tried in these pages to convey not only my excitement about the
results of our projects, but that of the people who get caught up
in them. Participation in Zooniverse projects gets science done,
but it’s more than that. A study led by Karen Masters, Galaxy
Zoo’s project scientist (and now the elected spokesperson of the
entire Sloan Digital Sky Survey), which asked questions of volun-
teers while they were classifying, showed that people who par-
ticipate in such projects learn things.
They learn things, in fact, that they couldn’t have learned from
the projects themselves. In other words, taking part in, say,
Galaxy Zoo, inspires people to go out and seek out more infor-
mation about the Universe. The projects act as an engine of
motivation, creating a cohort of people who are actively seeking
information they never knew they wanted. Think of Planet
Hunters finding the details of transiting planets, or Old Weather
participants digging into naval history, or any of the other byways
and distractions we’ve inspired. Gamifying the experience—hid-
ing the science behind a thin veneer of play, making it feel less
like real science and more like any other game on your phone—
might make projects more efficient, but it would kill this most
important side effect of participation stone dead.
Three PaThs 241
You can see this in the studies researchers have done of the
effect of even the simple gamification—the ranks on board the
ship—that we added to the Old Weather project. Volunteers
they interviewed said that the game worked as designed, in that
it made them more likely to do more work, but there was
another, more disturbing result. Instead of describing partici-
pating as fun and interesting, they suddenly used language that
made it seem like work. One volunteer, anonymous in the
study, said that though they made it to captain they found it
stressful trying to stay ahead. So our options in reality two,
where we need to resort to either hiding the task within a game
or to using the kind of manipulation of reward that makes people
feel like they’re burdened with a task, are between projects
which don’t change people’s thoughts about whether they can
participate in science and those that feel like you’ve taken on a
second job. In this reality, science is still reserved for the clever few who capture the attention and time of others through
design, using the resulting effort for their own purposes.
Participants are motivated not by curiosity, but by competi-
tion. It may be effective, but it seems a long way from the best
that we could manage.
I want us to live in a third reality. This one is going to take some
work, I think. It’s a universe in which we don’t need to rely on
advances in machine learning to get the best out of the wealth of
scientific data that we now have access to. It’s one where human
intuition and pattern recognition are still needed to get the most
from data, even when machines are good at classification them-
selves. I feel pretty confident that this is indeed part of the reality we live in; though the recent advances in machine learning have
been breathtaking, I think that our science is weird enough and
our requirements exact enough that there will be a human
element to it for a long while yet.
242 Three PaThs
However, as I’ve repeatedly stressed, our ability to collect
information about the Universe continues to astound. We
shouldn’t expect the pace at which new data flows in to decrease,
nor should we expect it to become less open.* Because I want to
keep communities of volunteers consciously participating in sci-
ence, with no hiding in games, this means accepting that we
won’t be able to rely on citizen scientists to do all the work.
We’ve already opened the door to a solution. The supernova
project showed that when humans and machines classify in con-
cert, the outcome can be better than either working alone. I
reckon that machines, as they improve while the surveys grow,
will take on more of the burden, leaving the volunteers to review
the unusual, the unexpected, and the interesting. The work
comes in deciding how to divide the effort in such a way that
allows the most interesting objects to be found; this probably
means wading through a lot of confusing images or, for some
projects, a lot of junk with little inherent interest. We need to
understand how participants in Zooniverse projects want to
work alongside the robot colleagues that my clever machine-
learning colleagues are building for them.
This isn’t a problem unique to us. In clicking your way around
the material that Facebook chooses to show when you log in,
you are in some sense collaborating with its algorithms. You are
providing information about what the site should do next, which
it responds to by showing you things it thinks you want to see.
* I’m somewhat dismayed that the LSST project has now taken money from international collaborators, including those of us in the UK, to help fund its operating costs in exchange for privileged access to data. I hope the leadership will see sense and, despite the need for cash to keep the lights on and the servers humming, find a way to go back to what was once imagined as the most open of projects, with data freely shared and available to everyone. The sky the telescope will scan, image, and monitor, after all, belongs to no one, and there is certainly plenty of science to go round.
Three PaThs 243
More precisely, it will show you some combination of what it
thinks you want to see, content that is most likely to expand the
time you choose to spend on Facebook and content such as
adverts that is profitable for the platform.
I hope that makes your skin crawl just a little. I think we’re just
beginning to understand how our attention is being manipulated
on the internet, and to work out how to talk about it. I think that
setting up a project like Galaxy Zoo, but with machine-learning
classifiers actively working alongside human ones, is a fascinat-
ing problem which allows us to think about what we want. Even
simple examples pose dilemmas. One worry is that if we allow a
neural network to take the images it is most certain about away
from classifiers then our poor humans might lose the brightest
and most interesting images faster than the faint blobs, which are
harder to classify. If we assume that people are, for all that they
tell us they’re in it for the science, partly interested in the brightest and most interesting galaxies, even if subconsciously the
dopamine hit of suddenly seeing something spectacular keeps
them classifying, then in allowing the machine to remove pre-
cisely these galaxies from circulation we have built a project
which gets progressively less appealing over time.
Yet we have a problem. I can’t just put the bright galaxies back
in the pot without compromising on the promise, implicit in any
citizen science project and explicit on the Zooniverse, that any
work done by someone will actually be used. I
experienced an
early warning that this was going to be difficult when we shut
down our original supernova project. As I said earlier, that deci-
sion was triggered when the researchers involved switched to an
automatic classifier, and told me that they would no longer use
the results of of citizen scientists’ efforts.
On the face of it, an easy decision. The classifications were not
being used, so we no longer had a project worth participating in.
244 Three PaThs
Yet the participants, many of them dedicated people who would
come back whenever new data was released, searching for super-
novae time and time again, were not happy. I ended up calling a
few of them to understand their views, and they were pretty
unanimous in the fact they wanted their work to be used, for sure.
No one I spoke to would participate in the project if we told them
that we would just throw the results away. But they didn’t under-
stand why the researchers would switch to an automated system.
The researchers, I think, saw the automated system as just eas-
ier to understand. Given that a modern convolutional neural net-
work can be essentially a black box, as inscrutable at least as the
average person, I’m not sure that this is justified, but I see why
they might think like that. We didn’t think clearly enough about
what this would feel like to the volunteers. One day they were
contributing classifications that made a real difference scientific-
ally. The next day they weren’t, even though from their perspec-
tive nothing had changed; it’s not as if they suddenly got worse.
What had seemed to be a collaborative project was suddenly
looking rather one-sided.
I think what we got wrong here was the lack of control we gave
the volunteers, suddenly wrenching their project away from
them, and I think that’s the key to how to cope with the com-
plexities of this third reality, when we combine human and
machine classifications. If we give people control over what they
see, they can make their own decisions about how they want
things to run. I really like the idea of a project that says, ‘We know if we give you more beautiful galaxies (or spectacular penguins,
or interesting texts) you’re likely to stay around longer. These
classifications won’t count, but do you want to see these images
anyway? If so, how often?’
That seems honest and interesting, and I hope will lead to a sys-
tem that can cope with the majority of the data heading our way.
Three PaThs 245
If we don’t do something like this, I worry that we’ll miss out on the most unexpected of finds. On the contrary, I think we’re likely—if
we get it right—to be overwhelmed with interesting things.
Imagine a typical night, a few years from now when LSST is
operating. As the Sun sets over the Chilean Andes, the dome
containing the telescope opens up to allow it to cool in the cold
night air. As the sky darkens, the enormous beast of a machine
inside starts to methodically work its way across the sky, never
pausing in one place for very long but often flicking back to
where it has already been, to keep an eye out for asteroids and
other rapidly moving or changing phenomena.
As the telescope and its camera work away, the images it takes
are flowing digitally away from the mountaintop observatory
and out into the world. They will soon end up at the US National
Supercomputing Center in Illinois, where code will compare
each one to previous images of the same field, checking the
brightness of millions of objects. In any given image, millions of
times a night, some object or other will be found to have changed
in brightness, or to have apparently appeared from nowhere—or
vanished completely.
LSST deals with this by issuing an alert, a public declaration of
something happening in the sky. Its massive database, eventually
laden with the fruits of ten years of surveying, will provide details of the history of each source. And then it’s up to the rest of us.
Software ‘brokers’ will try to filter this unprecedented torrent of
data, sending the cosmologists pristine type 1a supernovae and
planetary scientists a steadily growing list of candidate asteroids
and Kuiper Belt Objects. One of these brokers will be listening
too, but it will have a different job, directing objects to the screens of volunteers around the world.
Alerts will ping on a thousand mobile phones; something has
happened in the sky, and we need your help. By the time the Sun
246 Three PaThs
rises in Chile, tens of thousands or maybe hundreds of thousands
of images will have been inspected by a crowd consisting of both
the astronomically passionate and the mildly curious. Sunrise in
Chile means that it’s nearly night in Australia, and we’ll need to
have identified the most interesting things by the time telescopes
there are opening for the evening.
Maybe the centre of a nearby galaxy has brightened because
something is falling into its black hole. Maybe a slow-moving
object looks like a promising contender to be the latest member
of the swarm of bodies out around Pluto. Maybe we caught a
planet in transit in front of a star, or just the star itself behaving badly. Whatever the case, contributions from people like you
will help determine what happens next. As the Earth spins, tele-
scopes in Hawai’i and the Canary Islands and in South Africa join
in; for the most energetic events, information from space-based
satellites will be added to the mix.
For each of these events, triggering the worldwide network of
observatories to stare at the right place is merely the start.
Understanding what they are telling us will take a lot of time, and
will overwhelm professional astronomers like myself. As data
becomes more open, we’ll see networks of citizen astronomers
spring up to discuss and debate their favourite objects. Some of
the participants are undoubtedly already experts in the field;
some will bring skills that are of great use, and others just a will-
ingness to learn. They will talk to and collaborate with the
increasing number of scientists who have discovered just how
powerful working in this way really is.
Between us, in this best of all possible worlds, we will have
built a new way of exploring the Universe: something that takes
the best features of Galaxy Zoo and Planet Hunters and all the
other projects from the last decade and turns them into some-
thing even more inclusive, more powerful, and above all else
Three PaThs 247
much more fun, as volunteers control not only the discovery
but the investigation of the things that are uncovered. I hope
that if we look back in a decade’s time at twenty years of citizen
science through these projects, there will be a completely new
crop of strange anomalies and curious objects to talk about.
I would be very, very surprised to find myself in any reality other
than this one.
It does, however, need you. You and your ver
y human talent
for pattern recognition and for being distracted from a task. You,
with your curiosity and interest and willingness to spend just a
few moments in your day doing something in contemplation of
the Universe. You, with just a little time to join with millions of
others so that collectively we can all achieve amazing things.
Making the best of our capacity as a species to explore the
Universe, and to understand the world around us, I believe,
depends on finding a way that everyone on the planet can partici-
pate as an active observer and interpreter of the data that’s now
available. If we really can get everyone to join in—even if only for
a few minutes—with this great endeavour, who knows what we
might find, sitting out there and just waiting to be discovered.
REFERENCES
I haven’t tried to give a complete bibliography of works about citizen science, or even about the Zooniverse. An updated and nearly complete list of publica-tions produced by Zooniverse projects is maintained at Zooniverse.org/publications, and the ‘Meta’ category there is a good starting point for those looking for academic studies of what we’ve been up to.
Preface
Christiansen, Jessie L. et al., 2018, The K2–138 System: A Near-Resonant Chain of Five Sub-Neptune Planets Discovered by Citizen Scientists, Astronomical Journal, 155, 2, https://arxiv.org/abs/1801.03874.
Lakdawalla, Emily, 2009, An ‘Amateur’ Discovers Moons in Old Voyager
Images, planetary.org, 5 August, http://www.planetary.org/blogs/emily-
lakdawalla/2009/2035.html.
chaPter 2
Bailey, Jeremy et al., 1998, Circular Polarization in Star-Formation Regions: Implications for Biomolecular Homochirality, Science, 281, 5377, 672.
Finkbeiner, Anne, 2010, A Grand and Bold Thing, Free Press. Provides a history of the Sloan Digital Sky Survey project.
Ivezić, Željko et al., 2018, LSST: From Science Drivers to Reference Design and Anticipated Data Products, https://arxiv.org/abs/0805.2366.
Reid, David and Chris Lintott, 1996, Astronomy at Torquay Boys’ Grammar School, Journal of the British Astronomical Association, 5, 265, http://articles.
adsabs.harvard.edu/full/1996JBAA..106..265R.
chaPter 3
Land, Kate et al., 2008, Galaxy Zoo: The Large-Scale Spin Statistics of Spiral Galaxies in the Sloan Digital Sky Survey—Clockwise and Anticlockwise
Galaxies, Monthly Notices of the Royal Astronomical Society, 388, 1686.
The Crowd and the Cosmos: Adventures in the Zooniverse Page 29
