Why read a book review when you you can read the book (for free via #oa #openscience)?


Reviews, recommendations, and ratings are an important component of contemporary online consumption. Rotten Tomatoes, Metacritic, and Amazon.com reviews and recommendations increasingly shape decisions. Science and technical books are no exception. Increasingly, I have checked reviews for a technical book on a purchasing site even before I downloaded the free book. Too much information, not too little informs many of the competing learning opportunities (#rstats ) for instance).  I used to check the book reviews section in journals and enjoyed reading them (even if I never read the book). My reading habits have changed now, and I rarely read sections from journals and focus only on target papers. This is an unfortunate. I recognize that reviews are important for many science and technical products (not just for books but packages, tools, and approaches). Here is my brief listicle for why reviews are important  for science books and tools.

benefit description
curation Reviews (reviewed) and published in journals engender trust and weight critique to some extent.
developments and rate of change A book review typically frames the topic and offering of a book/tool in the progress of the science.
deeper dive into topic The review usually speaks to a specific audience and helps one decide on fit with needs.
highlights The strengths and limitations of offering are described and can point out pitfalls.
insights and implications Sometimes the implications and meaning of a book or tool is not described directly. Reviews can provide.
independent comment Critics are infamous. In science, the opportunity to offer praise is uncommon and reviews can provide balance.
fits offering into specific scientific subdiscpline Technical books can get lost bceause of the silo effect in the sciences. Reviews can connect disciplines.

Here is an estimate of the frequency of publication of book reviews in some of the journals I read regularly.

journal total.reviews recent
American Naturalist 12967 9
Conservation Biology 1327 74
Journal of Applied Ecology 270 28
Journal of Ecology 182 0
Methods in Ecology & Evolution 81 19
Oikos 211 22

Details of journal data scrape here: https://cjlortie.github.io/book.reviews/


A good novel tells us the truth about its hero; but a bad novel tells us the truth about its author.

–Gilbert K. Chesterton

Tips for rapid scientific recordings


If a picture is worth a thousand words, a video is worth 1.8 million words. Like all great summary statistics, this has been discussed and challenged (Huffington Post supporting this idea and a nice comment at Replay Science reminding the public it is really a figure of speech).

Nonetheless, short scientific recordings, posted online are an excellent mechanism to put a face to a name, share your inspirations in science, and provide the public with a sense of connection to scientists. It is a reminder that people do science and that we care. I love short videos that provide the viewer with insights not immediately evident in the scientific product. Video abstracts with slide decks are increasingly common. I really enjoy them. However, sometimes I do not get to see what the person looks like (only the slide deck is shown) or how they are reacting/emoting when they discuss their science. Typically, we are not provided with a sense why they did the science or why they care. I think short videos that share a personal but professional scientific perspective that supplements the product is really important. I can read the paper, but if clarifications, insights, implications, or personal challenges in doing the research were important, it would be great to hear about them.

In that spirit, here are some brief suggestions for rapid scientific communications using recordings.


  1. Keep the duration at less than 2 minutes. We can all see the slider at the bottom with time remaining, and if I begin to disconnect, I check it and decide whether I want to continue. If it is <2mins, I often persist.
  2. Use a webcam that supports HD.
  3. Position the webcam above you facing down. This makes for a better angle and encourages you to look up.
  4. Ensure that you are not backlit. These light angles generally lead to a darker face that makes it difficult for the viewer to see any expressions at all.
  5. Viewers will tolerate relatively poor video quality but not audio. Do a 15 second audio test to ensure that at moderate playback volumes you can be clearly understood.
  6. Limit your message to three short blocks of information. I propose the following three blocks for most short recordings. (i) Introduce yourself and the topic. (ii) State why you did it and why you are inspired by this research. (iii) State the implications of the research or activity. This is not always evident in a scientific paper for instance (or framed in a more technical style), and in this more conversational context, you take advantage of natural language to promote the desired outcome.
  7. Prep a list of questions to guide your conversation. Typically, I write up 5-7 questions that I suspect the audience might like to see addressed with the associated product/activity.
  8. Do not use a script or visual aids. This is your super short elevator pitch. Connect with the audience and look into the camera.
  9. Have a very small window with the recording on screen, near the webcam position, to gently self-monitor your movement, twitches, and gestures. I find this little trick also forces me to look up near the webcam.
  10. Post online and use social media to effectively frame why you did the recordings. Amplify the signal and use a short comment (both in the YouTube/Vimeo field) and with the social media post very lightly promoting the video.

Happy rapid recording!

A set of #rstats #AdventureTime themed #openscience slide decks


I recently completed a set of data science for biostatistics training exercises for graduate students. I extensively used R for Data Science and Efficient R programming to develop a set of Adventure Time R-statistics slide decks. Whilst I recognize that they are very minimal in terms of text, I hope that the general visual flow can provide a sense of the big picture philosophy that R data science and R statistics offer contemporary scientists.

Slide decks

  1. WhyR? How tidy data, open science, and R align to promote open science practices.
  2. Become a data wrangleR. An introduction to the philosophy, tips, and associated use of dplyr.
  3. Contemporary data viz in R. Philosophy of grammar of graphics, ggplot2, and some simple rules for effective data viz.
  4. Exploratory data analysis and models in R. An explanation of the difference between EDA and model fitting in R. Then, a short preview of how to highlighting modelR.
  5. Efficient statistics in R. A visual summary of the ‘Efficient R Programming’ book ideas including chunk your work, efficient planning, efficient planning, and efficient coding suggestions in R.

Here is the knitted RMarkdown html notes from the course too https://cjlortie.github.io/r.stats/, and all the materials can be downloaded from the associated GitHub repo.

I hope this collection of goodies can be helpful to others.



The importance of #upgoESA experiment by @DrHolly #ESA2016

The ‘Up-Goer Five Challenge: Using Common Language to Communicate Your Science to the Public‘ session was an experiment.  It was a brilliant success. Enjoyable and profound because of the direct and indirect discoveries in how we communicate and share. Semantics are important. Scientific language conveys complexity. Complexity can become a barrier. Simpler language tends to highlight emotions. Using simpler words can change meaning but make the narrative more powerful.  The main direct discovery was that we function, as scientists and communicators, on a continuum from jargon to overly simple, and we need to find the sweet spot in using complexity appropriately in sharing our findings with others (and one another).

EAS NCRG Complexity

However, I propose the ‘experiment’ need not have been successful for us to learn. Experiments are about discovery. We learn as much from error as success in science. Trials are useful. The most exciting element of the up goer five model for talks was the fact that Dr. Holly Menninger proposed the session, it got approved, and many people participated (in speaking, attending, and the discussion). We need to try things out. We need to experiment with scientific communication just like we experiment with research systems and test hypotheses and predictions. There is a field of research in communication studies, and I am not proposing we must also become experts in that too. However, ESA meetings are a safe place for ecologists.  At the minimum, we can try some new things in how we communicate with one another and explore efficacy and potential for different audiences. There is likely no one best way for every context. Importantly, we can practice taking risks. Each of us needs to decide what we are comfortable with. Oral session, poster, or ignite for instance each come with different risks and challenges. The upgoESA model provided an alternative opportunity that came with new risks. However, we benefitted from the experiment and made some discoveries. Consequently, I propose we continue to look outward like Dr. Holly Menninger did and continue to bring new opportunities to future ESA meetings that explore how communicate. PechaKucha, slide karoke, video abstracts, streaming, micro-writing groups, hackathons, datashareathons, and more meetups are all viable experiments too. The session ‘Ecology on the Runway: An Eco-Fashion Show and Other Non-Traditional Public Engagement Approaches‘ was also an experiment with risks, entertainment, and a different set of messages.

We need to continue to hack the conference model and treat it like our own collective experiment to become better communicators. Plus, experiments are fun.


Posted in fun

Sharing strategies for #ESA2016 #openscience #scicomm

Meetings are an excellent opportunity to not only communicate your science but secure feedback. I propose the more you give, the more get.


There are at least the following five open-science products associated with any contribution (presentation or poster) to share with your colleagues and a much wider online audience prior to the meeting.  [ green text = hyperlinks ]

Open-science products to share for a meeting

  1. The slide deck or poster can be published on SlideShare.
  2. Your data-science workflow, code, and EDA can be published as an r-markdown on GitHub.
  3. The primary or derived summary data (if you are not ready to go public yet) can be published on figshare (and/or included in GitHub repo).
  4. Most journals accept submissions that have been pre-printed. Consider sharing your draft paper on PeerJ or bioRxiv. Not at that stage? Do a blog post instead.
  5. Record a video abstract to share the main finding of your talk and post to YouTube or Vimeo. This could attract a larger audience to the conference presentation and does an incredibly useful service in communicating science to the public and others that do not attend the conference.


I enjoy the process of science way too much, and I am easily distracted.  How did I do in preparing for my ESA talk this year on microenvironmental change under desert shrubs?  I scored a total of 4 our of 5 . Feel free to click on bolded text below to see materials and provide feedback. Each is absolutely a work-in-progress like the experiment itself (we need at least one more year of data). However, I am hoping it is a good time to share ideas now and see if we can do better next year in the field.

Deck on SlideShare

Code on GitHub

Data on GitHub

Video abstract (went a bit crazy here and did two). Field and in office versions. Very high cheese factor in both (hard to be natural on camera).

Science is a process. Share your steps.