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Research Data Management & Computational Reproducibility

Name: Research Data Management &amp; Computational Reproducibility
Author: Baturi

Free Download Research Data Management & Computational Reproducibility

Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Research Data Management & Computational Reproducibility, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 8/2026
Created by George Sentis
MP4 | Video: h264, 2560x1440 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 53 Lectures ( 2h 42m ) | Size: 2.6 GB

An introduction to FAIR data, RDM, Git, Conda, Docker and reproducible workflows using bioinformatics examples.

What you'll learn

⚡ Understand how FAIR principles and research data management practices apply to bioinformatics and biomedical research projects.
⚡ Recognize key approaches for improving computational reproducibility using Git, Conda, Docker, Snakemake, metadata and checksums.
⚡ Learn what should be documented about data, software, environments, provenance and analysis decisions to support reproducibility.
⚡ Follow the creation of a project directory showing how reproducibility practices can be incorporated into a bioinformatics project.

Requirements

❗ Basic familiarity with bioinformatics or computational biology is helpful but not required. No prior knowledge of FAIR or research data management is expected.

Description

Bioinformatics projects often involve large datasets, multiple software tools, changing environments and many small decisions that can be difficult to reconstruct later.
This course provides an introduction toResearch Data Management, FAIR principles and computational reproducibility in a bioinformatics context.
The course explains how data can be planned, documented, stored, shared and preserved more carefully throughout the research lifecycle. Topics include Data Management Plans, metadata, file organization, persistent identifiers, licensing, repositories, backup practices and basic data protection considerations.
The computational section introduces common tools and concepts used to improve reproducibility, includingGit, Conda, Docker, Snakemake, Nextflow, checksums, random seeds and literate programming. The emphasis is on understanding what each tool contributes, rather than providing advanced technical training in every technology.
A small computational project is used as a practical demonstration of how reproducibility practices can be incorporated into a realistic bioinformatics folder structure and workflow. The demonstration focuses on project organization, metadata, software environments, validation, workflow automation and documentation. It is not intended to teach RNA-seq analysis itself or to provide comprehensive training in workflow development, container engineering or research data governance.
The course is intended for bioinformaticians, computational biologists, data professsionals, researchers and graduate students who want a structured overview of current reproducibility and research data management practices and examples of how these ideas can be applied in everyday computational research.

Who this course is for

⭐ Bioinformaticians, computational biologists, researchers and graduate students who want an introduction to FAIR data, research data management and computational reproducibility.

Homepage

https://www.udemy.com/course/dama-rdmcr

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