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

Statgraphics

Statgraphics

Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...

Statistical Analysis System (SAS)

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...

Statistical Package for the Social Sciences (SPSS)

Statistical Package for the Social Sciences (SPSS)

The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...

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...

Data Collection by Experiments

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...

Introduction to R

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...

You might also read

Related Articles

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

Sort by

Same author

Evaluating the method reproducibility of deep learning models in biodiversity research.

PeerJ. Computer science·2025

Same author

Datathons: fostering equitability in data reuse in ecology.

Trends in microbiology·2024

Same author

How widespread use of generative AI for images and video can affect the environment and the science of ecology.

Ecology letters·2024

Same author

Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study.

Metabolites·2024

Same author

BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain.

Biodiversity data journal·2023

Same author

Reference bioimaging to assess the phenotypic trait diversity of bryophytes within the family Scapaniaceae.

Scientific data·2022

Same journal

Updates and validation of the Compi RNA-seq pipeline with a case study in Alzheimer's disease.

Journal of integrative bioinformatics·2026

Same journal

Fragment-level FAIRness: annotating scientific data and its provenance using data fragment selectors.

Journal of integrative bioinformatics·2026

Same journal

Integrating cross-omics research through FAIR Digital Objects with DataPLANT.

Journal of integrative bioinformatics·2026

Same journal

Pheno-App 2.0 - a mobile app for collecting phenotypic data in plant research.

Journal of integrative bioinformatics·2026

Same journal

Evolving bioinformatics services - the journey of KPI metrics with Scorpion.

Journal of integrative bioinformatics·2026

Same journal

The community engagement and empowerment cycle: FAIRagro's framework to foster cultural change towards FAIR RDM practices in agrosystem science and beyond.

Journal of integrative bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

A flexible statistics web processing service--added value for information systems for experiment data.

Dennis Heimann¹, Jens Nieschulze, Birgitta König-Ries

¹Max-Planck-Institute for Biogeochemistry, Hans-Knoell-Str. 10, D-07745 Jena, Germany. dheimann@bgc-jena.mpg.de

Journal of Integrative Bioinformatics

|April 23, 2010

Summary

This summary is machine-generated.

Life science data management now integrates complex analysis tools. This study introduces a flexible statistics web service for easy integration and extension of statistical functionalities in information systems.

More Related Videos

gP2S, an Information Management System for CryoEM Experiments

gP2S, an Information Management System for CryoEM Experiments

Published on: June 10, 2021

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

Related Experiment Videos

Last Updated: Jun 13, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

gP2S, an Information Management System for CryoEM Experiments

gP2S, an Information Management System for CryoEM Experiments

Published on: June 10, 2021

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

Area of Science:

Life Sciences
Bioinformatics
Computational Biology

Background:

Data management in life sciences has evolved beyond storage to include analysis and visualization.
Integrating statistical tools into existing systems is often costly and complex due to hard-coded solutions or extensive adaptation of generic approaches.

Purpose of the Study:

To present a novel approach for providing statistical functionality via a web service.
To enable easy integration and extendability of statistical tools within information systems.

Main Methods:

Development of a statistics web service architecture.
Utilization of XML configuration files for service setup and extension.
Description of the data exchange process between client and service.

Main Results:

The proposed web service allows for straightforward integration into any information system.
Statistical functionality can be extended by simply adding new application descriptions to configuration files.
A practical example demonstrates the successful implementation and functionality of the service.

Conclusions:

The statistics web service offers a flexible and cost-effective solution for integrating advanced analytical capabilities into life science information systems.
This approach simplifies the process of adding and managing statistical tools, promoting greater adaptability and efficiency.