Introducing Office Hours

Our Research & Instruction team is offering a new way to get quick answers to your questions via Zoom. Starting in June you can drop in to Research Office Hours every Monday or Thursday. We are also continuing our Bioinformatics Office Hours on Wednesdays.

Research Office Hours

Have a quick question? Drop by Lane Library’s  biweekly office hours to ask a librarian about:

  • Navigating the scholarly literature and creating literature reviews.
  • Managing your citations and bibliographies.
  • Managing your ORCID iD.
  • Assessing research impact of you and/or your team
  • Managing and sharing data.

Register with your Stanford-affiliated email to receive the Zoom link. You can join at any time during the hour. This service is only available for the Stanford Medicine community.

Available on Zoom every Monday from 1:00 PM- 2:00 PM and Thursday from 3:00 PM – 4:00 PM PST.

For more involved questions, please reach out to your liaison librarian to set up a consultation.


Bioinformatics Office Hours

Lane Library’s Bioinformatics Office Hours are a resource for Stanford medical students and researchers to get help using bash, R, Python, Git, or Sherlock (Stanford’s research computing cluster) for academic purposes. Library staff with bioinformatics backgrounds will be available every week to assist with a range of issues.

Available on Zoom every Wednesday from 1:00 PM -2:00 PM PST. To make an appointment, please use the form below. You can also drop-in and sign in using your Stanford credentials.

We are happy to assist you by:

  • Recommending tools to accomplish specific tasks
  • Providing information and resources about tools that you are unfamiliar with
  • Walking through specific and isolated bugs in your code (we won’t debug your whole program)
  • Providing suggestions for improving your code

While we are excited to help you, we will not:

  • Answer questions about topics outside the scope of applying bash, R, Python, Git, or Sherlock for academic/research purposes
  • Teach you how to program or use applications/tools (like RStudio)
  • Consult on which statistics or machine learning models to apply to your data
  • Debug large amounts of broken code

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