dataset for the Fiji macro SurfCut (https://github.com/sverger/SurfCut; 10.5281/zenodo.2635737)</p> <p> </p> <p><strong>Material and methods:</strong></p> <p>Plant material and growth conditions</p> <p><em>Arabidopsis thaliana </em>wild type Col-0 and the microtubule reporter line <em>GFP-MBD</em> (WS-4, (Marc et al. 1998) were used. Seeds were cold treated for 48 hr to synchronize germination. Plants were then grown in a phytotron at 20°C, in a 16 hr light/8 hr dark cycle on solid Murashige and Skoog medium (MS medium, Duchefa, Haarlem, the Netherlands) with 0.8% agar, 1% sucrose, and no vitamin.</p> <p> </p> <p>Confocal microscopy</p> <p>Cell contour staining in the case of PC_PI_Col0_(1-8).tif and SAM_PI_Col-0.tif was performed by staining the cell wall with Propidium Iodide (PI). Plants were immersed in 0.2 mg/ml propidium iodide (PI, Sigma-Aldrich) for 10 min and washed with water prior to imaging. For imaging, samples were either placed on a solid agar medium and immersed in water, or placed between glass slide and coverslip separated by 400 μm spacers to prevent tissue crushing. Images were acquired using a Leica TCS SP8 confocal microscope, equipped with a water immersion objective (HCX IRAPO L 25x/0.95 W). PI excitation was performed using a 552 nm solid-state laser and fluorescence was detected at 600–650 nm. GFP excitation was performed using a 488 nm solid-state laser and fluorescence was detected at 495–535 nm. Stacks of 1024x1024 pixels (pixel size of 0.363 x 0.363 micron) optical section were generated with a Z interval of 0.5 μm.</p> <p> </p> <p><strong>File list:</strong></p> <p>Light grown hypocotyl, <em>GFP-MBD</em> reporter line:</p> <p>- Hypocotyl_GFP-MBD.tif</p> <p>Cotyledon’s pavement cells, PI staining:</p> <p>- PC_PI_Col0_1.tif</p> <p>- PC_PI_Col0_2.tif</p> <p>- PC_PI_Col0_3.tif</p> <p>- PC_PI_Col0_4.tif</p> <p>- PC_PI_Col0_5.tif</p> <p>- PC_PI_Col0_6.tif</p> <p>- PC_PI_Col0_7.tif</p> <p>- PC_PI_Col0_8.tif</p> <p>Shoot apical meristem, PI staining:</p> <p>- SAM_PI_Col-0.tif</p> <p> </p> <p><strong>Reference:</strong></p> <p>Marc, Jan, Cheryl L. Granger, Jennifer Brincat, Deborah D. Fisher, Teh-hui Kao, Andrew G. McCubbin, and Richard J. Cyr. 1998. “A GFP–MAP4 Reporter Gene for Visualizing Cortical Microtubule Rearrangements in Living Epidermal Cells.” <em>The Plant Cell</em> 10 (11): 1927–39. https://doi.org/10.1105/tpc.10.11.1927.</p>
Supplementary information for D4.6 CEMCAP comparative techno-economic analysis of CO2 capture in cement plants
<p>Supplementary information for D4.6 CEMCAP comparative techno-economic analysis of CO2 capture in cement plants</p>
A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants
<p>This folder contain the data relative to the contact based pipe inspection presented on M. Tognon et al. "A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants." IEEE Robotics and Automation Letters 4.2 (2019): 1846-1851.</p>
Sugarcane Culturable microbiome prospection for plant growth promotion traits in Cynodon dactylon
<p>Data set of running experiments for the prospection of traits for plant growth promotion of bacterial communities from sugarcane tissues: rhizospheric soil, roots, stalks, and leaves. For this experiment, we are using a model plant: Cynodon dactylon known as Bermuda grass.</p>
Dataset for research paper "Heracleum sosnowskyi plants frost-resistance assessment in laboratory and field experiments"
<p>Dataset for research paper "Heracleum sosnowskyi plants frost-resistance in laboratory and field experiments". </p> <p>The <em>Heracleum sosnowsky</em> plants has low freezing tolerance and die in temperature range minus 6–12 °С. Snow cover provides stable soil temperature (not lower than minus 3 °С) and is the only factor that ensures the survival of <em>H. sosnowsky</em> plants in the regions with cold winter. The <em>H. sosnowsky</em> frost tolerance is higher in autumn (up to minus 12 °С) and became lower at spring (minus 5–7 °С). These results can be explained by absence of deep dormancy in <em>H. sosnowskyi</em> meristem tissues and gradual change of carbohydrate content in them during the cold period. The seeds have high freezing tolerance after its formation but lost it after stratification. The field experiments were carried out by participants of citizen science project “Moroz”. It was shown that <em>H. sosnowskyi</em> plant eradication probability with the help of snow removal completely depends on weather conditions. This method can be used only on the territories where the use of herbicides is prohibited and only in the regions with minimal temperature in January – Febrary not higher than minus 25 °С. The dataset with all measurements made during the experiments is available on the site of “Moroz” project (<a href="http://proborshevik.ru/%20">http://proborshevik.ru</a>).</p>
Mechanisms of unbalanced sediment transport linked to anthropogenic disturbance in world's largest tidal power plant
<p>Analysis_code.zip and Mooring_toolbox.zip contain matlab m-files. The code was designed to analysis the changes from the mooring data.</p> <p>Data.zip contains a mooring observation data in Lake Sihwa. The velocity profiles and backscatter intensities were measured with a downward-looking acoustic Doppler current profiler (ADCP) (RDI, 600 kHz WorkHorse Sentinel). The mooring system was designed to measure the near-bed temperature, salinity, and turbidity using conductivity-temperature-depth (CTD) sensors (RBR, XR-420, and Concerto) and an optical backscatter sensor (OBS) (Seapoint Turbidity Meter).</p> <p>Waterlevel.zip has water level data in the ocean and Lake Sihwa.</p> <p> </p> <p> </p>
Data and model scripts for "Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub"
<p>Model code and data as used for the first revision submitted to New Phytologist, Sept. 2019. </p> <p>Models are coded in JAGS and run in R via the package "rjags." Two model versions are presented:</p> <p>1) "mod_SAM.R" and "script_SAM.R" run the full, time-varying SAM model, utilizing both the plant water potential ("res.Rdata") and the environmental covariate data ("cov2.Rdata") and associated initial values ("initsSAM.Rdata")</p> <p>2) "mod_SIMPLE.R" and "script_SIMPLE.R" run the simple, time-invariant model, utilizing only the plant water potential data ("res.Rdata") and associated initial values ("initsSIMPLE.Rdata")</p>
DS_Ctrl_Mutriku: Controllers assessment at Mutriku OWC plant (Spain)
<p>Data obtained for the technical assessment of control algorithms with the biradial turbine installed in the Mutriku OWC plant: i) numerical results; ii) sea trial results. </p>
JoseBSL/Geonet: Climate mediates pollinator species roles in plant-pollinator networks
<p>Code and data from the article "Climate mediates pollinator species roles in plant-pollinator networks".</p>
ScienceDex guides
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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.