Monday, August 15, 2011

Identifying Who Citizen Scientists Are by What They Do

Photo Courtesy: Booksworm
To help define what citizen science is we need to ask what a citizen scientist does.  This will help us cull through the proposed definitions for ones that may be too restrictive and show us ways to improve the academic proposals with real-world examples. 

After reviewing that 70+ citizen science posts I've put up this year, I created a table of all the projects and identify key characteristics of each.  This should help us narrow the definitions, as well as help characterize the various types of citizen science activities currently going on and help us identify key aspects of successful projects.  A summary list of projects we've posted soon once I've completed analysis for another project I'm working on. I've posted an early version of the list here that you are welcome to review.  But below are some key findings you may find interesting that may help us answer our question:

  • Areas of Science:  The large majority of projects reviewed have been in the area of astronomy and ecology, as well as meteorology and engineering.  Many of these also have secondary areas of science (such as ecology projects involving biology) and can also be sub-divided into other areas of science (such as biology sub-dividing into zoology and  botany).  Although useful for better classifications and as data for search engines, this overlap does not change our general description of the overall citizen science field.

  • Types of Citizen Scientist Involvement:  This looked at how each project utilizes the citizen science participants engaged in the program.   Although the scientific emphasis is what papers are written about and where we get the technological payoff we need to look at what people actually do in the projects.  Overall I found six (and a half!) general types of involvement as described below.  I also found that these roughly correlated to the amount of expertise required to participate, an important finding designers of future projects may want to consider:

    • Distributed Computing:  Citizen scientists participate by allowing project-specific programs to run on their computers.  Utilized for computationally-intensive research, huge number-crunching tasks can be divided into small chunks that personal computers can handle, with the results of all participants reassembled and analyzed by the project team.  Although some initial user set-up effort may be required, normally the program runs in the background with little or no intervention required from individual participants.   The SETI@Home project is a perfect example of a distributed computing project.
    • Transcription: Users are given existing data and are asked to transcribe it into another form usable by project scientists.  Examples are Herbaria@Home and OldWeather, projects that scan historical paper records from over a hundred years ago  and ask participants to read the information and log it into the project database.  Most of this is relatively simple recording of other people's work and can be performed by less-experienced or beginner citizen scientists.
    • Observational Measurement: Users are asked to perform simple quantitative measurements that do not require complex analysis or qualitative reasoning in the measurement.  Projects such as Snowtweets requests users make simple measurements of snow cover with just a simple ruler, and does not require any interpretation of the data or analysis of the type of snow being measured.  These are also highly accessible to novice participants.
    • Observational Analysis:  This is strongly related to the Observation category above and does not truly fit into its one category, thus my considering it a "half-category".  Many of these are "Identification" type projects, such as the North American Breeding Bird Survey or the Valley of the Khans project.  Although there are quantitative aspects similar to the Observational Measurement projects they are differentiated by the need to evaluate each measurement qualitatively or to interpret the meaning of each measurement.  For example, understanding the differences between subtle shades of feather colors or bird calls, or the unique geography indicating potential archaeological sites.  These are often accessible to novice citizen scientists but more expert users may get more out of it and may participate longer that less expert users.
    • Research Analysis: These are projects that offer data to users for analytical or research purposes but generally don't provide any formal hypotheses for testing.  Instead they make the information available and allow users to utilize it in their own projects and create their own hypotheses for testing.  An example is the U.S. government's Data.gov site, which offers data from numerous official sources but does not ask users for any specific analyses or results.  Since there is very little structure these projects normally attract more experienced citizen scientists.
    • Game: Projects that use the entertainment value of puzzles and games to engage participants in performing the scientific tasks necessary to complete the project.  For example, the FoldIt project which has turned the challenge of protein-folding into competitive games with rules, strategies, and scoring to organize the difficult work of understanding complex chemical bonding.  This also helps less-experienced citizen scientists get involved by providing an easy and fun-to-use structure for their participation.
    • Challenge: Similar to Games, these projects organize competitions around specific goals with participants competing for prizes of tangible worth.  The prizes can be quite large (up to $20 million in some cases) though many Challenges of smaller size are also available.  The incentivization of prizes is meant to drive competitive innovation and encourages participants to work intensely on the sponsor's behalf, but it also requires significant work for Challenge sponsors to set up, administer, and judge the competition.  Examples are the Ansari X Prizes which have helped reinvigorate this type of project and remains a solid model for future projects.  The many higher-value projects tend to attract more experienced users, but lower-value prizes are available for less experienced citizen scientists.
Now that we've discussed the variety of different projects falling under the "Citizen Science" title, does it give you any more ideas about what a proposed definition should or should not include?  Does it reveal weaknesses in any of the previously proposed definitions.  Let me know in the comments below so we can all benefit from each other's ideas.

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