Annual Grant Writing Symposium

Each spring the UC Davis Health Office of Research Grants Facilitation Team brings their multiple years of experience as researchers, authors, editors, manuscript reviewers, grantees, grant reviewers, NIH scientific review officer, NIH program officer, and patent holders to offer a one-day symposium on research grant writing.

This symposium (designed for faculty, researchers and post-docs) will focus on the basics of getting started in grant writing and maintaining success. The continual challenges to personal bandwidth, changes in funding, and grant funding policy affect both new investigators starting out and the experienced investigators who serve as mentors.

Workshop goals:

  • Finding information and help when you need it
  • What needs to be managed throughout the process: pre-submission, submission, post-submission
  • Keeping up with the new policies on human subjects and clinical trials
  • Knowing the grant mechanisms for each level of development and circumstance
  • Gaining a competitive edge when writing the proposal
  • Understanding what just happened: summary statements, funding lines, and the response to reviews
  • Building your personalized approach to grant writing and funding to include how to use the information in this symposium, self-review, working with a team, working with a mentor

Presented by: UC Davis Health Grants Facilitation Team

Co-sponsored by: UC Davis Health Clinical and Translational Science Center (CTSC)

Audience: Open to all faculty and postdoctoral scholars

Our 2020 symposium was held on May 7, 2020. The date of the 2021 symposium has not been set yet.

Scientific Writing for Publication

Asked to describe what he did for a living, an academic colleague thought about it for a moment and said, “I’m a scientific writer.” Without discounting the other things health sciences faculty do, writing is surprisingly central.

In this seminar, we will discuss the whys and hows of academic publishing. Topics to be covered include:

  • Why publish
  • What editors are looking for
  • Writing tips Insights into the editorial process
  • How to get attention for your work

In addition, participants will have the opportunity to review materials submitted and reviewed by the Journal of General Internal Medicine, where the instructor was Co-Editor-in-Chief from 2009 through 2017.

Presented by:  Richard L. Kravitz, M.D., M.S.P.H., Professor of Internal Medicine, Co-Vice Chair for Research, Director, UC Center Sacramento

Audience: Open to all faculty.

The date of this workshop has passed. We hope to offer it again in 2021.

Computational Thinking for Problem Solving (online)

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling.

The course is at an introductory level with various practical assignments.

View the e-Learning course

Process Mining: Data Science in Action (online)

Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don't need to be a computer scientist to think like a computer scientist! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact.

In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program. This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through computational thinking.

The course is at an introductory level with various practical assignments.

View the e-Learning course