Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

732
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
732
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.5K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.5K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.5K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.5K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

995
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
995
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

325
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
325
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.7K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2022
Same author

Ten simple rules for making a vocabulary FAIR.

PLoS computational biology·2021
Same author

Community standards for open cell migration data.

GigaScience·2020
Same author

Semantic concept schema of the linear mixed model of experimental observations.

Scientific data·2020
Same author

Abdominal organ perfusion and inflammation in experimental sepsis: a magnetic resonance imaging study.

American journal of physiology. Gastrointestinal and liver physiology·2018
Same author

Exploration of potential biochemical markers for persistence of patent ductus arteriosus in preterm infants at 22-27 weeks' gestation.

Pediatric research·2018
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K

Interoperable and scalable data analysis with microservices: applications in metabolomics.

Payam Emami Khoonsari1, Pablo Moreno2, Sven Bergmann3,4

  • 1Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.

Bioinformatics (Oxford, England)
|March 10, 2019
PubMed
Summary
This summary is machine-generated.

We developed a scalable and interoperable Virtual Research Environment (VRE) for metabolomics data analysis using microservices, Docker, and Kubernetes. This approach simplifies complex workflows for researchers with minimal IT expertise.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.8K
Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry
11:39

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Published on: June 9, 2019

9.6K

Related Experiment Videos

Last Updated: Jan 28, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.8K
Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry
11:39

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Published on: June 9, 2019

9.6K

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Developing scalable and robust data analysis workflows for scientific research is challenging.
  • Integrating diverse software tools into a cohesive and performant system requires significant effort.

Purpose of the Study:

  • To introduce a generic method for creating scalable and interoperable scientific workflows.
  • To develop a Virtual Research Environment (VRE) for metabolomics data analysis with minimal IT expertise requirements.

Main Methods:

  • Utilized microservice architecture to encapsulate software tools as Docker containers.
  • Employed Kubernetes for orchestrating containerized tools into scientific workflows.
  • Developed a VRE accessible via a web portal for launching workflows on demand.

Main Results:

  • Demonstrated dynamic scaling of the VRE with increasing computational resources.
  • Achieved interoperability by integrating major software suites for a comprehensive metabolomics workflow.
  • Validated the method on mass spectrometry, NMR spectroscopy, and fluxomics studies.

Conclusions:

  • The microservice-based VRE provides a generic, scalable, and interoperable solution for scientific data analysis.
  • This approach facilitates large-scale integrative science across various disciplines.
  • The VRE simplifies complex workflows, making advanced data analysis accessible to novice users.