I encourage all levels of bio/ecologists to employ microcontrollers for
their projects because it is economical, reliable, and, most importantly,
flexible. It gives me a sense of freedom for generating ideas
and experimental methods because my experiment is no longer limited by
'function' or 'capability' of commercial device. But I found difficult to
acquire even basic skills and understanding in engineering and programming
language at first. For that matter, I would like to share some useful
websites for beginners:

1. Arduino forum: https://forum.arduino.cc
2. Raspberry Pi forum: https://www.raspberrypi.org/forums/
3. Tutorial: http://www.instructables.com
4. Tutorial: https://learn.adafruit.com
5. Tutorial:

If you don't have any experience in microcontrollers, you need to buy a
starter kit rather than only a microcontroller. Even the simplest project
(e.g., LED on/off) needs an electrical component (e.g., resistor,
breadboard, wire, cable, power) to run.

I use Arduino microcontroller for simulating sea level rise in natural
wetlands. Please find my publication explaining the design and construction
of the system in detail from:

Title: Design and construction of an automated irrigation system for
simulating saltwater intrusion in a tidal freshwater
Wetlands <https://link.springer.com/article/10.1007/s13157-016-0801-4>

Hope this helps.

Dong Yoon Lee, Ph.D.
Postdoctoral researcher at Virginia Commonwealth University
On Thu, Oct 13, 2016 at 11:29 AM, David Inouye <ino...@umd.edu> wrote:

> An interesting article in Science:
> http://science.sciencemag.org/content/353/6306/1360
> Summary
> Many science research projects rely on specialized electronic devices and
> software to gather data that often come with a high price tag. Advances in
> open-source hardware and software are occurring at an astounding rate, but
> scientists are often slow to take advantage of these for purposes beyond
> their original scope. Here, we advocate that open-source technology can be
> easily applied in science research to collect large data sets, at the same
> time reducing costs and increasing the repeatability of experiments.
> --
> Dr. David W. Inouye
> Professor Emeritus
> Department of Biology
> University of Maryland
> College Park, MD 20742-4415ino...@umd.edu
> Principal Investigator
> Rocky Mountain Biological Laboratory
> PO Box 519
> Crested Butte, CO 81224

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