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Related Concept Videos

Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Sample Handling01:02

Sample Handling

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Transportation of samples from the collection point to the laboratory, as well as storage and preservation techniques, are crucial for maintaining sample integrity and ensuring accurate and reliable test results.
Samples should be transported carefully from collection points to the laboratory. They should be properly sealed and clearly labeled to prevent cross-contamination. To preserve the sample integrity, optimal temperature conditions during transport are essential. This could involve using...
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Sampling Theorem01:15

Sampling Theorem

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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The ITS2 Database
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The ITS2 Database

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BioSamples database: an updated sample metadata hub.

Mélanie Courtot1, Luca Cherubin1, Adam Faulconbridge1

  • 1EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, UK.

Nucleic Acids Research
|November 9, 2018
PubMed
Summary
This summary is machine-generated.

The EMBL-EBI BioSamples database now centralizes over 5 million sample metadata records, enhancing data exchange and accessibility. Infrastructure upgrades support increased submissions and improved search functionalities for researchers worldwide.

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Data Management

Background:

  • The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) hosts the BioSamples database, a critical resource for biological sample metadata.
  • The database has experienced significant growth, expanding from 2 million to over 5 million samples between 2014 and 2018.
  • Previous infrastructure limitations necessitated upgrades to handle increasing data volumes and user demands.

Purpose of the Study:

  • To detail recent major enhancements to the BioSamples database at EMBL-EBI.
  • To describe infrastructure and data content expansions to improve sample metadata management.
  • To outline new features facilitating data exchange and user accessibility.

Main Methods:

  • Implemented infrastructure upgrades to the BioSamples database backend storage, APIs, and user interface.
  • Established fast, reciprocal data exchange with sister BioSample databases and International Nucleotide Sequence Database Collaboration (INSDC) partners.
  • Redesigned the website to incorporate advanced search filters for efficient data retrieval.

Main Results:

  • Data content has more than doubled, exceeding 5 million samples.
  • Achieved worldwide common representation and centralization of sample metadata through established data exchange.
  • Upgraded platform now accommodates anticipated increases in submissions from major projects like the Human Cell Atlas and the European Genome-phenome Archive (EGA).

Conclusions:

  • The BioSamples database is now the authoritative repository for all INSDC sample metadata and an ELIXIR Deposition Database.
  • The upgraded system offers enhanced scalability, resilience, and faster sample submission turnaround.
  • New functionalities cater to current and future research needs, improving sample metadata accessibility and utility.