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Projects Funded Under PA-16-142

Listed below are the details of the projects funder under PA-16-142.

Advanced Training in Quantitative Skills, Research Methodology and Team Science
Principal Investigator: Myles Huge Akabas, M.D., Ph.D., Albert Einstein College of Medicine
This project is to incorporate additional training in quantitative skills, research methodology and team science into the MSTP and other predoctoral programs. The activities will include integration of new methodological content to existing workshops and courses; developing new courses, workshops, online tutorials and other resources to teach different aspects of scientific rigor and reproducibility and the tools to implement principles in practice; and organize activities and working groups that promote rigor and reproducibility and team science. The effectiveness of the activities will be assessed by using a combination of course evaluations and feedback from academic advisors and mentor pairs and collection of pre-/post-training activity surveys from students.

Development and Evaluation of a Business Strategy Skills Course for Biomedical Graduate Students
Principal Investigator: Karl Mark Ansel, Ph.D., University of California, San Francisco
This project is to develop and evaluate a business strategy skills course for biomedical graduate students. Course topics will include fundamentals of business strategy, tools for strategic analysis and strategic collaborations. Course format includes a blended classroom with interactive in-class discussions following review of online videos and podcasts. The success of the course will be assessed via mandatory course evaluations. Course evaluations will be administered via online surveys at two time-points: prior to the first workshop to provide a point of comparison to see what students learned and after completion of each weekly topic. Evaluation of students’ understanding of course concepts will be determined by asking facilitators to return assessments of the in-class discussions.

Characterization of Antibodies or Antibody Constructs
Principal Investigator: William M Atkins, Ph.D., University of Washington
This project will develop a laboratory course focused on relevant characterization of antibodies or antibody constructs, with hands-on experience. This will complement an existing lecture course on Biophysical Enzymology and Protein Therapeutics that covers many biophysical methods. The course will be 3 weeks of intensive lab time. Data collection will take place during the 3-week period and students will have an additional week to analyze data and write a report. The course will be assessed by student evaluations, the Corporate Advisory Board of the School of Pharmacy at the University of Washington, and input from the outside lecturers that give talks.

Develop Curriculum and Assessment Tools for Experimental Design, Statistical Analysis and Interpretation of Data
Principal Investigator: Richard Carthew, Ph.D., Northwestern University
This project is to develop curriculum and assessment tools for a new 10-week course for training in scientific rigor and reproducibility that will be embedded in the graduate curriculum in the Interdisciplinary Biological Sciences graduate program at Northwestern University. The course activities include experimental design and data analysis, making data structures that humans and computers can each read and process, a basic introduction to the reproducibility software package Git, and how to use GitHub scales reproducible code and analyses for individuals or large groups. The impact of the course will be assessed by testing students’ knowledge of the fundamentals of experimental design, statistical analysis and interpretation of data, at the beginning of the course in comparison with test results obtained after instruction.

Training in Computational Techniques and Application of Reproducible Research Methodologies
Principal Investigator: David J. Christini, Ph.D., Weill Medical College of Cornell University
This project proposes to expand access to a series of well-regarded small-group workshops that teach computational techniques essential for the practical and effective application of reproducible research methodologies. This will be achieved by leveraging emerging tools for online learning so that an unlimited number of students may acquire the basic concepts and mechanics independently and at their own pace. These courses will be made freely available to the public. The courses will be supplemented by shorter, more frequently held direct engagement group lab sessions where the skills learned are consolidated and put into practical use in a flipped classroom setting using content developed specific to the needs of Weill Cornell’s students’ research activities. The impact of the proposed project will be self-assessed by students of their abilities and understanding before and after each course.

New Course in Experimental Design and Data Handling, Analysis, Interpretation and Presentation
Principal Investigator: Lori L. Isom, Ph.D., University of Michigan
This project proposes to develop a credit-bearing graduate level course in practical statistics and experimental design to provide basic working knowledge and best practices that can be directly applied to trainees’ everyday laboratory research activities. The course topics will include: descriptive statistics, measures of data spread, hypothesis testing and comparisons, Student’s t-test, analysis of variance, power analysis, nonlinear regression, dose-response analysis, data transformation and normalization, and effective data presentation through graphs and tables. The impact of course will be assessed by evaluating instructional delivery, course planning and assessment of student learning.

Theoretical, Computational and Experimental Training Modules in Molecular Biophysics
Principal Investigator: Juliette T. Lecomte, Ph.D., Johns Hopkins University
This project plans to develop 10 modules for training: five theoretical and computational modules and five experimental modules. The theoretical and computational modules include instruction in the UNIX operating system, basic fitting and modeling procedures and specialized mathematical operations, data processing skills, statistics and data analysis and molecular dynamics. The experimental modules include diffraction and scattering methods, nuclear magnetic resonance spectroscopy, single molecule and super resolution techniques, solutions biophysics and analytical ultracentrifugation. Each module will be assessed by electronic and anonymous survey at the end of the module period, collecting feedback from thesis advisors and graduate board oral examination process.

Training in Theory and Application of Statistical Methods
Principal Investigator: Lynne E. Maquat, Ph.D., University of Rochester
This project is to add additional activities focused on the theory and application of statistical methods and other best practices to complement the existing training. The new training activities will include a new annual summer mini-course to train students in effective design and communication of basic research, creation of a statistical science web portal and wiki, and monthly sessions in scientific best practices. The impact of the project will be assessed by the statistical training oversight committee and anonymous feedback from students after each session.

Development of a Course in Quantitative Measurement and Analysis
Principal Investigator: Timothy J. Mitchison, Ph.D., Harvard Medical School
This project is to develop a course, Quantitative Measurement and Analysis, which will be a compulsory course of the Systems Biology Graduate Program first year students and open to all Harvard graduate students. The course will begin with a series of foundation lectures followed by two main modules. These modules will cover instruction on building an assay, characterizing its performance and obtaining reproducible and statistically significant results. The impact of the proposed curriculum will be assessed by confidential student evaluations, success of the student’s qualifying exams and evaluation by co-directors of the program.

A Novel Statistics-Based Course for Life Science and Engineering Students
Principal Investigator: Martin L. Yarmush, Ph.D., Rutgers, The State University of New Jersey
This project is to develop a novel statistics-based course specifically tailored to the needs of Life Science and Engineering students. The course activities will include: use of enquiry-based learning to strengthen understanding of statistical analysis; emphasis on practical rather than theoretical aspects of statistical analysis methods; analysis of real data generated by the student pool; teaching the importance of considering data analysis as a pre-requisite to experimental design; and training students how to use open-source, easy-to-use statistical analysis tools such as R, as well as commercially available graphing and statistics packages, such as GraphPad Prism. The evaluation plan for the course includes an anonymous short online survey by students early in the course to provide feedback to the instructors to make alterations early in the semester followed by a Likert scale course evaluation at the end.

This page last reviewed on April 24, 2017