In 2018, we wrote a short editorial to introduce new users to estimation graphics. We first released this as a preprint, which was received well.
We then submitted this short piece, with the software for consideration by Nature Methods. After some time, and addition of several features, this was accepted and published. The publication was also met with a warm reception by scientists.
Several other statistics-reform advocates recommended the software for scientists to use in their data analysis. Lewis Halsey, who previously highlighted the volatility of p-values, noted that DABEST and the estimationstats web app are the best methods to produce publication-quality estimation graphics.
Most recently, the Editor-in-Chief of eNeuro, the Society for Neuroscience's open-access journal, announced that he was changing the journal's policy to encourage the use of estimation statistics. This was backed up with a great explanation of the why and how of estimation by Robert J. Calin-Jageman and Geoff Cumming. The Editor-in-Chief Christophe Bernard wrote a personal account of how he came to adopt estimation:
This is a very promising development. We hope that this policy change at eNeuro spreads further, as we believe journal policy, when paired with the right tools for analysts, is a powerful way to transform statistical practice.
Neuroscience and Behavioral Disorders Program
Duke-NUS Medical School
While the structure and function of the pre-synapse is relatively well-understood, the complex structure attached to the post-synaptic membrane—known as the post-synaptic density—remains largely mysterious. The Human Frontier Science Program has funded a three-year project to study post-synaptic structure and function. This ambitious project aims to understand the dynamic organization of the post-synaptic scaffold, using a range of methods including: genomics, transcriptomics, proximity labeling, mass spectrometry, Drosophila neurogenetics, protein modeling, X-ray crystallography, cryo-electron microscopy, and electron tomography.
The Claridge-Chang Lab at Duke-NUS Medical School is seeking to appoint a Postdoctoral Research Fellow with a strong background in molecular neuroscience to join this project. The collaborative project will involve interactions with a number of other groups, including the Copley, Hoelz, Manser, and Robinson labs. The Fellow will investigate the structure and function of the postsynaptic scaffold.
Duke-NUS Medical School in Singapore is a collaboration between Duke University and the National University of Singapore. It has a mission for training educating physicians, training biomedical scientists, and condcuting research. The Claridge-Chang Lab focuses on analysing essential brain functions with behavioural, genetic, anatomical and physiological methods, with an aim to discovering some of the basic components and mechanisms that underlie psychiatric and cognitive dysfunction. The laboratory uses neurogenetic methods combined with ongoing molecular, instrumentation and analytical tool development and application to understand the circuits and molecules that support learning. For additional information please read other pages on www.claridgechang.net.
Candidates must have:
You will work to conduct research activities, including (but not limited to): planning; organizing; conducting; and communicating research studies within the overall scope of one or more research projects. Your main duties would include—but are not limited—to the following:
Interested applicants are welcome to email a detailed resume, supporting documents and contact details for at least two referees to firstname.lastname@example.org. Please note that your cover letter should indicate the reference number (Ref no: PN/ACC/RF-HFSP/201906). Only short-listed candidates will be contacted.
In November 2018, we celebrated Farhan's new job as an Assistant Professor at Hamad Bin Khalifa University in Qatar. While we were sad to see him leave, we are thrilled that he will be starting the next phase in his career. He is more than ready to take on the challenges of teaching and doing independent research.
On the 19th, 20th and 27th of May 2018, members of the lab volunteered to help introduce Drosophila research to children at Singapore's Science Centre.
James, Joses, Sangyu, and Quyen spent three weekend afternoons interacting with kids showing them Drosophila flies and experiments in person. Hundreds of children had fun looking at flies!
Four decades of memory genetics
We systematically reviewed over 30 years of Drosophila memory genetics. While replication is sparse, the replicated genes have high reproducibility. There is currently almost no evidence connecting genes into plasticity pathways.
Click to set custom HTML
Over Easter weekend 2018 we launched estimationstats.com on Twitter. There was a lot of interest, and the information spread to scientists in many different fields. The upsurge in usage provoked some server memory outages. Requests included a version in R, and an option that can tolerate very large sample sizes (which the swarmplot cannot handle). We're happy that people seem to find it helpful, and welcome feedback on what doesn't work and what does.
We mourn the loss of our colleague Katarina Chlebikova, a brilliant student and passionate scientist.
Katarina graduated with a degree in Biological Sciences from the University of Cambridge, doing her major project on learning and memory with Johan Alsiö. In the lab Jun - Sep 2013 she worked on Drosophila defense behaviors. Katarina went on to do her MSc at Edinburgh University before returning to Duke-NUS as a PhD student. She pursued her doctoral studies in the lab from Jan 2016. Tragically, Katarina passed away January 2018.
MS Word offers a huge range of advanced features. However, Google Docs is vastly superior for collaborative composition.
After getting sick of the default font in matplotlib, and frankly annoyed by how difficult it seems to use custom fonts, Joses sat down and figured it out.
These instructions below are for Mac OS X, and assume you have installed Jupyter.
1. Install homebrew: go to and follow the instruction (no plural) here http://brew.sh/
2. Install font converter `fondu` with homebrew using the command:
brew install fondu
in Terminal. We will used `fondu` to convert the font of choice to 'ttf'.
3. Then find the font file you want to add. Open FontBook in your Utilities folder, find your font of choice, right-click on it, and select "Show in Finder".
4. Copy the font family to the folder "\Library\Fonts".
5. If this font already has the file extension `ttf`, you can skip to step 6.
If the font has file extension `dfont`, enter the following lines in Terminal:
fondu <filename of font>
This will convert the .dfont file to a .ttf file.
6. Now we need to delete the font cache for matplotlib. In terminal, enter the lines
to navigate to the matplotlib temp directory and list its contents.
7. There should be a file named either `fontList.cache` or `fontList.py3k.cache`. Use
rm <name of fontlist cache>
to delete it.
8. Now we need to edit the matplotlib parameters file. Start a new Python notebook in Jupyter and run the command
in a new cell. This should give you something like:
which is the location of the parameters file.
9. Open this file in a text editor and scroll down to the Font section.
10. Find the line beginning with `font.sans-serif` and/or `font.serif`. Remove the '#' at the beginning of these lines.
11. Add the name of the font family to these lines. For example:
font.serif : Alegreya, Droid Serif, Bitstream Vera Serif, ...
font.sans-serif : Helvetica Neue, Source Sans Pro, Bitstream Vera Sans…
I have added Alegreya and Droid Serif to the serif family of fonts; Helvetica Neue and Source Sans Pro have been added to the sans-serif family. Matplotlib will look for fonts in running order, so put the fonts you want to use as default immediately after the colon.
11. Save the edited `matplotlibrc` file.
12. Restart your Jupyter notebook and run the following lines.
import matplotlib.pyplot as plt
and scroll to the Font section again to check that your settings have taken effect.
13. You can use the following matplotlib commands to change font family (sans-serif or serif) and the font. For example:
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = 'Droid Serif’
You'll need to restart the notebook and run these lines again if you change the font.
14. To ensure that your edited parameters file will not be ignored when using seaborn, you need to import seaborn as
import seaborn.apionly as sns
This is only possible with the latest version of seaborn (v0.7.1).