I was searching for the excitation and emission spectra for mNeonGreen. I was able to find an image, but no values for the spectra. Here is an approximation of the spectra (xlsx format, still haven’t figured out csv for wordpress). I got these values using IgorThief.ipf a very handy tool that allows the extraction of XY coordinates from a bitmapped plot (below). mNeonGreen is available from Allele Biotechnologies.

I recently gave a talk at a retreat for new PIs working at QMUL. My talk was focussed on tips for getting started, i.e. the nitty gritty of running an efficient lab. It was a mix of things I’ve been told, worked out for myself or that I’d learned the hard way. PIs are expected to be able to do lots of things that can be full-time jobs in themselves.

I have just finished reading this excellent book, Statistics done wrong: a woefully complete guide by Alex Reinhart . I’d recommend it to anyone interested in quantitative biology and particularly to PhD students starting out in biomedical science. Statistics is a topic that many people find difficult to grasp. I think there are a couple of reasons for this that I’ll go into below.

I put a recent code snippet put up on the IgorExchange. It’s a simple procedure for averaging a set of 1D waves and putting the results in a new wave. The difference between this code and Average Waves.ipf (which ships with Igor) is that this function takes the average of all points in the wave and places this single value in a new wave. You can specify whether the mean or median is used for the average.

This is a long post about Journal Impact Factors. Thanks to Stephen Curry for encouraging me to post this.

I saw this great tweet (fairly) recently: I thought this was such a great explanation of when to submit your paper. It reminded me of a diagram that I sketched out when talking to a student in my lab about a paper we were writing. I was trying to explain why we don’t exaggerate our findings. And conversely why we don’t undersell our results either. I replotted it below:

We were asked to write a Preview piece for Developmental Cell. Two interesting papers which deal with the insertion of amphipathic helices in membranes to influence membrane curvature during endocytosis were scheduled for publication and the journal wanted some “front matter” to promote them. Our Preview is paywalled – sorry about that – but I can briefly tell you why these two papers are worth a read.

Our most recent manuscript was almost ready for submission. We were planning to send it to an open access journal. It was then that I had the thought: how many papers in the reference list are freely available? It somehow didn’t make much sense to point readers towards papers that they might not be able to access. So, I wondered if there was a quick way to determine how papers in my reference list were open access.

Following on from the last post about publication lag times at cell biology journals, I went ahead and crunched the numbers for all journals in PubMed for one year (2013). Before we dive into the numbers, a couple of points about this kind of information. Some journals “reset the clock” on the received date with manuscripts that are resubmitted. This makes comparisons difficult.

My interest in publication lag times continues. Previous posts have looked at how long it takes my lab to publish our work, how often trainees publish and I also looked at very long lag times at Oncogene. I recently read a blog post on automated calculation of publication lag times for Bioinformatics journals. I thought it would be great to do this for Cell Biology journals too.

I thought I’d share this piece of analysis looking at productivity of people in the lab. Here, productivity means publishing papers. This is unfortunate since some people in my lab have made some great contributions to other peoples’ projects or have generally got something going, but these haven’t necessarily transferred into print. Also, the projects people have been involved in have varied in toughness.