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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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...

You might also read

Related Articles

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

Sort by
Same author

Long-term variability in sugarcane bagasse feedstock compositional methods: sources and magnitude of analytical variability.

Biotechnology for biofuels·2016
Same journal

Current Trends in Multidrug Optimization.

Journal of laboratory automation·2017
Same journal

From the Editor-in-Chief: The 2013 JALA Ten: Call for Nominations.

Journal of laboratory automation·2017
Same journal

From the Editor-in-Chief: The JALA Special Issues on Robotics in Laboratory Automation.

Journal of laboratory automation·2017
Same journal

Informatics and Computing.

Journal of laboratory automation·2017
Same journal

Informatics and Computing.

Journal of laboratory automation·2017
Same journal

Automated Systems.

Journal of laboratory automation·2017
See all related articles

Related Experiment Video

Updated: May 29, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Applying open-source software to laboratory data management.

Glenn A Murray1, David P Crocker

  • 1Department of Chemical Engineering, Colorado School of Mines, Golden, CO, USA.

Journal of Laboratory Automation
|September 13, 2011
PubMed
Summary
This summary is machine-generated.

Small research projects face challenges managing large datasets due to advanced techniques. Open-source frameworks offer affordable, robust data management solutions for these facilities.

More Related Videos

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform
11:08

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Published on: January 13, 2019

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Related Experiment Videos

Last Updated: May 29, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform
11:08

Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

Published on: January 13, 2019

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Area of Science:

  • Data Science
  • Scientific Computing
  • Research Management

Background:

  • Modern experimental techniques and internet data availability generate large datasets.
  • Existing data management systems are often unsuitable or unaffordable for smaller research projects.
  • The need for scalable and cost-effective data management solutions is growing.

Purpose of the Study:

  • To explore options for scientific data management systems tailored to smaller projects.
  • To present considerations for selecting and implementing open-source data management solutions.
  • To illustrate the application of these solutions through a case study.

Main Methods:

  • Review of available open-source data management frameworks.
  • Analysis of suitability and affordability for resource-limited projects.
  • Development and presentation of a practical case study.

Main Results:

  • Identification of several viable open-source frameworks for scientific data management.
  • Demonstration of how smaller facilities can build effective data management solutions.
  • Successful implementation of a data management system in a case study.

Conclusions:

  • Open-source frameworks provide accessible and robust solutions for scientific data management in smaller projects.
  • Strategic selection and implementation of these tools can overcome resource limitations.
  • The presented case study offers a practical model for other facilities.