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2020-03-13-nih-get-you-funded.md (36897B)

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 title: "Notes on the book 'How the NIH Can Help You Get Funded'"
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 image: https://source.unsplash.com/OfMq2hIbWMQ
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   - "physician-scientist"
   - "academic writing"
   - funding
   - "career development"
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 # How the NIH Can Help You Get Funded
 
 [Mike Becich](https://www.dbmi.pitt.edu/person/michael-j-becich-md-phd) is a wonderful researcher and entrepreneur, and chairman of the Department of Biomedical Informatics at the University of Pittsburgh. I met him on the residency and fellowship interview trail, and, among other things, we talked about grant strategy and how to build a career doing research in biomedical machine learning/informatics/data science/etc. He was kind enough to give me a copy of the book <ins>How the NIH Can Help You Get Funded</ins>, which is exactly what it sounds like: a how-to and strategy book for academic research scientists, most of whom depend on funding from the National Institutes of Health to support their work and salaries. The book is a publication of [Oxford University Press](https://global.oup.com/academic/product/how-the-nih-can-help-you-get-funded-9780199989645?cc=us&lang=en&) and also available from [Amazon](https://www.amazon.com/How-NIH-Can-Help-Funded/dp/0199989648).
 
 These are my notes from and synthesis of this book. The synthesis was made by going through my notes and extracting things I thought were key and may be useful for quick review; the full notes I took as I was reading are copied below that.
 
 If you notice any mistakes, misconceptions, or flatout wrongheadedness, please shoot me an email. Also, though the general outline of how the NIH works is fairly stable, specifics change from year to year and this book, as with almost every book of its genre, was outdated upon publication. It will be at least a few years before I apply for funding, at which point an even larger number of specific details will be out of date, so what I am looking for is general strategy and key history.
 
 # Synthesis
 This book is a bit hard to summarize, as it has a mix of very practical and technical information, with general guidance, strategy, and advice (to use the parlance du jour, strategy and tactics). I figured this would be the case going in, hence this public store of notes that I can quickly review.
 
 Some general things:
 - The NIH is about science, but is run by, and full of, people. These people have personalities, hopes, fears, egos, soft spots, families, and digestive systems. If you keep this front of mind as you communicate your science, you'll have a Good Time.
 - Of all the people at the NIH, your main points of contact are the Program Officers (POs). It is a completely sane and compassionate approach to try and make their lives easier. Also realize that their job is to build a portfolio of cool and important work for their organization, and that they often had prior lives as PIs themselves. So do your homework, know what stresses and timelines they are under, and do your best with your science and your personal communication to develop a relationship of mutual respect and care.
 - Also think of your proposal itself as building a relationship with all its readers - you want to be nice to them (make it easy to read and rate - many specific tips and tricks herein), and to make it exciting and fun to imagine what success will look like for your project, the team, the institution that is considering sponsoring it, the patients who will benefit, and the world at large. (Don't resort to corporate technobabble BS, as this makes it less fun/very irritating to read).
 - The NIH is also a government organization, and is therefore subject to all manner of red tape, budget constraints (and cuts), fickle bureaucrats, and delays. If you know the timelines and possibilities going in, you'll have a Better Time than You Would Otherwise.
 - The R01 Holy Grail makes more sense to me now, since going through this book while also talking to friends who are early in their careers as physcian-scientists. The grant is attached to you, not your institution. You have a large amount of freedom to do what you want with it (with the exception of a small number of funding mechanisms, the NIH pretty much gives you money and gets out of your way - you have to show productivity, but exactly how you accomplish that is your choice). It can be renewed, and this renewal is a bit easier if you're an early investigator. If you want to be a physician-scientist, having funding will let you lighten up on the patient load. You can also hire brilliant people, and having the R01 is a draw for top talent.
 - R01 is a pretty good way to fill 2/3 of the triumvirate of what makes careers happy: competence/expertise (you are by definition an expert in your field), autonomy, and relatedness (you bring this - a sense of connection to the people you work with and for, and the cause).
 - The "publish or perish" thing makes more sense to me now as well. Tenure seems to be going away at many institutions, or is veryveryvery difficult to get, so a researcher's life is a never-ending cycle of applying for grants that have a government-mandated average length of 4 years. If you want a renewal, or a successful new grant, you have to show productivity. Though there is some understanding that certain kinds of work take a long time to bear fruit, you basically have to have first and/or last author pubs in decent-to-good journals rolling out with some consistency to remain competitive. This book helped me own my identity as a physician-scientist (in training), and all the trappings that go with it, including the high likelihood that I will live most of my career on these funding cycles.
 - Speaking of owning my career, the book also makes me curious about non-NIH funding mechanisms, e.g. through other govt orgs or third parties. This book mentions some, including the Dept of Defense and National Science Foundation. If anyone has suggestions for places I should check out that may be conducive to a research career in bioinformatics/data science/machine learning for medicine, particularly blood cancers, with major interests in healthcare disparities and community/population health, shoot me an email. I also want to see patients, so certain industry careers may be out - but I know that industry is also a complex and varied space, with as many shapes of careers as there are individuals, so would be open to hearing about those avenues as well.
 - As I finished this book I was also overwhelmed with gratitude to my  undergraduate alma mater, Brigham Young University, for giving me singular opportunities for research across the institution that led me to a career in research, to my medical school alma mater, Cleveland Clinic Lerner College of Medicine, for setting me up to be successful, by giving me time and headspace to think about which skills I wanted to develop, plenty of support to get a head start on them, and mentors in every conceivable area. Next, I am so, so excited and grateful for Vanderbilt University Medical Center and the Harrison Society for welcoming me into their fold. The clinical training will be fantastic, and the way they set up their physician-scientists for success (== R01 and other funding mechanisms) occupies a truly rarefied place among academic institutions in the United States. I'll admit I'm scared of the publish-or-perish thing still, but knowing there is a staggering level of support and deep precedence for success helps assuage the imposter syndrome and insecurity.
 - Overall, I'm glad I read this book, and am grateful to Becich and other mentors and friends in science and medicine for getting me to think about the arc of my career and how to increase the likelihood of success. The timing was great (Spring 2020), as I will be entirely engaged in the practice of clinical medicine for the next 2-4 years before jumping back into research. I will likely revisit this book or its successor, as well as these notes, as I scan the horizon for opportunity and learn, as I practice clinical medicine, where help is needed most.
 
 # Notes
 
 ## National Institutes of Health
 This chapter provides a background on the NIH.
 - NIH = 27 Institutes and Centers (ICs)
 - began in 1937 with the National Cancer Institute (NCI), then in 1938 cornerstone laid for NIH campus
 - part of executive branch, but Congress authorizes and appropriates funding
 - budget grew ~2x from 1998-2003, but then failed to keep pace with inflation or dropped. This is problematic because ICs would like to plan for multiple years ahead and provide stable commitments to researchers doing important but longer-than-a-year research. The payline (the score and percentile at which grants are funded) is subject to variations in appropriations.
 - budget allocation (rough percents): 80% extramural funding, 11% intramural funding, 5% salaries/admin, 2.5% formal training programs (80+11+5+2.5 = 98.5%)
 - Program Officers (POs) are extramural staff associated with each I/C. The authors make the point first here, and repeatedly throughout the text, that *applicants should contact their PO at each stage of the application, review, and award process*.
 - Office of the Director: coolest thing here is the Office of Strategic Coordination's Common Fund Programs, including the NIH Director awards. These fund cross-cutting, high-risk, possibly high-yield projects (see [here](https://commonfund.nih.gov/highlights) for current highlights), touting the program as the "venture capital" space within the NIH. Some of the data science-y stuff funded by the NIH was/is via the Common Fund, e.g. development of a [data commons](https://grants.nih.gov/grants/guide/notice-files/NOT-RM-17-031.html). The language is purposefully obtuse, matching the tone of Silicon Valley VC, as I think it has to be when they are looking for (again, purposefully) underspecified opportunity to fund interesting work that doesn't quite fall under any IC purview but has potential to be generally useful.
 - A "grant" is different than a loan, contract, or cooperative agreement. The idea is for the IC to determine if your proposed project fits within their strategic plan to have "significant and lasting impact on the field and public health," and then they get out of the way to let you carry out your plan as you see fit, and evaluate your productivity and promise as you apply for renewals, etc.
     - This jives with what I learned early on in anthropological/ethnomusicological research: it is widely understood that everything might (will) change when you get in the field, but funding and approval bodies need to see evidence of innovation, possible impact, and careful thought before they hand out money and rubber stamps. You then have freedom to do your work as best you know how, and react to unexpected realities as you see fit.
     - Contracts are for specific needs (e.g. developing an animal model, operating a facility); cooperative agreements are similar to grants but involve substantial "scientific or programmatic" involvement from the federal government (i.e. they are less independent).
 - At this point, the rest of the book is summarized and a timeline is given. The timeline is a concordance of the timelines of Congress, the ICs, the principle investigator (PI), and the standard funding opportunity announcements.
 
 ## Institutes and Centers
 - More on the program officer (PO).
     - POs often were investigators in the field, and have a deep interest in its success.
     - They see the PIs as "their investigators"
     - It's ok and encouraged to contact POs in the several ICs that might cover your work, and work with them to determine strategy at every key point along the way
     - ICs are differents, and POs are different, so don't expect your friend's experience to necessarily match your own
 - Advisory Councils
     - main job is to determine how well the proposed research fits with the mission of the IC, and if the scientific review was adequate. They don't say much about the scientific merit of a project, as that is the job of the review.
     - also review concepts for future initiatives, and *cleared concepts* are the fodder for Requests for Applications - keep the pulse on cleared concepts, and you can get a head start on preparing your applications
 - ICs
     - a list of each IC and key data is presented. Some notes on ideas I found interesting:
         - Love this sentence on clinical vs. basic science research "The NHLBI believes all the clinical research should have some return on investment, unlike basic science research, which should be designed to explore, not build, and in which return decades later is not uncommon." For one recent example, the Shapley value was developed in the 1950s-60s, but was too computationally expensive for all but rather restricted use, and now undergirds some of the most important paradigms in explainable machine learning systems (SHAP was published in 2017, IIRC). I think we have an uneasy relationship with this concept, that basic and theoretical researchers should essentially be encouraged to play, and play their best, knowing full well that much of the research will never be particularly useful, that some of what will be useful will not be useful for decades, and that it is very, very difficult to determine beforehand which is which. The embrace of intellectual freedom and creativity always butts up against funding constraints, and people want ROI.
         - NCI R01 success rates financial year 2012: 12.5% for new, 29.4% for renewals, 11.8% for supplement.
         - NHLBI R01 success rates in financial year 2012: 13% for new, 25.4% for renewal, 33.3% for supplement.
 
 ## Center for Scientific Review and the Peer Review Process
 - "Scientific review groups" is the official name for the colloquially known "study sections"
 - Some alphabet soup decoded:
     - P - program project/center
     - U - cooperative agreement
     - T - training
     - K - career development
     - N - contracts
     - F - fellowship
 - How to get your application rejected quickly:
     - Don't have a cover letter from the IC approving the submission if your budget exceeds $500,000 direct costs in any 1 year
     - Don't comply with the formatting restrictions
     - Have the wrong budget type (modular vs. detailed, depending if >/< $250k)
     - Ignore specific requirements for your application type
     - Fail to pass the sniff test for A2 applications (revision of an unfunded A1) - apparently they use a combination of NLP and a manual review process to find out if you're just swinging away without changing bats
 - A0/A1/A2
     - A0 - first/new application
     - A1 - first revised/amended application
     - A2 - second revised application, no longer allowed (max attempts = A1)
     - To convert A2 to A0:
         - 2/3 of aims must be new
         - research plan must be substantively changed
         - OR submit under new mechanism OR in response to an RFA (request for applications) if it happens to match your science
 - Application ID number decoded:
     - Ex: 1R01 CA1234567-01 A1; 5R01 GM000091-62
     - First example: New (type I) R01, in the NCI (CA), number 1234567, first year of funding (01), first amendment (A1)
     - Type 1: new; Type 2: competing renewal; Type 3: competitive supplement; Type 5: noncompetitive supplement
     - Second example: Noncompeting renewal (type 5) RO1, in the National Institute of General Medical Sciences (NIGMS, abbrev GM here), number 91, in the 62nd year of funding. Cool example - was for the "Structure and Function of Enzymes---Role of Metals." Zinc fingers ftw?!
 - Study section assignment tip: almost always go with the one they give you, not necessarily the one you wanted. Their interest is in getting you a good review, and know the ins and outs likely better than the individual PI.
 - Check the [CSR](https://public.csr.nih.gov/StudySections) website and [RePORTER](https://projectreporter.nih.gov/) to find out which study groups have which interests.
     - The CSR website gives more granular details on the study section topics, RePORTER gives results and other data, so CSR -> RePORTER is the usual workflow (though I could imagine reasons for RePORTER -> CSR)
 - Also ask your program officer, mentors, and colleagues which study sections you should target.
 - **Think about your study sections at the outset**
     - If your audience is specific, you can save space and write directly to their knowledge and interests.
     - This extends all the way to: know their names and what they've published, and create your steel-man arguments (and a little [Carnegie-esque](https://creativesamba.substack.com/p/feeding-the-baby-rabbits) baby rabbit/Edgar Thomson Steel Works action) by citing their work.
     - Also make sure your work gets in front of them and stuck in their heads with some "presuasion" techniques: present at the conferences they are at, invite them to your seminar series, get their take on what is important in their field now and going forward. Just don't bring up any specific proposals (i.e. don't break the rules, but feel free to water the field).
 - **Hours spent in different parts of the review process** (this is huge):
     - You spent: 100s of hours
     - Primary reviewer spends: several hours
     - Secondary reviewer: less than primary reviewer
     - Unassigned panel members: zero hours to a few minutes (maybe read abstract and specific aims)
     - Panel discussion: 10-15 minutes
     - SO be kind, it's an elevator pitch not a thesis. ?Read [Brief](https://thebrieflab.com/book_brief/) again?
 
 ## Getting at Mechanism
 Aha! This is part of why I picked this book. I've always been curious about the specific mxns the NIH uses, and when to use which.
 - R01
     - ~1/2 of extramural NIH funding is R01
     - up to 5y length - average length mandated to be 4y, and new and early stage applicants more likely to get 5y award
         - when you are no longer early or new, then proposing work with shorter time frames is a bit sexier and more likely to get funded
     - renewable
     - favors early state investigators (reviewed in their own group, also can reapply within the same funding cycle)
 - R21
     - "starter" grant
     - success rate actually lower than R01, d/t large number of increased applications
     - NIH recommends that the R21 *not* be used as an entry for junior investigators, because:
         - cannot be renewed
         - generally limited to ~$275k over 2y
         - no payline break for new or early applicants
     - kinda seems like one to avoid, overall - what are the upsides? Not clear from this reading.
 - R03
     - "Small research grant"
     - $50k per year for 2y, nonrenewable
     - time limited, focused - e.g. collect pilot data, perform data analysis, develop assay or model
     - sometimes limited to K awardees and new investigators
     - sometimes used to respond to reviewer concerns for R21s, so it would be a (long path) R21 application -> R03 application -> R03 award -> R01 application and award
 - R13
     - Conference awards, supports specific costs, usually less than $20k (???)
     - Needs letter from IC R13 PO
     - 60% success rate
 -  R15
     - Academic Research Enhancement Award (AREA)
     - Limited to academic components w/in institutions with < $6m in funding from >=4 of the past 7y.
     - $300k direct costs over 3y, renewable
     - Seems like a mini-R01 for early researchers at underfunded/newer programs
 - R33
     - 4 offered per year.
     - Phase II for successful R21.
     - Often solicited by funding opportunity announcements and ICs.
     - Milestone driven (must be quantifiable) w/ Gantt chart or similar timeline
 - R34
     - Clinical Trial Planning Grant (phase III trials)
     - E.g. data collection tools, manuals, recruitment strategies, data sharing and multiple IRB submissions if multisite, also pilot studies
 
 Small business grants
 - Small Business Innovation Research (SBIR) R41 and R42
     - >=40% of the work performed by small business, >=30% by a nonprofit research institution, rest either split or include a third party
 - Small business Technology Transfer (STTR), R43
     - Primary PI primarily employed by small business. >=67% of the work performed by small business if phase I, >=50% if phase II.
 
 P grants
 - Program projects (P01) or Center (P20, P30, P50, P60)
 - Themes that would benefit from collaboration, and would be unlikely to be successful otherwise
 - Emphasis on supporting new and nontraditional researchers
 - Could be basic or have a clinical component
 - Need a large number of R01 and other awards to justify, and if you are interested in starting one of these work closely with the PO
 
 K awards
 - Career Development Awards
 - Setup:
     - Award is both for the work itself (e.g. specific aims) and to prepare the investigator for R01, so write it this way - emphasize how the training and mentorship will not only make the project more likely to succeed, but will also set you up for a successful career in your field
     - This sounds a lot like the ASH awards, with similar strategies:
         - make sure your primary advisor is going to be an actual advisor, not just a big name on the proposal (the reviewers will probably know if this is the case, and you would be seemingly paradoxically less likely to get the award)
         - go ahead and have that big name as a secondary advisor/part of an advisory panel, and use their expertise to get a broad view of the field and how to succeed in it (if they have time for specific mentoring, gravy)
         - write in the first person, about *your* career goals and accomplishments thus far
         - start the Candidate section with your long-term goals, to frame the conversation and show the reviewers that you are going somewhere
         - acknowledge your gaps and show how the K award will help you fill them - if you have no major gaps, go for the R01, not the K
         - have great letters from your mentor and advisory team, that you participate in writing, showing a personalized recommendation and plan, and the qualifications of the mentor(s). Make sure the details in the letters all jive.
     - Have some first author papers, to the extent possible (reviewers are understanding if you are a clinical scientist and stuff takes forever to get to the manuscript phase)
     - A collection of poster presentations that never -> manuscript is a bad look
     - Review papers are +/- helpful, depends on the journal, but might be useful to show commitment to and understanding of the field
 - The grant itself:
     - Less is more, be ambitious and innovative but realistic about what can be accomplished in the time frame
 - All the Ks:
     - Different ICs use the same codes for different things, so check the K Kiosk at the NIH website.
     - But generally:
         - K08, K23, K24: applicants with clinical degrees
         - K01, K02: basic scientists
         - K01, K08, K23, K25: junior investigators
         - K02, K05, K18, K24: established investigators
         - K22, K99/R00 (kangaroo!): postdocs (also needs academic appt during award, and need to submit R01 before end of 2nd year of the 3y award). K99 while a postdoc, then R00 activated when faculty appt received
         - K12: clinical research career dev, established PI submits application to NIH then trainees compete for slots. Coupled with intensive training and resources (similar to the PSTP programs, with biostats support, grant-writing committees and extensive pre-review, etc.)
         - KL2: similar to K12, Clinical and Translational Science Award (CSTA)
 
 F and T awards
 - F: fellowship, T: training
 - Like Ks, but required to pay back 1y of support through research and teaching (not patient care).
 - Can be obtained predoctoral (e.g. MD-PhDs in training - up to 6y) or postdoctoral - up to 3y.
 
 Summary: Tons of alphabet soup. Most of us will focus on R01 and ways to get to R01 (K,F,T, maybe R21/R03). For more specific stuff, it's always changing anyway, so look it up and talk to your PO.
 
 ## Telling Your Story Well
 - Specific Aims may be the only page everyone reads, and then likely cursorily, so grab attention and cut the cruft
 - One way to cut the cruft: know your audience, give only the background that is absolutely necessary and unlikely to be known/agreed upon by all
 - The reviewer is going to be writing bullet points for the summary statement - make this easy for them, do all the work so they don't have to
 - How important, relatively, are the sections of the report?
 - Table 8.1 gives the correlation coefficients bw overall impact score and 5 criterion scores for 2010. The ones that are likely to be of most interest to me:
 
 | Institute 	| Approach 	| Significance 	| Innovation 	| Investigator 	| Environment 	| N    	|
 | -----------	 | ----------	 | --------------	 | ------------	 | --------------	 | -------------	 | ------	 |
 | NCI              | 0.80             | 0.67             | 0.59             | 0.53             | 0.45             | 5396     |
 | NHLBI            | 0.82             | 0.67             | 0.64             | 0.56             | 0.48             | 3157     |
 
 - So Approach is most correlative, then Significance. In other words, it appears that the way you are doing something is the most important thing (though note that every part of the score is indeed part of the score).
 - On specific aims:
     - write it first, and rewrite it often
     - Should address:
         - why the work is important
         - why are you the one to solve it
         - what problems you will solve
         - how you will do so
         - what the impact will be
     - Send a bare-bones version to the PO and colleagues, and take note of the questions they ask
     - Narrative order (pretty standard): 1. significance of problem, +/- prior contributions of author if available, 2. your take on the problem, lead the reviewer to your hypothesis and approach by integrating prior work and prelim data, 3. hypothesis and overall approach
     - Picture/diagram/table: can be very useful, but don't just repeat the text, and don't make it harder to understand than just reading the proposal.
     - One problem with overambitious aims: the reviewers will know that you probably have no idea how long it takes to actually [establish an animal model, recruit patients, build the software, etc.] and you are seen as not having sufficient experience
     - Aims complementary but not conditional
     - Generate useful data whether or not your hypothesis is confirmed
     - **check out the sample R01s from the NIAID** - the link in the book is old, but here's a PDF of the Wahlby grant applicaiton they mention (Image analysis for C elegans) - [Wahlby](https://emergencymed.arizona.edu/sites/emergencymed.arizona.edu/files/sample_nih_proposal_4.pdf), cool for me bc it's fairly ML/AI, and here's the Ratner application, notable for using a schema to communicate the experimental flow - [Ratner](https://www.niaid.nih.gov/sites/default/files/ratnerfull.pdf). Because these were kind of hard to find, I put copies at the following links:
     - [Ratner PDF](/assets/pdf/ratnerNIHproposal.pdf)
     - [Wahlby PDF](/assets/pdf/wahlbyNIHproposal.pdf)
     - ![pic](/images/ratneraims.png)
     - Can also conclude with a paragraph "Overall Impact" summarizing what you'd like the reviewer to use as the take-home message in their presentation. "..what will be possible after your research has been completed that is not possible or known now."
 
 - On approach:
     - modular approach based on aims
         - briefly restate rationale and hypothesis, integrate relevant prelim data, summarize design and individual experiments, conclude with analysis, interpretation of results, potential problems, alternative approaches.
         - Consider an illustration
     - key methodology, mentioned by name as needed, but avoid minutiae (it seems that knowing what is and isn't significant is another sign of experience and thoughtfulness).
     - cite pubs, especially if you wrote them, demonstrating feasability
     - have good stats - consult with your statistician early and often, and have them review before the study section sees your proposal
     - indicate how you will interpret the data, especially if they are surprising, and what you will do if the data ends up more fuzzy than expected. I wonder if it would be useful to do what Sekeres recommends and mock up the tables and figures beforehand, to make sure you're collecting the right stuff and analyzing it the right way? (especially if you use the Rmd approach that builds on Sekeres', of actually writing the code that makes the figs, and populating them with mock data, to be filled in as the real stuff comes in)
     - Consider the proposed work as part of a 10y plan - for you, and for the reviewers (given that renewing R01 is a thing you will pursue and they will encourage). It's not a single project, but a piece of a career.
 
 - On significance:
     - Significance assumes your work was successful - will the field substantively change as a result? Why does your project deserve funding?
     - consider splitting into sections: Importance of the Problem, Knowledge to Be Gained/Impact on the Field
     - know your reviewers, and freely cite their papers as appropriate - they probably think their own work is important and impactful, and you want them nodding along in agreement, if not pounding the table with "Amen!"
 
 - On innovation:
     - why is your work different and better than the current approaches?
     - methods do not need to be innovative, if their application is likely to yield innovative findings
     - consider splitting into sections: Technical Innovation, Conceptual Innovation
     - another good place for a bullet point, feed the reviewer their line at the meeting
 
 - On the introduction:
     - If it's an A1, first page will be response/rebuttal.
         - Remember the reviewer is always right - tell your friends your visceral reaction, write it down, then shred it, rinse and repeat a few times until you've cooled off.
         - Remember the reviewers that see your rebuttal may be different than the initial reviewers
         - Take on the big issues, leave the minor stuff - focus on the Resume and Summary of Discussion (individual critiques may have been resolved in the discussion and not revised in the written doc)
         - "Sometimes your harshest critics can become your biggest fans on resubmission" - and how. I've experienced this repeatedly.
         - Quote verbatim
         - Use bullets and indentations to delineate comments and responses
         - *brief* appreciative acknowledgement, then get to the meat (obsequiousness is ugly)
         - make sure you are addressing the actual concerns, run it by your PO and friends
 
 - Other sections
     - Protection of Research Subjects is another area in which your goal is to comfort the reviewers, assure them you know what you're doing (i.e. a great PoRS is unlikely to get you a top score, but a bad one is a red flag).
         -   included in this is your sample size calculation - if not done, or done poorly, will reflect poorly. Biostatisticians are your friends, and will be on the review committees.
     - Make each biosketch support the overall narrative. Don't copypasta. Seems that the PI (me) should be willing to write/rewrite others' biosketches for jiviness, and send for approval (a la letters of support)
     - Budgets either modular (module = $25k) or detailed. Detailed if project >$250k, involves foreign institutions, or is one of a few specific mxns that always require it
         - a too-low budget is also often a sign that you don't know what you're doing
 
 - On writing in general
     - Use language from the criteria in your section subheadings to make it easy for your reviewers - imagine they have a list of checkboxes they are going through. It's much better to not be creative here, so they can find what they are looking for
     - Think pagewise: what 2-3 points should no one miss on this page? Consider (sparing) use of bold, italic, etc., but favor white space and emphasis by subtraction.
     - q: which fonts *does* the NIH allow?
     - page 123: nice list of how to structure writing for max impact (i.e. use psychological principles: make the first word the subject if possible, put the impactful thing at the end of the sentence, build schema through introductory and summary sentences, etc.)
     - writing in first person is often less wordy and creates more excitement, buy-in, so use it! (I know there's some difference of opinion on this one, but I'm into bringing personality back to academic and scientific writing, so count me in)
     - omit needless words. Use modifiers if they have meaning (e.g. "weakly fluorescent").
     - Don't say your work is "innovative." Show it. OK to use the word "novel" in the strictly technical sense, but even then use sparingly.
 
 ## Getting by with a Little Help from Your Friends
 - Homework for talking to your PO: NIH Guide, target IC website, RePORTER. Remember they are not only a go-between, but an advocate.
 - Know when your PO truly can't tell you anything - wait for your summary statement (not just percentile/score) before asking for revision advice, then notice of award
 - write so that an educated friend could get through your narrative in <1h, ideally less, certainly not more
 - might be nice to go through all these notes, quick runthrough of the book again, to pull out all the PO-related tips
 
 ## Before and after Your Study Section Meets
 - Cleared Concepts are available either on dedicated websites or in the Council Minutes (might be a good target for a webscrape - are these minutes in plaintext or PDF?)
 
 ## Is the Check in the Mail?
 - Proportion of applications discussed: NCI 55.6% of grant applications, 46.6% career development applications. NHLBI 57.6% grants, 65.9% career dev. Also, these two are the big dogs in terms of pure number of grants reviewed - the Appendix in this book has nice summaries of all the ICs, would be worth looking into other sources for funding for the type of research I'm into: data science, healthcare disparities, etc.
 - PO is the one who advocates for select pay and pay by exception (see, not just a go-between!)
 - It's ok to let your PO know if you have tenure evaluation or some other time-sensitive career event coming up so they put you on speed dial, but know that there are tons of other PIs also asking for that special treatment
 - Early stage investigators can resubmit 6wk after receipt of summary statement, but make sure it's actually a good idea to do so (how much stronger would the A1 be if you had more prelim data, etc.?)
 - It's a Good Idea to start revising your A0 immediately after you turn it in. It will either help you advance scientifically (the research itself or drafting manuscripts, abstracts) or get you a head start on your A1, or both.
 
 ## The Check is Not in the Mail...
 - A1 must be w/in 37mo of A0, but this is too much time. Either the field will have advanced to the point that you need a new A0 to be current, or the field is stagnant and funding is unlikely (though I would submit that there's a slim possibility that the time for the first A0 was a little too early, but you should be reworking it during that time anyway and reevaluating)
 - on long term strategy:
     - see if your work might be of interest to multiple ICs. Example they give is HPV in the context of head and neck cancer - could involve NCI, National Institute of Dental and Craniofacial Research (NIDCR), National Institute of Allergy and Infectious Diseases (NIAID)
     - remember the NIH isn't the only player in the game. DoD, NSF, Agency for Healthcare Research and Quality, etc., etc.
 
 ## The Check is in the Mail, but...
 -  Change of Institution - awards are typically tied to you, not your institution, but there is red tape and negotiation (and tact!) necessary when making a move
 - Carryover of funds: if <25% of annual budget, no approval or explanation needed
 - No Cost Extension: usually easy to get, no application, lets you carry funds in the year after the award period, can extend to 2y with approval of PO.
 
 ## Appendix
 - some interesting organizations to check out, tailored to my interests (webpages in the book, links may be added here at a future date):
     - NCI - Cancer Control and Population Sciences; Cancer Treatment and Diagnosis
     - NHLBI - Blood Diseases and Resources
     - NIEHS - Risk and Integrated Sciences; Susceptibility and Population Health
     - NIGMS - Biomedical Technology, Bioinformatics, and Computational Biology
     - National Institute of Minority Health and Healthcare Disparities
     - NLM might be fun for NLP work, web scraping stuff, etc.
 - A related to-do: I know the researchers I follow that have R01 funding, but haven't looked deeper. Which orgs are funding the cool data science and crossover stuff for my favorite PIs?