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ShareScore release 0.9.0
Dataset results
20 results for “variety identification”
Table S1. Geographical localization of Pinus pseudostrobus Lindley. specimens used in the study and concordance of morphological identification with real-time PCR-HRM assay for Pinus pseudostrobus varieties pseudostrobus, apulcensis, oaxacana and coatepecensis, based on the cluster pattern.
<p>File encloses geographical location of collected Pinus pseudostrobus samples in México, as well as haplotype grouping obtaines from HRM analysis</p>
FIGURE 9 in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 9. Syntype of Onobrychis kabylica (Reverchon E., JE 00010093!). Copyright: Herbarium Haussknecht.
FIGURE 2 in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 2. Collection sites of O. conferta subsp. conferta in Dyr El Kef (A and A'), Fedj El Hdoum (B and B'), Ain Dyssa (C and C') and O. conferta subsp conferta var Maktaris in Makthar.
Data from: Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels
Nitrogen is one of the most important nutrient indicator for the growth of crops, which is closely related to chlorophyll content of leaves and thus influences the photosynthetic ability of crops. In this study, visible and near infrared (vis/NIR) reflectance spectroscopy combined with multi-variate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, several spectral indices combined with partial least squares (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subject to different nitrogen levels, and a coefficient of determination (R2) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. Experimental results demonstrated that vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.
FIGURE 8. Onobrychis kabylica. A in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 8. Onobrychis kabylica. A—pods, B—inflorescence, C—pod, D—calix.
FIGURE 1 in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 1. Distribution map of Onobrychis conferta and O. kabylica in Tunisia.
FIGURE 7 in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 7. Collection site of O. kabylica in Fedj El Hdoum, Tunisia.
Data from: Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels
Open the record for dataset details and reuse information.
Identification of self- and pathogen-targeted miRNAs from resistant and susceptible Theobroma cacao variety to black pod disease
GEO Series GSE244955. Theobroma cacao. 4 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Transcriptome wide identification of Dicer binding in human and C. elegans reveals a variety of substrates (HEK RNA-Seq)
GEO Series GSE55326. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome wide identification of Dicer binding in human and C. elegans reveals a variety of substrates (CEL RNA-Seq)
GEO Series GSE55329. Caenorhabditis elegans. 3 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome wide identification of Dicer binding in human and C. elegans reveals a variety of substrates (CEL PAR-CLIP)
GEO Series GSE55325. Caenorhabditis elegans. 3 samples. Type: Other; Non-coding RNA profiling by high throughput sequencing.
Transcriptome wide identification of Dicer binding in human and C. elegans reveals a variety of substrates (HEK PAR-CLIP)
GEO Series GSE55324. Homo sapiens. 3 samples. Type: Other; Non-coding RNA profiling by high throughput sequencing.
Transcriptome wide identification of Dicer binding in human and C. elegans reveals a variety of substrates (CEL small RNA)
GEO Series GSE55332. Caenorhabditis elegans. 2 samples. Type: Other; Non-coding RNA profiling by high throughput sequencing.
Transcriptome wide identification of Dicer binding in human and C. elegans reveals a variety of substrates
GEO Series GSE55333. Caenorhabditis elegans; Homo sapiens. 34 samples. Type: Expression profiling by high throughput sequencing; Other; Non-coding RNA profiling by high throughput sequencing.
Transcriptome wide identification of Dicer binding in human and C. elegans reveals a variety of substrates (small RNA AGO-IP)
GEO Series GSE55331. Homo sapiens. 2 samples. Type: Other; Non-coding RNA profiling by high throughput sequencing.
FIGURE 4. Onobrychis conferta subsp. conferta. A in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 4. Onobrychis conferta subsp. conferta. A—flower, B—keels, C—standard, D—wings, E—androecium.
FIGURE 3. Onobrychis conferta subsp. conferta var Maktaris. A in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 3. Onobrychis conferta subsp. conferta var Maktaris. A—Above ground, B—inflorescence, C—pods.
FIGURE 6 in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 6. (A) 3 rows of spines and 6-11 crest spines in Onobrychis conferta subsp. conferta and (B) 2 rows of spines and 4-6 crest spines in O. conferta subsp. conferta var Maktaris.
FIGURE 5 in Genus Onobrychis (Fabaceae) in Tunisia: distribution, rediscovery, ecology, and description of two new variety with an updated identification key in the country
FIGURE 5. Lectotype of Onobrychis conferta (Pomel A.N., P00084442!). Copyright: Lunds Universitet.
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)
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.