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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...

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Related Experiment Video

Updated: Dec 12, 2025

PIPEMAT-RS: Development and Validation of a Standardized MATLAB Pipeline for Resting-State EEG Preprocessing

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Published on: June 6, 2025

Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data

Mikkel Elle Lepperød^1,2, Svenn-Arne Dragly^1,3, Alessio Paolo Buccino^1,4,5

¹Center for Integrative Neuroplasticity, University of Oslo, Oslo, Norway.

Frontiers in Neuroinformatics

|August 15, 2020

Summary

Neuroscience labs face data management challenges with large datasets. Expipe offers a lightweight framework for organizing experimental data and metadata, improving analysis and reproducibility.

Keywords:

Python (programming language)analysis data base (DB)data management data sharing open source software (OSS)

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Area of Science:

Neuroscience
Computational Biology
Bioinformatics

Background:

Experimental neuroscience increasingly uses diverse acquisition techniques across multiple spatiotemporal scales.
Large datasets exceed typical storage capacities, necessitating efficient data retrieval and exploration.
Existing software tools lack the agility to manage evolving metadata specifications in dynamic experimental pipelines.

Purpose of the Study:

To address the lack of agile data management tools in neuroscience laboratories.
To develop a flexible framework for storing, organizing, and retrieving experimental data and metadata.
To simplify the workflow from experiment execution to data analysis.

Main Methods:

Development of Expipe, a lightweight data management framework.
Implementation of functionality for storing and organizing experimental data and associated metadata.
Design for flexibility in defining metadata schemas to accommodate changing experimental needs.

Main Results:

Expipe provides a simplified approach to managing experimental data and metadata.
The framework facilitates efficient retrieval of metadata without needing to access complete datasets.
Expipe is designed to be flexible, adapting to evolving experimental specifications.

Conclusions:

Expipe offers a user-friendly solution for experimental neuroscience laboratories.
The framework enhances data provenance, reproducibility, and project sharing.
Expipe addresses critical data storage and retrieval challenges in modern neuroscience research.