Mentoring Resources
Undergraduate Astronomy Research Tutorial Repository
The Astronomy Research Tutorial Repository serves as an excellent resource for undergraduate students learning how to program in the Python language, as well as introducing several other astronomy-specific software packages. This is a work in progress and contributions are always welcome. Please check back periodically for new additions, and stay tuned for the set of python modules for beginners to be released in Fall 2020!
Graduate Student Research Agreement Template
Student-mentor relationships are both extremely rewarding, but also challenging. This agreement has been developed to provide (graduate) students with a set of guidelines for their role performing independent research in astronomy. The purpose of this document is to provide a clear set of expectations, which will help make students and mentors more successful in their journey, enhancing the students development, and ensuring that the student and mentor are on the same page. A generic version of the Graduate Student Research Agreement can be downloaded and adapted; please acknowledge those that have contributed to making this document: Prof. Kate Whitaker (UMass Amherst), Prof. Stella Offner (UT Austin), Prof. Cara Battersby (UConn), Prof. Julia Kamenetzky (Westminster College), and Prof. Jeyhan Kartaltepe (RIT).
Compilation of Undergraduate Research Opportunities
This compilation of undergraduate research opportunities, compiled in Fall 2020, including the program name, an estimate of the application due date, and a link to the webpage. Some information may not be up-to-date; corrections and/or additions are encouraged - please email me!
Effective Plotting and Presentations Workshop
This workshop was originally developed for the UMass Astronomy Department Fall 2020 Journal Club (and made public in September 2020). Included in the 2-session workshop is a “Data Challenge”, where participants are asked to download a spreadsheet of the astrophysics arXiv submissions between March 15th and August 10th of 2019 and 2020 (i.e., before and during COVID-19). Using code released by Professor Megan Frederickson, the CSV file contains two gender columns counting the total number of men and women (albeit an imperfect process, gender is automatically assigned through an applied algorithm whenever first names are specified).