A comprehensive dataset of 750K+ car images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

A comprehensive dataset of 40K+ road sign images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

A comprehensive dataset of 750K+ furniture images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

A comprehensive dataset of 50K+ texture images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification, and segmentation.

Biochar is being evaluated as an amendment to improve soil characteristics to increase crop yields, revitalize degraded soils and facilitate the establishment of plant cover. Unfortunately, there are few rapid tests to determine potential effects of biochar on soil and associated plant responses. Seed germination (emergence of hypocotyl) is a critical parameter for plant establishment and may be a rapid indicator of biochar effects. We adapted Oregon State University Seed Laboratory procedures to develop a “rapid-test” to screen for effects of biochar on seed germination and soil characteristics. Soils were amended with 1% biochar by weight and placed in 11.0 cm square x 3.5 cm deep containers fitted with premoistened blotter paper. Seeds were placed in a uniform 5 x 5 pattern and covered with 15 g of the soil-biochar mixtures. Two South Carolina Coastal Plain soils, the Norfolk (Fine-loamy, kaolinitic, thermic Typic Kandiudults) and Coxville (Fine, kaolinitic, thermic Typic Paleaquults), were used. Eighteen biochars were evaluated produced from 6 feedstocks [pine chips (PC), poultry litter (PL), swine solids (SS), switchgrass (SG); and two blends of PC and PL, 50% PC/50% PL (55), and 80% PC/20% PL (82). For each feedstock biochars were made by pyrolysis at 350, 500 and 700°C for 1-2 hours. Percent germination and shoot dry weight were evaluated for cabbage, carrot, cucumber, lettuce, oat, onion, perennial ryegrass and tomato. Soil pH, electrical conductivity (EC) and extractable phosphorus (EP), factors which can affect seed germination and early seedling growth, were determined after plant harvests. Germination primarily was affected by soil type with few biochar effects. Shoot dry weight was increased for carrot, lettuce, oat and tomato; primarily with biochars containing PL. Soil pH and EC increased with PL, SS, 55 and most 82 treatments across soil types and plant species. Soil EP increased substantially with SS and PL and to a lesser extent with 55 and 82 for both soils across species, and with SG pyrolyzed at 550 and 750°C soil for the Norfolk soil across species. Thus, this rapid-test method can be an early indicator of the effects of biochar on seed germination and important soil health characteristics which can be affected by biochar and effect seed germination. This dataset is associated with the following publication: Olszyk, D.M., T. Shiroyama, J.M. Novak, and M.G. Johnson. A Rapid-Test for Screening Biochar Effects on Seed Germination. Communications in Soil Science and Plant Analysis. Taylor & Francis Group, London, UK, 49(16): 2025-2041, (2018).

A comprehensive dataset of 350K+ bridge images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification, and segmentation.

SEED-Data-Edit

SEED-Data-Edit is a hybrid dataset for instruction-guided image editing with a total of 3.7 image editing pairs, which comprises three distinct types of data: Part-1: Large-scale high-quality editing data produced by automated pipelines (3.5M editing pairs). Part-2: Real-world scenario data collected from the internet (52K editing pairs). Part-3: High-precision multi-turn editing data annotated by humans (95K editing pairs, 21K multi-turn rounds with a maximum… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part1-Unsplash.

Download the Soybean Seeds Dataset featuring 5513 categorized images of soybean seeds, including intact, spotted, immature, broken, and skin-damaged types.

The dehusking of seeds by granivorous songbirds is a complex process that requires fast, coordinated and sensory-feedback-controlled movements of beak and tongue. Hence, efficient seed handling requires a high degree of sensorimotoric skill and behavioural flexibility, since seeds vary considerably in size, shape and husk structure. To deal with this variability, individuals might specialise on specific seed types, which could result in greater seed handling efficiency of the preferred seed type, but lower efficiency for other seed types. To test this, we assessed seed preferences of canaries (Serinus canaria) through food choice experiments and related these to data of feeding performance, seed handling skills and beak kinematics during feeding on small, spindle-shaped canary seeds and larger, spheroid-shaped hemp seeds. We found great variety in seed preferences among individuals: some had no clear preference, while others almost exclusively fed on hemp seeds, or even prioritized novel seed types (millet seed). Surprisingly, we only observed few and weak effects of seed preference on feeding efficiency. This suggests that either the ability to handle seeds efficiently can be readily applied across various seed types, or alternatively, it may indicate that achieving high levels of seed-specific handling skills does not require extensive practice. Methods

Food choice experiments were conducted where individual birds fed on a tray consisting of 9 different seed types for 10 minutes. These experiments were recorded with a Sony RX1000M4 camera, and videos were manually analysed to count which seeds the individuals selected. Data on seed choice was used to calculate Manly's Selectivity Index of 2 seed types (hemp and white millet) and Levin's niche breadth index. These metrics were then related to data on feeding performance, seed handling skills and beak kinematics during feeding on canary seed and hemp seed. This data was taken from Andries et al. (2023).

A comprehensive dataset of 500K+ macro insect images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification, and segmentation.

This dataset contains canopy measurements of Taxodium distichum taken outside of the Wetland and Aquatic Research Center in Lafayette, Louisiana. The measurements were used to produce data on tree canopy area and seeds produced per Taxodium distichum tree.

757863 Global export shipment records of Seeds Seeds with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The collection includes seeds of weeds collected in agricultural fields, fallows, balks, etc., as well as crop seeds. These seeds were collected by Asst. Kulpa, the founder of this collection and also come from botanical gardens from different parts of the world, e.g. from Brussels (Belgium), Riga (Latvia), Coimbra (Portugal), Jasi and Bucharest (Romania), Strasbourg (France), Dresden, Gatersleben and Leipzig (Germany), Uppsala (Sweden), Vienna (Austria), Basil (Switzerland), Amsterdam (Netherlands), Brno (Czech Republic). Most of the material was received in the 1980's and 1990's.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

davidberenstein1957/uplimit-synthetic-data-week-1-with-seed dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset contains 90 rice seed species and 96 kernels per species resulting in 8,640 rice seed kernels in total. The dataset was collected in 2017 using the following two imaging systems:

1. Visible - Near Infrared (VIS/NIR) Hyperspectral Imaging Device System (~385nm - ~1000nm) consisting of a Specim V10E Imaging Spectrograph and Hamamatsu ORCA-05G CCD camera.
2. RGB - Fujifilm X-M1 with a 35mm/F2.0, ISO 400.

For each species, 96 kernels have been captured in two imaging bundles with 48 kernels in each bundle. For each imaging bundle, the 48 kernels were carefully positioned on a sheet of white paper and arranged in an 8x6 matrix. This rice seed matrix was then positioned on a translational stage and imaged using the HSI and RGB cameras described above.

The following three files result from a single acquisition:

• .hdr: The HSI ENVI header file (More information on the ENVI format can be found at the Harris Geospatial Solutions documentation.
• .raw: The HSI datacube data.
• .jpg: The RGB image.

The filename convention used is the (short) species name followed by a dash, followed by the bundle number (i.e. 1 or 2), followed by the filename suffix. For instance, the data for the BC15 rice seed variety are contained in the following 6 files:

• BC15-01.hdr
• BC15-01.raw
• BC15-01.jpg
• BC15-02.hdr
• BC15-02.raw
• BC15-02.jpg

The data were captured in 9 batches across multiple days. All the data from the same batch are contained in a dedicated folder. For instance the folder Data-VIS-20170111-2-room-light-offindicates that the data are in the VIS/NIR range, captured on the 11th of January 2017 and this was the second batch for that day with the room lights off. Two halogen bulbs were used for illumination and these were accurately positioned to provide balanced lighting across the scene. To ensure stability, the halogen bulbs were switched on and allowed to reach constant operating temperature before the data were acquired in a dark room to minimise any other sources of illumination variance.

For the purposes of calibration each HSI image contains in the scene a 100% reflective spectralon tile which is a highly reflective Lambertian scatter. For the dark reference, each folder contains an HSI image with the lens-cap covering the camera. The dark reference can be founds in each folder under the filename black.hdr/black.raw.

A full index of the data for each species is provided in the index.csv file. The file contains the following columns:

• Species Full Name: The full species name (as used in filenames).
• Species Short Name: A shorthand of the species name.
• Bundle Number: Imaging Bundle Number (each bundle contains 48 kernels) every species has 2 bundles.
• Folder: The name of the folder containing the data (as described above where each folder contains a batch of images captured in a single imaging session).
• File Name: The stem of the filename. Note: that there are 3 suffixes for each stem (.hdr, .raw, .jpg)

The HSI system was used to capture 256 wavelengths in this experiment and the exact wavelengths corresponding to the data provided are included in the file wavelengths.csv.

Both camera systems were fixed on a rigid frame for the duration of the experiments. To permit possible registration between the two cameras, a chessboard pattern has been imaged and the acquired files are also contained in the folder chessboard.

Note: The bundle 01 for the species NDC1 was originally acquired during the batch Data-VIS-20170111-2-room-light-off. However, the file was corrupted and hence, the acquisition was repeated during the batch Data-VIS-20170203-1-room-light-off. As a result, the NDC1-01 files are in the Data-VIS-20170203-1-room-light-off folder.

A comprehensive dataset of 850K+ weather images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

1288 Global import shipment records of Sweet Corn Seeds with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Small mammal seed predation x seed size datasets.

All variables included in the two datasets are detailed on the README tab associated with each file.

Please note that the seed removal dataset was updated to rebut a technical comment by Chen et al. (2021). The updated seed removal dataset is associated with a separate Dryad archive (https://doi.org/10.5061/dryad.hqbzkh1fp).

The dataset_2 comprises NMR raw data acquired on 19 accessions having atypical outer mucilage properties (Fabrissin et al., 2019). Accessions are from the Versailles Arabidopsis stock center (http://publiclines.inra.fr/naturalAccession/index) and are listed by their Versailles identification number in a four-digit format. Seed lots were produced from plants in two cultivations series c or d. Each raw data file is identified by a name which is the combination of the seed lot (lot2), the cultivation serie (C or D) followed by “imb-“, the imbibition time (t0 or H23) and the accession code. Data correspond to intact seeds imbibed in water (code 6). Data were recorded using a Time-Domain spectrometer (Minispec BRUKER, Germany) operating at 0.47T (resonance frequency of 20 MHz) at 20°C. The FID-CPMG sequence was used using the following parameters : a 90° pulse close to 2.8 µs, a dwell time of 0.4 µs for a FID duration of 60 µs, 16 scans, a recycle delay of 5 s, an echo time of 0.2 ms with 8000 or 5000 data points, depending on the imbibition time.