Data in the Field

Making use of Data Skills

Authors: Erin Becker BLOG

How our community uses data skills in their work

Data Carpentry learners and instructors come from a variety of backgrounds and research disciplines. Many of us, myself included, don’t think of ourselves as “computer people.” We’re here because, at some point in our career, we found ourselves doing repetitive and error-prone computational tasks by hand when someone looked over our shoulder and said “Why on earth are you doing it like that!?” This person may then have leaned over our keyboard, typed for a few minutes, and finished the work that would have taken us the rest of the day. At least in my case, this happened routinely, every time a certain person happened to walk past my work station, until it eventually became more work to try to justify my process (“But I only have to do this once”. I’d say) than to capitulate and try to learn the computer magic.

That’s not to say that learning these skills was easy or fast, or that I feel proficient in many of them even years later. But once I committed to trying (mostly to avoid those annoying conversations) I started noticing that these dull, monotonous tasks could be minimized and yes, some of them could even be fun. And of course, as it turns out, you hardly ever have to do anything only once.

Some of you may have had an efficiency advocate like I had to peer over your shoulder and pester you into doing things the “right” way. Some of you may have gotten frustrated on your own and sought out tools to make your lives easier. For others, it may have been a desire to make sure your work was reproducible, rather than simply efficient, that drew you into this realm. Regardless of what got you to first engage with computers as helpful tools rather than magic boxes, you’ve probably developed your own set of practices for making the work that you do efficient and reproducible.

We’re excited to learn about how the diverse members of our community use computational tools, particularly those in the Data Carpentry curriculum, in their daily work. The Data Carpentry blog will be running a new series titled “Data in the Field” to highlight the many ways in which our community members integrate “good enough” practices for data management and analysis into their research.

Stay tuned for posts in this series every Monday. Upcoming contributors include:
- Naupaka Zimmerman, Asst. Professor of Biology at University of San Francisco
- Damien Irving, Postdoctoral Research Fellow, CSIRO Oceans and Atmosphere
- Marian Schmidt, PhD student in Ecology and Evolutionary Biology at the University of Michigan
- Christie Bahlai, Professor of Integrative Biology at Michigan State University
- Sean Pue, Professor of Linguistics at Michigan State University

We’d love to hear from you! If you’re interested in contributing a post in this series please contact ebecker@datacarpentry.org.


Some questions to consider as you plan your post. These are simply suggestions - feel free to structure your post in a way that makes sense for you.

What is your current field of research/work? Please describe a project you’re working on in terms that a non-specialist will understand. Tell us about the types of computational tools that you need to know in order to work on this project. What do you use them for and why are they important to your research?

How did you come to learn those tools? More broadly, how did you first become exposed to these sorts of computational tools? When did you realize you needed to learn how to use these sorts of tools in order to do the work you’re interested in doing? How did you go through the process of learning what you know now? For example, did you have a very supportive advisor? Did you carve out time for self-study? Did you have a community of learners that you worked with?

What do you want to learn next? What are you currently working on learning in terms of computational tools or what do you hope to learn soon? More broadly, how do you plan on continuing to improve your skill set?

What advice do you have for new learners? What do you wish someone had told you when you first started learning how to use these sorts of computational tools in your work? Do you have any words of wisdom?

« Previous

Dialogue & Discussion