This lesson is still being designed and assembled (Pre-Alpha version)

lesson home

Introduction to Data Science and AI for senior researchers

next episode

Welcome to this workshop

Overview

Teaching: 10 min
Exercises: 0 min
Questions

  • What is the purpose of this training?

  • What are the learning goals and objectives?

  • What will this workshop not cover?

  • What next steps should be taken after this course?

Objectives

  • Contextualising Data Science, AI and ML for Biomedical Researchers

  • Understanding the scope and coverage of the topics within this workshop

  • Getting access to complementary and additional resources

  • Being aware of the next possible steps to take after this workshop

Outline

Data Science for Biomedical Researchers

Biosciences and biomedical researchers regularly combine mathematics and computational methods to interpret experimental data. With new technologies supporting the generation of large-scale data as well as successful applications of data science, the use of Artificial Intelligence (AI) in biomedicine and related fields has recently shown huge potential to transform the way we conduct research. Recent groundbreaking research utilising AI technologies in biomedicine has led to an enormous interest among researchers in data science as well as AI approaches to extracting useful insights from big data, making new discoveries and addressing biological questions. It is more important than ever to engage researchers in understanding best practices in data science, identifying how they apply to their work and making informed decisions around their use in biomedicine and related fields.

Glossary

Short definitions of selected terms that are used in the context of this workshop:

  • 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
  • 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
  • Computational Project: Applying computer programming and data science skills to scientific research.
  • Computational Reproducibility: Reproducing the same result by analysing data using the same source code (in a computer programming language) for statistical analyses.
  • Data Science: An interdisciplinary scientific field to extract and extrapolate information from structured or unstructured data using statistics, scientific computing, scientific methods, processes, algorithms and systems, while integrating domain and discipline-specific knowledge. Definition on Wikipedia
  • 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 having to be specifically coded for the task. Definition by Builtin
  • Reproducibility: The results obtained by an experiment, an observational study, or in a statistical analysis of a data set should be achieved again when the study is replicated by different researchers using the same methodology. Definition on Wikipedia

Over the last decade, several tools, methods and training resources have been developed for researchers to learn about and apply data science skills in biomedicine, often referred to as biomedical data science. However, to ensure that data science approaches are appropriately applied in domain research, such as in biosciences, there is a need to also engage and educate scientific group leaders and researchers in project leadership roles on best practices.

The Data Science for Biomedical Scientists project helps address this need in training by equipping experimental biomedical scientists with computational skills. In all the resources developed within this project, we consistently emphasise how computational and data science approaches can be applied while ensuring reproducibility, collaboration and transparent reporting. The goal is to maintain the highest standards of research practice and integrity.

What is biomedical data science?

The term “data science” describes expertise associated with taking (usually large) data sets and annotating, cleaning, organizing, storing, and analyzing them for the purposes of extracting knowledge. […] The terms “biomedical data science” and “biomedical data scientist” […] connote activities associated with the creation and application of methods to new and large sources of biological and medical data aimed at converting them into useful information and knowledge. They also connote technical activities that are data-intensive and require special skills in managing the large, noisy, and complex data typical of biology and medicine. They may also imply the application of these technologies in domains where their collaborators previously have not needed data-intensive computational approaches. Russ B. Altman and Michael Levitt (2018). Annual Review of Biomedical Data Science

In this training material for Introduction to Data Science and AI for senior researchers, we introduce data science and Artificial Intelligence (AI). Providing contexts and examples from biomedical research, this material will discuss AI for automation, the process of unsupervised and supervised machine learning, their practical applications and common pitfalls that researchers should be aware of in order to maintain scientific rigour and research ethics.

Targeted measures and opportunities can help build a better understanding of best practices from data science and AI 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 supervisor roles in research projects who want to lead rigorous and impactful research through computational reproducibility, reusability and collaborative practices.

Target audience

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.

Learning Outcomes

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

Modular and Flexible Learning

We have adopted a modular format, covering a range of topics and integrating real-world examples that should engage mid-career and senior researchers. Most senior researchers can’t attend long workshops due to lack of time or don’t find technical training directly useful for managing their work. Therefore, the goal of this project is to provide an overview (without diving into technical details) of data science and AI/ML practices that could be relevant to life science domains and good practices for handling open reproducible computational data science.

We have designed multiple modular episodes covering topics across two overarching themes, that we refer to as “masterclasses” in this project:

  1. Introduction to Data Science and AI for senior researchers (THIS training material)
  2. Managing and Supervising Computational Projects

Each masterclass is supplemented with technical resources and learning opportunities that can be used by project supervisors or senior researchers in guiding the learning and application of skills by other researchers in their teams.

Do I need to know biology 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 introduce data science, AI and related concepts in detail. The training material “Managing and Supervising Computational Projects” is developed in parallel under the same project that discusses best practices for managing reproducible computational projects. 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 and Assumptions

In defining the scope of this course, we made the following assumptions about the target audiences:

  • You have a good understanding of designing or contributing to a scientific project throughout its lifecycle
  • You have identified a computational project with specific questions that will help you to reflect on the skills, practices and technical concepts discussed in this course
  • have a computational project in mind for which funding and research ethics have been approved and comprehensive documentation capturing this information is available to share with the research team.
  • This course does not cover the processes of designing a research proposal, managing grant/funding or evaluating ethical considerations for research.

Mode of delivery

Each course has been developed on separate repositories as standalone training materials and will be linked and cross-referenced for coherence purposes. This modularity will allow researchers to dip in and out of the training materials and take advantage of a flexible self-paced learning format.

In the future, these courses could be coupled with pre-recorded introductions and training videos (to be hosted on the Turing online learning platform and The Turing Way YouTube channel).

They can also be delivered by trainers and domain experts, who then mix and match lessons from across the two courses and present them in an interactive workshop format.

Next Steps after this Training

After completing this course we recommend these next steps:

  • Go through the “Managing and supervising computational Projects” course (unless already completed)
  • Explore the set of resources provided at the end of each lesson for a deep dive into the topics with real-world examples
  • Establish connections with other courses and training materials offered by The Alan Turing Institute, The Crick Institute, The Carpentries, The Turing Way and other initiatives and 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 to further enhance theoretical and technical skills
  • Collaborate with other scholarly experts such as librarians, research software engineers, community managers, statisticians and experts with specialised skills in your organisation who can provide specific support in your project.

In this course, we are introducing data science, AI and related concepts. Another workshop materials developed under the Data Science for Biomedical Scientists project, Managing and Supervising Computational Projects, discusses best practices, tools, and strategies of project management in reproducible computational projects. Although both courses were developed to complement each other they can also be booked separately. Both courses discuss challenges, solutions and examples of machine learning and AI applications within biomedical research and related fields. The recommendations are transferable across many other disciplines within Life Sciences.

Funding and Collaboration

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.

Pulling together existing training materials, infrastructure support and domain expertise from The Alan Turing Institute, The Turing Way, The Carpentries, Open Life Science and the Turing ‘omics interest group, we will design and deliver 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 with 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.

Exercise/Discussion/Reflection

License

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

Key Points

  • This workshop is developed for mid-career and senior researchers in biomedical and biosciences fields.

  • This workshop aims to build a shared understanding of data science and AI in the context of biomedicine and related fields.

  • Without going into underlying technical details, the contents provide a general overview and present selected case studies of biomedical relevance.

lesson home

next episode