Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials

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

Assessment of susceptible chemical modification sites of trastuzumab and endogenous human immunoglobulins at physiological conditions.

Communications biology·2018

Same author

Assessment of Telomere Length, Phenotype, and DNA Content.

Current protocols in cytometry·2017

Same author

International Society for the Advancement of Cytometry cell sorter biosafety standards.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2014

Same author

Natural killer T cells in advanced melanoma patients treated with tremelimumab.

PloS one·2013

Same author

How to develop a standard operating procedure for sorting unfixed cells.

Methods (San Diego, Calif.)·2012

Same author

Isolation of synaptic terminals from Alzheimer's disease cortex.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2012

Same journal

Filament Extrusion-Based Conductive TPU Composite Scaffolds Enable Superior Neuronal Growth and Synaptic Maturation In Vitro.

Engineering in life sciences·2026

Same journal

Less Genome, More Gain: Genome Reduction Enhances Transaminase-Producing <i>E. coli</i> in a Scale-Down Bioreactor.

Engineering in life sciences·2026

Same journal

Design and Characterization of a 3D-Printable Membrane Aeration Module for Small-Scale Bioprocess Prototyping.

Engineering in life sciences·2026

Same journal

Molecular Pharming: Advances, Applications, and Future Prospects in Biotechnology and Medicine.

Engineering in life sciences·2026

Same journal

Topic: Continuous Enzymatic Peracids Synthesis in Pickering Emulsions: Influence of Nanoparticles Modification, Aqueous Phase Composition and Operational Parameters.

Engineering in life sciences·2026

Same journal

Optimized Bioconversion of Naringenin to Hesperetin in <i>Escherichia coli</i> With Halide Methyltransferase-Mediated <i>S</i>-Adenosylmethionine Regeneration.

Engineering in life sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 16, 2025

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Published on: June 16, 2023

A scalable software framework for data integration in bioprocess development.

Ingrid Schmid¹, Joachim Aschoff¹

¹BU Life Science, infoteam Software AG Bubenreuth Germany.

Engineering in Life Sciences

|July 7, 2020

Summary

This summary is machine-generated.

The iLAB software framework enhances lab workflow integration by connecting devices and data. This middleware standardizes data for improved analysis and decision-making in bioprocess development.

Keywords:

Bioprocess optimization Data integration Device integration Middleware SiLA

More Related Videos

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

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

Published on: November 22, 2019

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

Published on: September 22, 2011

Related Experiment Videos

Last Updated: Dec 16, 2025

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Published on: June 16, 2023

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

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

Published on: November 22, 2019

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

Published on: September 22, 2011

Area of Science:

Biotechnology
Laboratory Informatics

Background:

Lab workflow effectiveness is limited by poor device and data integration.
Automation and lab informatics have advanced, but integration remains a challenge.

Purpose of the Study:

To describe the application of the iLAB software framework in bioprocess development.
To demonstrate how iLAB integrates devices and data for enhanced lab workflows.

Main Methods:

Utilized the iLAB software framework as a middleware solution.
Integrated devices using standardized protocols and converted data to a standard format.
Collected and administered process and result data from high-throughput bioreactor systems in a central database.

Main Results:

iLAB successfully collected and consolidated process parameters and measured values from diverse bioreactor systems.
Enabled visualization, analysis, and comparison of screening and offline data.
A filter algorithm facilitated searching for matching parameters and comparing correlated datasets across different systems.

Conclusions:

The iLAB database model consolidates data, transforming it into actionable information.
Provides a robust foundation for enterprise-level management decisions in bioprocess development.
iLAB improves lab efficiency through seamless device and data integration.