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22,710 results for “Plants for planting”
Plant Health Status
<p>These are the results of the predictions from the xylella classifier on the validation dataset, which contains 1200 health statuses, with 400 for each health status condition. As we can see from the confusion matrix, the model almost perfectly recognizes Asymptomatic health status, distinguishing it clearly from Mild and Evident symptomatic statuses.</p>
Fig. 2 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 2. Holotype of Isoetes coreana Y.H. Chung & H.K. Choi.
Fig. 3 in Correction of the holotype citations of three vascular plants at the herbarium of the National Institute of Biological Resources, Korea
Fig. 3. Holotype of Huperzia jejuensis B.Y. Sun & J. Lim.
Fig. 4 in New distribution record of northern lineage plant of Stellaria filicaulis (Caryophyllaceae) from South Korea
Fig. 4. Distribution of Stellaria filicaulis Makino in Yeoncheon-gun, Gyeonggi-do, South Korea.
Animal versus plant protein and adult bone health: a systematic review and meta-analysis from the National Osteoporosis Foundation- S1 File
<p>All calculations and meta-analyses for the systematic review "Animal versus plant protein and adult bone health: a systematic review and meta-analysis from the National Osteoporosis Foundation" were conducted in Stata SE 13 (Stata Corp) using this analytical dataset <strong>(S1 File).</strong></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: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Contracts for the eradication of plants of the hogweed (Heracleum sosnowskyi Manden.) in Russia from 2011 to 2017.
<p>Contracts for the eradication of hogweed (<em>Heracleum sosnowskyi </em>Manden.) plants in Russia from 2011 to 2017 are described. Data on the cost of contracts, site areas, mowing, herbicide use, mapping were obtained at http://zakupki.gov.ru</p>
tci20130907-02 - Plant walk
<p>This dataset contains files recorded for during a plant walk around Rouku village, Western Province, Papua New Guinea. The two main participants are Nakre Ruth Abia and her sister Janet Abia. They show several plant species that are used in daily life. The botanist who is doing the plant identification is Kipiro Damas. The recordings were made by Julia Colleen Miller and Christian Döhler.</p> <p>The dataset includes:</p> <ul> <li>audio file (tci20130907a-02.wav)</li> <li>video file (tci20130907v-02.mpg)</li> <li>transcription file (tci20130907a-02.eaf)</li> </ul> <p>The material was recorded by Christian Döhler as part of a language documentation project for his PhD. The project was located at the <a href="http://chl.anu.edu.au/">School of Culture, History and Language</a> at the <a href="http://anu.edu.au">Australian National University, Canberra</a>. For the most part it was funded by the <a href="http://dobes.mpi.nl/">DOBES project</a> of the <a href="https://www.volkswagenstiftung.de/en/foundation">Volkswagen Foundation</a>.</p>
Process plant data integration and querying
<p>This data set includes:</p> <ul> <li>Process plant data derived from heterogeneous sources</li> <li>An OWL ontology of the entities in the data sources</li> <li>Mapping for data in the sources to the ontology (<em>done using Cellfi</em>e,a <em>Protégé plugin. Also save as .json to use</em>)</li> <li>Data integrated from all the sources</li> <li>Sample SPARQL queries (<em>Q5 and Q6</em>)</li> </ul> <p> </p>
Scans of leaves dissected in phyllotactic order: complementation of ago7 mutant A. thaliana plants with truncated promoter transgenes
<p>This transgenic complementation experiment is described in a paper by Hoyer et al. (2019): <a href="https://doi.org/10.1002/pld3.102">https://doi.org/10.1002/pld3.102</a></p> <p>Adaxial (ad) and abaxial (ab) surfaces of rosettes were photographed in between removal of leaves, to enable checking that leaves were removed in the correct phyllotactic order.<strong> </strong>A manifest file with file SHA-1 sums is included.</p> <p>Metadata and LeafJ measurements have been made available separately, to facilitate updates -- see <a href="https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata">https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata</a>, archived as <a href="https://doi.org/10.5281/zenodo.1340636">https://doi.org/10.5281/zenodo.1340636</a></p> <p>Seed was plated and growth started on 2016-08-26. Leaves were dissected and scanned 33 and 35 days post-stratification (2016-09-28 and 30).</p>
Time-lapse photograph dataset: complementation of ago7 mutant A. thaliana plants with truncated promoter transgenes
<p>This transgenic complementation experiment is described in a paper by Hoyer et al. (2019): <a href="https://doi.org/10.1002/pld3.102">https://doi.org/10.1002/pld3.102</a></p> <p>Time-stamped photographs are provided in twelve directories by camera (twelve overlapping fields of view) and a manifest file with file SHA-1 sums is included.</p> <p>Metadata have been made available separately, to facilitate updates -- see <a href="https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata">https://github.com/jshoyer/raspi-photo-and-leaf-scan-metadata</a>, archived as <a href="https://doi.org/10.5281/zenodo.1340636">https://doi.org/10.5281/zenodo.1340636</a></p> <p>Seed was plated and growth started on 2016-08-26.</p>
A detailed flowsheet of a 100 MW Solid Oxide Fuel Cell plant modeled in ASPEN PLUS
<p>Files required for modeling a 100 MW Solid Oxide Fuel Cell Plant (Power-to-Gas Mode) in Aspen Plus. The plant was designed by Butera et al.[1]. A detailed capital cost estimate was made using the AspenTech Process Economic Analyzer and written in ESA.xlsx. </p> <p>Further details are available upon request: alexandru.botan@phystech.edu</p> <p>[1] Butera, G., Jensen, S.H., and Clausen, L.R. "A novel system for large-scale storage of electricity as synthetic natural gas" submitted to J. Energy</p> <p> </p> <p> </p>
Results of questionnaire on live plant health nematode collections
<p>In the framework of the Euphresco project 2016-F-186 'Inventory of living collections of cyst and root knot nematodes in Europe and their maintenance techniques (Cyst and Melo Collect)', a survey was organised during the Autumn 2017 with the aim to collect information on the live nematode collections in different countries. 32 (reference) collections in Austria, Belgium, Canada, Czech Republic, France, Germany, Italy, Latvia, Mexico, Netherlands, Portugal, Spain, United Kingdom, and United States of America participated in the survey.</p> <p>Of the 32 mentioned collections the preferred nematode populations are clearly different between the USA and Europe: the tropical <em>Meloidogyne</em> species are the most represented in North America while <em>Globodera</em> species are in Europe.</p> <p>The comparison of the protocols for live nematode storage for short and long periods of time showed high variability, although in general, <em>Globodera</em> spp. can be stored at 4 °C for longer time (>20 years) than <em>Meloidogyne</em> spp., whose optimal storage time is 8 months at 14 °C, when kept in soil.</p> <p>The need to verify the population for its trueness, with other words, the frequency of identification differs; various possibilities are mentioned between never and almost monthly to frequently, each time the nematodes are extracted from the soil (90 days for PCN, 16 weeks for <em>Meloidogyne</em>) to yearly or only upon arrival. The frequency depends on the amount of time people can afford to put into this work and the risk assessment for getting cross contamination.</p> <p>The conditions for rearing and maintaining were inventoried as well. For <em>Meloidogyne</em> spp., host differences for rearing and maintaining them are rarely observed (often <em>Solanum lycopersicum</em> -tomato- is used for both), although <em>Ficus carina</em> for long maintenance of <em>Meloidogyne</em> spp. and <em>Solanum dulcamara</em> for <em>M. fallax</em> have been used in France (pers. comm. Fabrice Ollivier, ANSES). For cyst nematode species their specific host is used for both rearing and maintenance purposes: <em>Solanum tuberosum</em> (potato), <em>Nicotiana tabacum</em> (tobacco), or <em>Glycine max</em> (soybean) are used respectively for the potato cyst nematodes (<em>Globodera rostochiensis</em> and <em>G. pallida</em>), <em>G. tabacum</em> and <em>G. glycines</em>.</p> <p>The project report is also available on Zenodo https://zenodo.org/record/1442881#.W7PaxmgzbIU</p>
Expression data of the flowering time genes in chickpea, extracted from Ridge et al. (2017). Plant Physiology 175, 802-815.
<p>This data is supplementary to the following paper: Gursky, V.V., Kozlov, K.N., Nuzhdin, S.V., and Samsonova, M.G. (2018) Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation from the <em>FLOWERING LOCUS T </em>Gene Homologs in Chickpea. <em>Frontiers in Genetics</em>. 9:547. doi: 10.3389/fgene.2018.00547</p> <p>The data was obtained by digitizing Figure 5 of the following paper: Ridge, S., Deokar, A., Lee, R., Daba, K., Macknight, R. C., Weller, J. L., and Tar'an, B. (2017). The chickpea Early flowering 1 (Efl1) locus is an ortholog of arabidopsis ELF3. <em>Plant Physiology </em>175, 802-815. doi:10.1104/pp.17.00082</p> <p>The archive contains files (in csv format) with the expression data of each of the following ten genes: <em>FTa1</em>, <em>FTa2</em>, <em>FTa3</em>, <em>FTb</em>, <em>FTc</em>, <em>AP1</em>, <em>FD</em>, <em>TFL1a</em>, <em>TFL1c</em>, and <em>LFY</em>, for the cultivars CDC Frontier and ICCV 96029 and for two growth conditions (long day, LD, and short day, SD). Each file is named according to the following scheme: <Gene name>_<Cultivar name>_<Growth conditions>.csv. Each file contains values in the following three columns (separated by commas): time (in days after sowing), relative transcription level (%ACTIN), and standard error. In the case of the genes <em>AP1</em>, <em>FD</em>, <em>TFL1a</em>, <em>TFL1c</em>, and <em>LFY</em>, the standard error was assumed equal to the size of the points in the figure when the actual error range was smaller than that size (and, thus, not visible in the figure). In the case of the genes <em>FTa1</em>, <em>FTa2</em>, <em>FTa3</em>, <em>FTb</em>, and <em>FTc</em>, the standard error was recorded as 0 for such points (the error for these genes was not used in the study).</p> <p>The data was extracted with the help of the web-based tool <em>WebPlotDigitizer</em> (https://automeris.io/WebPlotDigitizer).</p>
Freezing point of water, water content and proportion of freezing water in Heracleum sosnowskyi plants
<p><strong>Method plant tissue of analysis using differential scanning calorimetry (DSC)</strong></p> <p>Differential scanning calorimetry was used to assess the risk of tissue plant damage by low negative temperatures. We measured the freezing point of water, the water content and the proportion of freezing water in plant samples using the DSC – 60 Shimadzu calorimeter (Japan). The samples were buds and seeds of <em>Heracleum sosnowskyi</em> (hogweed) plants. The samples were placed in an aluminum pan with a volume of 0.1 cm<sup>3</sup>. The sample was cooled at a rate of 1 °C/min from +5 °C to -30 °C. The water crystallization temperature was determined at the beginning of the phase transition peak. After measurements, the material was dried at 105 °C to a constant dry mass.</p> <p>The following calculations were applied:</p> <ol> <li>A – DSC data (water crystallization temperature, ºC);</li> <li>B – wet weight sample (mg);</li> <li>C – dry weight sample (mg);</li> <li>D = B-C – (water content in sample, mg);</li> <li>E – DSC data (heat from water freezing, mJ);</li> <li>F= E× 335 – (frozen water in sample, mg);</li> <li>335 –The heat of frozen for wateris approximately (mJ/mg);</li> <li>G = D-F – (non-freezing water, mg);</li> <li>H = (100%/D) × F – (frozen water in sample, %);</li> <li>I = (D/B) ×100 – (water content in sample, %);</li> <li>J= D/C (mg H<sub>2</sub>O/mg dry weight sample).</li> </ol> <p> The results of the analysis are presented in the file «The_results_DSC_analysis_of_buds_seeds.xls».</p> <p>Measurements were carried out in September 2018. The samples were buds and seeds of <em>Heracleum sosnowskyi</em> (hogweed) plants. Geographical coordinates vegetation plants 61.645333, 50.733196.</p>
Strong gene activation in plants with genome-wide specificity using a new orthogonal CRISPR/Cas9-based Programmable Transcriptional Activator.
<p>This data set correspond to the supporting data generated in the manuscript: Strong gene activation in plants with genome-wide specificity using a new orthogonal CRISPR/Cas9-based Programmable Transcriptional Activator. </p>
Raw diffraction images of plant vacuolar iron transporter VIT1 (with Zn and Co)
<p>Diffraction images of full-length VIT1 related to PDB codes <a href="https://www.rcsb.org/structure/6IU3">6IU3</a> (Zn-bound) and <a href="https://www.rcsb.org/structure/6IU4">6IU4</a> (Co-bound). All data were collected from loop-harvested (micro)crystals on BL32XU, SPring-8 using EIGER X 9M detector.<br> <br> 6IU3: Helical (20, 45, or 90°/crystal) datasets were collected automatically using ZOO system at a wavelength of 1 Å.<br> 6IU4: Small-wedge (10°/crystal) and helical (120°/crystal) datasets were collected at a wavelength of 1.28 Å.<br> <br> The crystals belonged to space group <em>C</em>222<sub>1</sub> with unit cell parameter a~47, b~290, c~46 Å. All datasets were processed and merged using KAMO pipeline with XDS.</p> <p>Related entries: <a href="https://zenodo.org/record/2532134">metal binding domain</a>, <a href="https://zenodo.org/record/2532138">data for phasing by Hg-SIR</a> <br> </p>
Raw diffraction images of plant vacuolar iron transporter VIT1 (metal binding domain)
<p>Diffraction images of the metal binding domain of VIT1 related to PDB codes <a href="http://www.rcsb.org/structure/6IU5">6IU5</a> (Zn-bound), <a href="http://www.rcsb.org/structure/6IU6">6IU6</a> (Ni-bound), <a href="http://www.rcsb.org/structure/6IU8">6IU8</a> (Co-bound), and <a href="http://www.rcsb.org/structure/6IU9">6IU9</a> (Fe-bound).</p> <p>Helical data (180°/crystal) were collected from loop-harvested crystals on BL41XU, SPring-8 at the peak and low-remote wavelengths of specific metals (Zn: 1.2820/1.3000; Ni: 1.4850/1.5220; Co: 1.6050/1.6480; Fe: 1.74009/1.79148 Å).</p> <p>The crystals belonged to space group <em>P</em>3<sub>1</sub> with unit cell parameter a~85, c~98 Å.</p> <p>Related entries: <a href="https://zenodo.org/record/2532136">full-length with Co/Zn</a>, <a href="https://zenodo.org/record/2532138">full-length data for phasing by Hg-SIR</a></p>
Raw diffraction images of plant vacuolar iron transporter VIT1 (phasing data by Hg-SIR)
<p>Diffraction images of full-length VIT1 for Hg-SIR phasing. All data were collected from loop-harvested crystals on BL32XU, SPring-8 at a wavelength of 1 Å using the EIGER X 9M detector.</p> <p>For Hg-bound VIT1, six helical (71-210°/crystal) datasets were collected. From native (without Hg) crystals, 24 helical (20-90°/crystal) datasets were automatically collected. The crystals belonged to space group <em>C</em>222<sub>1</sub> with unit cell parameter a~47, b~290, c~46 Å.</p> <p>Related entries: <a href="https://zenodo.org/record/2532136">full-length with Co/Zn</a>, <a href="https://zenodo.org/record/2532134">metal binding domain</a><br> </p>
Supplementary data for "An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system"
<p>The code for the Unit Commitment & Economic Dispatch model that was used in this work is available at: https://gist.github.com/vitali87/20688c161d7b5ad598b5d52b524f4585</p> <p>Sample output data can be found in the "Example Outputs.zip" file. This corresponds to the case outlined in the article that simulates a system with 5 Allam Cycle plants without Liquid Oxygen Storage, for the winter test week.</p> <p>To run the UCED model:</p> <ul> <li>Download "UC AIMMS Allam Cycle Model" code from the github and save as an AIMMS project file.</li> <li>Save the file in a folder that contains all the necessary input datasets, found in the "Universal Inputs for UCED Model.zip" file, and the example outputs, found in the "Example Outputs.zip" file, which are to be overwritten. Do not change the name of the input or output files.</li> <li>Open the project and execute the following procedures: <ul> <li>"Main Initialisation" - to initialise the problem</li> <li>"Read from Excell" - to read data from the input files</li> <li>"Main Execution" - to begin running the problem</li> </ul> </li> <li>Once the run is complete, execute "Run External Procedure" to overwrite the output files with the new data.</li> </ul> <p>To change the test week:</p> <ul> <li>Open "Demand Profiles" in 'sets' and change the set definition. Enter "C1" for the winter week and "C21" for the summer week. Another week can alternatively be selected. For example, entering "C45" would allow the model to run with the weather and demand data from the 45th week in the year 2010. </li> <li>Save and close the set.</li> </ul> <p>To change the number of plants in the system:</p> <ul> <li>Open "PCCSGenerators" in 'sets' and change the set definition. To run with 5 Post Combustion Capture plants, end the list of generators after plant number 5 by commenting the remaining plants. This is done by using "!" after the 5th plant name in the string. Then save and close the set.</li> <li>Repeat the above step for the "ACGenerators" and "AirSeparationUnits" sets, to change the number of Allam Cycle plants in the system.</li> </ul> <p>To add or remove oxygen storage capability from the Allam Cycle plants:</p> <ul> <li>Open the "Main Initialisation" procedure.</li> <li>To run the model without oxygen storage: <ul> <li>make sure the following command is stated: "AC_ASU_coupled := 0;"</li> <li>save and close the procedure</li> </ul> </li> <li>To run the model with oxygen storage: <ul> <li>make sure the following is command is stated: "AC_ASU_coupled := 1;"</li> <li>make sure that the number, 'X', of "map_AC_to_ASU('Gas_CCS_AC_X') := 'ASU_X';" commands that are active matches the number of active Allam Cycle plants in the model</li> <li>save and close the procedure.</li> </ul> </li> </ul>
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)
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DANDI Archive for NWB datasets
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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.