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263 results for “S3”
Figure S3 in Surveys of the bee (Hymenoptera: Apiformes) community in a Neotropical savanna using pan traps
Figure S3. Landscape aerial images of the five trails and the five points at each trail used to collect bees using pan traps at Rio Preto State Park, Minas Gerais, Brazil. Satellite images from Google Earth. Scale bar = 100 meters.
Figure S3 in Unusual morphology in the mid-Cretaceous lizard Oculudentavis
Figure S3. Segmented skull elements. Related to Figures 1, 2. A–D, Oculudentavis naga; E–H, O. khaungraae. Dorsal view of the frontal and nasal (A, E); dorsal view of the parietal (B, F); lateral view of the left (C), and right (G) postorbitals, and lateral views of the left squamosal (D, H).
Dataset S3: Oxfordiana motturii gen. et sp. nov. supplemental information (SRXMT data for BU 5265.1)
<p><strong>Dataset S3.</strong> SRXMT 8-bit BMP tomographic dataset of BU5265.1. The dataset consists of 2180 8-bit bitmap images compressed as a ZIP archive. Image brightness/contrast optimized and despeckling applied. Note that images from the tomographic stack beginning and end, without specimen data present, have not been included. [ZIP/BMP format 23.1 GB]</p>
Supplementary Table S3 - Article: Transcriptome Analysis Provides Novel Insights into Salinity Stress Response in two Egyptian Rice Varieties with Different Tolerance Levels
<p><strong>Table S3.</strong> Repository data for the global analysis produced for cv Giza 178. </p> <p>A, Up regulated genes observed when comparing salt stressed plants vs unstressed controls. </p> <p>B, Down regulated genes in Giza 178 observed when comparing salt stressed plants vs unstressed controls.</p> <p>C, Gene Ontology enrichment analysis (GOEA) results for Giza 178 up regulated genes. </p> <p>D, GOEA results for Giza 178 down regulated genes. </p>
Figure S3
<p><strong>Classifiers for CD31.</strong> CD31 stained micrograph of a KPC (<strong>a</strong>) and CKS (<strong>c</strong>) tumor specimen and their corresponding CD31 classifier (b,d). CD31 positive pixels are visualized in yellow (<strong>b</strong>) or red (<strong>d</strong>) respectively on red or yellow background. </p>
Dump of bioRxiv S3 bucket
<pre><code class="language-bash">aws s3 ls s3://biorxiv-src-monthly/ --request-payer --recursive --human-readable --summarize > dump.txt && xz dump.txt</code></pre> <p> </p>
'Stekelvarken S3' Kazemat (Dutch WWII bunker)
3D model of a Dutch bunker type S3 'stekelvarken' ('porcupine'). This bunker type was designed in 1939 and housed one light machine gun. The gun could be positioned in one of the three hatches, and the other two could be locked off with a 2cm thick steel door. The bunker's walls were made of 80cm thick reinforced concrete. A total of 763 bunkers of this type have been built in the days before WWII in the Netherlands. During the invasion by the German army in may 1940 the S3 bunkers proved to be vulnerable because of their relatively thin walls and big shooting holes. Source: http://www.grebbelinie.nl/page/s3 Source: Objaverse 1.0 / Sketchfab
Data S3. 2010 MTI and 2020 losses
<p>This dataset contains the 2010 MTI values computed for the mangrove patches within 130 sample cells worldwide, and the values of registered losses within the same patches by 2020.</p>
Appendix S3
<p>Testing the road-based pattern for distribution records within Brazil. A buffer of 10 km placed over federal inter-state roads is marked in gray; records falling inside that buffer in green, records outside of it in blue.</p>
Chromatin fiber invasion and nucleosome displacement by the Rap1 transcription factor_Fig.2,3,S3,S4,S7
<p>Raw microscopy movies for colocalization TIRF experiments (Rap1 binding) with various chromatin templates for Mivelaz M., et al, 2019 (<a href="https://doi.org/10.1016/j.molcel.2019.10.025">https://doi.org/10.1016/j.molcel.2019.10.025</a>)</p> <p>for Figures Fig.2,3,S3,S4,S7</p> <p>see attached documentation for more details</p>
FIGURE S3 in Accounting for differences in element size and homogeneity when comparing Finite Element Models: Armadillos as a case study
FIGURE S3. Map of Von Mises stress distribution in the 20 FEA models of Cingulata mandibles.
Supplemental Figure S3. Variation of the His plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis).
<p><strong>Supplemental Figure S3.</strong> Variation of the His plasma concentration (μmol/L) over 6 time points in the experiment for each treatment group (CTRL, MetLys, MetLysHis). Day 1 is the reference, d18 and d29 are in the depletion period, d54 and d79 in the RP-AA period and d113 in the cross-back period. Day 29, d79 and d113 also represent the last d of each period. During the whole experiment all treatment groups received a low protein diet (CTRL). The cows in the MetLys group received rumen-protected (RP) Met (Excential Rumenpass MET, Orffa Additives) and RP-Lys (AjiPro-L, Ajinomoto H&N) during the RP-AA period. The cows in the MetLysHis group received RP-Met, RP-Lys and RP-His (experimental RP-His product, Ajinomoto Co.) during the RP-AA period. Significant differences (<em>P</em> ≤ 0.05) between groups within a d are indicated by different letters as determined by Tukey’s test.</p>
Exposing the CSI - S3
<p>This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.</p>
Figure S3 in Supplementary Materials for Precipitation is the main axis of tropical plant phylogenetic turnover across space and time
Figure S3. Overview of species tree inference workflow with ASTRAL-3.
Macroalgal deep genomics illuminate multiple paths to aquatic, photosynthetic multicellularity - Supplementary Data - ANALYSES - Data S3
<p>Macroalgae are a polyphyletic group of multicellular aquatic organisms vital to global climate maintenance and have a wide variety of commercial applications. The lack of genomic datasets and poor physiological records preclude understanding their ecological roles and industrial potential. We <em>de novo</em> sequenced 121 macroalgal genomes from various climates spanning five major latitude parallels. The resultant genomic datasets reveal genetic bases for niche habitation facilitated by morphological complexity in diverse and extreme regions and illuminate the evolutionary mechanisms behind macroalgal diversification and specialization. Adhesome genes (e.g., cadherins, integrins, and lectins), extracellular matrix enzymes, and cytoskeletal organization regulating genes (e.g., spondins, Rho-type GTPases) predominantly distinguished macroalgal genomes from their microalgae correlates. Deep neural networks could accurately classify an alga as micro- or macro- from set of significance-ranked genomic features (n = 251, entropy R<sup>2</sup> > 0.99, RASE = 0.001) as well as adhesome gene sets (n = 110, entropy R<sup>2</sup> > 0.86). By deciphering the macroalgal adhesome, a clear picture of the genetic basis for the development and maintenance of complex algal tissues could be resolved. Sequences from giant viruses were rampant in the macroalgal genomes and coded for zinc-finger transcription factors, ankyrins, Rieske proteins, and other exotic codomains. Lineage-specific retentions of transcription factors, cadherins, integrins, polysaccharide-acting enzymes, and receptor kinases, many with predicted viral origins, outline the divergent mechanisms facilitating multicellularity in these three macroalgal lineages. This work sheds new light on the evolution of multicellularity in three phyla (Rhodophyceae, Chlorophyceae, and Ochrophyceae v. Phaeophyceae) through the lens of large-scale genomics and paves the way for the genomic exploration of macroalgal biology.</p> <p> </p> <p><strong>Data S3.</strong> <strong>Analysis data files.</strong> This dataset includes data files for the analyses presented in the manuscript, including</p> <p>(A) Decontamination analysis, including iterative BLEACH contamination calls, GFF coordinates for contaminants, and downsampling analyses of decontaminated genomes. Related to Fig. S1.</p> <p>(B) HMMsearch results for decontaminated assemblies for PFAMs. Related to Figs. 2-6.</p> <p>(C) Ternary analyses including dcGO enrichment for >80% purity sets for the three phyla. Related to Fig. 2.</p> <p>(D) Comparative genomics analyses of micro- and macroalgal genomes, including intersection, response screening, metabolic pathway, GO enrichment, and aNN modeling analyses. Related to Fig. 3.</p> <p>(E) Adhesome analysis including HMMsearch results for adhesome domains and codomains and response screening analyses between phyla, habitat, climate, and micro- vs. macroalgae. The neural network model using the 110 adhesome PFAMs is also included in this dataset. Related to Fig. 4.</p> <p>(F) Endogenous viral element analyses, including VFAM HMMsearch results, EVOPs, macroalgal sequences with EsV-1-7 codomains and comparative analyses including response screens and hierarchical clustering results. Related to Fig. 5.</p> <p>(G) All computational scripts used for analyses in this study. Scripts are either ‘.sh’ or ‘.sbatch’ files for execultion in a Linux environment with a SLURM (<a href="https://github.com/SchedMD/slurm">https://github.com/SchedMD/slurm</a>) high-performance computing (HPC) scheduler. Related to all analyses.</p>
Fig. S3 in High Genetic Diversity of Amoebae Belonging to the Genus Mayorella (Amoebozoa, Discosea, Dermamoebida) in Natural Habitats
Fig. S3
PARTNER II Trial: Placement of AoRTic TraNscathetER Valves II - S3 Intermediate
ClinicalTrials.gov study NCT03222128. IPD Sharing: NO. Countries: 1. Publications: 10.
Tables S2-S3. SMR-HEIDI results for 8q24.21 locus between back pain and other phenotypes.
<p><strong>Supplementary Table S2. SMR-HEIDI results for 8q24.21 locus between back pain and other complex traits. </strong></p> <p><strong>Column headers of Tables S2:</strong></p> <p>Secondary Trait Name: Name of complex trait<br> Dataset: Name of dataset (UKB Neale's lab or UKB Gene Atlas)<br> Index_SNP: RsID of SNP used as target in SMR-HEID analysis<br> Proxy_SNP: RsID of top SNP in the selected locus - SNP presented in both GWAS data with minimum P-values in back pain GWAS<br> r(proxy_SNP,index_SNP): linkage disequilibrium coefficient between index and proxy SNP<br> beta_SMR: Beta SMR for proxy SNP<br> p_SMR: P-value of beta SMR<br> qFDR-BH SMR: P-value of beta SMR after Bonferroni correction<br> p_HEIDI: P-value of HEIDI test<br> n_HEIDI: Number of SNPs used in HEIDI test<br> SNPs_HEIDI: List of rsID of SNPs used in HEIDI test</p> <p> </p> <p><strong>Supplementary Table S3.</strong> <strong>SMR-HEIDI results for 8q24.21 locus between back pain and expression of genes.</strong></p> <p><strong>Column headers of Tables S3:</strong></p> <p>Secondary Trait Name: Name of complex trait<br> Tissue: Name of the tissue from which the samples were taken to study gene expression<br> Gene_name: Gene name corresponding to the transcript name<br> Transcript Name: Transcript name (ID)<br> Dataset: Name of dataset (CEDAR, GTEx_v6)<br> beta_SMR: Beta SMR for proxy SNP<br> p_SMR: P-value of beta SMR<br> qFDR-BH SMR: P-value of beta SMR after Bonferroni correction<br> p_HEIDI: P-value of HEIDI test<br> n_HEIDI: Number of SNPs used in HEIDI test<br> SNPs_HEIDI: List of rsID of SNPs used in HEIDI test</p> <p> </p> <p>Part of the article: Williams FMK et al. "Sequence variation at 8q24.21 and risk of back pain"</p>
Figure S3 in Supplementary information: Bat coronavirus phylogeography in the Western Indian Ocean
Figure S3. Mean CoV prevalence (mean ± 95% confidence interval) as function of the bat sampling season in Mozambique.
FIGURE S3 in A tale of two genera: the revival of Hoplodoris (Nudibranchia: Discodorididae) with the description of new species of Hoplodoris and Asteronotus
FIGURE S3. Phylogenetic tree estimated with Bayesian Inference (BI) and Maximum Likelihood (ML) for 28S. Numbers above branches refer to BI posterior probabilities (pp), while numbers below branches refer to ML non-parametric bootstrapping values (bs). Relationships not recovered during ML analysis are indicated by dashes.
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.