My Project applications are complete. Decided to offer some sage (old guy) advice on technical aspects of writing a CIHR grant, or any grant proposal.
The Equipment…
A good part of the job of any research scientist is writing. This is why I’m surprised to see people still working as they did in grad school, on the venerable laptop. I rarely use a laptop, the screens are too small, they force poor ergonomics, they have iffy keyboards, they are near impossible to generate figures on, they break to easily or get stolen.
I use a variable-height desk (motorized, from IKEA), a desktop computer with redundant backup, battery UPS power supply, a high quality gaming keyboard with mechanical key action -they cost a lot. The actual brand and OS is irrelevant, for reasons that will be obvious below. The monitor is a 4K screen at 39″ -it’s huge, many pixels, because we do a lot of image analysis and cell biology. Another alternative is two or three 1080p screens.
The Timing…
Scientists tend to procrastinate. I think it’s inherent to the overworked lifestyle of the scientific mind, but it is the single worst habit in science, next to the removal of the bottom half of error bars in bar graphs (it’s wrong, it’s misleading, just stop doing it).
Step one is to set a timeline of grant writing activities, with a goal of completion of the entire proposal one week before institutional deadline. This means the proposal sits unread for a week, before final read prior to CIHR submission. Waiting for some awesome preliminary data? Bad practice, and this typically leads to poor quality preliminary data. Preliminary data does not mean poor data read with rose-colored glasses, it means publication-quality figures not yet published. Many proposals suffer from the idea that poor quality data is acceptable as ‘preliminary’.
It’s critical to leave the proposal for a week and re-read it. Can’t be done if you’re in the last hours to deadline.
The Software…
Until recently, I followed the classic paradigm of MS Word/Endnote/Reference manager/some draw program. The problem with this software is that they have had a far too comfortable market share for too long, the competition is gone, and we are left with mediocrity that can often be unstable. How many times have we be stuck for 30 minutes trying to get Endnote to see a reference? Ever try to embed figures in MS Word? It’s stochastic, at best. Does Microsoft care? Nope. But, there are inherent anachronisms inherent to this software: poor third party cross-talk and instability (sometimes, the file is corrupted and just cannot be rescued), file sharing is cumbersome and poorly implemented, and you can lose hours/days of work easily.
I’ve settled on the package of: MS Powerpoint, Google Docs, and the Google docs addon: Paperpile. Last, a simple screen capture utility like Windows Snipping tool.
We’ve all had those nightmares…a power surge in your lab blows out you desktop, and on the way home you drop your laptop, two days before final deadline. This can have many versions in the nightmare dreamscape, including meteor hitting your office and an ominous black raven pecking out your laptop keyboard. Sure, it can all be fixed with time, but time has run out…
Google Docs is cloud-based in real time (MS now has this with Office), so the actual input device is irrelevant, and nothing is lost. Sure, as I write this, someone undoubtedly hacked the server and the world is in a tailspin, but the truly paranoid can backup to two cloud sources. The best parts of Google Docs are the integration of Paperpile and Document sharing.
Paperpile takes the Google Scholar engine and mates it seamlessly with Docs. For years, I would struggle with the AWFUL Endnote/RefManager search by bouncing back and forth between Pubmed, Google and the software, often having to build a citation from scratch. Tedious.
Once you install full Paperpile (just pay for it), wonderful things happen in your Browser: any Google search or Pubmed search items have a button appear beside them.
Click and it’s in your library, and references are never missed (especially by PMID).
You can format references in any way (should be Nature -less space), because of the insanely stupid publishing industry that cannot settle on a single reference format (my theory is they also secretly work for the Canada “common” (LOL) CV.
For figure mockups, I use Powerpoint, with tools for bitmap corrections (crop, brightness, contrast, etc.). All aspects of figures are dropped into one PPT file, mocked up then captured as bitmap using the snipping tool.
You can even adjust levels again within Docs. Full figures in minutes.
The figure bitmap is then pasted into Docs, set as “wrap text” with O margins. What you see is what you will get in the final PDF generated by Docs. Very reliable. Magazine style, scalable figures.
The Writers…
Most PIs sit in their luxurious ivory tower offices and write, The Great Canadian Proposal™, like some deranged hermit working on a manifesto linking mayonnaise, immigration and global climate change.
Man… it sounds all so awesome. Totally clear.
I review a lot of proposals, thousands, between CIHR, NIH, and HSC, and some are as clear as mud, because it is one writer caught in their own feedback loop of awesomeness, often empowered by a “high impact” publication that somehow validates everything for another $1M.
USE YOUR LAB. It’s a critical training tool to teach you trainees how to write in the bizarre language of science. We blather on like idiots, jumping From Acronym to Acronym (JFATA), or even better, making up our own acronyms (MUOOA), JFATA and MUOOA enough and the proposal is FUBAR. The problem is sometimes acronyms overlap in different areas -this can confuse a reader quickly.
Interestingly, we tend to write superfluously as if we are speaking aloud and trying to impress someone at a business pitch. This is wordiness. Interestingly, it leads to words like, interestingly. If you have to state one observation alone as “interesting”, your proposal is in big trouble.
The proposal at second draft should be shared to the lab. I mean the whole lab, from undergrads to PDFs. In Google Docs, this means in real time you can see who is simultaneously reading and commenting, with different color cursors and comments are a click to “resolve and go away”.
DUMB IT DOWN. It is very likely you do not have an expert reading your grant in Canada. We are a tiny country of mostly cancer researchers in biomedical science, and thanks to CIHR reforms, anyone can still be reading your proposal as a non-scientist, and scoring it (it sounds stupid when you say it aloud). Thus, it should be understandable to any undergrad working in the lab. The worst thing you can do is get a colleague in the same research field reading drafts -this is still the Feedback Loop of Awesomeness (FLOA). My lab uses some biophysics maybe three guys in Canada have ever even heard of -this gets lost fast.
Figures: no more than 8, references, no more than 100. I once saw a record-breaking proposal with >40 figures and >400 references, statements with >12 reference tags. I forget what it was about, but it should have been about obsessive compulsive disorder (OCD)12,23,34-56, 42, 187, 199-204, 206, 208, 210-14.
One easy killer comment is if they need so many data figures, why not just publish it. Thankfully, CIHR put the end to this with 10 page totals.
Lessons from the Triage pile….
If you are going to propose new methodology, make sure you know what you are doing. You are NOT going to CRISPR edit 45 genes and validate. Do not suggest FRET experiments unless you understand the caveats.
The Big killers:
The Amazing HEK293 Cell. Derived by Frank Graham at McMaster. There should be a moratorium on HEK and HeLa cells for anything other than over-expression of proteins for purification, they neither represent normal cells, nor cancer cells, definitely not neuronal cells, and they are not the route to translational studies in humans. They have shattered, hyper-variable, polyploid genomes with both two many chromosomal anomalies to list, and are never the same, even within one lab. They are far from human. There are better alternatives for any disease. See ATCC or Coriell, however, Coriell is losing support because of so much scientific disinterest, no doubt because cell biology papers in major journals still publish studies of cell biology from one transformed and immortalized cell line and call it normal.
Pharmacology overdose. Take a “specific” drug with an established EC50, apply it at 100-10,000X. One wonders if these researchers, when they get a headache, take two aspirin or just quaff the whole bottle and hope for the best. These typical studies look at live versus very dead cells, and make specific conclusions, i.e.:
We measured NFKb levels, and they were altered, therefore this model died from a defective NFKb signaling pathway (also works for almost all clinical epidemiology studies) .
I’ll just look busy for 5 years…
Descriptive aims, also known as Yadda Yadda syndrome. The vague listing of stuff to do because everyone else does this. This is Canada, do this and you will get scooped by post doc #46-12b at some institutional Death Star in the US. More importantly, neither innovative nor interesting.
If I ask for less money, It will have a better Chance…
This is a new consequence of CIHR reforms, where PIs typically funded at $40k projects are now shooting for the moon at $100K. A single tech, PDF and 2 student full CIHR project is a $240,000+ proposal. Our dollar is no longer at par, which means expendables are now 25% more. My last CIHR operating grant period spend >$30,000 in publication fees. Bad budget requests can indicate the PI does not know the real costs of the Project, nor will be able to complete. Some pencil pusher will cut your budget, but no one will increase it for you.
My Model is Best Model, because.
Models systems have utility for most diseases, they also have caveats, and not all models work for all diseases, and you cannot use a single successful model in one disease and blindly justify it across your focus. There are pathways entirely absent in many model systems relative to humans.
Some reviewers have the opinion that the very poor success rate of genetic diseases research (we’ve got lots of genes, no therapies) has to do with over-dependence on animal model systems. Mice are not humans. They are shorter.
