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.
26
datasets available to search
ShareScore release 0.9.0
Dataset results
26 results for “rice paddy”
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>
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>
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>
Paddy rice methane emissions across Monsoon Asia
<p>Although rice cultivation is one of the most important agricultural sources of methane and contributes ~8 % of total global anthropogenic emissions, large discrepancies remain among estimates of global methane emissions from rice cultivation due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5 degrees) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combined paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning, and produced gridded up-scaling estimates of rice methane emissions at 5000-m resolution at 8-day intervals across Monsoon Asia, where ~87% of global rice area is cultivated and ~90% of global rice production occurs.<br> </p>
20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data
<p>This dataset provides 20 m annual paddy rice map for mainland Southeast Asia since 2019 .</p> <p>*** The data file is in “.tif" format</p> <p>*** Pixel size: 20 m</p> <p>*** Projection information: EPSG: 4326 (WGS84)</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Remotely Sensed Paddy Rice Map of South Korea (2017-2021)
<p>This dataset includes paddy rice maps in South Korea from 2017 to 2021 with 10 m resolution, which was produced by analyzing time-series Sentinel-1 images with recurrent U-Net deep learning architecture. The paddy rice maps are a product of deep learning model predictions and DO NOT represent ground truth information.</p> <p>The detailed algorithm for producing the dataset can be found in the following paper: <a href="https://doi.org/10.1080/15481603.2023.2206539">https://doi.org/10.1080/15481603.2023.2206539</a></p> <p>The used modeling architecture is indicated by "RU-net 3" in the paper, and the consisting products in the dataset is as follows:</p> <ul> <li>PR_in_[Year] : Probabiltiy of paddy rice cultivation area inside the paddy boundary (levee)</li> <li>PR_bd_[Year] : Probabiltiy of levee</li> <li>PR_in and PR_bd files have a scale factor vaule of 1,000,000.</li> <li>PR_bi_[Year] : Binary map of paddy rice, Due to the pixel size much larger than the levee width, the pixels labeled as levees inevitably include a large portion of the cultivation area. Therefore, binary map represent the sum of PR_in and PR_bd exceeding 0.5 threshold.</li> </ul> <p>Please cite the paper when using this dataset.</p> <p>This work was supported by the International Research and Development Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT [2021K1A3A1A78097879], and partially supported by the European Commission under contract H2020-CALLISTO [101004152]</p>
Data from: Spatial sorting caused by downstream dispersal: Implication for morphological evolution in isolated populations of fat minnow inhabiting small streams flowing through terraced rice paddies
Open the record for dataset details and reuse information.
Paddy Rice Labeling Sites in South Korea (2018)
<p>The paddy rice was visually interpreted at 30 sites in South Korea. The sites were selected at each province by a proportional stratified sampling method according to the paddy rice area statistics(Statistics Korea), so the dataset can be used for the validation on model generalization over the entire country. The paddy rice areas were visually interpreted by using Google Earth Pro and street view services(<a href="https://map.naver.com">https://map.naver.com</a>, <a href="https://map.kakao.com">https://map.kakao.com</a>) and updated to the state of 2018.</p> <p>Supported by the CALLISTO (No. 101004152) project, which has been funded by EU Horizon 2020 programs.</p>
Paddy Rice Maps South Korea (2017-2021)
<p>This dataset includes paddy rice maps in South Korea from 2017 to 2021 with 10 m resolution. The paddy rice maps are a product of deep learning model predictions and DO NOT represent ground truth information. The predictions were made by analyzing time series Sentinel-1 images based on the deep learning architecture that integrates U-Net and RNNs layers desined by eGIS/RS lab., Korea University. The deep learning model has been trained on the farm map produced by Korean Ministry of Agriculture, Food and Rural Affairs(MAFRA). The validation accuracy and Cohen's kappa value are 96.50%, 0.7857 each which were calculated from the 40% of the farm map. For more information please contact to the KU-eGIS/RS lab.</p> <p><br> Supported by the CALLISTO (No. 101004152) project, which has been funded by EU Horizon 2020 programs.</p>
APRA500: a 500 m annual paddy rice dataset for monsoon Asia using multisource remote sensing data
<p>This dataset provides 500m-grid paddy rice maps of monsoon Asia (some countries) from 2000 to 2021.</p> <p>*** Updated paddy rice map for 2021</p> <p>*** The data file is in “.tif" format</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 500 m</p> <p>*** Projection information: EPSG: 4326</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Data for: Improvements in the Land and Crop Modeling over Flooded Rice Fields by Incorporating the Shallow Paddy Water (Submitting to the Journal of Advances in Modeling Earth Systems)
<p>We incorporated the shallow paddy surface water layer into the Noah-MP land surface model to improve its performance of surface heat fluxes over flooded rice paddies. Field measurements from two crop sites, i.e., SAITO (early rice) and SAGA (late rice), were used to initialize and evaluate the modified Noah-MP model (Maruyama, 2021). Additionally, we investigated the roles of some key parameters in the land and crop modeling. </p> <p>Note that, all numerical experiments in this study were conducted at the field scale using the offline version of Noah-MP (Niu et al., 2011) running within the High-Resolution Land Data Assimilation System (HRLDAS v3.9; Chen et al., 2007). Please refer to the official HRLDAS/Noah-MP unified Github repository (<a href="https://github.com/NCAR/hrldas">https://github.com/NCAR/hrldas-release</a>) for the original model codes.</p> <p>The related model code modifications and model outputs were included in this dataset. Surface observations for the nearest AMeDAS or meteorological observatory stations were obtained from the Japan Meteorological Agency website (<a href="https://www.jma.go.jp/jma/indexe.html">https://www.jma.go.jp/jma/indexe.html</a>), and were also provided in this dataset.</p> <p> </p> <p>References</p> <p>Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R. D., et al. (2007). Description and evaluation of the characteristics of the NCAR high‐resolution land data assimilation system. <em>Journal of Applied Meteorology and Climatology</em>, 46(6), 694-713. <a href="https://doi.org/10.1175/JAM2463.1">https://doi.org/10.1175/JAM2463.1</a></p> <p>Maruyama, A. (2021). Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies (version 2) [Data set]. Mendeley Data. <a href="https://doi.org/10.17632/tv23z95r5g.2">https://doi.org/10.17632/tv23z95r5g.2</a></p> <p>Niu, G., Yang, Z., Mitchell, K., Chen, F., Ek, M., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. <em>Journal of Geophysical Research, </em>116, D12109. <a href="https://doi.org/10.1029/2010JD015139">https://doi.org/10.1029/2010JD015139</a></p>
FIGURE 4. Dichaetura filispina n in Two new Japanese species of Gastrotricha (Chaetonotida, Chaetonotidae, Lepidodermella and Dichaeturidae, Dichaetura), with Comments on the Diversity of Gastrotrichs in Rice Paddies
FIGURE 4. Dichaetura filispina n. sp., line drawings of holotype. a, Dorsal view; b, ventral view. Scale bar 5 μm. Abbreviations: BC, basal constriction; BFS, basal furca spine; DS, dorsal scale; F, furca; FS, furca spine; INT, intestine; MR, mouth ring; PH, pharynx; SC, sensory cilium; VC, column of ventral cilia.
FIGURE 3. Dichaetura filispina n in Two new Japanese species of Gastrotricha (Chaetonotida, Chaetonotidae, Lepidodermella and Dichaeturidae, Dichaetura), with Comments on the Diversity of Gastrotrichs in Rice Paddies
FIGURE 3. Dichaetura filispina n. sp. light micrographs of the holotype. a, Coronal view; b, dorsal scale; c, furca spine and basal furca spine. Scale bars 5 μm. Abbreviations: BC, basal constriction; BFS, basal furca spine; DS, dorsal scale; F, furca; FS, furca spine; INT, intestine; MR, mouth ring; PH, pharynx; SC, sensory cilium.
FIGURE 1. Lepidodermella acantholepida n in Two new Japanese species of Gastrotricha (Chaetonotida, Chaetonotidae, Lepidodermella and Dichaeturidae, Dichaetura), with Comments on the Diversity of Gastrotrichs in Rice Paddies
FIGURE 1. Lepidodermella acantholepida n. sp., light (a, b) and electron (c) micrographs of the holotype. a, Coronal view; b, dorsal scale and dorsal terminal plate; c, dorsal terminal plate. Scale bars 10 μm in (a) 5 μm in (b, c). Abbreviations: CE, cephalion; DS, dorsal scale; DT, dorsal terminal plate; F, furca; INT, intestine; MR, mouth ring; PH, pharynx; TB, tactile bristle.
FIGURE 2. Lepidodermella acantholepida n in Two new Japanese species of Gastrotricha (Chaetonotida, Chaetonotidae, Lepidodermella and Dichaeturidae, Dichaetura), with Comments on the Diversity of Gastrotrichs in Rice Paddies
FIGURE 2. Lepidodermella acantholepida n. sp., line drawings of holotype specimen. a, Dorsal view; b, ventral view; c, dorsal scale; d, dorsal terminal plate. Scale bars 5 μm. Abbreviations: CE, cephalion; DS, dorsal scale; DT, dorsal terminal plate; F, furca; HY, hypostomion; MR, mouth ring; SC, sensory cilium; TB, tactile bristle; TP, terminal plate; VC, ventral cilium; VS, ventral scale.
NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019
<p>This dataset provides annual paddy rice maps of Northeast and Southeast Asia from 2017 to 2019.</p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: Northeast and Southeast Asia</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 10 m</p> <p>*** Projection information: EPSG: 4326</p> <p>*** CropType: Paddy rice.</p> <p>*** Year: Values from 2017 to 2019</p> <p>*** The file name consists of year (Y), latitude range (N/S), and longitude range (E). For example, the file name '2019Y_40_45N_140_145E' means the map of paddy rice in the range of 40-50 degrees North and 140-145 degrees East for 2019.</p>
Rice paddy soils are a quantitatively important carbon store according to a global synthesis
<p>Overview of all experimental observations from global rice field experiments that were used for calculating paddy SOC stocks and the meta-analysis.</p>
Figure 4 in Anuran assemblage and its trophic relations in rice-paddy fields of South India
Figure 4. Dietary niche breadth of two anurans represented across males, females, and juveniles. Bars indicate upper and lower limits. Taxonomically distinct units are used for analysis. Dark grey bars represent M. ornata and pale grey bars represent M. caperata. Juveniles of M. ornata were not captured.
Figure 2 in Anuran assemblage and its trophic relations in rice-paddy fields of South India
Figure 2. The median number of prey items recovered from two anuran species. No juveniles of M. ornata were encountered.
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.