My areas of support include:

• Programming/scripting for data gathering and analysis, especially in Python and R
• Determining how to organize your project's or group's files and code
• Planning, organizing, and documenting project data for reproducibility
• Writing data management plans for grant proposals
• Depositing and describing project data for preservation and sharing
• Finding and reusing data that others have shared
• Data visualization principles and how-to
• Licensing your deposited data for reuse
• Citing data and software usage in publications
• Data deposits in D-Scholarship, Pitt's institutional repository
• Collaborative version control for your projects using Git

I am a certified Carpentries Instructor and hold a Creative Commons Certificate.

Researchers often find themselves needing to pick up new ancillary skills to get the job done, and this includes computer coding and similar tools, especially for working with data. If you're in this situation, and lack the time to pursue courses in Computer Science, my trainings are for you.

No prior knowledge or experience is required. I particularly invite researchers positioned in labs and research groups, so that you can co-learn a new skill with your lab collaborators, but any ad-hoc group of learners in the Pitt community may request a training. Scheduling, timing (number of hours, sessions), and precise topics studied are flexible. Trainings may include asynchronous components (e.g., "assignments" to practice your new skills) if desired. Trainees are also free to contact me for follow-up support once the training is concluded.

Trainings are informed by evidence-based pedagogy and designed to teach foundational, practical skills for cleaning up and combining data, performing analysis, and creating plots. As a self-taught coder, I empathize with the struggle many researchers face in trying to acquire a new technical skill or two "on their own"--but it doesn't have to be that way.

Technical training topics include:

• The Command Line aka Terminal (as found on Linux servers and macOS)
• R, a statistical and general-purpose programming language commonly used for in biology, data science, statistics, and other fields
• Python, a general-purpose programming language commonly used in data science, digital humanities, physics, other fields, and industry.
• Git, a powerful tool for collaboratively managing files over time with versioning, and GitHub, an online platform for sharing research data and code.
• SQL (Structured Query Language), a language for interacting with relational databases.

I am also available for brief (~30 minutes) presentations on such topics as these:

• Data sharing for Open Science
• Data management planning for grant proposals
• Writing documentation for your code and data (to make it more usable by others, and future you)

On-request trainings are provided, free of charge, to Pitt affiliates but may be constrained or time-delayed depending on competing job duties.

Want to chat about your project's data, or need help or advice? You can schedule an appointment on the calendar, or if those times don't work, please write to me to set up a time with you or your team.

I also offer one-off workshops open to the wider Pitt community and general public. The topics discussed are the same as in my on-request training, but these are typically shorter and in a more demonstrative than hands-on format. These are good for getting a first taste and a general sense of what the tool is like.

If you are interested in a workshop but unable to attend, please let me know. I would be happy to share materials with you as well as arrange one-on-one time for questions you have.

Fall 2021:

• Introduction to Creative Commons Licensing and Licensing Your Data for Open Science, cotaught with Jonah McAllister-Erickson
• Data Visualization Best Practices, Including Introduction to Tableau Public, cotaught with Boris Michev
• In the Dealing with Data Series:
  • Command Line Basics
  • Introduction to Git Parts I and II
  • Querying Databases with SQL
• In the Coding and Computational Methods Series:
  • Introduction to R, Part I and Part II
  • Code Testing for Replicable Research
• Monthly half-hour info sessions about data management plans and data sharing

ORCID: https://orcid.org/0000-0002-8382-1300

• Kerzendorf, Wolfgang E., Ferdinando Patat, Dominic Bordelon, Glenn van de Ven, and Tyler A. Pritchard. 2020. “Distributed Peer Review Enhanced with Natural Language Processing and Machine Learning.” Nature Astronomy, April. https://doi.org/10.1038/s41550-020-1038-y.

• Grothkopf, U., Meakins, S., & Bordelon, D. (2018). ESO telbib: learning from experience, preparing for the future. Proceedings of the SPIE, 10704. https://doi.org/10.1117/12.2311667

• Grothkopf, U., Bordelon, D., Meakins, S., & Emsellem, E. (2017). On the Availability of ESO Data Papers on arXiv/astro-ph. The Messenger, 170, 58–61. https://doi.org/10.18727/0722-6691/5056

• Patat, F., Boffin, H., Bordelon, D., et al. (2017). The ESO Survey of Non-Publishing Programmes. The Messenger, 170, 51–57. https://doi.org/10.18727/0722-6691/5055

• Leibundgut, B., Bordelon, D., et al. (2017). Scientific Return from VLT instruments. The Messenger, 169, 11–15. https://doi.org/10.18727/0722-6691/5032

• Bordelon, D., Grothkopf, U., et al. (2016). Trends and developments in VLT data papers as seen through telbib. Proceedings of the SPIE, 9910. https://doi.org/10.1117/12.2231697

Currently enrolled: Post-Baccalaureate student, University of Pittsburgh

Master of Library and Information Science (MLIS), Louisiana State University

B.A., English, concentration in Writing & Culture (Folklore, Linguistics), LSU
B.A., Spanish, LSU
B.A., History, LSU