Hacking the principles of #openscience #workshops

In a previous post, I discussed the key elements that really stood out for me in recent workshops associated with open science, data science, and ecology. Summer workshop season is upon us, and here are some principles to consider that can be used to hack a workshop. These hacks can be applied a priori as an instructor or in situ as a participant or instructor by engaging with the context from a pragmatic, problem-solving perspective.

Principles

1. Embrace open pedagogy.
2. Use and current best practices from traditional teaching contexts.
3. Be learner centered.
4. Speak less, do more.
5. Solve authentic challenges.

Hacks (for each principle)

1. Prepare learning outcomes for every lesson.

2. Identify solve-a-problem opportunities in advance and be open to ones that emerge organically during the workshop.

3. Use no slide decks. This challenges the instructor to more directly engage with the students and participants in the workshop and leaves space for students to shape content and narrative to some extent. Decks lock all of us in. This is appropriate for some contexts such as conference presentations, but workshops can be more fluid and open.

4. Plan pauses. Prepare your lessons with gaps for contributions.  Prepare a list of questions to offer up for every lesson and provide time for discussion of solutions.

5. Use real evidence/data to answer a compelling question (scale can be limited, approach beta as long as an answer is provided, and the challenge can emerge if teaching is open and space provided for the workshop participants to ideate).

Final hack that is a more general teaching principle, consider keeping all teaching materials within a single ecosystem that then references outwards only as needed. For me, this has become all content prepared in RStudio, knitted to html, then pushed to GitHub gh-pages for sharing as a webpage (or site). Then participants can engage in all ideas and content including code, data, ideas in one place.

 

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

Politics versus ecological and environmental research: invitation to submit to Ideas in Ecology and Evolution #resist

Dear Colleagues,

Ideas in Ecology and Evolution would like to invite you to submit to a special issue entitled
‘Politics versus ecological and environmental research.’

The contemporary political climate has dramatically changed in some nations. Global change marches on, and changes within each and every country influence everyone. We need to march too and can do so in many ways. There has been extensive social media discussion and political activity within the scientific community. One particularly compelling discussion is best captured by this paraphrased exchange.

“Keep politics out of my science feeds.”
“I will keep politics out of my science when politics keeps out of science.”

The latter context has never existed, but the extent of intervention, falsification by non-scientists, blatant non-truths, and threat to science have never been greater in contemporary ecology and environmental science in particular.

Ideas in Ecology and Evolution is an open-access journal. We view the niche of this journal as a home for topics that need discussing for our discipline. Ideas are a beautiful opportunity sometimes lost by the file-drawer problem, and this journal welcomes papers without data to propose new ideas and critically comment on issues relevant to our field both directly and indirectly. Lonnie Aarssen and I are keen to capture some of the ongoing discussion and #resist efforts by our peers. We will rapidly secure two reviews for your contributions to get ideas into print now.

We welcome submissions that address any aspect of politics and ecology and the environment. The papers can include any of (but not limited to) the following formats: commentaries, solution sets, critiques, novel mindsets, strategies to better link ecology/environmental science to political discourse, analyses of political interventions, summaries of developments, and mini-reviews that highlight ecological/environmental science that clearly support an alternative decision.

Please submit contributions using the Open Journal System site here.

Warm regards,

Chris Lortie and Lonnie Aarssen.

A rule-of-thumb for chi-squared tests in systematic reviews

Rule

A chi-squared test with few observations is not a super powerful statistical test (note, apparently termed both chi-square and chi-squared test depending on the discipline and source). Nonetheless, this test useful in systematic reviews to confirm whether observed patterns in the frequency of study of a particular dimension for a topic are statistically different (at least according to about 4/10 referees I have encountered). Not as a vote-counting tool but as a means for the referees and readers of the review to assess whether the counts of approaches, places, species, or some measure used in set of primary studies differed. The mistaken rule-of-thumb is that <5 counts per cell violates the assumptions of chi-squared test. However, this intriguing post reminds that it is not the observed value but the expected value that must be at least 5 (blog post on topic and statistical article describing assumption). I propose that this a reasonable and logical rule-of-thumb for some forms of scientific synthesis such as systematic reviews exploring patterns of research within a set of studies – not the strength of evidence or effect sizes.

An appropriate rule-of-thumb for when you should report a chi-squared test statistic in a systematic review is thus as follows.

When doing a systematic review that includes quantitative summaries of frequencies of various study dimensions, the total sample size of studies summarized (dividend) divided by the potential number of differences in the specific level tested (divisor) should be at least 5 (quotient). You are simply calculating whether the expected values can even reach 5 given your set of studies and the categorical analysis of the frequency of a specific study dimension for the study set applied during your review process.

total number of studies/number of levels contrasted for specific study set dimension >= 5

[In R, I used nrow(main dataframe)/nrow(frequency dataframe for dimension); however, it was a bit clunky. You could use the ‘length’ function or write a new function and use a ‘for loop’ for all factors you are likely to test].

Statistical assumptions aside, it is also reasonable to propose that a practical rule-of-thumb for literature syntheses (systematic reviews and meta-analyses) requires at least 5 studies completed that test each specific level of the factor or attribute summarized.

Example

For example, my colleagues and I were recently doing a systematic review that captured a total of 49 independent primary studies (GitHub repo). We wanted to report frequencies that the specific topic differed in how it was tested by the specific hypothesis (as listed by primary authors), and there were a total of 7 different hypotheses tested within this set of studies.  The division rule-of-thumb for statistical reporting in a review was applied, 49/7 = 7, so we elected to report a chi-squared test in the Results of the manuscript.  Other interesting dimensions of study for the topic had many more levels such as country of study or taxa and violated this rule. In these instances, we simply reported the frequencies in the Results that these aspects were studied without supporting statistics (or we used much simpler classification strategies). A systematic review is a form of formalized synthesis in ecology, and these syntheses typically do not include effect size measure estimates in ecology (other disciplines use the term systematic review interchangeably with meta-analysis, we do not do so in ecology). For these more descriptive review formats, this rule seems appropriate for describing differences in the synthesis of a set studies topologically, i.e. summarizing information about the set of studies, like the meta-data of the data but not the primary data (here is the GitHub repo we used for the specific systematic review that lead to this rule for our team). This fuzzy rule lead to a more interesting general insight. An overly detailed approach to the synthesis of a set of studies likely defeats the purpose of the synthesis.

Tips for rapid scientific recordings

Preamble

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.

Tips

  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!