Overview

This page is for sharing useful resources that don’t fit neatly into an existing CfRR course yet (for example: external short courses, excellent references, or recommended tools).

To add a resource, please open a Pull Request (PR) editing this file. For contribution guidance, including the process of opening a PR please see the Join Us! section of the website, particularly the contribution guidance.

A–Z

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A

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B

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C

• The Carpentries — Teaching foundational coding and data science skills to researchers.

• CodeRefinery — Training workshops and resources for better research software development.

C and C++

• Core references

  • C reference

  • C++ reference

• Tooling

  • Compilers: GCC docs and Clang docs

  • Build systems: CMake tutorial

D

• DES RAP Book - step‑by‑step open book on building discrete‑event simulation (DES) models in Python and R as part of a reproducible analytical pipeline (RAP), with worked healthcare examples and template repositories. Although centred on DES as an example, there is lots of guidance that is broadly relevant (e.g., environments, version control, structuring research as a package, documentation, linting, github actions, code review, licensing, citation, sharing and archiving).

E

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F

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G

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H

• Healthcare Services Analytics and Decision Science Atlas - a directory of open-source tools, packages, and projects for analytics and decision science in healthcare.

I

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J

• Julia for healthcare modelling and data science - online book with some examples on using Julia in healthcare modelling (including SimJulia).

K

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L

• Learning and Development Academy - UK ITSS — Training and professional development resources from the UK IT Support Staff organization.

• The Little Book of DES - an open online book guiding you through creating discrete-event simulation models for healthcare (and beyond) in SimPy (python).

M

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N

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O

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P

• A Philosophy of Software Design, 2nd Edition — A book by John Ousterhout covering software design principles.

• Python for Health Data Science - an open textbook explaining key concepts in python - algorithms and computational modelling, statistical programming, and managing data science projects.

Q

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R

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S

• Software Carpentry Lessons — Open-source lessons teaching fundamental programming and data science skills.

T

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U

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V

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W

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X

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Y

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Z

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