The first book that I start with is written by Jeffrey Leek. It is not a Data Science book by itself but rather an introductory set of tips on how to aspire to make science today.
The title of the book is "How to be a modern scientist" that you can get here. Actually, the series of posts that I start with this one is a consequence of reading this book. It is a way to acknowledge value in a twofold manner: first, I praise the book and congratulate the author and second, I share with the community a biased version of value that they could obtain by reading this book. These two processes are currently also present in the scientific community, together with more traditional aspects such as scientific paper reading, and, certainly, writing.
Let me try to share with you the main learning points I collected from this book. As always, here it goes my personal disclaimer: the reading of this very personal and non-comprehensive summary by no means replaces the reading of the book it refers to; on the contrary, this post is an invite to read the entire work.
Paper writing and publishing
There are currently three elements in modern science, what you can write, what you can code and the data you can share (the data you have based your investigation on).
The four parts the author states that a scientific paper consists of are great: a set of methodologies, a description of data, a set of results and, finally, a set of claims.
A key point is that your paper should tell or explain a story. That is why the author talks about "selecting your plot" for your paper i.e. once you have an answer to your question is when you start writing your paper.
These chapters distinguish between posting a preprint of a paper (for example in arxiv.org and submitting the paper to a peer-reviewed journal. For junior scientists, the mix the author mentions of using a preprint server and a closed access journal is very adequate.
Peer review and data sharing
The author proposes some elegant ways to carefully and timely review papers and also mentions the use e.g. of blogs to start sharing a serious and constructive review.
Regarding data sharing, his suggestion is to use a public repository that would remain accessible throughout time such as e.g. figshare.com.
Scientific blogging and coding
A way to market your papers can be via blogging. The three recommended platforms are blogger.com, medium.com and wordpress.org.
The author also reminds us that the Internet is a medium in which controversy flourishes.
In terms of code, the suggestion for general code is github.com and bitbucket.com.
Social media in science
An useful way to promote the work of others and your work.
Teaching in science
Post your lectures on the Internet (be aware of any non-disclosure agreements with the University or the educational institution to teach at. Share videos of your lectures and, if resources allow it, create your own online course.
Books and internal scientific communication
Three platforms are suggested: leanpub.com, gitbook.com and amazon kindle direct publishing.
Regarding internal communication, slack.com is one of the proposed tools to keep teams in sync.
Scientific talks and reading scientific papers, credit and career planning and online identity
This are the last sections of the book: some hints on preparing scientific talks, reading papers constructively and, very important, giving credit to all those community members who have help you out either by writing something you use or by creating frameworks you use. A key suggestion is to use as many related metrics as possible in your CV and in your presentations.
Finally, the books ends up with some useful (and common sense based) tips on career planning and online identity.
Thanks to the author of How to be a modern scientist!
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| Happy revealing! |
