18  Drop-in Hours

Mission & Goals
  • Mission of the drop-in hours / help desk: to provide a drop-in resource pathway to engagement with the ALVSCE researcher community.

  • Scope: to augment other campus resources including ResBaz, Library, UITS, Stats Consulting, Carpentries, and others.

  • Focusing on research with novel challenges related to computational and data-intensive (CDI) research

18.1 Individual office hours

  • Set up appointments for 1-2 hours each week
    • Google Calendar: create a new event in calendar view, selecting “Appointment slots” option instead of “Event”. These can be recurring.
    • YouCanBook.me
  • Add link to your People page

18.2 Announcement

Do you have hurdles in your research related to data and analysis? Would one-on-one conversations with a data scientist help you solve these specific problems?

What: CCT Data Science Drop-In Hours is a way for University of Arizona’s College of Agriculture and Life Sciences (CALS) researchers, such as you, to get focused and specific help with data science problems in your research.

Why: To facilitate and accelerate data-intensive research in CALS

When: Every Tuesday, 8-10am (MST)

Where: Our gather.town space

We collaborate closely with other groups on campus to get you the help you need, when you need it.

For more information email Dr. Kristina Riemer, Director of Data Science at CALS: kristinariemer@arizona.edu

Visit the CCT Data Science website to read about our mission, vision, and services

18.2.1 Communications Channels

Weekly reminders

We post weekly reminders of our Drop-In Hours the day before they’re held. These reminders contain the following information:

  1. Where we meet (see above)

  2. The time(s) of Office Hours

  3. The overall purpose of Office Hours

  4. The main focus of the upcoming Office Hours (such as R, Docker, …) but we’re not restricted to only that topic

  5. An invitation to come and participate, or just hang out with us

List of channels weekly reminders are posted on:

  • Mastodon
  • UA Data Science Slack

19 References / further reading