This is a post by Gary Wolf, one of the new Directors of the Open Humans Foundation. The post was originally published on the Quantified Self blog.
I’ve recently joined the board of directors of Open Humans, joining the current board along with two other new directors, Marja Pirttivaara and Alexander (Sasha) Wait Zaranek. I’m honored to be in their company, and I want to take advantage of joining the board to explain how, in my view, Quantified Self and Open Humans fit together. Both communities include many people working in science and technology who take an interest in biometric data. But this isn’t enough to define a common purpose, and in fact a much deeper connection between Open Humans and Quantified Self has developed over the last few years, as each community has approached, from nearly opposite directions, a common problem: How can we make meaningful discoveries with our own personal data?
Open Humans has its roots in the Personal Genome Project,
whose purpose was to supply scientists with human genomic data so that
they could make discoveries more quickly. The geneticist George Church
created a project to sequence the genome of individual volunteers who
agreed to donate their genomic data non-anonymously, creating a common
data resource. Since many important genomic questions cannot be answered
with genome data alone, volunteers also shared other information about
themselves. The Personal Genome Project inevitably became a somewhat
more general personal data resource for science; however, with its focus
on genomic data, much relevant data, including the kind of data that
could be collected in daily life, remained out of scope.
When I first met Jason Bobe, who co-founded Open Humans with Mad Price Ball, he was keenly interested in this question of how to connect personal genomes with other personal data sets. Jason had worked with George Church on the Personal Genome Project. He and Mad saw Open Humans as an analogous effort, but one that would allow volunteers to contribute any kind of data. The Personal Genome Project was now a decade old. Perhaps, with deep personal data sets to work with, scientists could deliver on the promise of genomics to revolutionize medicine, a promise that had been long frustrated by the complexity connecting genomic data with real world outcomes.
I understood the goal. A few years earlier, I’d written a long Wired story about the taxonomic collaboration between Daniel Janzen and Paul Hebert. Janzen, along with his other accomplishments, was among the world’s most knowledgeable field biologists. Hebert had developed a genomic assay that promised to identify animals using an extremely small region (about 650 base pairs) of their mitochondrial DNA. Hebert was confident in his technique, but needed to prove its utility. How could the genomic data he was collecting be paired to real world ecological knowledge? At their field station in the Guanacaste Preserve in Costa Rica, Janzen and his partner Winnie Hallwachs, along with their students and colleagues, collected hundreds of butterflies and moths, identified them, snipped off a leg, and shipped it to Guelph, a city in Canada, where Hebert ran the sequence. Slowly, painstakingly, they connected the genomic data to the real world data. More than just proving that Hebert’s technique worked, they also brought a new degree of resolution to the ecological picture; showing, for instance, that individual specimens, though visually almost identical as adults, may belong to distinct evolutionary clades and feed on different plants. In my first conversations with Jason, I saw this as how Open Humans should work. It promised to provide the “field biology” for the genomic studies of the Personal Genome Project.
Unfortunately, as attentive readers, link followers, and experts in
the history of overconfidence in science may already have realized,
there’s a pretty serious flaw in my analogy. Paul Hebert was using the
genome to distinguish strands in evolutionary history, mostly at the
level of species. He wanted to know, given a leg, what kind of creature
it was from. Answering relevant health questions requires understanding
the world at a far more detailed level, down to extremely small
differences among individuals of the same species. The trick that Hebert
used is never going to work; and, for many of the health related
questions we care about, nobody knows the tricks that will work. Fifteen
years after the launch of the Personal Genome Project, it continues to
supply data resources to basic science, but its relevance to medicine
remains mostly a promise.
In the Quantified Self community the focus has always been on
individual discovery: How can we learn about ourselves using our own
data? Many of the questions addressed by people doing their own QS
projects relate to health and disease. Browse the archive of Quantified Self Show&Tell
presentations and you’ll find projects on Parkinson’s disease,
diabetes, cognitive decline, cardiovascular health, depression, hearing
loss, and many other health related issues. The kind of “everyday
science” practiced in the Quantified Self community can be understood as
being the opposite of the genome-wide association studies. Instead of
finding small, telling differences among groups of people, the everyday
science of the Quantified Self finds large effects within a single
person who is both subject and scientist.
This comes with its own kinds of difficulties. People doing
Quantified Self projects related to health face a number of discouraging
barriers, including lack of access to their own data and medical
records, bureaucratic roadblocks and exorbitant costs in ordering their
own lab tests, problems in acquiring the requisite domain knowledge to
test their ideas and interpret their data, and – perhaps most
discouraging to people who are dependent on medical professionals for
some aspect of their care – lack of recognition in the health care
system that self-collected data can be useful for making decisions about
treatment.
In the 11 years since Quantified Self started, participants have
tried many different ways to overcome these barriers, both individually
for their own projects and systematically through creating tools and
advocating for better policies. One of the lessons from this work is
that while the focus of self-tracking projects is typically on
individual learning, the methods required to make sense of our data
often require collaboration. Existing systems are not designed to
provide support for the kind of highly individualized reasoning we do;
therefore, we have to build a new system. Key requirements of this new
system include: private, secure data storage; capacity to integrate data
from commercial wearable devices; fine-grained permissions allowing
sharing of particular data with particular projects, and withdrawal of
permission; capacity for ethical review both to protect individual
participants and to enable academic collaborations.
Two years ago, we organized our first participant-led research project in the Quantified Self community. A group of about two dozen of us measured our blood cholesterol as often as once per hour, exploring both individual questions about the patterns and causes of variation in our blood lipids and a common group question about lipid variability. We had a pressing need for some collective study infrastructure, but there was no available tool that worked for our needs. We took a DIY approach and at the end of the project we’d learned a tremendous amount both about our own varying cholesterol and about the process of self-directed research. (Our paper, “Approaches to governance of participant-led research,” has recently been published in BMJ Open; our paper on our collective discovery about lipid variability has been accepted for publication in the Journal of Circadian Biology; we’ll add a URL when we have it.)
At the conclusion of our study, one of the participant organizers Azure Grant, decided to press ahead with another participant-led study on ovulatory cycling. Azure had already presented a self-study on using continuous body temperature to predict ovulation at a Quantified Self conference. Now, she wanted to organize a group of self-trackers to try something similar, but integrating newer measurement tools to acquire higher resolution data. Among these tools was the new version of the Oura ring, which offered body temperature, heart rate, and sleep data. This idea put new demands on our study infrastructure. Thanks to generous collaboration from Oura engineers, we could offer participants access to detailed data from their rings. But how could this data be stored privately and controlled by each individual, while also being available using fine-grained permissions to their fellow participants and study organizers? How could this data be integrated with other data types they might decide to collect during the project? Where was there infrastructure for a “field biology” of the self?
We turned to Open Humans. The personal reasons were as important as
the technical ones. Mad Ball, along with her work leading Open Humans,
is a long time participant in the Quantified Self community, who has
consistently advocated for non-exploitive approaches to handling
personal data, and has contributed the results of her own self-directed
research. (See Mad’s recent talk on “A Self-Study Of My Child’s Genetic Risk.”) And Bastian Greshake Tzovaras, the Open Humans research director, quickly proved to be an extremely sensitive and skilled collaborator. Bastian co-founded openSNP,
a grassroots effort that outgrew Personal Genome Project by supporting
citizen science participation. (Currently, there are more genotyping
datasets publicly shared in openSNP than all other projects in the world
combined.)
With help from Mad and Bastian and the Open Humans infrastructure, we
built our next stage study workflows with encouraging speed and
harmony. Fundamentally, we found ourselves aligned on the core idea that
research processes designed around personal data sets should be built
to protect individual agency, even where this requirement creates
friction for academic collaborators. The rarity of this commitment may
only be obvious to those few people who have gotten painfully deep into
the workflows of study infrastructure. (And I recognize that a post of
this length that is this deep in the weeds can have very few readers!)
But, in a way, that’s one of the beautiful things about this stage of
building a new knowledge infrastructure. We’re far into it enough to
have evidence that we’re on the right track. But we’re still close
enough to the beginning that each step is a significant contribution and
a potential model to build on.
I very much hope that over time – and the sooner the better – our shared ideas about individual agency and everyday reasoning are embodied in tools and policies that are so commonplace that no single organization is responsible for them. But for now, it’s impossible not to recognize that Open Humans is an indispensable resource, defining an approach that needs to be developed and expanded, and managed by a team that has deep insight into the challenges and potential of participatory science. I look forward to building more connections between our two communities.
Open Humans Blog 