Category Archives: open science

Debris Flow Experiments (Spring 2017)

This spring I taught an undergraduate Geomorphology class at Duke. For the last few weeks of class, I broke out my debris flow flume. I have written about this debris flow previously, and is described here on the Sediment Experimentalist Network site. Also posted is a slo-mo video of a typical debris flow.

Students planned and executed an experiment of their choosing — an example of a  ‘Course-based Undergraduate Research Experience’ (CURE). Though there has been some work done with ‘scaled down’ debris flows (e.g., de Haas et al. 2015) there seemed to be lots of room for the students to do something new.

Both groups ended up investigating various mitigation measures for slowing or stopping debris flows. This involved 3D printed several pieces as mitigation structures, from solid walls of various sizes and angles:


…to plates with various densities of upright rods/sticks to function as tree/vegetation mimics:


Each group ended up writing up their work as a paper (data and plots included), and I’m happy to share them here:

  • Paper 1 focused on solid walls
  • Paper 2 focused on the ‘green infrastructure’ mimics.

Arduino and Raspberry Pi in Geoscience research

Nature reported last week on the uptick in usage of Arduino and Raspberry Pi for research. The idea of building research tools with open source hardware has been covered before (see Pearce 2012 for an example), but this recent article had a nice plot of the # of papers/year that mention these boards (using PubMed and Scopus) .

After the article last week, I wondered how many Geoscience articles actually use an Arduino or Raspberry Pi….

Using the Web of Science, there are less than 10 articles under the ‘Geosciences Multidisciplinary’, ‘Geology’, and ‘Geography Physical’ topics that use the word ‘Raspberry Pi’ or ‘Arduino’ in the title, key words, or abstract. Not much uptake in the Earth sciences I guess.

Though the articles that use the Arduino are very neat, such as a system for geophone data acquisition, a microscope focus stacker, an earth flow monitoring tool, and temperature-sensing waders.

I have seen other Earth science research using these boards — by attending poster sessions at AGU that highlight low cost tech, and I have read about the Raspberry Shake, which could generate a host of papers in the future…

My interest here comes from dabbling with these two tools in the past. With the Arduino I have actually built a few things, including a primitive Optical Backscatter Sensor (OBS), a datalogger, and an ultrasonic distance sensor (see below; pic from 2014). I hope to get back to that dabbling some day..

FullSizeRender.jpg

Open Access charges for journals that publish geomorphology research

Here I compiled the Open Access charges for journals that publish geomorphology research (i.e., Gold Open Access; Author Pays). I’m sure some are missing — let me know which publications I should add to this list.

Keep in mind that some journals have page charges even if the articles are not Open Access, some journals provide open access after a given time period, and other journals ONLY publish Open Access. Your institution may also have an agreement with a publisher about paying the fee (i.e., they will pay for you)…

I hope to periodically update this list.. The data below was collected on March 26th 2017.

Journal Cost (various currencies)
Earth and Space Science (AGU + Wiley) $1800
Earth’s Future (AGU + Wiley) $1800
GRL (AGU + Wiley) $2500
Water Resources Research (AGU+ Wiley) $3500
JGR – Earth Surface (AGU+ Wiley) $3500
Reviews of Geophysics (AGU + Wiley) $3500
ESurf (EGU + Copernicus) €50-120/ journal page
GSA Journals $2500
Geomorphology (Elsevier) $3300
Earth Surface Processes and Landforms (BSG+ Wiley) $4200
Progress in Physical Geography (Sage) $3000
Marine Geology (Elsevier) $3300
PLoS ONE $1495
Scientific Reports (Nature) $1675
PNAS $1100- 1450
Nature Communications (Nature) $5200
Zeitschrift für Geomorphologie (Schweizerbart) €140 per article + €119 per published page

Our tiny debris flow flume

Last semester I taught an undergraduate level geomorphology class at UNC-Chapel Hill. It was a blast. In addition to reading lots of primary literature, and editing wikipedia, we conducted a class experiment. I built a small debris flow flume based on de Haas et al. (2015) and we did a few experiments. A description of the flume can be seen here on the Sediment Experimentalist Network site, and a slo-mo video of our debris flow can be seen here.

But what did we do with the flume? After watching the USGS debris flow videos and thinking about articles by John McPhee (one and two), the students decided to focus on how ‘baffles’ (obstructions in the outwash plain) can work as a debris flow mititgation strategy and modify debris flow runout (see an example of this type of research by Choi et al., 2014). The UNC students wrote up some preliminary results, and if you want more details (or the data), let me know… Eventually I will get it all up on figshare.

For now, here is a picture of our baby debris flow:

IMG_8130.JPG

 

References to AGU Journals in Wikipedia: JGR-B, JGR-P and JGR-ES

Wikipedia page views are immense. Editing Wikipedia to include more references to journals is one way to get more science into the public eye. Additionally, Wikipedia is a portal to peer-reviewed science.  But how many Earth and Space science papers are actually cited in Wikipedia?

For this post, I’m focusing on articles published by AGU. From an earlier investigation, I found 1599 citations to AGU publications in Wikipedia. But how are these 1599 citations spread across the journals? Let’s look at works published in JGR-Planets, JGR-Biogeoscience and JGR-Earth Surface because they have a similar number of publications per year — with 123, 196 and 126 articles published in 2016 (see the AGU publication stats). (Compare these numbers to the other 4 sections of JGR: ~400 articles in 2016 for Solid Earth and Oceans, and ~800 articles in 2016 for Space Physics and Atmospheres).

A quick note on the data: I first downloaded all of the articles records for a given journal from the Web of Science. Using the article DOI numbers, I used the rAltmetric package created by rOpenSci to find Wikipedia mentions listed in the Altmetric database. Note that this was done in Dec. 2016 and Wikipedia changes constantly, so treat this data as a snapshot.

The top panel is the percent of articles (published in a given year) that are referenced in Wikipedia. The bottom panel is the number of articles (published in a given year) referenced in Wikipedia. Also plotted is the data for GRL.

JGR-wiki.jpeg

JGR-Planets steals the show here..

For # of articles cited, GRL does well too.

I’ll post results for the other 4 JGR sections in a future post. In the meantime:

  • Here is an open dataset of scholarly citations in Wikipedia, from Wikipedia Research.
  • Here is an early analysis of the issue of scholarly citations in Wikipedia.
  • This type of analysis has also been done for the PLoS Journals.
  • I wrote an article that compared month page views of relevant Wikipedia pages, my website, and one of my articles (the only one with publicly available article level metrics) — Wikipedia page views are orders of magnitude higher.

Open Structure-from-Motion Data (pt.2)

I wrote a post last summer about kite-based structure-from-motion data released on figshare in collaboration with a few colleagues (a fun side project). At the time, I couldn’t find a more specific repository for the raw images, GPS data, orthophotomosaic, and digital surface model. During AGU 2016 I stopped by the OpenTopography booth and apparently I can contribute my topographic data to the OpenTopography repository. A good way to make sure that small bespoke structure-from-motion datasets are stored in a place where people might find the data (i.e., find the SfM data when scanning through other relevant lidar data). My continued hope is that others may find these datasets useful, even if it seems that most data in repositories remains uncited (see here and here)

I originally published the data with the hopes of citing it in my own coastal dune research — and  I did manage use and cite this data on my AGU 2016 poster. This idea of citing a published dataset in a paper seems to be part of the ‘Geoscience Paper of the Future‘ concept (Gil et al., 2016David et al., 2016) where datasets, code, models, data analysis, etc. are all published seperately (i.e., given a DOI) and cited in the final manuscript. Among other benefits, these open science methods aid reproduceability and reuse by other researchers.

IMG_2863_edited.jpg

Photo credit: Mary Lide Parker / UNC Research

p.s.— check out Mary Lide’s work, it’s awesome.

 

Correlation between article downloads and citations (in ESurf)

Previous work has established a correlation between article downloads and citation (e.g., Perneger, 2004; Brody et al., 2006; Yan and Gerstein, 2011). The only exclusively Earth science journals I know that make article level metrics viewable are published by Copernicus (see here for info on their article level metrics).

To test for correlation between citation data and article level metrics (in a variety of forms), I downloaded (from the Web of Science) the 2015 cumulative citation counts for all articles published in Earth Surface Dynamics (ESurf) during 2013 and 2014. Admitedly this is a small sample (45 papers). I compared these citation records with the cumulative article level metrics for each article (through December 2015).

The article level metrics are broken into three categories: pdf downloads, xml downloads and html views. For a given paper, pdf downloads account for an average of 42% of total engagements, html views are 52%, and xml downloads are 5%.

Here is what the data look like:

Citations vs all engagement (combined html, pdf and xml):

Total.jpeg

Citations vs html views:

Rplot

Citations vs pdf downloads:

pdf.jpeg

finally, Citations vs xml downloads:

xml.jpeg

The correlation cofficient (r) for these plots:

  • 0.64 for citations and all engagements
  • 0.49 for citations and html views
  • 0.70 for citations and pdf downloads
  • 0.25 for citations and xml donwloads

In this data, article level metrics tend to be correlated with article citations. Some large outliers for html views are likely because this dataset spans the first few ESurf papers, and we were all checking out 1) how the manuscripts looked online; 2) the typsetting; and 3) the open review format..

Longer data records (perhaps from other Copernicus journals) will help to firm up these correlations. However, there is currently no way (that I know of), to obtain the Copernicus data without a lot of manually work —  i.e., there is no API like PLoS.

A general reference for this work has been:

Haustein S (2014) Readership metrics. In: Cronin B, Sugimoto C, editors. Beyond Bibliometrics: Harnessing Multi-dimensional Indicators of Performance. Cambridge, MA: MIT Press, 2014 (note: This whole book is an great reference)

(again, plots here were made using Tufte in R)