WG3 CLIMO COST action Short Term Scientific Mission

The CLImate Smart Forestry in MOuntain Regions (CLIMO) COST action focusses on adaptation to climate change through climate-smart forestry practices. The CLIMO COST action is currently looking for candidates for short term scientific missions (STSM). These missions have a focus on early career scientists who want to work within the context of the COST action.

In particular, working group (WG) 3, which focuses on technological aspects of measuring forest processes, is looking for interested candidates to study wireless technology and sensor networks (e.g. temperature of the canopy, visual parameters through phenocams, etc) as well as physiological aspects of forests disturbances such as (persistent) droughts using stable isotope or other dendrochronological measurements. There are also opportunities to work with existing data within the context of data visualization and development of web-based tools to monitor canopy phenology responses to climate change. Different topics within the framework of WG3 are also welcome. Candidates will work out of INRA Bordeaux under guidance of Dr. Lisa Wingate (WG3 lead) and myself.

Currently there are two calls in 2017 (see below) with the deadline for the first call approaching fast. Apply while you still can !!

Calls for 2017

  • First STSM call submission deadline: 24, July 2017
    • call opens: 24, June 2017
  • Second STSM call submission deadline: 15, October 2017
    • call opens: 15, September 2017

Application procedure

Full details on the application procedure can be found on the STSM webpage of the CLIMO COST action.

Level Theta S images

My Virtual Forest project is still running strong and generates tons of spherical images (currently ~50GB). However, the post on which the camera sits is not perfectly level.  The Theta S camera normally compensates for this using an internal gyroscope which detects pitch and roll of the camera.  Yet, when downloading images directly from the camera no adjustments are made and the pitch and roll data is merely recorded in the EXIF data of the image.

As such I wrote a small bash script which rectifies (levels the horizon) in Theta S spherical images using this internal EXIF data. This is an alternative implementation to the THETA EXIF Library by Regen. I use his cute Lama test images for reference. All credit for the funky images go to Regen. Below is the quick install guide to using my script. I hope it helps speed up people’s Theta S workflow.


Download, fork or copy paste the script from my github repository to your machine and make it executable.

$ chmod +x theta_rectify.sh


$ theta_rectify.sh image.jpg

The above command will rectify the image.jpg file and output a new file called image_rectified.jpg.

Visual comparison between my results and those of Regen’s python script show good correspondence.


The script depends on a running copy of exiftools, imagemagick and POVRay. These tools are commonly available in most Linux distros, and can be installed on OSX using tools such as homebrew. I lack a MS Windows system, but the script should be easily adjusted to cover similar functionality.

On reviewing, publons and privacy

Recently I came across Publons as a way to get credit for your reviewing efforts. At first I was rather intrigued. It does sound like a good idea as I often bemoan the burden which is reviewing at times, and the little reward it brings (often because of the quality of the work). I was even more intrigued as five years ago I was runner-up in Elsevier’s peer-review challenge, trying to resolve the ailing peer-review system. The winner suggested something along the lines of Publons.

However, Publons or any badge system want to make you believe that your review is worth something outside it’s academic context. Yet, it does not contribute to a workable solution for the core problem of the academic peer-review process which is ease-of-use and the quality of the review. It perverts the peer-review system with false incentives and a race to the bottom if poorly executed. Publons claims for publishers state a decrease in review times and accepted reviews. This suggests that Publons change incentives to accept reviews, and potentially the number of accepted manuscripts. Assuming that time is a limited resource for most scientists, increasing the number of reviews should decrease the time spend on them, letting errors slip through.

Even less surprising is that given that this is a for profit venture and they do not go lightly when it comes to privacy. Checking the privacy statement (below) basically states that all the data you submit (full reviews if possible) can be used for data mining, and reselling to advertisers or publishers alike.

In short, Publons is a niche data broker, contrary to sweeping approach Google and Facebook use. Added value is generated in the form of a virtual badge, with little or no real world value, providing only an extra account to track and performance anxiety that goes with it and the privacy you sign away. The badges potentially shift the reviewer acceptance rates due to time restrictions and moral hazard. We should not be speeding up science, we should be increasing rigour, reproducibility and quality.




CitSci Jungle Rhythms project finished!

After an amazingly short ~475 days the Jungle Rhythms citizen science project finished !

I hereby would like to thank all citizen scientists who contributed to the project and made it into a success, transcribing ~30K site years of tropical phenology data in no time.

I will now start the post-processing. I will report on this shortly as I make my way through the large dataset consisting of more than 300K classifications and additional remarks collected from the Talk forums. Intermediate results have shown that the data collected are of an outstanding quality, illustrating the swift and accurate work by citizen scientists. I’m confident the complete dataset will be of an equal high quality.

Once more, my thanks and gratitude go out to all volunteers who made this possible !

Robots are -also- after your academic job

Rapid development in machine learning and artificial intelligence has allowed previously complex consumer applications and jobs to be automated at an ever increasing pace. But make no mistake, machine learning will also displace academic jobs with only those remaining which wield the right tools combined with the best and brightest ideas.

Voice assistance allow you to order anything online, translation services process natural language ever better, ordered packages are picked by robots, and soon a drone might deliver them. All these advances in automation are due to recent software and hardware developments which result in job loss.

Yet, in academia the rise of automation and ‘robots’ stirs less of a concern. The general feeling remains that core functions of academic jobs are too complex to be sufficiently automated. This core function is the creation of new hypothesis to be tested. However, a large part of academic work, especially in ecology, still relies on encyclopedic knowledge, collection management and often tedious work. Encyclopedic knowledge and many of these mundane academic tasks can be reduced to harder classification or sorting problems (of samples stored in collections and data sets).

As with the soon to be obsolete Uber driver, harder classification problems,  such as driving a car, become easier to solve using advanced machine learning techniques. One can argue that automation will free up time to do more hypothesis testing, and increases the pace of science. This arrival of machine learning into mainstream research can be seen in a number of recent high profile publications from classifying leaves in paleobotany to mapping poverty.

I argue that in the near future it will be a requirement to wield the tools to implement (large scale) machine learning approaches to remain competitive in most academic fields. With austerity and budget cuts, a failure to do so might lead to some academics being replaced by evermore sophisticated algorithms and not necessarily lead to a shift in their job content. A urgent shift is needed in the skill sets of many scientists, extending past statistics into the fields of computer science and machine learning.



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