Summary and Setup

The Managing Open and Reproducible Computational Projects training material covers best practices for managing and supervising computational projects in biology and related fields through data science methods, analysis, interpretation, and reporting processes. Through lessons learned in this training, researchers will enhance their understanding and guide the integration of rigorous and reproducible scientific methods for designing reproducible, transparent and collaborative computational projects. Furthermore, the guidance provided for managing and supervising early career researchers in conducting computational (data-driven/informed) research will help ensure transparency and research integrity throughout the project design, methodology, analysis, interpretation and reporting process.

This training material is developed under the Data Science for Biomedical Scientists project. It massively reuses The Turing Way chapters and builds on The Carpentries and Open Life Science practices. Hosted by the Tools, practices and systems (TPS) research team, all materials are shared under CC-BY 4.0 License. Although the training course is tailored to the biomedical sciences community, materials will be generally transferable and directly relevant for data science projects across different domains. Anyone interested in collaboration and improvements of this material is welcome to connect with the development team on GitHub (see the repository).

Funding Acknowledgement: The first iteration of this project was funded by The Alan Turing Institute - AI for Science and Government (ASG) Research Programme from October 2021 to March 2022.

Managing Open and Reproducible Computational Projects

Overview

Questions

  • What is the purpose of this training?
  • Who are the target audience?
  • What will they learn at the end of this training?

Objectives

  • Describe the motivation, purpose, target audience and expected outcome of this training

Target audience

This resource is designed for experimental biologists and biomedical research communities, with a focus on two key professional/career groups:

  1. Group leaders without prior experience with Data Science and ML/AI - interested in understanding the potential additionality and application in their areas of expertise.
  2. Postdoc and lab scientists - next-generation senior leaders, who are interested in additionality, but also the group more likely to benefit from tools to equip them with the requirements to enable the integration of computational science into biosciences.

Targeted measures and opportunities can help build a better understanding of best practices from data science that can be effectively applied in research and supported by senior leaders. Senior leaders, in this context, can be academics or non-academics working in advisors, experts or supervisors roles in research projects who want to lead rigorous and impactful research through computational reproducibility, reusability and collaborative practices.

Do I need to know biology and AI/ML concepts for this training material?

The short answer is no!

Although the training materials are tailored to the biomedical sciences community, materials will be generally transferable and directly relevant for data science projects across different domains. You are not expected to have already learned about AI/ML to understand what we will discuss in this training material.

In this training material, we will discuss best practices for managing reproducible computational projects, regardless whether they include AI/ML components or not. The training material “Introduction to Data Science and AI for senior researchers” is developed in parallel under the same project that introduces data science, AI and related concepts in detail. Although those are helpful concepts, it is not required to go through that training material to understand the practices we discuss in this training material.

Both the materials discuss problems, solutions and examples from biomedical research and related fields to make our content relatable to our primary audience. However, the best practices are recommended and transferable across different disciplines.

Prerequisites

In defining the scope of this project for our target audience, we make some assumptions about the learner groups:

  • Our learners have a good understanding of designing or contributing to a scientific project throughout its lifecycle.
  • They have identified a computational project with specific questions that will help them reflect on the skills, practices and technical concepts discussed in this training
  • We assume that the research team of any size is (either partially or fully) established, and hence, we will not discuss the recruitment of team members.

This lesson is developed alongside the Introduction to Data Science and AI for senior researchers lesson. Our learners are encouraged to go through Introduction to Data Science and AI for senior researchers lesson to learn about data science and AI/ML practices that could be relevant to life science domains, where the best practices for Managing Open and Reproducible Computational Projects can be practically applied.

  • This training doesn’t cover the processes of designing a research proposal, managing grant/funding or evaluating ethical considerations for research.

Jargon Busting

Below we provide a simple definition of some terms that we use in this project in the context of scientific research:

  • Best Practices: Set of procedures that have been shown by research and experience to produce optimal results and that are established or proposed as a standard suitable for widespread adoption. Definition by Merriam Webster
  • Data Science: An interdisciplinary scientific study that uses mathematics and computational tools to extract insights from big structured and unstructured data.
  • Computational Project: Applying computer programming and data science skills to scientific research.
  • Reproducibility: When the same analysis approach is applied to the same data, it should give the same answer - this answer should be reproduced by others using the same analysis and data originally used.
  • Computational Reproducilibity: Reproducing the same result by analysing data using the same source code (in a computer programming language) for statistical analyses.
  • Artificial Intelligence (AI): A branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Definition by Builtin
  • Machine Learning (ML): A subset of artificial intelligence that gives systems the ability to learn and optimize processes without having to be consistently programmed. Simply put, machine learning uses data, statistics and trial and error to “learn” a specific task without ever having to be specifically coded for the task. Definition by Builtin

Learning Outcomes

At the end of this lesson (training material), attendees will gain a better understanding of:

  • Acquiring the necessary/essential basic tools to produce good quality and reproducible computational projects, including AI/ML related
  • designing computational projects using collaborative, reproducible and open science approaches
  • examining challenges and potential pitfalls in applying best practices in Data Science
  • identifying tools and practices required for managing specific kinds of computational projects
  • applying a systematic framework introducing computational protocols in their groups as project leads and/or managers

Next Steps after this Training

After this masterclass we recommend our learners take these next steps:

  • Go through the “Introduction to Data Science and AI for senior researchers” masterclass (if not already completed)
  • Explore the set of resources provided at the end of each lesson for deeper dive into various technical topics required to learn or guide the application of Data and computational research best practices in real-world projects
  • Establish connections with other training and training materials offered by The Alan Turing Institute, The Crick Institute, The Carpentries, The Turing Way and other projects/organisations involved in the maintenance and development of this training material
  • Connect with other research communities and projects in open research, data science and AI that offers opportunities to develop/enhance technical skills
  • Collaborate with domain experts such as librarians, research software engineers, community managers, statisticians or people with specialised skills in your organisation who can provide specific support in your project.

Collaboration and aims

This course was initiated and mainly maintained by the Data Science for Biomedical Scientist by The Alan Turing Institute. Large contribution were made by the OLS community, during book sprint events as well as asynchronous work.

Pulling together existing training materials, infrastructure support and domain expertise from The Turing Way, The Carpentries, Open Life Science and the Turing ’omics interest group, we have been designing and delivering a resource that is accessible and comprehensible for the biomedical and wet-lab biology researchers.

This project will build on two main focus areas of the Turing Institute’s AI for Science and Government research programmes: good data science practice; and effective communication to stakeholders. In building this project, we will integrate the Tools, practices and systems (TPS) Research Programme’s core values: build trustworthy systems; embed transparent reporting practices; promote inclusive interoperable design; maintain ethical integrity and encourage respectful co-creation.

Funding

Data Science for Biomedical Scientists is funded by The Alan Turing Institute’s AI for Science and Government (ASG) Research Programme. It is an extension of The Crick-Turing Biomedical Data Science Awards that strongly indicated an urgent need to provide introductory resources for data science in bioscience researchers. This project extension will leverage strategic engagement between Turing’s data science community and Crick’s biosciences communities.

Julien Colomb was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project number 327654276, CRC1315, a “Collaborative Research Centre” in the domain of the neuroscience.

License

All materials are developed online openly under CC-BY 4.0 License using The Carpentries training format and The Carpentries Incubator lesson infrastructure.

Keypoints

  • This material is developed for mid-career and senior researchers in biomedical and biosciences fields.
  • This training aims to build a shared understanding and facilitate the integration of computational reproducibility in data science and research.
  • This material has been developed by the Turing way community as an open educational resource.

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