Suggested viewing/reading material for young women scientists
- Grace Hopper, namesake of a NERSC supercomputer, explaining the concept of a nanosecond
- TED talk on viewing stress differently, and the physical implications
- Women Scientists and Engineers Council (WSEC) @ LBNL
Water use data
This recounting of the faculty job search continues to be one of my favorite honest pieces on what it’s like to interview. Interviewing at a national lab like LBNL has some similarities – depending on what position you’re applying for, you may need to give a job talk, and certainly you’ll meet with some of your potential future collaborators. Thus, faculty job search advice can be very helpful for a national lab interview.
In terms of negotiating, it’s always helpful to get information about how much people there currently make (and what their initial offers were). Glassdoor is a helpful resource since employees can anonymously share information like salaries and bonuses, but don’t take it as gospel. In my experience, the fact that younger people are more likely to be users of the site means that the salaries can be skewed, usually on the low side. If you’re lucky, friends and colleagues will share useful information with you as well, typically with the caveat that you can’t reveal your source. That, in my experience, is extremely valuable.
After struggling with a 270-page dissertation that crashed frequently in MS Word, I became a whole-hearted LaTeX convert, and have since used it to put together journal paper manuscripts, my CV, and a number of other documents. For those considering making the jump, I highly recommend it, and there are many wonderful resources online to help make the transition easier. There’s never really a need to create a document from scratch – if you are looking to write a letter, a paper, or even a course outline, chances are someone has put a sample template online that will look great. ES&T, for example, provides default LaTeX files for submissions, which eliminates the need to think about getting the formatting right, linking table/figure references, remembering to line numbers, etc. BibTeX is wonderful at managing references as well. I was able to download everything I needed here. However, after upgrading my Mac to Mavericks, it all mysteriously stopped working because of an inability to find a file called “kpsewhich”. For those with the same problem, a quick explanation and fix are provided here.
Selecting a programming language that works best for life-cycle assessment and scenario analysis-type modeling is challenging – many researchers still rely on Excel and the community has not yet coalesced around a particular set of tools that we all agree are best-suited for our work. I have tried Python, R, and Matlab, and each one has its pros and cons. Python and R are ideal because they are free for everyone. Python is more widely used, and hence the online documentation is more extensive. R is the programming language I use for almost all of my current projects. It makes managing data tables and 3+ dimension datasets extremely simple. That being said, Pandas (the data analysis package for Python) seems great, and I am just now familiarizing myself with it. R Studio is available free of charge to academic users and provides a nice GUI for managing packages. Canopy is also available to academics, and is a nice GUI for package management in Python. For statistical analysis, or any sort of analysis that requires basic math, R is a wonderful, fairly intuitive language. My only complaint so far has to do with optimization – namely, I had a difficult time finding a linear optimization package that worked as well as those available in Matlab or Python. Fortunately, there is a package called R.matlab that allows me to start up and run code in Matlab within my R program. Python can also be called in a similar fashion. It seems that no particular direction is better than another, but being able call multiple languages from the same program provides flexibility that is wonderful for interdisciplinary research.