How to use twitter for academia

What is twitter useful for?

  1. Stay up to date with new research
  2. Find jobs, grant opportunities, academic meet-ups
  3. Network and become (more) visible
  4. Attend conferences virtually (check out the all-twitter brain conference  and of course in Twitter)
  5. Get access to paywalled papers (e.g. #canihaspdf)
  6. Find help & resources
  7. Get input from other scientists on data, stats, writing, etc.
  8. Teach the public, and learn from other twitter users

To learn more, watch the video from the live session where Nikola (nikola.me), Tommi  (mindsync.wordpress.com) and me talked about our personal experiences with academic twitter:

Here are some more practical tips on how to get the most out of twitter (what we couldn’t cover yesterday during the live session): 

    • Whom to follow?
      • Other scientists (and labs), journalists, funding bodies, science enthusiasts and communicators
      • See who your followers are following in turn, or whose content they retweet
      • Use public lists that others have created for specific topics. (Here’s a good one for CogNeuro people: https://twitter.com/neuroconscience/lists/cogneuro)
    • Keep your newsfeed manageable
      • Use services like TweetDeck (tweetdeck.twitter.com) – this allows you to split up your main feed into “sub-feeds” which are more manageable, based on topic or any other criterion you want.
      • Websites such as Buffer (buffer.com) and Hootsuite (hootsuite.com) will let you schedule tweets – you can make a queue of tweets which will then be posted automatically at (ir)regular intervals. This saves your time, and also benefits your followers who live in different time zones.
      • Turn off most if not all notifications! Social networks are built to be addictive – having your phone buzz every time someone tweets or replies to you will become very distracting once you start following more people. Making sure that you use Twitter at times that suit *you* is key.
      • Remember to weed your following list periodically – unfollow people you are no longer interested in, or add new people who you just met at conferences, etc.
    • Your public persona
      • It is generally a good idea to keep your personal and professional social media accounts separate, especially if you are an early career researcher.
      • If you want to keep your account professional, make a separate private account. Then you’ll have the flexibility to post about anything else you want, while not angering all the people who followed you for your science content.
    • How to tweet and content ideas
      • Sharing links – share why a news story, publication, video, or image captured your interest
      • Post short updates on your research; make these posts longer by using threads (almost like a blog post)
      • Start a dialogue or conversation by tagging other users, or using hashtags. Some good ones are: #phdchat #postdoc #ECRchat #scicomm #openscience
      • Do an online journal club – Twitter can be great to talk about new papers you’ve read, ask questions, etc.
      • Public AMAs (ask-me-anything sessions) – answer questions from the public about your work. One good example of this is @IAmSciComm, which has a new scientist host their account each week, and talk to thousands of science interested followers!
      • Live-tweet talks at conferences – you don’t need to take it too seriously, even a couple of bullet points per talk will contribute, and if more than one person does it it will create a nice public record of a conference. It is very common to get thank you messages from people for sharing info from conferences they couldn’t attend.

More practical tips can be found in a recent Cogtales post.

How to stay on top of trends and findings in your field.

Keeping up with the literature and current issues is challenging. But thanks to different tools you can make this an easier task. The best tool in my experience is Google Scholar (https://scholar.google.com). If you don’t have a profile yet, make one today. You can use it to follow colleagues’ publications, track citations of seminal papers, and get recommendations based on your usage or core papers in your field.

If you are interested in the output of a certain lab and they are not active on google scholar, you can use tools like https://www.followthatpage.com to track their publications. This obviously only works for labs that have a frequently updated website.

Twitter is an amazing tool to stay up to date with current discussions and topics in your field. It takes a while until you figure out whom to follow, but it’s worth the investment.

Another useful tool to find older but still important papers is Mendeley. Technically this is a citation manager and library tool, but you get recommendations based on the articles in your library. Most of the time I find them very useful.

Journal updates are useful but should be limited to a few journals. The maximum of what I find manageable is 3. I follow Brain and Language, Behavioural and Brain Sciences, and Neuroscience and Biobehavioural Reviews. I only scan the headlines when the articles come in and decide if and what i will read in detail.

Researchgate can be useful if a lab is very active, but most of the time it is only selectively helpful to keep in touch/utd with a certain group. These ~10people I interact with on Researchgate only are the single reason why I still have my profile there.

Search alerts for journal databases like PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) or Web of Science (webofknowledge.com) can be great tools if you figure out good search  terms and restrictions. I have not succeeded with this, but I found this blog with seemingly useful recommendations: https://bitesizebio.com/419/18-ways-to-improve-your-pubmed-searches/. There are also tools like Pubcrawler or Pub-Chase (which I will certainly try out next because it looks great!).

The most important thing is to get 2 types of reading integrated into you academic life:

  1. Quick scanning of the newest output and deciding what  to read (should be done weekly at least).
  2. Extensive reading and broader researches for articles.

More details and tips in the video:

A practical guide to linear mixed effect models in Rstudio

In this episode of the Academic Crisis Line, Stacey Humphries and I gave a practical introduction to linear mixed-effects models. We talked about the background and key concepts about LMEMs, focused around 5 key questions that people often have when starting to encounter LMEMs for the first time.

  1. Why is a LMEM better than an ANOVA?
  2. What are fixed- and random-effects?
  3. What is the difference between a random intercept and a random slope?
  4. Should I include random slopes in my model?
  5. How can I tell if my model predictors significantly affect my dependent variable?

For answers to all these questions and more, check out the video! We also briefly walked through some practical aspects of running these analyses in R, but unfortunately the stream died at this point. We tried to re-record the parts that were lost, so this is annoyingly split over 3 videos. We hope it is useful nonetheless.

Video links:

Part 1:

 

Part 2:

Part 3:

 

We mentioned a lot of different papers and links in the video which are listed here for your convenience, along with a few others you might find interesting:

You can find the visualisations of random intercepts and random slopes that we walked through here: http://mfviz.com/hierarchical-models/

Bodo Winter put together some fantastic tutorial for LMEMs:
http://www.bodowinter.com/tutorial/bw_LME_tutorial1.pdf
http://www.bodowinter.com/tutorial/bw_LME_tutorial2.pdf

Clark (1973) The language-as-fixed-effect-fallacy: http://www.sciencedirect.com/science/article/pii/S0022537173800143

Baayen et al. (2008) Mixed-effects modeling with crossed random effects for subjects and items http://jakewestfall.org/misc/BDB2008.pdf

Barr et al. (2013) Random effects structure for confirmatory hypothesis testing: Keep it maximal https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881361/

Bates et al. (2015) Parsimonious mixed models https://arxiv.org/pdf/1506.04967.pdf

Matuschek et al. (2017) Balancing Type I error and power in linear mixed models http://www.sciencedirect.com/science/article/pii/S0749596X17300013

Baayen et al. (2017) The cave of shadows: Addressing the human factor with generalized additive mixed models http://www.sciencedirect.com/science/article/pii/S0749596X16302467

Luke (2017) Evaluating significance in linear mixed-effects models in R https://link.springer.com/article/10.3758/s13428-016-0809-y

Summary of Luke (2017) by Richard Morey: https://featuredcontent.psychonomic.org/putting-ps-into-lmer-mixed-model-regression-and-statistical-significance/

Brysbaert (2018) Power analysis and effect size in mixed effects models: A tutorial https://psyarxiv.com/fahxc

 

 

 

If you have any questions that we didn’t cover, please feel free to tweet us (@_SHumphries, @FranHartung, @Ph_Dial), or email us (hstace[AT]pennmedicine.upenn.edu, fhartung[AT]pennmedicine.upenn.edu).

Upgrade to digital project planning. A follow up on ‘how to juggle multiple projects’.

I recently I switched from Post-its and paper sheets (Original post: How to juggle multiple projects) to Trello (trello.com), an online based (free!!) project management tool. It’s simple, visual, and easy for collaborating. I love and highly recommend it!

Here is my brief tutorial:

Live saving archiving and documentation strategies.

What are the best practices in documentation, archiving, publishing of code, data, and other materials? How to keep your file structure organized on your own computers?

This series featured two awesome guests from University of Pennsylvania:

Steven Weissberg (https://stevenmweisberg.com/) from the Chatlab at Upenn (http://ccn.upenn.edu/chatterjee/) talked about OSF and how to use it for open science and archiving (from 5:14).

Giulia Frazetta (https://github.com/gfrazzetta) from Geoffrey Aguirre’s lab at UPenn ( https://github.com/gkaguirrelab) talked about versioning and how to make the best use of github (from 11:39).

 

Quick summary of the most important points:

  1. Always keep future you in mind. And with future, we don’t mean the next few weeks, but the next few years.
  2. Keep your files on your computer tidy. Organize things which you are likely to recycle (e.g. slides, writing, graphics) together. Organize your research material (e.g. data, analysis files, stimuli) in project folders and stay consistent with folder structure and naming.
  3. Don’t panic-save everything. Archive data files at the most important steps in a separate folder (raw, preprocessed, analysis#1, analysis#2, wide format) and use the  copies in your analysis directory for ongoing analyses and trying out things. Overwrite old files when moving on (Don’t panic, you have that safety copy).
  4. Write up the documentation of your study directly into a manuscript file which will later be the basis of a published paper. Writing up the experiment protocols, materials and methods descriptions, analysis steps, and the results right away will save you a lot of time and frustration.
  5. Use OSF (https://osf.io) to archive your materials and data. This is also great for working with multiple people on one project. Keep this as tidy as possible. As soon as you make it public eventually (which we recommend for most projects) this should be the best reflection of your work as a scientist with highest standards.
  6. If your work entails any kind of coding or writing analysis scripts, github is a great way to keep version control and publish your code.
  7. Always work as if a stranger would have to pick up your project at any time and finish it for you.