Citizen Science and Employment in High Technology Industries

Last week I wrote an article on Industry Niches for Citizen Science (Part 1) that tried to identify high-technology sectors of the U.S. economy.  Although the papers I found were not definitive they gqve us a great head start. So let's continue pushing forward to see what we find.

As a reminder, the list of high-tech industries was determined through a number of different factors. The first was based on the relative employment by each of people in various high-technology occupations.  But what are those occupations and how do they connect to the work of citizen scientists?    Reviewing the Hecker article again,

"High-technology occupations are scientific, engineering, and technician occupations, the same group of occupations used to define high-tech industries in this and earlier studies. They include the following occupational groups and detailed occupations: computer and mathematical scientists, Standard Occupational Classification (SOC) 15-0000; engineers, SOC 17– 2000; drafters, engineering, and mapping technicians, SOC 17– 3000; life scientists, SOC 19–1000; physical scientists, SOC 19– 2000; life, physical, and social science technicians, SOC 19–4000; computer and information systems managers, SOC 11–3020; engineering managers, SOC 11–9040; and natural sciences managers, SOC 11–9120...Some technology-oriented workers are engaged in R&D, increasing scientific knowledge and using it to develop products and production processes; others apply technology in other activities, including the design of equipment, processes, and structures; computer applications; sales, purchasing, and marketing; quality management; and the management of these activities."

This is still academic.  Relating it back to citizen science, I now ask the question of what are the jobs these people do, and what is the citizen scientist activity equivalent to those jobs?  In other words, What do these people actually do?  Here's a new version of the list but with list of common citizen scientist activities attached to each.

1. Computer systems design and related services - Computer coding (hardware and software), Electrical engineering
2. Software publishers - Computer coding (software)
3. Architectural, engineering, and related services - Electrical, engineering
4. Scientific research and development services - Biology, Chemistry, Physics, Ecology, Medicine, Electrical engineering, Mechanical engineering, Bioengineering, Computer coding (hardware and software)
5. Internet service providers and web search portals - Computer coding (hardware and software), Electrical engineering,
6. Computer and peripheral equipment manufacturing - Computer coding (hardware and software), Electrical engineering
7. Internet publishing and broadcasting - Writing, Computer coding (software)
8. Navigational, measuring, electromedical, and control instruments manufacturing - Computer coding (hardware and software), Electrical engineering, Design
9. Data processing, hosting, and related services - Computer coding (hardware and software), Electrical engineering
10. Aerospace product and parts manufacturing - Electrical engineering, Design
11. Communications equipment manufacturing - Computer coding (hardware and software), Electrical engineering
12. Semiconductor and other electronic component manufacturing - Computer coding (hardware), Electrical engineering
13. Pharmaceutical and medicine manufacturing - Biology, Chemistry, Medicine, Mechanical engineering, Bioengineering
14. Other telecommunications - Electrical engineering
15. Oil and gas extraction - Geology, Chemistry, Mechanical engineering
16. Forestry - Biology, Ecology
17. Commercial and service industry machinery manufacturing - Electrical engineering, Mechanical engineering
18. Manufacturing and reproducing magnetic and optical media - Electrical engineering, Mechanical engineering
19. Basic chemical manufacturing - Chemistry, Mechanical engineering
20. Professional and commercial equipment and supply merchant manufacturing - Chemistry, Physics, Electrical engineering, Mechanical engineering, Computer coding (hardware and software)
21. Industrial machinery manufacturing - Mechanical engineering
22. Federal government, excluding postal service - Biology, Chemistry, Geology, Physics, Ecology, Medicine, Electrical engineering, Mechanical engineering, Bioengineering, Computer coding (hardware and software)
23. Management, scientific, and technical consulting services - Biology, Chemistry, Geology, Physics, Ecology, Medicine, Electrical engineering, Mechanical engineering, Bioengineering, Computer coding (hardware and software)
24. Audio and video equipment manufacturing - Computer coding (hardware and software), Electrical engineering, Design
25. Electric power generation, transmission, and distribution - Electrical engineering, Mechanical engineering
26. Resin, synthetic rubber, and artificial synthetic fibers and filaments manufacturing - Chemistry, mechanical engineering

This starts to give us some insight, especially if we start to "clump" some of these industries together based on the type of work they do. Even if the scientific field is somewhat different there are general similarities.  For example, 

• Manufacturing industries (numbers 3, 8, 10, 11, 12, 13, 17, 18, 19, 20, 21, 24, and 26)
• IT and computer coding (numbers 1, 2, 5, 6, 7, 11, 12, and 18). 
• Industries that (positively or negatively) focus on Environmental concerns, such as Forestry (16), Oil and gas extraction (15) and Management, scientific and technical consulting services.
• "Traditional" science and technology fields (numbers 1, 2, 3, 4, 5, 6, 8, 9,10, 11, 12, 13, 19, and 23) and more "Applied" (7, 14, 15, 16, 17, 18, 20, 21, 22, 24, 25, and 26).

There are even a few other items to note, such as the interesting addition of "Writing" in the seventh-largest high-technology field of Internet publishing and broadcasting.  There are also some that span a wide variety of products and activities, such as Federal Government, excluding postal service (#22) and Management, scientific, and technical consulting services (#23). 

The inclusion of "Consulting" in the last item is most interesting to me.  This means a lot of different things to a lot of people, and in fact can even be included with it's counterpart of the Federal government, since much consulting (even scientific/technical) is actually performed on behalf of the government.  So this is potentially a very large opportunity as far as high-technology employment is concerned.

But what most intrigues me is the idea of consulting and what it can mean for citizen science.  Typically the term "consulting" implies firms bringing in outside experts to offer advice and direction in a specialized area.  This can include advice on an area outside of the companies focus (e.g., a chemist advising on a specific reaction of interest to a pharmaceutical firm) or to perform a project for a defined length of time (e.g.,, an IT consultant upgrading computer systems of a chemical firm).  

In some ways we already see examples of both types in existing platforms such as the Innocentive challenge web site.  The Ideation and Theoretical challenges are very similar to the model of bringing in outside advice, while the Reduction-to-Practice is similar to the short-term project model.  Obviously there is overlap and it is not a 100 percent match, but the concept is pretty close.  

Both offer real possibilities for citizen scientists.   For the first, where outside experts provide advice, there is no reason these need to be "Professionals" or degreed experts in those fields.  It can be anyone with the proper knowledge and experience the company is looking for.  Even better is that often these people can be brought in from areas widely different than the firm, providing a unique perspective that is often to key important breakthroughs.   For the second, it just requires citizen scientists to take their ideas and put them into practice, either in developing a product, performing a service, or taking on some larger challenge.

All these opportunities go by different names.  Statistically they may be called consultants.  Innocentive calls them Solvers.  But in every case just replace that word with "citizen scientist".

Of course there are many more to apply this concept to entrepreneurship and helping citizen scientists profit from their work.  But I'll leave much of that for San Jose and many future posts!

More Research on the Economic Value and Engagement of Citizen Scientists

Regular readers know that I'm very interested in the factors that get people involved in citizen science projects and keeps them motivated to stay.  By its very nature this is one of the most important issues citizen science needs to master.  It was also of the first research areas I was interested in and it played an important role in my "Keys to Citizen Science Success" and later poster presentation. So its great to see some good, peer-reviewed research coming out on these very same issues.

My concern has always been that while many people may initially be interested in the general idea of citizen science, and may find a project that sparks some initial activity, it doesn't take long before they lose the passion and leave the project (or the field) altogether.  My gut has always told me this is a problem and now some recent research backs me up.  Apparently it really is true that while most people start projects with a rush of excitement it quickly fades and the actual work is done by a just a small handful of individuals.

The paper's official title is "Crowd science user contribution patterns and their implications", written by Henry Sauermann and Chiara Franzoni and published this month in the Proceedings of the National Academy of Sciences.  A popular press article from Ars Technica sums it up well, "Most participants in 'citizen science' projects give up almost immediately".  Similarly, in the words of the paper's authors, "...we find that most contributors participate only once and with little effort, leaving a relatively small share of users who return responsible for most of the work."  All this fits right in with some of my previous findings that found "Motivate the User" was an important key to successful citizen science projects. As I said at the time:

"People need a push sometimes. Just because they joined your project and have learned how to participate, that doesn't mean they'll stick around. This is especially true for projects where users perform the same tasks multiple times (such as identifying whale songs or counting craters). Users may lose interest after 10-15 minute. So project scientists need ways to keep things interesting by offering new goals as initial ones are met."

There is a lot to this article with data from over 100,000 users spread over six months on seven different Zooniverse sites and a comparison with sites like CitizenSort. As popular and highly successful citizen science platforms these are a great place to start.  However, one issue I have with the authors' analysis is that the participants involvement is at the "Contributory" level of citizen science.  You can read much more about the levels of citizen science involvement here, but this is one of the early stages that don't require much involvement from participants.  Of course people can CHOOSE to get heavily involved, but they can perform the required tasks with minimal training or follow-up, and without a large time/financial commitment. So the findings that many people don't stick with projects may be a self-fulfilling prophecy for these types of citizen science research.

It should also be noted that the authors attempted to quantify the financial impact of the citizen scientists' work. Depending on the model used, they found an average savings of over $200,000 per project. For all projects analyzed there was a total savings of over $1.5 million, or $15 per initial participant.

As someone interested in developing citizen science business models, evidence demonstrating the value of citizen science in either reducing the costs of research is very important.  Given the highly leveraged nature of these projects, where the large majority of costs are in initial software development and raw data collection, a $15 per user benefit is quite tempting.  Adding some costs for marketing and to handle the bandwidth for submitting individual results, this  cost-effective business tool can be used to both save money for companies AND support investments in additional citizen science projects.  In other words, benefiting people on both sides of the research process.

Interestingly, I'd be remiss if I didn't say that the researchers talk about some early evidence (from previous papers) hinting that may be stronger motivators than financial incentives.  I'm pretty skeptical of this for a variety of reasons, but obviously I can't pre-judge.  For instance, it only looks at projects at the "Contributory - Novice - Entry Stage" level of citizen science research. So I'll be taking a closer look at those portions in a future post.

Just one more reason for you to keep coming back!

Industry Niches for Citizen Science - Part I

My current long-term goal in citizen science is to identify business models that will both benefit from, and support the work of, citizen scientists.  This is a symbiotic relationship that can push our field to the next level.  But while some admirable steps have been taken by various entrepreneurs, there is still much room for potentially profitable but currently untested models.  Exploring them together is my goal.

One of my first approaches has been looking at the crowdfunding of citizen science projects to see what types of things the public (or future customers) are interested in, and to find ways future citizen science entrepreneurs can raise funds for their ideas. Consider this a look at selling citizen science to public (retail) consumers.

Another great place to start is looking at the types of business that rely heavily on science and technology to run their operations, develop innovative products, or both.  These would be like selling citizen science to business (commercial) consumers. What are they, how can they benefit from citizen science, and how can citizen scientists get support from them?

An easy place to start is just a brainstorming session...not only does it lay a quick framework but it's also a great way to think about the problem from a citizen science side first.  That way, high-tech fields that are less relevant to citizen science won't come up immediately and we can build our model with the appropriate emphases. That being said, we still want to some rigor around the process.  So we need to do some research.

One of the first places to look are government sources, specifically the North American Industry Classification System (NAICS codes).  This is a listing of nearly every type of business operating in the country and is the basis for a wide amount of statistical reporting.  But it is not based on the products or types of workers; instead it is designed to classify industries based on the "...primary purpose of...facilitating the use of economic data." (http://www.census.gov/eos/www/naics/history/docs/issue_paper_1.pdf).  So while this gets us started, and may help us attach meaningful statistics to our eventual citizen science model, I'm not sure this gets us where we need to go.

Another problem with NAICS codes is they do not provide any information on the science or technology basis of the classified industries.  So we need a new source for that too.  Looking around a bit I found this article (by Daniel Hecker at the U.S. Bureau of Labor Statistics) developing a list of high-technology fields based around the NAICS model. Although a number of criteria are often used to define high-technology fields (such as production of high technology products and intensity of R&D employment), this Hecker article was forced (for a variety of reasons) to rely solely on those industries employing a high proportion of science, engineering, and technical occupations. From that paper,

"An industry is considered high tech if employment in technology-oriented occupations accounted for a proportion of that industry’s total employment that was at least twice the 4.9-percent average for all industries. With this relatively low threshold, 46 four-digit NAICS industries are classified as high tech.15 Within that group, three levels of high technology were specified. Level I Monthly Labor Review July 2005 59 includes the 14 industries in which these occupations accounted for a proportion that was at least 5 times the average or greater and constituted 24.7 percent or more of industry employment. Level II includes the 12 industries in which the high-tech occupations were 3.0 to 4.9 times the average (constituting 14.8 percent to 24.7 percent of total employment), and Level III includes the 20 industries with a proportion that was 2.0 to 2.9 times the average (making up 9.8 percent to 14.7 percent of total employment). 

These high-tech industries are a heterogeneous group in terms of production processes and output, covering a broad range of industries. Level I includes the computer and electronic products, aerospace, and pharmaceutical and medicine manufacturing industries; the computer software, Internet, and data processing industries in the information sector; and three professional, scientific, and technical services industries..."

Although the data is from 2002 and the data from from 2005, it's still a great place to start. Extracting from his table rankings,

Level I

• Computer systems design and related services
• Software publishers
• Architectural, engineering, and related services
• Scientific research and development services
• Internet service providers and web search portals
• Computer and peripheral equipment manufacturing
• Internet publishing and broadcasting
• Navigational, measuring, electromedical, and control instruments manufacturing
• Data processing, hosting, and related services
• Aerospace product and parts manufacturing
• Communications equipment manufacturing
• Semiconductor and other electronic component manufacturing
• Pharmaceutical and medicine manufacturing
• Other telecommunications

Level II

• Oil and gas extraction
• Forestry
• Commercial and service industry machinery manufacturing
• Manufacturing and reproducing magnetic and optical media
• Basic chemical manufacturing
• Professional and commercial equipment and supply merchant manufacturing
• Industrial machinery manufacturing
• Federal government, excluding postal service
• Management, scientific, and technical consulting services
• Audio and video equipment manufacturing
• Electric power generation, transmission, and distribution
• Resin, synthetic rubber, and artificial synthetic fibers and filaments manufacturing

Interestingly, while the crux of this analysis is centered around employment Hecker does delve a bit into industry classifications based on those producing high technology products.  This does not follow the NAICS system and instead uses his own industry definitions.  His top ten high technology product industries are:

• Biotechnology
• Life science technologies
• Optoelectronics
• Information and communications
• Electronics
• Flexible manufacturing
• Advanced materials
• Aerospace
• Weapons
• Nuclear technology

These are informative and will be very useful if we opt to go further and pull stats on these fields. But for my purposes, helping people think of entrepreneurial opportunities in citizen science, they leave me wanting much more.  There is little connection to the underlying science and no recognition of the underlying niches of each field...these niches being the place where citizen science innovation could thrive.

After all this discussion that is what we are looking for.  Not the broad industry characteristics or trends, but the niches within those industries.  This approach holds promise to me for many reasons, but primarily because citizen science is very much a grass roots phenomenon (almost by definition) and as such does not have the clout or resources to take on huge corporations immediately.   Instead it must build itself up, gathering resources, infrastructure, and public acceptance before its potential can be met. Integrating citizen science into science industries holds much potential, but needs to start out small and build itself up from there.

There is so much to say on not just why we need to be targeting specific niches, but also ideas on what those niches should be.  Sadly way too many though.  So I'll have to hold those thoughts for now, but stay tuned next week where we'll talk much more about this topic and start tying things together a bit.

More Thoughts of Crowdfunding Citizen Science Successfully

Photo Courtesy:
Flickr User 401K (2012)

Last year I spent much time reading through hundreds (if not thousands) of crowdfunding projects to identify ones that support citizen science, and citizen science-like, projects.  This time was well spent and led me to a number of findings about citizen science in general.  But I also learned a lot about the crowdfunding process.

First off, as I've documented before, requesting funds based on the public's goodwill for citizen science has not yet shown much effectiveness.  There are a variety of reasons, including (in my estimation) that the term itself is still somewhat unknown to many people outside the field, and that it does not (yet) drive people to make contributions.  So project designers need to do more to attract funding.

A good example of this is the uBiome project. The designers certainly talk about citizen science a lot in the description, and (as discussed at length before) the project does indeed fall in the citizen science category.  But they offered a lot more to backers than just their thanks; they also offered an actual product and service to backers.  So by funding the project the user gained something tangible (the product) as well as knowledge (test results).  But what that really does is make the project interactive with funders and provides an interactivity that many other projects don't offer. That allowed them to catch on quickly, and then ride that success to allow selling even more after the initial funding period expired.

There is also another reason citizen science projects may have a tougher time than others...some people don't yet think they are as 'exciting'.  While we know citizen science is exciting the public may not always agree.  At least, not if we rely on project descriptions alone.  Because of this it is hard to ignite a passion in potential funders that will get them to open up their wallets.   In "Crowdfunding the Next Big Thing: Money-Raising Secrets of the Digital Age", author Gary Spirer repeatedly describes how entrepreneurs often have difficulty with crowdfunding while artists and other creative types have much more success.  In his words,

"Why can't the entrepreneur succeed very well at crowdfunding donations with rewards, but creative artists, game developers, and musicians can?  The reason is that the creative types have learned how to build audiences and fans.  They know how to entertain.  They know how to appeal to emotions.  They know how to tell compelling stories."

At a gut level I have to agree with some of this statement.  Looking over all the project descriptions many seemed to fall into the very trap Spirer describes.  The projects are written more for scientists or others in the field already, sometimes using specialized jargon or not fully describing advanced concepts.  This makes them less accessible to a lay reader.  Also, while they may state an excitement or describe a passion, the reader doesn't FEEL that passion; it doesn't come through in the writing.  That is in art form and not a science (no pun intended) and goes to show why artists may be initially more successful.

Admittedly this is just an observation. It is not a strict fact or rule of nature.  Potential project designers and crowdfunders can overcome this type of problem if aware of it and if they spend the energy to overcome it.  Fortunately the art of science communication continues to be recognized as an important skill and there are many resources for people who want to learn.  So this is a great place to start for people looking to truly take advantage of the potential for crowdfunding their research.

This ability to incite passion in their fans is also a key to their building networks of eager fans...networks that citizen scientists and professional researchers often don't have.  As the co-founder of Rocket Hub states (as reported by Spirer),

"...entrepreneurs often don't do as well with the rewards type of crowdfunding because they are not as experienced in building the needed networks or followers, as, say, a creative-type rock group.  Rock groups know that to survive they need fans to buy their records and tickets to concerts."

Again, this comment is focused on entrepreneurs and business people, but I think researchers and citizen scientists are in the same boat.  We don't have avid fans that follow us and give us their money based on our reputation. We are strangers to the public funder which sets the bar that much higher. We have to build trust, demonstrate the strength of our scientific idea, and prove we are the ones best suited to do it. Without an existing network that is very hard to do.

It should go without saying, but when starting a project it is imperative to convince your friends, family, and colleagues make donations early. Don't be shy about asking everyone in your contact list. If you believe in your research, you should believe enough to ask the people who know you best to vouch for it. It may not be easy, but acting shy will only hurt you. In "The Crowdfunding Book: A How-to Book for Entrepreneurs, writers & Inventors", author Patty Lennon writes,

"Many of us have been raised to believe that promoting ourselves is wrong.  So it is not surprising that so many "good" people struggle with marketing.  Promoting yourself is not about bragging.  Promoting yourself is about letting people know about the amazing gifts you bring to the world, so that if they need those gifts or know someone that needs those gifts, you can reach them."

Sadly I saw examples of this shyness in a number of the proposed projects I read through. Obviously not all proposals will be successful and meet their goals, especially when goals are optimistic. But projects with just a few backers (less than ten) or less than $500 in support seem to not be tapping into those networks. Some of those appear on the listing of projects but many others don't...as described in my previous post these are the ones that received so little funding (less than 1-2% of the total request) or such as small dollar amount (under $100) I couldn't even consider these "serious" attempts.

Starting with an existing network of backers is also crucial for gaining initial momentum.  A quick spurt of support when a project opens not only helps set a good pace, but also serves as an indicator to other people that the project is worth supporting.  In essence, that early support helps "vouch" for the project to future potential supporters who come across it.  They may not know who you are, but they like the idea and see that OTHERS like the idea as well.  So they choose to join the crowd.  Early support also helps search engines, the press, social media, and the crowdfunding sites themselves notice you.  These sites like to showcase winners, and demonstrating early support may help you get extra coverage and free publicity.   All continuing the virtuous cycle of increased funding that itself spurs more funding.

These are a few of the things I learned, and am continuing to learn, by going through these various projects and reading up on emerging experts in crowdfunding.  There is still a lot more to learn though, and even more to apply to citizen science business models.  So stay tuned to this blog as we continue researching, analyzing, and learning together.