Students and participants interested in learning, updating or improving their scientific writing skills are welcome to enroll. Critical analysis of peer-reviewed research publications and National Institutes of Health (NIH) and National Science Foundation (NSF) grant proposals are a plus.

Dates: April 8- May 20

Format: ZOOM

Time: Thursdays 3-4 30 pm ET.

Credits: G (1-0-5)

Instructor: Dr. Pratik Shah. Ph.D. is a Principal Investigator and Principal Research Scientist at The MIT Media Lab. Pratik's research lab creates real-world evaluation of emerging technologies in genomic engineering, machine learning, and medicine to improve health and diagnose and cure diseases. Recent work from his lab has been published in Nature Digital Medicine, Cell press, Journal of American Medical Association, and workshops at Proceedings of National Academies of Science Engineering and Medicine. Pratik serves on the grant reviewer board and health informatics study sections of non-profit foundations and the Center for Scientific Review at National Institutes of Health. He is also a peer reviewer and associate editor for leading data science, emerging technology and basic science journals such as Nature Medicine. Pratik has BS, MS, and Ph.D. degrees in biological sciences and completed fellowship training at Massachusetts General Hospital, the Broad Institute of MIT and Harvard, and Harvard Medical School.

Enrollment: Please fill out this form with your academic and professional background, prior experience and interest and career goals to request enrollment.

Course Description: Designed for grant proposal and research publication student writers, this course presents a general overview as well as the level of detail for creating well-written scientific documents. Each component of the grant and research publication writing process will be addressed, including: documenting your laboratory research data and observations in LaTeX; writing measurable objectives; making high quality figures in Python and MatLab. Developing testable hypotheses and an empiric research plan to test them.

Components of grant proposal application

• Strategies for developing proposal outline: Abstract, Summary, Significance, Innovation, Approach and Bibliography

• Detailed research plan with power of statistical analyses and rigor of prior research

• The grant review process at funding agencies: Scientific program officer, Study sections and Council reviews.

• How to find, understand and navigate NIH, NSF and other government and non-profit funding announcements and eligibility criteria

• Biosketch vs. Curriculum vitae vs. Resume

• Demystification of codes R01, R21, K99, R00, T32, etc.

• How to read and understand grant guidelines and requests for proposals/applications (RFP/RFA) to match your research interests and projects

• Impact scores and percentiles and resubmissions

Course Goals: Prepare a complete grant proposal (computation + any field you are working in) to an agency of choice with the instructor. This includes a 1 page significance and specific aims section, 5 page research plan and a minimum 2 year $80,000 budget with multiple categories. Peer review (with the instructor) several examples of publicly posted prototype grant proposals. Participate in a mock online study section as reviewers of proposals from other students in the course and assign scores and prepare summary statements. Learn transferable skills for writing high-quality research papers.

Topics include:

- Hypothesis generation and testing

- Literature review for establishing significance, innovation and rigor of prior research

- Scientific writing

- Research design and data analyses

- Scientific peer-review process and response to reviewers

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Fields

Biological: Recombinant DNA mutation & complementation, cellular pathways, next-generation sequencing, mass spectrometry for proteomics, high performance liquid chromatography for metabolomics, sequence alignments, BLAST searches, KEGG pathway analyses, homology mapping

Computational and engineering: Convolutional neural networks, Bayesian inference, deep learning, Hidden Markov Models, auto-encoders, recurrent neural networks, Reinforcement learning, Markov Decision Process, image processing, bioinformatics, clustering and classification, kmeans, Human-computer interactions, Sensors, R, Perl

Clinical: Biomarker assays and interpretations, surrogate endpoints, interventions and treatments, adverse events, randomized control trials

Statistical: p-value, students’ t-test, Pearson’s correlation coefficient, Principal Component Analysis, causal inference, Mann-Whitney U Test

Art: Painting, architecture, sculpture, design, literature, music, performing arts + technology projects and funding

Learning outcomes

• Identifying specific topics from your research or course material and lectures to design a strategy for implementation of scientific writing and statistical methods, algorithms.

• Test, validate, and interpret results to be included in grant proposals or scientific publication.

Reading tasks

Recent peer-reviewed scientific publications and example grant proposals prior to their discussion in class.

Final project: One selected topic can be used by teams of students to develop into a final project presentation proposing strategy for grant proposal writing. In class demonstrations will provide training and examples for final project proposal prototypes. A brief two-page NIH style proposal (example R03 proposal format here), class presentations and software code for successful submission to the course GitHub site at the end of the term are encouraged.

Notes

1. Please make notes during class relevant to each topic that you wish to discuss more. Details on accessing and editing class notes with LaTeX, will be shared

2. Opportunity to engage with invited speakers and program managers and center directors at the forefront of funding agencies (NIH, NSF, DARPA etc.), technology (IBM, Google, Apple etc.) and non-profit foundations.

Textbook and research articles reading

There is no required textbook for the course. All required articles and readings will be available for download from the MIT CANVAS site.

Suggested resources:

Winning Grants Step by Step (3rd ed.) by Mim Carlson and Tori O‟Neal McElrath. Jossey Bass (2008)
The Artist’s Guide to Grant Writing by Gigi Rosenberg. Watson-Guptill (2010)
The Scientist's Guide to Writing: How to Write More Easily and Effectively throughout Your Scientific Career. Stephen B. Heard. (2017)
Writing Science: How to Write Papers that get Cited and Proposals that get Funded. OUP USA. Joshua Schimel (2012)