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.
1,997
datasets available to search
ShareScore release 0.7.1
Dataset results
1,997 results for “rice”
Rice straw degradation analysis with Kraken2/Bracken annotation
<p>Metadata and annotation of the reads obtained from the rice straw degradation process using Kraken2/Bracken.</p>
Light-regulated gene expression and alternative splicing data from rice seedlings.
<p>This data contains analyzed data from the experiment conducted on rice seedlings under dark and light conditions. Seeds of rice (Oryza sativa spp. japonica cv. Nipponbare) were sown in the dark and germinated on day 2 and continued to grow in the dark for another 6 days. 3 biological replicates of the dark-grown etiolated shoots were harvested on day 8 after sowing. The remaining dark-grown seedlings were exposed to continuous white light at 120 mol/m2/sec for 48 hours or another 2 days (Days 9 and 10 after sowing). Three replicates of the light-treated green-colored seedling samples were harvested at the end of day 10. Harvested samples were frozen in liquid nitrogen and stored at -80C until further processing.</p>
Microbiome homeostasis on rice leaves is regulated by a precursor molecule of lignin biosynthesis
<p>A GWAS pipeline for identification of the loci associated with >3000 bacterial species (Selected from over 6000 bacterial species of rice Phyllosphere).</p><p> </p>
RiceFloodIT: Water Management in the Italian Rice Paddies Estimated from MODIS data
<p>This repository includes two datasets used in Ranghetti et al. (2018) and Ranghetti & Boschetti (2022) to analyse the magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period in the Italian rice district: methods used to generate these data from MODIS remote sensing imagery are described in these papers.</p> <ul> <li><strong>ffavg_2021.csv</strong>: this dataset includes values of yearly FF<sub>avg</sub> (averaged Flooding Fraction) at pixel level. Each record represent the FF<sub>avg</sub> value of a specific pixel in a specific year. <ul> <li><strong>x</strong> and <strong>y</strong> identifies the latitude and longitude of each record (in UTM32 coordinates);</li> <li><strong>subdistrict</strong> represent the sub-district ID of each pixel ("A" to "G");</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ff</strong> is the FFavg value (range 0-1);</li> <li><strong>count</strong> is the number of MODIS images used to generate each FF<sub>avg</sub> aggregated value.</li> </ul> </li> <li><strong>ws_2021.csv</strong>: this dataset includes values of WS (proportion of Water-Seeded rice surface) at sub-district and district levels. <ul> <li><strong>subdistrict</strong> represent the sub-district ID of each record ("A" to "G", plus "all" which identifies values aggregated at district level);</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ws</strong> is the WS value (range 0-1);</li> <li><strong>count</strong> is the number of pixels used to generate each WS aggregated record.</li> </ul> </li> </ul> <p>Current data version (2021.01) includes estimated values in the period 2000-2021.</p> <p>References:</p> <p>Ranghetti, Luigi, Elisa Cardarelli, Mirco Boschetti, Lorenzo Busetto and Mauro Fasola. 2018. “Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies.” <em>Remote Sensing</em> 10 (3). doi:<a href="https://doi.org/10.3390/rs10030416">10.3390/rs10030416</a>.</p> <p>Ranghetti, Luigi and Mirco Boschetti. 2022. “Updated trends of water management practice in the Italian rice paddies from remotely sensed imagery.” <em>European Journal of Remote Sensing</em> 55 (1), pp. 1-9. doi:<a href="https://doi.org/10.1080/22797254.2021.2002726">10.1080/22797254.2021.2002726</a>.</p>
Indicative distribution map for Ecosystem Functional Group F3.3 Rice paddies
<p>This archive contains indicative distribution maps and profiles for <strong>F3.3 Rice paddies</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Kawe Gidaa-naanaagadawendaamin Manoomin Tribal-University Research Collaborative, University of Minnesota, Manoomin / Psiη (Wild Rice) Density Survey for Northern Minnesota and Wisconsin Waters
Wild Rice (Ojibwemowin: Manoomin; Dakodiapi: Psiŋ; Latin: Zizania palustris) abundance, harvest, and water level data, across the upper Great Lakes region collected by tribal organizations.
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL rice simulations
<p>This data set contains output data from simulations with the model LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, plant day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
UAV Imagery Dataset for Paddy Rice Panicle Detection
<p>Accurate panicle segmentation is a key step in rice field phenotyping. Deep learning methods based on high spatial resolution images provide a potential solution to increase the throughput as well as the accuracy of panicle identification. The quality and volume of the dataset are crucial to training an accurate and robust deep learning model. Panicle segmentation tasks require particularly costly annotations. Here we open a paddy rice panicle dataset, acquired by DJI Mavic Pro in 2018, to public use for rice panicle phenotyping. </p> <pre>@article{wang2021paddy, title={Paddy Rice Imagery Dataset for Panicle Segmentation}, author={Wang, Hao and Lyu, Suxing and Ren, Yaxin}, journal={Agronomy}, volume={11}, number={8}, pages={1542}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} }</pre>
High resolution rice yield data of Jiangsu, China
<p>This dataset presents rice yields of Jiangsu Province, China during 2001-2020, the spatial resolution is 1 km. </p> <p>The dataset was generated using Random Forest Regression algorithm and multi-phase remote sensing data, the accuracy is <span>R2 = 0.65, RMSE = 388.79 kg/ha, and rRMSE = 4.48%.</span></p>
Effect of sticky rice germ oil droplet spraying on chrysanthemum thrips resistance and metabolome
<p>This dataset contains experimental results from full plant assays with Chrysamthemum plants that were conducted to test the effectiveness of sprayng solutions containing sticky rice oil droplets for trapping of small arthropods on plants. The experiments were conducted at the Institute of Biology Leiden, Leiden University the Netherlands.</p> <p>The first dataset contains the results of the full plant assays with thrips.</p> <p>The second dataset contains the results of 1H NMR and GC-MS signals of leaf samples of sprayed chrysanthemum plants.</p> <p> </p> <p>Version history:</p> <p>Version 2: Included the RAW data on % coverage of plants for the two plant assays that had been left out during earlier submission</p> <p>Updated the metadatasheets within the excel files to be more complete.</p> <p>Version 3: Included a new excel sheet in the GC-MS and NMR data file in which a subset of the RAW HS-GC-MS and 1H NMR data, namely those peaks and delta signals that were identified and matchedd to compound id after untargeted analysis, are presented together with the name of the compounds or classes of compounds as mentioned in the manuscript.</p> <p>No changes were made to the plant assay data file</p> <p> </p> <p>In the "Dataset_TBierman_RGO_thrips_1HNMR_GC-MS_V3" excel file:</p> <p>Sheets: "Processed 1H NMR data" and "Processed HS-GC-MS data"</p> <p>contain processed 1H NMR and GC-MS data of chrysanthemum leaves, harvested after 10 or 25 days, of plants that were sprayed with water or vegetable-oil derived adhesives and infested with thrips or not.</p> <p>Sheet: "Quantitative data selected comp" contains a subset of the data where signals were found significant in the untargeted analysis have been annotated to their compound identity.</p> <p>In the "Dataset_TBierman_RGO_thrips_plantassay1_and_2_V3" excel file:</p> <p>Sheets "Plant_assay_1_RGO_thrips_d10_25" and "Plant_assay_2_RGO_thrips_d25" contain the raw plant assay data</p> <p>Sheets "Plant_assay_1_RGO_coverage" and "Plant_assay_2_RGO_coverage" contain the summary values of the estimated coverage with adhesive oil droplets of each respective experiment on the left side while on the right side the raw data is presented </p> <p> </p>
Paddy Rice Mapping Learning Material(Sentinel-1 & labeling) in South Korea
<p>This dataset includes time series Sentinel-1 images and paddy rice labeling in South Korea for ML/DL model training. It consists of 7,762 training patches and 5,180 validation patches for each patch consists of 256 x 256 pixels. The dataset is saved in hdf5 format separated into training/valdation data, image/labeling, and part number which can be accessed by key: {tr/va}_{im/lb}_{0~4}.</p> <p>According to the phonological stage of paddy rice, the Sentinel-1 images were acquired through 8-time steps from May 10 to October 20 in 20 days’ interval. In order for the images to capture similar features of rice invariant to more or less difference of growth, minimum and maximum value composite were used at transplanting season and ripening season each. The acquisition year for each patch varies from 2017 to 2019 since it was matched to that of labeling source.</p> <p>The paddy rice labeling is a rasterized version of farm map produced by Korean Ministry of Agriculture, Food and Rural Affairs(MAFRA). The original source data was produced by visual interpreted by high-resolution satellite images and aerial photos referring the other national GIS data and it is accessible through the national open data platform (<a href="http://data.nsdi.go.kr/dataset/20210707ds00001">http://data.nsdi.go.kr/dataset/20210707ds00001</a>). As the data is distributed in a vector format, it was converted to 10 m x 10 m raster format which is compatible to the Sentinel-1, and used for labeling the images.</p>
2022 Rice Crop-type Data for Western Tanzania
<p>Rice Crop-type data from Katavi Region Tanzania was collected by the NASA Harvest Program at the University of Maryland, the Sokoine University of Agriculture, and Flamingoo Food Limited under the Optimizing Crop Yield Data Collection for Supply Chain Enhancement project (more at: https://cropanalytics.net/optimizing-yield-data/) funded by ENABLING CROP ANALYTICS AT SCALE (ECAAS) is a multi-phase initiative that aims to catalyze the development, availability, and uptake of agricultural ground and remote sensing data and applications in smallholder production systems more at (https://cropanalytics.net/)</p>
Transposable elements in rice detected by TEF
<p>Next generation sequence data of '<a href="https://www.gene.affrc.go.jp/databases-core_collections_wr_en.php">World Rice Core Collection</a>' and '<a href="https://www.gene.affrc.go.jp/databases-core_collections_jr_en.php">Rice Core Collection of Japanese Landraces</a>' distributing NARO genebank have been analyzed by the software '<a href="https://pubmed.ncbi.nlm.nih.gov/36418944/">Transposable Elements Finder</a>'. This data is an additional supplementary data of the TEF paper.</p> <p>Transposition evidences were detected by direct comparison of NGS short reads between Japonica rice Nipponbare (wrc01) and other cultivars. Nearby 21,000 kinds of head and tail sequence pairs of TE have been identified by TEF. Head and tail sequences of TE, chromosome number, position, and name of detected rice cultivar were listed. </p> <ul> <li>This version is TE list of <em>Oryza sativa</em> detected by TEFv1.4.</li> <li>Positions of TE transpositions were mapped on <a href="https://rapdb.dna.affrc.go.jp/download/archive/irgsp1/IRGSP-1.0_genome.fasta">Os-Nipponbare-Reference-IRGSP-1.0</a> distributed from <a href="https://rapdb.dna.affrc.go.jp/">The Rice Annotation Project Database</a>.</li> <li>Accession Numbers of NGS data are listed in Japanese page '<a href="https://www.gene.affrc.go.jp/databases-core_collections_wr.php">World Rice Core Collection</a>' and in NCBI SRA page '<a href="https://www.ncbi.nlm.nih.gov/Traces/study/?acc=DRP006572&o=acc_s%3Aa">Rice Core Collection of Japanese Landraces</a>'.</li> <li>The TEF software is available at: <a href="https://github.com/akiomiyao/tef">https://github.com/akiomiyao/tef</a></li> <li>Miyao, A., Yamanouchi, U. Transposable element finder (TEF): finding active transposable elements from next generation sequencing data. <em>BMC Bioinformatics</em> <strong>23</strong>, 500 (2022). <a href="https://doi.org/10.1186/s12859-022-05011-3">https://doi.org/10.1186/s12859-022-05011-3</a></li> </ul>
Albert Richard Rice (r2420)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Albert Richard Rice<br><u>musiXplora-ID</u>: r2420<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/r2420">https://musixplora.de/mxp/r2420</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 09 June 1951<br><u>Place of Birth</u>: Pasadena/CA<br><u>First Mentioned</u>: 1986<br><u>Sectors</u>: Bibliothek, Hochschule, Museum, Musikforschung<br><u>Professions (Historical)</u>: Kurator<br><u>Professions (Musical)</u>: Instrumentenkundler, Klarinettist, Musikforscher<br><u>Professions (Non-Musical)</u>: Bibliothekar, Schriftsteller<br><u>Other Places of Activity</u>: Claremont/CA, Los Angeles<br><br><br><u>Ereignisse:</u><br><table><tbody><tr></tr><tr><td>Related</td><td></td><td>Tagung</td><td>6003430</td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Data archive for "Modified rice bran arabinoxylan as a nutraceutical in health and disease — A scoping review with bibliometric analysis"
<p>v1.0.0 Release with the publication of the paper on PLoS One</p> <p>Ooi, S. L., Micalos, P. S., & Pak, S. C. (2023). Modified rice bran arabinoxylan as a nutraceutical in health and disease—A scoping review with bibliometric analysis. PLOS ONE, 18(8), e0290314. https://doi.org/10.1371/journal.pone.0290314</p> <p><strong>Full Changelog</strong>: https://github.com/sooi10/RBACScoping/commits/NetworkAnalysis</p>
Dataset for "Whole-genome de novo assemblies reveal structural variations and organelle-to-nucleus DNA transfers in Asian and African rice""
<p>DXCWR_O.rufipogon_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. rufipogon</em> DXCWR.</p> <p>DXCWR_O.rufipogon_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly DXCWR_O.rufipogon_scaffolded_anchored.fa.</p> <p>DXCWR_O.rufipogon_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly DXCWR_O.rufipogon_scaffolded_anchored.fa.</p> <p>IRGC104165_O.glaberrima_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. glaberrima</em> IRGC104165.</p> <p>IRGC104165_O.glaberrima_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly IRGC104165_O.glaberrima_scaffolded_anchored.fa.</p> <p>IRGC104165_O.glaberrima_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly IRGC104165_O.glaberrima_scaffolded_anchored.fa.</p> <p>W1411_O.barthii_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. barthii</em> W1411.</p> <p>W1411_O.barthii_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly W1411_O.barthii_scaffolded_anchored.fa.</p> <p>W1411_O.barthii_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly W1411_O.barthii_scaffolded_anchored.fa.</p> <p>W2014_O.nivara_scaffolded_anchored.fa.gz</p> <p>--Scaffolded and anchored genome assembly for <em>O. nivara</em> W2014.</p> <p>W2014_O.nivara_scaffolded_anchored.gff.gz</p> <p>--Gene annotation for the genome assembly W2014_O.nivara_scaffolded_anchored.fa.</p> <p>W2014_O.nivara_scaffolded_anchored_repeatmasker.gff.gz</p> <p>--Repeat annotation for the genome assembly W2014_O.nivara_scaffolded_anchored.fa.</p>
Unravelling the modus operandi of phytosiderophores during zinc uptake in rice: the importance of geochemical gradients and accurate stability constants
<p>Dataset for figures in the article 'Unravelling the modus operandi of phytosiderophores during zinc uptake in rice: the importance of geochemical gradients and accurate stability constants'.</p>
Root images of rice plant
<p>These root images of rice plant were used for<em> </em>analysising root morphological indexs like length, diameter, surface area and volume by the image anaiysis software like <em>WinRhizo</em>. These grayscale images of roots were obtained using an EPSON1680 scanner.</p>
3D image of an assembly of rice grains
<p>3D reconstruction of an assembly of rice grains. The image was acquired on the collaborative microtomography platform at Laboratoire Navier (http://navier.enpc.fr).</p> <p>Ordinary, long-grain rice is poured in a plastic container, 50mm in diameter. The average length of the grains is about 6.5 mm.</p> <p>The X-ray source is a Hamamatsu L10801 X-ray source (maximum voltage: 230V, maximum current: 1mA). Combined with the the Paxscan Varian 2520V flat-panel X-ray imager (1536x1920 pixels, pixel pitch 127µm), this setup leads to a voxel size of approx. 0.030mm. The specimen was scanned at 100kV and 300µA with an imager frame-rate of 6 images per second. To reduce noise, 12 radiographs were averaged to produce one projection; the total number of projections was 1440.</p> <p>3D reconstruction was carried out using standard tools developed by RX Solutions France. Contrast, resolution and signal-to-noise ratio were all excellent, so that the most basic reconstruction procedure resulted in very high quality 3D images.</p> <p>The dataset is provided as a set of 689 TIFF images, 1747×1751 pixels. It is used as illustrative material for a series of posts called “Orientation correlations among rice grains” on my blog (see related identifiers below).</p> <p> </p>
Figure 4 in Purification and Characterization of Midgut α-Glucosidase from Larvae of the Rice Green Caterpillar, Naranga aenescens Moore
Figure 4. Irreversible thermoinactivation of the N. aenescens α-glucosidase at 35 (▲), 40 (■) and 45 °C (•). Different letters indicate that the relative activity of enzymes is significantly different from each other by Tukey's test (P <0.05).
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.