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.
969
datasets available to search
ShareScore release 0.7.1
Dataset results
969 results for “wheat”
3D Tomography Images of wheat grains for several development stages
<p>Images of wheat grains acquired by 3D tomography at various stages of the early development of the grain. This data set serves as companion for the article "Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development" submitted to the "Plant Methods" journal.</p> <p><strong>Grain samples</strong></p> <p>A total of 41grains obtained at height different stages was imaged. The files correspond to the collections of grains at each stage:</p> <ul> <li>060 degree-days: 5 grains</li> <li>080 degree-days: 5 grains</li> <li>100 degree-days: 5 grains</li> <li>120 degree-days: 5 grains</li> <li>180 degree-days: 6 grains</li> <li>210 degree-days: 5 grains</li> <li>270 degree-days: 5 grains</li> <li>310 degree-days: 5 grains</li> </ul> <p>A more detailed description is provided in the file "<a href="https://zenodo.org/api/files/923a7508-b325-4264-8553-6a62092bb84d/wheatGrainTomoDataset.pdf">wheatGrainTomoDataset.pdf</a>".</p> <p><strong>Image format</strong></p> <p>All images are in TIFF format.</p> <p>Two kinds of images are provided: the volumes of the whole grains after conversion tu 256 gray levels, and the results of the segmentation of the grains as described in the manuscript. </p>
ECOBREED WP2 durum wheat data related to Kuzmanovic et al. (2020)
<p>Agronomic data (Sheet 1) and quality data (Sheet 2) of durum wheat (Triticum durum) check varieties and durum wheat breeding lines with multiple alien gene introgressions. The data are related to the publication of Kuzmanovic et al. (2020) Agronomy 10, 486. doi:10.3390/agronomy10040486</p>
Data from: Thermal niches of wheat curl mite, Aceria tosichella (Acari: Eriophyidae): congruence between physiological and geographical distribution data
<p><strong>Filename: parm.csv</strong></p> <p>Population growth rate of the two wheat curl mite (WCM) lineages reared in different temperatures.</p> <ol> <li>lineage - mitochondrial lineage (MT-1 or MT-8)</li> <li>temp - the rearing temperature (ºC)</li> <li>n - no. of replications of the experiment</li> <li>r, r.lower, r.upper - estimated intrinsic population growth rate and its 95% confidence intervals</li> </ol> <p><strong>Filename: aceria.csv</strong></p> <p>Data on field sampling localities, WCM abundance and thermal niche suitabiity.</p> <ol> <li>julian - Julian date</li> <li>x, y - geodetic coordinates (EPSG: 2180)</li> <li>stems - no. of stems collected</li> <li>MT1, MT8- mitochondrial lineage (MT-1 or MT-8)</li> <li>TNS1, TNS8 - thermal niche suitability for lineages</li> </ol>
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
ECOBREED WP2 T2.1 Winter common wheat (Triticum aestivum) - Late maturity group
<p>Description of the winter common wheat (Triticum aestivum) late maturity group nursery. Tested within T2.1 in Germany (by Secobra), Czech Republic (by Selgen) and Slovakia (by NPPC) in 2019/2020.</p>
ECOBREED WP2 T2.1 Winter durum wheat (Triticum durum) nursery
<p>Description of the winter durum wheat (Triticum durum) nursery. Tested within T2.1 in Austria (by BOKU), Hungary (by MTA-ATK) and Italy (by UNITUS). Results included from the season 2018/19.</p>
AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations
<p>This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios.</p> <p>The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019).</p> <p>References</p> <p>Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481</p> <p>Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542</p> <p> </p>
Data: Breeding progress for pathogen resistance is a second major driver for yield increase in German winter wheat at contrasting N levels
<p>This is the experimental data set of Zetzsche, et. al. (2020, Scientific Reports: doi.org/10.1038/s41598-020-77200-0) based on a three-year field trial (2014/15, 2015/16, 2016/7) of 178 German elite winter wheat cultivars.</p> <p>The table (QLB_BRIWECS_WW_fieldtrial_adjustMeans_treatments.csv) subsumes the adjusted mean values of four fungal disease scores (average ordinates) and six yield-related traits investigated at four treatments (T1: 110 kg N ha<sup>-1</sup>, no fungicides; T2: 110 kg N ha<sup>-1</sup> + fungicide; T3: 220 kg N ha<sup>-1</sup>, no fungicides; T4: 220 kg N ha<sup>-1</sup> + fungicide) of two replicates each over three years. Data of each trait are considered independent for all four treatments. Details of the plant material, the experimental site, the trail design as well as the phenotyping of the diseases and agronomical traits are given in the material and methods section of the related publication. Further metadata on the plant material and the trial design are provided in the Supplementary information of the publication.</p>
Global Wheat Head Dataset - 2020 challenge version
<p>The latest version is V4.</p> <p>This is the only official version of the Global Wheat Head Dataset presented in David et al. (2020) . It's a corrected version of the dataset published on Kaggle, and the one used for the Codalab challenge.</p> <p>Test labels are available on request by filling the form <a href="https://docs.google.com/forms/d/e/1FAIpQLSciaWUwQDNFP199Xb0Iqt2fY67tQI0hAZBJCCfvwd5OuIVQ3A/viewform?usp=sf_link">here </a> or contacting <strong>etienne.david@outlook.com</strong></p> <p>If you use the dataset for your paper, please cite: <a href="https://doi.org/10.34133/2020/3521852">https://doi.org/10.34133/2020/3521852</a></p> <p>If you want to benchmark your solution and get localization and counting metrics, please submit to the codalab challenge: </p>
Carbon fluxes data over Indian spring wheat agro-ecosystem
<p>The data consists of the following:</p> <ol> <li>Site-scale carbon flux data for an IARI experimental wheat site for the growing season 2013–2014 in New Delhi (28°40' N, 77°12' E).</li> <li>The simulation data in NetCDF format comprises carbon fluxes such as GPP, NPP, Ra, Rh, and NEE.</li> <li>Harvested wheat area of spring wheat across the Indian wheat-growing regions.</li> <li>Site-scale NEP (gC/m2/mon) measured at Meerut (29°05′33″N, 77°41′53″E; growing season 2009-2010) and Saharanpur (29° 52′ 19.139″ N and 077° 34′ 01.621″ E; growing season 2014-15) extracted from published work (Patel et al., 2011; Patel et al., 2021, respectively)</li> </ol>
Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot
<p>Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot</p> <blockquote> <p><strong>X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil </strong>- [<a href="https://doi.org/10.1002/ppj2.20036">https://doi.org/10.1002/ppj2.20036</a>]</p> </blockquote> <p>The ZIP file contains:</p> <ul> <li><code>MCT1_Rcode.R</code> - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT1... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>MCT2_Rcode.R</code> - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT2... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>R_RooThProcessing.R</code> - R code for aggregating root traits from RooTh software.</li> <li><code>Modelling folder</code> - OpenSimRoot with model parameters and root data used in manuscript.</li> </ul>
Raw data: Diversity in root architecture of durum wheat at stem elongation under drought stress
<p>Raw data on above and below ground traits from a greenhouse drought stress experiment with six durum wheat varieties performed at Tuscia University, Viterbo, Italy. Measurements were performed at stem elongation stage; recorded traits: plant shoot length, dry weight, number of leaves and tillers; total root length, root surface area, mean diameter, volume, number of tips, forks, crossings, root dry weight and root angle. Root measurments were performed on the whole root system and the topsoil area (upper 5 cm). </p>
Bread wheat genomes graph pangenome
<p>A Giraffe and a GFA-formatted minigraph assembly of sixteen bread wheat cultivar genome assemblies.</p> <p>15-wheat10+.bed.gz is the relinearised graph from gfatools gfa2bed</p> <p>15-wheat10+.gfa.gz is the graph in GFA format as built by minigraph</p> <p>index.min, index.dist, index.giraffe.gbz are the same graph formatted for Giraffe alignments with vg giraffe v1.34.0 or later.</p> <pre><code class="language-bash">vg autoindex -w giraffe -g 15-wheat10+.gfa -t 16 -T ./ -V 1 --target-mem 850G</code></pre> <p>It is possible to convert the gfa graph to vg format:</p> <pre><code class="language-bash">vg convert -v -g 15-wheat10+.gfa > 15-wheat10+.vg</code></pre> <p>To use the index in alignments using vg v1.34.0 or later:</p> <pre><code class="language-bash">vg giraffe -Z index.giraffe.gbz -m index.min -d index.dist -f your_reads.fq > mapped.gam</code></pre> <p> </p>
Data from 168 fungicide trials in wheat fields across Europe 2014-2018
<p>The data set comprises records of disease incidence, crop growth stage and yield from untreated and treated plots.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The plot produced is also provided as a PNG file.</p> <p>The field trials were conducted by Corteva Agriscience, Germany.</p>
Data from 56 fungicide trials in wheat fields across Europe 2017-2019
<p>The data set comprises records of disease incidence and yield from untreated and treated plots.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The plot produced is also provided as a PNG file.</p> <p>The field trials were conducted by BASF, Germany.</p>
Collation and orthology-based identification of hormone-related genes in bread wheat
<p>Plant hormones coordinate a plethora of developmental processes in plants, including responses to abiotic and biotic stressors. Here, we collate the findings of previous studies identifying bread wheat (<em>Triticum aestivum</em>) genes related to hormonal processes (<strong>biosynthesis</strong>, <strong>transport</strong>, <strong>signalling</strong>, and <strong>catabolism</strong>) and collect wheat orthologues from hundreds of additional hormone-related genes utilising the Ensembl Plants Compara database. We have initially conducted this procedure for <strong>abscisic acid</strong>, <strong>auxins </strong>(IAA and IBA), <strong>brassinosteroids</strong>, <strong>cytokinins</strong>, <strong>ethylene</strong>, <strong>gibberellins</strong>, and <strong>strigolactone</strong>, yielding a total of over 1,700 putative wheat orthologues. We aim to provide a community resource to aid gene annotation and subsequent analyses. We warmly welcome feedback from the community.</p> <p>Please refer to the file <strong>README.pdf</strong> for further details, including methods and references.</p>
Supplementary data: Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?
<p>Agronomic and quality data of organic wheat (<em>Triticum aestivum</em>), mean comparisons and supplementary figures related to the publication "Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?" by Grausgruber et al. (2024) published in the Journal of Cereal Science.</p>
Global Wheat Head Dataset 2021
<p>This is the full Global Wheat Head Dataset 2021. Labels are included in csv.</p> <p>Tutorials available here: https://www.aicrowd.com/challenges/global-wheat-challenge-2021</p> <p> </p> <p>🕵️ Introduction</p> <p>Wheat is the basis of the diet of a large part of humanity. Therefore, this cereal is widely studied by scientists to ensure food security. A tedious, yet important part of this research is the measurement of different characteristics of the plants, also known as Plant Phenotyping. Monitoring plant architectural characteristics allow the breeders to grow better varieties and the farmers to make better decisions, but this critical step is still done manually. The emergence of UAV, camera and smartphone makes in-field RGB images more available and could be a solution to manual measurement. For instance, the counting of the wheat head can be done with Deep Learning. However, this task can be visually challenging. There is often an overlap of dense wheat plants, and the wind can blur the photographs, making identify single heads difficult. Additionally, appearances vary due to maturity, colour, genotype, and head orientation. Finally, because wheat is grown worldwide, different varieties, planting densities, patterns, and field conditions must be considered. To end manual counting, a robust algorithm must be created to address all these issues. </p> <p>💾 Dataset</p> <p>The dataset is composed of more than 6000 images of 1024x1024 pixels containing 300k+ unique wheat heads, with the corresponding bounding boxes. The images come from 11 countries and covers 44 unique measurement sessions. A measurement session is a set of images acquired at the same location, during a coherent timestamp (usually a few hours), with a specific sensor. In comparison to the 2020 competition on Kaggle, it represents 4 new countries, 22 new measurements sessions, 1200 new images and 120k new wheat heads. This amount of new situations will help to reinforce the quality of the test dataset. The 2020 dataset was labelled by researchers and students from 9 institutions across 7 countries. The additional data have been labelled by Human in the Loop, an ethical AI labelling company. We hope these changes will help in finding the most robust algorithms possible!</p> <p>The task is to localize the wheat head contained in each image. The goal is to obtain a model which is robust to variation in shape, illumination, sensor and locations. A set of boxes coordinates is provided for each image.</p> <p>The training dataset will be the images acquired in Europe and Canada, which cover approximately 4000 images and the test dataset will be composed of the images from North America (except Canada), Asia, Oceania and Africa and covers approximately 2000 images. It represents 7 new measurements sessions available for training but 17 new measurements sessions for the test!</p> <p>📁 Files</p> <p>Following files are available in the <code>resources</code> section:</p> <ul> <li> <p><code>images: the folder contains all images</code></p> </li> <li> <p><code>competition_train.csv , competition_val.csv, competition_test.csv : contains the splits used for the 2021 Global Wheat Challenge</code></p> <ul> <li> <p><code>Val contains the "public test", which is the test set of Global Wheat Head 2020</code></p> </li> <li> <p><code>Test contains the "private test".</code></p> </li> </ul> </li> <li> <p><code>Metadata.csv : contains additional metadatas for each domain</code></p> </li> </ul> <p>💻 Labels</p> <ul> <li>All boxes are contained in a csv with three columns <code>image_name</code>, BoxesString and domain</li> <li><code>image_name</code> is the name of the image, without the suffix. All images have a .png extension</li> <li>BoxesString is a string containing all predicted boxes with the format [x_min,y_min, x_max,y_max]. To concatenate a list of boxes into a PredString, please concatenate all list of coordinates with one space (" ") and all boxes with one semi-column ";". If there is no box, BoxesString is equal to "no_box".</li> <li>domain give the domain for each image</li> </ul> <p> </p> <p>If you use the dataset for your research, please do not forget to quote:</p> <pre>@article{david2020global, title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods}, author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A and others}, journal={Plant Phenomics}, volume={2020}, year={2020}, publisher={Science Partner Journal} } </pre> <p>@misc{david2021global,<br> title={Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods},<br> author={Etienne David and Mario Serouart and Daniel Smith and Simon Madec and Kaaviya Velumani and Shouyang Liu and Xu Wang and Francisco Pinto Espinosa and Shahameh Shafiee and Izzat S. A. Tahir and Hisashi Tsujimoto and Shuhei Nasuda and Bangyou Zheng and Norbert Kichgessner and Helge Aasen and Andreas Hund and Pouria Sadhegi-Tehran and Koichi Nagasawa and Goro Ishikawa and Sébastien Dandrifosse and Alexis Carlier and Benoit Mercatoris and Ken Kuroki and Haozhou Wang and Masanori Ishii and Minhajul A. Badhon and Curtis Pozniak and David Shaner LeBauer and Morten Lilimo and Jesse Poland and Scott Chapman and Benoit de Solan and Frédéric Baret and Ian Stavness and Wei Guo},<br> year={2021},<br> eprint={2105.07660},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p>
Sensitivities to temperature and evaporative demand in wheat relatives
<p>Data used in the paper "Sensitivities to temperature and evaporative demand in wheat relatives" accepted by Journal of Experimental Botany.<br> There are three tables:</p> <ul> <li>Metadata: genotype information</li> <li>Definition: Definition, symbol and unit of the variables used</li> <li>Genotype variables: set of variables used for analyses, figures and tables in the article. </li> </ul>
Phenotypic diversity of root architecture and genotypic variation in durum wheat under salt stress
<p>Supplementary data consists of Principal Components values for traits detected under salt and control conditions (S1); Markers' locations onto the durum wheat reference genome associated with QTL (S2); Markers associated with genes from NCBI database (S4); PCR results and alleles distribrution</p>
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.