Statistics 159/259, Spring 2022 Course Summary
Statistics 159/259, Spring 2022 Course Summary¶
This course teaches “the why and how” of reproducible and collaborative research by combining questions of good computational practice in science, open science and statistical data analysis, in the context of today’s research environment. We will interleave practical topics in software engineering and statistical computing with broader discussions on elements of the philosophy of science and the foundations of statistics.
More details can be found in the syllabus.
Communication: class Piazza.
Lectures will be recorded and posted in the Kaltura system (visible via bCourses), but attendance is mandatory. (Much of the pedagogical value of the class is in participating in discussions and code reviews).
Course readings that are not easy to find free on the web or through the UC Berkeley Library will be posted to bCourses.
We will use Jupyter notebooks. We will start with hosted notebooks on our Stat 159 JupyterHub. Later in the term, we will discuss installing Jupyter on your own device. The JupyterHub server will have all the packages you need pre-installed.
Assignments should be submitted by pull request to your private repositories using the GitHub Clasroom.
Whenever you need to work with GitHub, remember to activate GitHub authentication from the JupyterHub, by running the command
github-app-user-authat a terminal and following the instructions. If once authenticated you can’t push to a given repo, it may be that you forgot to add that repo/org to your setup of the authentication app, go here to configure the app’s permissions.
A note on the Berkeley Library EZProxy: Some of the resources listed here are scientific articles available only behind journal paywalls. If you haven’t already, you should configure your web browser to use the campus library EZProxy so you can access them even if you are working from an off-campus network.
Textbook and supporting materials¶
While not strictly a textbook for this course, we will rely heavily on the excellent, openly licensed: Research software engineering in Python. We will complement it with these other scientific python resources:
Katy Huff’s - Effective Computation in Physics.
Jake van der Plas’ A Whirlwind Tour of Python.
Josh Bloom’s Python for Data Science Berkeley Course.
Getting started with Python for research, a gentle introduction to Python in data-intensive research.
Effective Pandas, a book by Tom Augspurger, core Pandas developer.
And we’ll use these Earth Science resources for our domain focus:
Git and git workflows
Miscellaneous computing tutorials
Above are a list of books and websites mostly focusing on computational skills, and this is a list of all the bibliography we’ll refer to in the course. Some of these will become assigned readings, while others are available for your reference.
PLOS Ten Simple Rules¶
The PLOS Ten Simple Rules collection has many short, valuable papers full of relevant, practical advice in this space. A few that stand out, though many (if not most) are worth your time, are “Ten simple rules for …”:
Open Source Software and Open Science
The art of research
National Academies Reports¶
These are key reports produced by the National Academies of Science, Engineering and Medicine. They were created by teams of world experts in the field, and inform policy in multiple areas:
Reproducibility and Replicability in Science, 2018. The previous link contains multiple resources on this topic, including overview videos, from a large effort comissioned by the National Academies of Science, Engineering and Medicine. For reading, this NCBI link has both HTML and PDF download options.
Other general references on reproduciblity and open science¶
Keith Baggerly and the Potti & Nevins Cancer Scandal:
Barba 2016, Top-10 Readings in Reproducibility, a syllabus on reproducible research by Prof. Lorena Barba. Of particular interest from Barba is the Barba 2012, Reproducibility PI Manifesto, with slides available here, as well as
The Practice of Reproducible Research, Case Studies and Lessons from the Data-Intensive Sciences. An online (and printed) book produced by Berkeley researchers. It includes the excellent Achieving Full Replication of our Own Published CFD Results, with Four Different Codes.
Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico. A special issue of Philosophical Transactions of the Royal Society A dedicated to this topic, with multiple valuable articles, of which the following are just a few:
Reproducibility and earth/climate science¶
One of the National Academies reports above commissioned a paper by Bush et al. (2020) titled Perspectives on Data Reproducibility and Replicability in Paleoclimate and Climate Science.