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Dataset results
346 results for “Ship”
A hybrid method combining harmonic polynomial cell method and STF strip theory for solving marine hydrodynamics and ship motions
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Psychological well-being of travellers on long-distance shipping voyages: A systematic review protocol
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SAR Ship Detection
<p>you can use the file.</p>
GOOD FISH QUARANTINE PRACTICE FOR FINFISH BIOSECURITY, WELL-BEING, AND DISEASE-FREE STATUS BEFORE RECEIVING AND SHIPPING JUVENILE (EPENIPHELUS SPP)
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BeMed_Shipping Workshop Assessment_Questionnaire Data
<p>Data from the evaluation questionnaire that was distributed to BeMed workshop participants.</p>
SHIP gut microbiome dataset for glaucoma analysis
<p>The Study of Health in Pomerania (SHIP) is a population-based study. SHIP consists of the two independent cohorts: SHIP-START and SHIP-TREND. The aim of the study is the investigation of common risk factors, subclinical disorders and manifest diseases in the population of Northeast Germany. 16S rRNA gene sequencing was performed as described elsewhere (1,2). In brief, fecal samples were collected at home, and then transported to the laboratory by the participants or courier. The DNA was isolated (PSP Spin Stool DNA Kit; Stratec Biomedical AG, Birkenfeld, Germany) and stored at −20°C until analysis. 16S rRNA gene sequencing of the V1–V2 region was performed on a MiSeq platform (Illumina, San Diego, California, USA). MiSeq FastQ files were created using CASAVA 1.8.2. For amplicon-data processing the open-source software package DADA2 (v.1.10) was used (3,4). Samples were normalized to 10,000 16S rRNA gene read counts.</p> <p>16S rRNA gene sequencing data from SHIP was used for replication analysis to investigate the association between glaucoma prevalence and the gut microbiome (Vergroesen, JE et al.). A total of 2.546 SHIP participants were included in the replication analysis.</p> <p>[1] Frost, F., Kacprowski, T., Rühlemann, M., Bülow, R., Kühn, J.-P., Franke, A., Heinsen, F.-A., Pietzner, M., Nauck, M., Völker, U., Völzke, H., Aghdassi, A. A., Sendler, M., Mayerle, J., Weiss, F. U., Homuth, G., & Lerch, M. M. (2019). Impaired Exocrine Pancreatic Function Associates With Changes in Intestinal Microbiota Composition and Diversity. Gastroenterology, 156(4), 1010–1015. https://doi.org/10.1053/j.gastro.2018.10.047</p> <p>[2] Frost, F., Kacprowski, T., Rühlemann, M. C., Franke, A., Heinsen, F.-A., Völker, U., Völzke, H., Aghdassi, A. A., Mayerle, J., Weiss, F. U., Homuth, G., & Lerch, M. M. (2019). Functional abdominal pain and discomfort (IBS) is not associated with faecal microbiota composition in the general population. Gut, 68(6), 1131–1133. https://doi.org/10.1136/gutjnl-2018-316502</p> <p>[3] Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods, 13(7), 581-3. https://doi.org/10.1038/nmeth.3869</p> <p>[4] Frost, F., Kacprowski, T., Rühlemann, M., Pietzner, M., Bang, C., Franke, A., Nauck, M., Völker, U., Völzke, H., Dörr, M., Baumbach, J., Sendler, M., Schulz, C., Mayerle, J., Weiss, F. U., Homuth, G., & Lerch, M. M. (2021). Long-term instability of the intestinal microbiome is associated with metabolic liver disease, low microbiota diversity, diabetes mellitus and impaired exocrine pancreatic function. Gut, 70(3), 522–530. https://doi.org/10.1136/gutjnl-2020-322753</p>
GO_SHIP_P16NS_2005_2006_515Y_926R
<p>This dataset contains relative abundance and taxonomic information for planktonic organisms collected by filtering whole seawater (~1L P16S, ~2L P16N) collected at various depths onto 0.2 µm 25mm Supor filter filters during two latitudinal transects of the Pacific Ocean (P16S[http://bcodata.whoi.edu/CLIVAR_AEROSOL/CLIVAR_P16S_2005ado.pdf], P16N[http://bcodata.whoi.edu/CLIVAR_AEROSOL/CLIVAR_P16N_2006ado.pdf]) in 2005/2006. Samples were collected at sea by Elisa Halewood and Meredith Meyers (Carlson Lab, UCSB) as part of the GO-SHIP repeat hydrography program (then known as CLIVAR). Samples were stored as partially extracted lysates in sucrose lysis buffer at -80°C until analysis. DNA was isolated, purified, amplified, and sequenced by Jesse McNichol, Yubin Raut, & Bruce Yanpui Chan (Fuhrman Lab, USC) in 2020 and 2021.</p> <p>Relative abundance information was generated by PCR amplification of extracted DNA using a universal primer pair that amplifies both 16S and 18S simultaneously (515Y/926R) and subsequent denoising to amplicon sequence variants (ASVs) using a custom analysis pipeline based on qiime2 and DADA2 (Callahan et al., 2016; Bolyen et al., 2016; https://github.com/jcmcnch/eASV-pipeline-for-515Y-926R). The final dataset contains merged abundance information from both 16S and 18S ASVs using the same denominator. Merging of 16S and 18S ASV tables was accomplished using a dataset-specific correction factor that adjusts for a known bias against longer 18S sequences during Illumina sequencing (Yeh et al., 2021). </p> <p>Organisms quantified range from small Bacteria and Archaea to protists (photosynthetic and non-photosynthetic) as well as larger planktonic organisms such as Arthropods, Salps, and Jellyfish. Please note that some organisms will be quantified more than once using this approach, such as eukaryotic algae for which we have ASV information from both chloroplast 16S and nuclear 18S.</p> <p>This dataset also contains several environmental covariates, including nutrient and CTD data derived from publicly-accessible GO-SHIP records (CLIVAR and Carbon Hydrographic Data Office; https://cchdo.ucsd.edu/), DNA concentrations from extractions (a proxy for community biomass), community 18S fraction (a measure of whether the sample is prokaryote- or eukaryote-dominated), as well as manual assignments of whether a sample was from the euphotic or non-euphotic zone.</p> <p>Raw reads are available on NCBI. Relevant scripts, bioinformatic intermediates, and output files are available on OSF and github as part of the Global rRNA Metabarcoding of Plankton (GRUMP) umbrella project. The github page (https://github.com/jcmcnch/Global-rRNA-Univeral-Metabarcoding-of-Plankton) contains links to all relevant underlying data, including processing steps used to link environmental covariates with sequencing data, as well as dataset-specific correction factors for merging 16S and 18S data.</p>
The Sakakibara Health Integrative Profile of Atherosclerotic-Carcinogenesis Hypothesis (SHIP-AC)
ClinicalTrials.gov study NCT04198896. IPD Sharing: NO. Countries: 0. Publications: 1.
SHIP (Selinexor in Hormone Insensitive Prostate Cancer)
ClinicalTrials.gov study NCT02146833. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Size-dependent physiological responses of shore crabs to single and repeated playback of ship noise
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Data from: Distracted decision-makers: ship noise and predation risk change shell choice in hermit crabs
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Data from: Shape up or ship out: migratory behaviour predicts morphology across spatial scale in a freshwater fish
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Carbon Monitoring System Flux for Shipping, Aviation, and Chemical Sources L4 V1 (CMSFluxMISC) at GES DISC
This dataset provides the Carbon Flux for Shipping, Aviation, and Chemical Sources.The NASA Carbon Monitoring System (CMS) is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System will use the full range of NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS will maintain a global emphasis while providing finer scale regional information, utilizing space-based and surface-based data and will rapidly initiate generation and distribution of products both for user evaluation and to inform near-term policy development and planning.
Hurricane and Severe Storm Sentinel (HS3) Statistical Hurricane Intensity Prediction Scheme (SHIPS) Intensity V1
The Hurricane and Severe Storm Sentinel (HS3) Statistical Hurricane Intensity Prediction Scheme (SHIPS) Intensity dataset was obtained from March 18, 2014 through September 30, 2014 during the Hurricane and Severe Storm Sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The SHIPS model provides tropical storm intensity forecasts for the Atlantic Ocean and the eastern and central North Pacific Ocean storms and invest areas. SHIPS uses GOES infrared imagery as input to the systems. These SHIPS data are available in ASCII format.
Concerted action of Helios and Ikaros balances the strength of B cell receptor signaling and controls the expression of inositol 5-phosphatase SHIP
GEO Series GSE20946. Gallus gallus. 4 samples. Type: Expression profiling by array.
Lung cell responses to ship diesel exhaust particles
GEO Series GSE63962. Homo sapiens. 12 samples. Type: Expression profiling by array.
Individual array-based gene expression patterns generated using total RNA prepared from whole blood of 991 participants of the SHIP-TREND cohort
GEO Series GSE36382. Homo sapiens. 991 samples. Type: Expression profiling by array.
s-SHIP expression identifies a subset of murine basal prostate cells as neonatal stem cells
GEO Series GSE73758. Mus musculus. 6 samples. Type: Expression profiling by array.
Remains of ship in Hooe Lake Plymouth UK
Archeological remains of a wooden ship in Hooe Lake in Plymouth UK. Source: Objaverse 1.0 / Sketchfab
[VISIR-2 ship weather routing model] raw data
<h2>VISIR-2 ship weather routing model raw data</h2> <p>This repository contains the raw data necessary to reproduce the results from scratch as presented in the Geosci. Model Dev. Discussions manuscript titled "<em>VISIR-2: ship weather routing in Python</em>". The paper will be linked here as soon as available online. </p> <p> </p> <h3>How to Use the Data</h3> <p>To reproduce the results from the manuscript, please follow these steps:</p> <p>1. download this repository to your local machine.</p> <p>2. download the Zenodo repository containing VISIR-2 source code from this <a href="../doi/10.5281/zenodo.8305526">link</a></p> <p>3. run VISIR-2 jobs following the instructions included in the manual that can be found inside VISIR-2 source code at <em>VISIR-2/Docs/Manual/</em>VISIR-2_userManual.pdf</p> <p> </p> <h3>Contact</h3> <p>If you have any questions or need further assistance, please feel free to contact us:</p> <p>- Mario Leonardo Salinas</p> <p>- Email: mario.salinas@cmcc.it</p> <p> </p>
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.