- Use Paperpile for references.
- Follow Practical Typography where possible.
- Margins larger than 1 inch. Narrower text columns are easier to read.
- Never use Arial, use Helvetica Neue instead if Arial is required.
- If Arial not required, use Source Serif Pro and Source Sans Pro.
- Use Serif for the body text, Sans for headers and figure legends.
- Use the Text Styles menu to style each paragraph by typeface, margins, type weight, and paragraph spacing.
- Main text paragraphs should have no space before, and a space afterwards.
- Paragraph headers should have no spaces before or after.
- Text Styles allows you to auto-generate a Table of Contents.
- Use 200-300 dpi PNG files for figures.
- When discussing a project, use Basecamp. When discussing the details of a manuscript, use the Docs Comment function. I admit there is a lot of overlap between the two.
- Try to avoid using email to discuss either.
When using Google Docs, there are a few tricks.
How might a fly model of anxiety help us understand the genetic causes of anxiety disorders? Read the paper (PDF).
Back in January, Nature Methods ran our letter that defined and explained 'estimation statistics.' Since journal policy allows posting an e-print of the accepted manuscript 6 months after publication, here is the manuscript version of "Estimation should replace significance testing."
This e-print is also available at Zenodo, http://dx.doi.org/10.5281/zenodo.60156.
Some notes on gene and protein nomenclature, based on this extensive guide here.
Using Illustrator to make figures give nice results, but is a pain to collate into a single pdf repeatedly during the drafting process, requiring the use of Adobe Acrobat Pro's Create PDF > Merge Files into a Single PDF, clicking "Add Files", then selecting the files you want to collate into a pdf.
A faster way is to use pdftk server, a command line tool that is made for pdfs, but seems to work fine for .ai files also. From the examples given on the pdftk page, doing this is as simple as launching Terminal and typing the relevant version of this:
$ cd yourdirectory
$ pdftk input1.ai input2.ai cat output new.pdf
The resulting pdf files still ends up pretty big (as large as doing the same operation in Acrobat), so one still has to reduce file size in Preview (File > Export... > Quartz Filter: Reduce File Size, click Save).
You can download and install pdftk server here.
For repeated collation, you can save your command lines into a text file for later use.
*Note that you can find yourdirectory by using the Finder to find one of the relevant files and pressing Command-I to get information. Then highlight and copy the path information under "Where" in the information window.
Here is a guide to installing Python for data analysis on a Mac, along with a few extra tips to get going.
The Anaconda distribution is a very convenient version of scientific Python that installs a lot of modules as well as a Launcher that offers three GUI apps:
One of the great things about Anaconda is the ease of updating everything. On one hand, most of what we do doesn't need updating, but on the other the whole SciPy ecosystem is evolving so quickly that it seems silly to update less than twice a year. The GUI apps can be updated by pressing buttons in the Launcher, while Anaconda as a whole can be updated with two lines.
As shown here, you just need to open the command line (e.g. Terminal on Mac) and type
$ conda update conda
$ conda update anaconda
The first line will update the conda package manager, the second line will use conda to update all the modules in Anaconda.
Update: Some packages that are not in Anaconda will require installation with pip. To install pip, follow these instructions here. If you have pip already installed you can update pip:
$pip install --upgrade pip
and then use it to install a bootstrap package:
$pip install scikits.bootstrap
This bootstrap package can then used as per this tutorial.
OK so I needed to import a .csv table into Matlab, but knew it could be tricky. A friend wrote some Matlab code for me once to do this and it was a page-and-half of fiddly code, filled with string trimming and required quite prescriptive column and row names.
I knew pandas could handle csv files nicely, but was keen to keep things in Matlab since the rest of the code was there etc. So I google for the best way to do it and got this. Its a whole blog called Abandon Matlab on how awful Matlab is! Has chapter and verse about how Matlab does not handle csv files with mixed data... I did not go looking for this.