As a scientist, you focus on your research and often spend a significant portion of your time performing experiments in the lab. If you are a hard-core experimentalist, you may even think that you can only progress in your career through your experiments. However, there are many opportunities for professional development to go alongside your bench research. Here we have compiled several ideas on how you can advance your scientific career in your spare time.

Learn a new area or technique

You can expand knowledge in your research field or learn a new area by attending online courses on learning platforms such as Coursera or Edx.org. Whether you are interested in machine learning or epigenetics, you can learn essential basics, which will help to advance your career. Many of these courses are free to enroll. However, you may need to pay a fee to receive a verified certificate or gain access to peer-graded assignments.

If you need to learn a new technique, you can benefit from Abcam training, Abcam’s free online training courses. You can lay your foundations in fluorescent cell imaging or explore other courses, including Antibody basics, ChIP, Flow cytometry, Immunohistochemistry (IHC), and Western blot.

Improve your writing skills

You don’t need to be an excellent writer to be a good scientist. However, you do need to be able to communicate your research effectively. To upgrade your writing skills, Stanford University offers a free online course “Writing in the Sciences”. This course teaches scientists how to become more effective writers, using practical examples and exercises. After taking a few modules of this course, you can put them immediately into practice and write a page on your current project.

You can find some useful writing tips in Tipbox’s articles about writing a PhD thesis and a scientific paper.

Learn a new software

It’s always a good idea to explore new software before you need to use it. For image analysis, many scientists use Image J. It is a powerful free tool that allows you to automatize such tedious processes as counting cells or calculating the signal area. ImageJ will also allow you to analyze image stacks, time-lapse, and videos. If you want to learn more about ImageJ and what functions it can offer, watch this 8-minute video. Alternatively, you can read about some basic concepts of ImageJ.

Adobe Photoshop is a great software to build beautiful panels of your images for posters, publications, or presentations. You can learn some basics of using Adobe Photoshop in scientific imaging by reading this article or watching these videos. The full version of Adobe Photoshop requires an annual license, but Adobe Photoshop Elements is available as a one-off purchase. Although Elements does not include the extensive features of the full version, it may be sufficient for your needs.

Reference software can save you plenty of time by keeping your references organized. If you don’t have one yet, install Zotero, Mendeley, or Endnote and start building your reference library. If you already have a reference software, dedicate some time to curating your library by removing duplicates, sorting items in folders, and adding the missing information.

Conduct a literature review

You may have accumulated a long list of papers that you want to read, once you finish your experiments. Well, the time has come. Make a big cup of tea, read those papers, and add them to the reference software you’ve just downloaded!

As a next step, start putting together a literature review, which you can use later in an introduction in your next paper or thesis.

Learn to code

Advances in technology have increased the amount of data we produce, resulting in the need to analyze large datasets. Learning to code can save you time in the future by automating the process, and there are many free resources – you just need to choose the most suitable language.

R and Python are among the most popular open-source programming languages for scientists. R is mainly used for statistical analysis, whereas Python provides a more general approach to data science. If you are choosing between R or Python, check out this advice. Once you decided about which language to study, you can subscribe to one of many online courses on R or Python.

We hope you’ve taken some inspiration from our tips and keep on learning even when your research is on pause. We would love to hear about your own experiences of professional growth. Please send your stories on tipbox@abcam.com.

Photo by Nick Morrison

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