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8,038 results for “validation.”
FIGURE 2. Kalanchoe uniflora. A. Growing epiphytically with the thin stems typically descending. B in A reassessment of combinations previously proposed or published for Kalanchoe subg. Alatae (Crassulaceae subfam. Kalanchooideae), and its valid publication at the rank of subgenus
FIGURE 2. Kalanchoe uniflora. A. Growing epiphytically with the thin stems typically descending. B. Flowers and leafy stems in lateral view. Both photographs by Gideon F. Smith.
In situ SIF dataset for validating OCO2/3 SIF products
<p>The matched in situ SIF observations for validating OCO2/3 SIF products</p>
FIGURE 7. Pleurosicya micheli. A in How many valid Pleurosicya (Teleostei: Gobiidae) species are known from the Red Sea?
FIGURE 7. Pleurosicya micheli. A: 22.0 mm SL, Maricaban I., Philippines; B: Anilao, Philippines; C: Sulawesi, Indonesia; D: Papua New Guinea. Photos by J.T. Williams (A), A. Ryanskiy (B–D).
FIGURE 6. Pleurosicya mossambica. A in How many valid Pleurosicya (Teleostei: Gobiidae) species are known from the Red Sea?
FIGURE 6. Pleurosicya mossambica. A: ROM 60057, 19.8 mm SL, Moheli I., Comoros Isl.; B: ROM 58028, 16.4 mm SL, Chagos Archipelago; C: Maldives. Pleurosicya cf. mossambica. D: ROM 74851, 16.0 mm SL, Palau. Photos by R. Winterbottom (A, B & D), A. Ryanskiy (C).
FIGURE 5. Pleurosicya mossambica, Red Sea. A in How many valid Pleurosicya (Teleostei: Gobiidae) species are known from the Red Sea?
FIGURE 5. Pleurosicya mossambica, Red Sea. A: Mangrove Bay, Al Quseir, 14 m depth; B: Marsa Shagra, Marsa Alam, 7 m depth; C: Dahab, Egypt, 8 m depth; D: Dahab, Egypt, 12 m depth. Photos by S.V. Bogorodsky.
FIGURE 4 in How many valid Pleurosicya (Teleostei: Gobiidae) species are known from the Red Sea?
FIGURE 4. Lateral and upper view of cephalic sensory pore system of Pleurosicya mossambica. From Goren (1984).
FIGURE 1 in How many valid Pleurosicya (Teleostei: Gobiidae) species are known from the Red Sea?
FIGURE 1. Maximum likelihood estimate of the phylogenetic relationships of Pleurosicya based on the mitochondrial gene cytochrome c oxidase subunit I (COI). Numbers near the branches indicate bootstrap values of SH-aLRT support (%)/ ultrafast bootstrap support (%) based on 1000 replicates. Specimens of Pleurosicya mossambica from the Red Sea and the Western Indian Ocean are colored in red and blue, respectively; asterisk denotes mislabeled sequences. Further information for this dataset is provided in Table 1.
FIGURE 2. Pleurosicya bilobata. A in How many valid Pleurosicya (Teleostei: Gobiidae) species are known from the Red Sea?
FIGURE 2. Pleurosicya bilobata. A: Dahab, Egypt, Red Sea; B: Dahab, Egypt, Red Sea. Photos by T. Malkerova (A), C. von Mach (B).
Resulting Pseudonymized Classification Data for "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Resulting pseudonymized classification data of the study "Automatic Core-Developer Identification on GitHub: A Validation Study". The corresponding input data, from which the output data have been derived, can be found here: https://zenodo.org/record/7775078</p>
Pseudonymized Raw Data for "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Pseudonymized raw data (i.e., commit data and issue data for 25 GitHub projects) that has been used as input for the study published as "Automatic Core-Developer Identification on GitHub: A Validation Study".</p> <p>The pseudonymized raw data has been extracted via the tools <a href="https://github.com/se-sic/codeface/">Codeface</a>, <a href="https://github.com/se-sic/GitHubWrapper/">GitHubWrapper</a>, <a href="https://github.com/mehdigolzadeh/BoDeGHa">BoDeGHa</a>, and <a href="https://github.com/se-sic/codeface-extraction/">codeface-extraction</a> (and additional manual corrections after sanity checks).</p>
FIGURE 5 in Validation of Illicium × rubellum (Schisandraceae), a natural hybrid from Taiwan
FIGURE 5. Illustration of Illicium × rubellum Lu ex W.-J. Huang & S.-W. Chung hyb. nov. A. Habit. B. Flower. C. Androecium and gynoecium. D. Stamen. E. Carpel. F1–F4. Tepals from inward to outward. Drawn by Che-Wei Lin.
FIGURE 3 in Validation of Illicium × rubellum (Schisandraceae), a natural hybrid from Taiwan
FIGURE 3. Flower comparison of Illicium arborescens, I. × rubellum and I. anisatum. A. I. arborescens. B. I. × rubellum from Mt. Guzilun, Chung 13722. C. I. × rubellum from Mt. Lepei, Chung 13730. D. I. anisatum. A1–D1. Flower. A2–D2. Tepals of a flower. A3– D3. Stamens of a flower. A4–D4. Carpels in side-view and top-view. Scale bars correspond to 5 mm. Photographed by Wei-Jie Huang.
FIGURE 4 in Validation of Illicium × rubellum (Schisandraceae), a natural hybrid from Taiwan
FIGURE 4. Overlapping peak of variable sites in ITS region. The number at the top indicate the central site in sequencing electropherograms. The first row and the last row are Illicium arborescens and I. anisatum respectively. The three rows in middle are I. × rubellum, which have overlapping peaks of two components that each one is same as in I. arborescens and I. anisatum at these sites.
FIGURE 2 in Validation of Illicium × rubellum (Schisandraceae), a natural hybrid from Taiwan
FIGURE 2. Morphology comparison of Illicium arborescens, I. × rubellum and I. anisatum. A. I. arborescens. B. I. × rubellum from Mt. Guzilun, Chung 13722. C. I. × rubellum from Mt. Lepei, Chung 13729. D. I. × rubellum from Mt. Lepei, Chung 13730. E. I. × rubellum from Tashueshan logging trail, WJH1649. F. I. anisatum. A1–F1. Flowers at anthesis. A2–F2. Flower buds. A3–F3. Midvein of leaf on upper surface. Note that midvein is impressed in I. arborescens and prominent in I. anisatum, whereas it can be impressed (E3), prominent (B3), prominent along a groove (C3) or grooved proximally and prominent distally (D3) in I. × rubellum. Photographed by Wei-Jie Huang.
FIGURE 1 in Validation of Illicium × rubellum (Schisandraceae), a natural hybrid from Taiwan
FIGURE 1. Distribution map of Illicium × rubellum, I. arborescens and I. anisatum in Taiwan. Data obtained from Taiwan Biodiversity Network (https://www.tbn.org.tw/).
Validation of StarinIQ software performance using metagenomic data from the ATCC gut microbiome genomic mix
<p>StrainIQ (Strain Identification and Quantification) is a novel tool that implements a new <em>n</em>-gram based algorithm for predicting and quantifying strain-level taxa from whole genome metagenomic sequencing data. We tested our method using simulated (GI tract reference genomes) and mock metagenomic datasets (from ATCC microbial genomic mix) and compared its performance with existing methods. The gut_even.zip file contains the metagenomic sequence data from the ATCC Gut Microbiome Genomic Mix (MSA-1006). From this original sequencing sequence (nearly 120X), we created additional datasets with 90X, 60X, 30X, 5X, 3X, and 1X coverage and tested StrainIQ performance on those sequencing datasets with varying coverage.</p> <p>Please find the new link for this data at https://zenodo.org/record/8132164</p>
Assessment of covalently binding warhead compounds in the validation of the cytomegalovirus nuclear egress complex as an antiviral target
<p>Supplementary to the publication:</p> <p>"Assessment of covalently binding warhead compounds in the validation of the cytomegalovirus nuclear egress complex as an antiviral target" (2023), published in Cells</p> <p>Raw images of the Figures 4, 10, 11 and 14</p>
TA-EG Questionnaire italian Translation - Validation Dataset
<p>This file contains the raw data of TA-EG questionnaire single item information and the comments provided for each item </p>
Sequencing data from validation experiment for base editing of SCN2A
<p>It is challenging to apply traditional mutational scanning to voltage-gated sodium channels (NaVs) and functionally annotate the large number of coding variants in these genes. Using a cytosine base editor and a pooled viability assay, we screened a library of 368 guide RNAs (gRNA) tiling NaV1.2 to identify more than 100 gRNAs that changed NaV1.2 function. We sequenced base edits made by a subset of these gRNAs to confirm specific variants that drove changes in channel function. Electrophysiological characterization of these channel variants validated the screen results and provided functional mechanisms of channel perturbation. The majority of the changes caused by these gRNAs were classified as loss-of-function along with two missense mutations that led to gain-of-function in NaV1.2 channels. This two-tiered strategy to functionally characterize ion channel protein variants at scale identifies the largest set of loss-of-function mutations in a single NaV1.2 study to date.</p>
FIG. 9 in Validation of hemodynamic stress calculation in coronary computed tomography angiography versus intravascular ultrasound.
FIG. 9. Violin plots representing body sizes for males and females Mexican Burrowing Toads (Rhinophrynus dorsalis) across the 4 Mexican states that had Ž5 males and 5 females. Sample sizes are presented above each plot.
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.