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9,113 results for “Sampling”
ID linking table for all samples that have been sequenced in the CRUK-ICGC Prostate Group projects
<p>Prostate cancer somatic genomic sequencing data generated from 2011 onwards under auspices of the <a href="https://icgc.org/icgc/cgp/70/508/71331">International Cancer Genome Consortium Prostate Cancer UK consortium</a> (CRUK-ICGC Prostate Group), co-led by Colin Cooper and Ros Eeles, with other Principal Investigators (Brewer, Neal, Bova, McDermott, Wedge, Lynch, Massie, and Foster) and others as listed in study publications. Funded by Cancer Research UK and other funders as listed in study publications. Contents and publications related to each dataset are described with each dataset (EGAD) entry.</p> <p>Our study was funded with the ambition of collecting Whole Genome DNA sequence data from 250 prostate cancers, with matching transcriptome and methylome data.</p> <p>The original aims of the project were:</p> <p>1. To understand the significance of multifocal prostate cancer. </p> <p>2. To understand the clinical heterogeneity of prostate cancer to devise markers for predicting outcome and for drug targeting.</p> <p>3. To understand the molecular basis of development and spread of castration-resistant and metastatic disease </p> <p>4. To understand the aetiology of prostate cancer particularly the large variation in incidences that occur in different populations and ethnic groups. </p> <p>This table contains links between all the IDs, in particular between those that have been published in research articles and the The European Genome-phenome Archive (EGA) where the raw sequencing data is held (https://ega-archive.org/studies/EGAS00001000262).</p>
Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore: Tegnùa Metamauco, Site 4, Rock samples
<p>Rock samples</p> <p>Research Activity: Geology of the Northern Adriatic Biogenic Reefs</p> <p>Project: Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore </p> <p>Scientific coordinators: Sandra Donnici (CNR) and Luigi Tosi (CNR)</p> <p>Scientific Divers: Andrea Bergamasco (CNR), Luigi Tosi (CNR)</p> <p>Surface coordinator: Sandra Donnici (CNR)</p> <p>Sampling Date: 2014.06.24</p> <p>Sampling Site: Tegnùa Metamauco</p> <p>Site Coordinates: 45.213506 N; 12.395296 E (DEG WGS84)</p> <p>Seabed Depth: 21.9 m</p> <p>Biogenic reef elevation: 2.6 m</p>
Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore: Tegnùa Chioggia, Site 4, Rock samples
<p>Rock samples</p> <p>Research Activity: Geology of the Northern Adriatic Biogenic Reefs</p> <p>Project: Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore </p> <p>Scientific coordinators: Sandra Donnici (CNR) and Luigi Tosi (CNR)</p> <p>Scientific Divers: Andrea Bergamasco (CNR), Luigi Tosi (CNR)</p> <p>Surface coordinator: Sandra Donnici (CNR)</p> <p>Sampling Date: 2014.07.17</p> <p>Sampling Site: Tegnùa Chioggia</p> <p>Site Coordinates: 45.230303 N; 12.489957 E (DEG WGS84)</p> <p>Seabed Depth: 22.2 m</p> <p>Biogenic reef elevation: 1.5 m</p>
Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore: Tegnùa Metamauco, Site 3, Rock samples
<p>Rock samples</p> <p>Research Activity: Geology of the Northern Adriatic Biogenic Reefs</p> <p>Project: Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore </p> <p>Scientific coordinators: Sandra Donnici (CNR) and Luigi Tosi (CNR)</p> <p>Scientific Divers: Andrea Bergamasco (CNR), Luigi Tosi (CNR)</p> <p>Surface coordinator: Sandra Donnici (CNR)</p> <p>Sampling Date: 2014.06.23</p> <p>Sampling Site: Tegnùa Metamauco</p> <p>Site Coordinates: 45.213046 N; 12.396041 E (DEG WGS84)</p> <p>Seabed Depth: 21.9 m</p> <p>Biogenic reef elevation: 2.6 m</p>
Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore: Tegnùa Delfino Bianco, Site 1, Rock samples
<p>Rock samples</p> <p>Research Activity: Geology of the Northern Adriatic Biogenic Reefs</p> <p>Project: Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore </p> <p>Scientific coordinators: Sandra Donnici (CNR) and Luigi Tosi (CNR)</p> <p>Scientific Divers: Andrea Bergamasco (CNR), Luigi Tosi (CNR)</p> <p>Surface coordinator: Sandra Donnici (CNR)</p> <p>Sampling Date: 2013.10.14</p> <p>Sampling Site: Tegnùa Delfino Bianco</p> <p>Site Coordinates: 45.205000 N; 12.406215 E (DEG WGS84)</p> <p>Seabed Depth: 21.8 m</p> <p>Biogenic reef elevation: 2.5 m</p>
Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore: Tegnùa Nautilus, Site 1, Rock samples
<p>Rock samples</p> <p>Research Activity: Geology of the Northern Adriatic Biogenic Reefs</p> <p>Project: Stratigraphy and genesis of the Biogenic Reefs in the Venice offshore </p> <p>Scientific coordinators: Sandra Donnici (CNR) and Luigi Tosi (CNR)</p> <p>Scientific Divers: Andrea Bergamasco (CNR), Luigi Tosi (CNR)</p> <p>Surface coordinator: Sandra Donnici (CNR)</p> <p>Sampling Date: 2014.10.21</p> <p>Sampling Site: Tegnùa Nautilus</p> <p>Site Coordinates: 45.212841 N; 12.387204 E (DEG WGS84)</p> <p>Seabed Depth: 21 m</p> <p>Biogenic reef elevation: 2.8 m</p>
Azure dataset sample for OpenDC Serverless simulator
<p>This dataset is representative of the sample used in the Serverless in the Wild paper [1] experiments. The dataset is used to reproduce the experiments in the OpenDC Serverless FaaS simulator [2]. The original dataset [3] has been sampled converted into the OpenDC Serverless trace format. </p> <p>This is part of Soufiane Jounaid's Bachelor thesis on designing, evaluating, and implementing the first FaaS platform simulator conducted within the AtLarge Research group in the Vrije Universiteit Amsterdam.</p> <p>[1] <a href="https://arxiv.org/pdf/2003.03423.pdf">Shahrad, M., Fonseca, R., Goiri, Í., Chaudhry, G., Batum, P., Cooke, J., ... & Bianchini, R. (2020). Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider. arXiv preprint arXiv:2003.03423.</a></p> <p>[2] <a href="https://drive.google.com/file/d/12hox3PwagpD0jNFA57tO4r2HqvOonkY3/view?usp=sharing">https://drive.google.com/file/d/12hox3PwagpD0jNFA57tO4r2HqvOonkY3/view?usp=sharing</a> </p> <p>[3] <a href="https://github.com/Azure/AzurePublicDataset/blob/master/AzureFunctionsDataset2019.md">https://github.com/Azure/AzurePublicDataset/blob/master/AzureFunctionsDataset2019.md</a></p>
Discrete flow cytometry of underway samples from the Gradients 2019 cruise using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the Gradients 2019 (Gradients 3/KM1906) oceanographic research cruise in the Northeast Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including picoeukaryotes and the cyanobacteria Prochlorococcus and Synechococcus. Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Survey data, models and dated samples of the Pliocene shorelines of Camarones, Argentina (Ver 1.1).
<p>The dataset cosists of a spreadsheet containing data on GPS surveys, dynamic topography extracted from published models (gplates.org), Shell preservation scoring, Strontium Isotopic Stratigraphy ages, and Global mean Sea Level calculations.</p> <p>Version 1.1 contains fixes to small errors and formulas.</p>
Netflow data without sampling for test (D2)
<p>NetFlow traffic generated using <strong>DOROTHEA</strong> (<strong>DO</strong>cker-based f<strong>R</strong>amework f<strong>O</strong>r ga<strong>TH</strong>ering n<strong>E</strong>tflow tr<strong>A</strong>ffic)</p> <p>NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>NetFlow flows have been captured without sampling at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <p>In the construction of the datasets, different percentages of flows considered attacks and flows considered normal traffic have been used.</p> <p>These datasets have been used to test machine learning models.</p>
Netflow data without sampling for training (D1)
<p>NetFlow traffic generated using <strong>DOROTHEA</strong> (<strong>DO</strong>cker-based f<strong>R</strong>amework f<strong>O</strong>r ga<strong>TH</strong>ering n<strong>E</strong>tflow tr<strong>A</strong>ffic)</p> <p>NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.</p> <p>NetFlow flows have been captured without sampling at the packet level. A sampling means that 1 out of every X packets is selected to be flow while the rest of the packets are not valued.</p> <p>The version of NetFlow used to build the datasets is 5.</p> <p>In the construction of the datasets, different percentages of flows considered attacks and flows considered normal traffic have been used.</p> <p>These datasets have been used to train machine learning models.</p>
Mean measured proximate content for 144 samples of salmo salar
<p>Data from a survey of nitrogen content in farmed Atlantic Salmon.</p> <p>This is a reduced data set derived from data obtained in a published survey: See Colwell, P., Ellison, S. L. R., Walker, M. J., Elahi, S., Burns, D. T., & Gray, K. (2011). Nitrogen Factors for Atlantic Salmon, Salmo salar, farmed in Scotland and in Norway and for the derived ingredient, “Salmon Frame Mince”, in Fish Products. <em>Journal of the Association of Public Analysts (Online)</em>, <em>39</em>, 44.</p> <p>The data set is reduced by a) restriction to fillet and whole fish (omitting frame mince), b) restriction to nitrogen and fat content c) averaging of duplicate original observations. The reduced data set forms a complete full factorial design on five factors affecting nitrogen content.</p> <p>The data set is in the tab-delimited text file Salmon_N_means_FW.txt; Data field descriptions are provided in Salmon_N_means_FW_fields.txt.</p>
Evolutionary history of the Galápagos Rail revealed by ancient mitogenomes and modern samples
<p>Beast v. 2.6.3 input (<em>.xml</em>) files and output (<em>.log</em> and <em>.trees</em>) files for phylogenetic analyses of rails, used to determined the evolutionary history of the Galápagos Rail <em>Laterallus spilonota</em>. There are two main datasets: coding sequences of the mitochondrial genome ('mtCDS'), partitioned per codon position, and a two mitochondrial/one nuclear marker dataset ('2mt1nc'). For each of the datasets, separate runs have been made in which the fossil calibration of Rallidae is applied to the stem of the present-day family ('calRallidaeStem') or the crown node ('calRallidaeCrown), and finally all runs have been replicated with three different starting seeds ('seed_NNNNNNNNN', with the different seeds 123456789, 456789123, and 789123456).</p> <p>We provide raw output (<em>.log</em> and <em>.raw.trees</em>) as well as maximum clade credibility ('mcc') trees (<em>.mcc.trees</em>), calculated after discarding 10% of the trees as burn-in, using median ('heights_median') or mean ('heights_mean') node heights as estimated node age.</p> <p>The runs used for Table 1 (and Figure 2) in the accompanying paper are:</p> <ul> <li>Dataset mtCDS, Rallidae calibration of stem: seed 123456789</li> <li>Dataset mtCDS, Rallidae calibration of crown: seed 456789123 </li> <li>Dataset 2mt1nc, Rallidae calibration of stem: seed 789123456</li> <li>Dataset 2mt1nc, Rallidae calibration of crown: seed 123456789</li> </ul> <p>This version of the data includes <em>Pellornis mikkelseni</em> among the fossils making up the calibration distribution for crown Gruiformes. In a previous version of this data deposit, that data point was represented by <em>Messelornis cristata </em>(see accompanying paper).</p>
Fig. 1 in Molecular phylogenetics of the African horseshoe bats (Chiroptera: Rhinolophidae): expanded geographic and taxonomic sampling of the Afrotropics
Fig. 1 Type localities for recognized species of Rhinolophus (black circles), as well as subspecies and synonyms (white circles); label names represent the specific epithets of currently recognized species. Biomes of Africa and neighboring regions indicated by color shading, dark yellow: Tropical and subtropical moist broadleaf forests; orange: Flooded grasslands and savannas; gray: Tropical and subtropical grasslands, savannas, and shrublands; olive brown: Deserts and xeric shrublands; gray-green: Tropical and subtropical moist broadleaf forests; peach: Mangroves; ochre: Mediterranean forests, woodlands, and shrub; dark tan: Tropical and subtropical dry broadleaf forests [14]
Fig. 5 in Molecular phylogenetics of the African horseshoe bats (Chiroptera: Rhinolophidae): expanded geographic and taxonomic sampling of the Afrotropics
Fig. 5 Species tree estimated in StarBEAST2 using the four nuclear intron dataset. Numbers adjacent to nodes indicate posterior probabilities. Terminal tips in the tree that are statistically well-supported (PP ≥ 0.95) from BPP are indicated by "*" preceding the clade name, and terminal tips that had PP <0.95 are indicated by "?" preceding the clade name. Species groups are from [13]
Training dataset: Mass spectrometry based proteomics of healthy human serum samples
<p>The two raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>Serum of a healthy person was obtained by centrifugation of full blood in a serum-gelmonovette. One serum sample was depleted for high abundant proteins, the other not.<br> For the non-depleted sample: 5µl of serum was diluted with 0.1% Rapigest, resulting in a concentration of 1mg/ml.<br> Depletion was performed with the Seppro IgY14 Spin columns which are able to deplete 14 high abundent blood proteins by immunoaffinity. For the depleted sample 9µl of serum was diluted with TBS/HCl/NaCl buffer and added to the Seppro IgY14 spin column. After depletion the sample was buffered with Hepes pH 8.0 and Rapigest was added to a final 0.1% Rapigest concentration. From here on, both samples were reduced by adding TCEP, alkylated by IAA and quenched with DTT in solution. Digestion was performed by adding trypsin in a ratio of 1:50 to the samples. After incubation at 37°C, 600rpm, over night, the sample clean-up was performed with the PreOmics desalting columns. iRT peptides were added and the sample was measured with a Q-Exactive Plus mass spectrometer. Besides the two raw files, we uploaded a fasta file that serves as human protein sequence database and the Galaxy MaxQuant training result files: protein groups, peptides, mqpar and PTXQC.</p>
Released Experimental Dataset for Sampled Automated Machine Learning
<p>Released Experimental Dataset of "Doing More with Less: Characterizing Dataset Downsampling for AutoML"</p> <p> </p> <p>Experiments were run for 5 and 60 minutes on 16 datasets:<br> 4 small: < 10.000<br> 5 medium: < 100.000<br> 7 large: > 100.000<br> </p>
Application of contact-resonance AFM methods to polymer samples (Raw Data)
<p>Raw data and figures of the article "Application of contact-resonance AFM methods to polymer samples", published in Beilstein Journal of Nanotechnology on 12 Nov 2020</p> <p> </p> <p>The raw data can be opened with the software "Igor Pro"</p>
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains
<p>We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.</p>
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.