Update index.md

courses/methods_2023/index.md

@@ -44,6 +44,8 @@ Fluency in multiple biophysical methods is often critical for answering mechanis
 
 This is a team-based class where students work in small groups develop their own analysis of real data. Statistical aspects of rigor and reproducibility in structural biology will be emphasized throughout lectures, journal club presentations, and hands-on activities. The website for the [2017](/courses/methods_2017/),  [2018](/courses/methods_2018/), [2019](/courses/methods_2019), [2020](/courses/methods_2020) editions are available online.
 
+*Course expectations*: This course will likely be different from most other courses you have taken before. Our expectation is that you will maximize your learning by being an active participant - there is significant out of class work required to enable this. Many learning opportunities will arise if you are willing to take advantage of the hands on experience and from those taking time to show you interesting aspects of the various methods. Data analysis will not follow a linear path - our expectation is that you start to become confortable with unknowns and messy results. It is likely that you will encounter issues using the various scientific software required for the course - while we will help troubleshoot, navigating these challenges effectively is a core expectation that we want to see students build towrads during the course.
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 *Ethics*: This course is more than a training experience; data analysis is part of ongoing active research projects, the results of which will be published to the broader scientific community. The community must be able to understand our work, replicate it, and have confidence in its findings. We must therefore ensure the integrity of the information we disseminate. To do so, it is essential that students perform and document their experiments and analyses as faithfully as possible. Mistakes and oversights are normal and to be expected, but they must not be ignored, concealed, or disguised. 
 
 *Respect*: This course is built around an open research project performed in teams. Successful completion of the course objectives will require that students work together effectively, so please respect the time and effort of your classmates and instructors. Moreover, as part of the research process, we will consider and debate a variety of ideas and approaches; however, we must not allow our position on a particular idea or argument to compromise our respect for its author. We therefore expect course participants to give all instructors and students, regardless of academic or personal background, their complete professional respect; anything less will not be tolerated.