Join Quantified Flu

Do changes in physiological signals that are measured through wearables (such as body temperature and resting heart rate) precede consciously experienced symptoms (such as coughs, sore throats, shortness of breath, etc.)? This question is on many people’s minds right now, and it was a hot topic during an Open Humans community call in early March. Collectively we wondered whether it is possible to explore and potentially improve, through collective self research & an open process, what individual value data has with wearable data and symptom tracking.

As a result of this we have launched Quantified Flu, a collective project that tries to answer this question through both retrospective data analyses and the ongoing reporting of symptoms. It allows you to annotate your existing wearable data by adding past dates on which you fell sick. For the ongoing symptom collection, you can set up a daily check-in time on which you will get an email asking you to either quickly report “no symptoms” or enter symptoms if you experience any. 

You can decide how much of this data you want to share: You opt-in to publicly share the data through a random identifier, allowing others to analyze that data and create new and improved data visualizations. In the long-run we might share aggregated and de-identified data as well, as long as we are confident that this respects individual privacy. If you are interested in joining this effort as a participant, head to Quantified Flu and sign up!
All of this was made possible in such a short amount of time thanks to our great community, including Gary Wolf, Ernesto Ramirez, Katarzyna Wac, Chris Ball, Beau Gunderson, Lukasz Baldy, Konstantin Vdovkin and many more. If you want to contribute data visualizations, adding support for more wearables or help with web development, join our Slack and visit the #quantifiedflu channel!

Come to our Community Calls

As mentioned before: The idea to run Quantified Flu came directly out of our community calls, and given the huge interest in them we have ramped up the frequency and are now doing them on a weekly basis: They take place every Tuesday at 10am Pacific / 1pm Eastern / 6 pm GMT / 7pm CEST and you are invited to join us. We always welcome seeing new faces! 

The calls are done via Zoom and we have a growing Google Document that contains all the details on how to join the calls, an agenda for the upcoming call as well as notes of the past ones. If you want to get reminders for the meetings in your own calendar, we do have a community calendar that you can subscribe to. 

If you want to catch up on the prior community calls, we are recording them and make them available on the Open Humans YouTube channel.

We held our first community call on December 10th – many thanks to the attendees and invited presenters, Karolina Alexiou and Rogier Koning! Future community calls will be held on the second Tuesday of each month.

Interested in attending on January 14? You can add the Google calendar event and visit our ongoing agenda & notes document to get information about joining the call.

Invited guest: Rogier Koning, Nobism, and Cluster Headaches

Rogier shared the Nobism app and data source in Open Humans, which he created to track symptoms, potential triggers, and treatments. Cluster headaches are one of the most painful things people can experience – they’re called “suicide headaches” – and patients understandably want to understand how to anticipate and reduce their own symptoms!

Rogier’s reports showed compelling visualizations produced with the Ubiqum project, which members can share their data with – illustrating how headaches occur over the course of months, at different times of the day – different individuals had different patterns for what time headaches were likely to occur. The effect of medications could be clearly seen in the patterns on the graphs!

One of our long-time community members, Ben Carr, noted he’s also a cluster headache patient, and reflected on his use of the app! He reflected on potential improvements and hadn’t realized he could also join the accompanying Ubiqum project. (Showing us there’s a potential need for prompting people!) Rogier also explained that the app isn’t limited to cluster headaches, and he’d welcome other chronic disease patients using it for new purposes.

Rogier has plans to expand community aspects of his work, and hopes to share more in the future!

Invited guest: Karolina Alexiou and GitHub data import

Karolina presented one of our latest data source additions, which might especially be of interest for programmers. Her GitHub data import gets all data on contributions made to (open source) code projects on GitHub, giving members a view of to which projects they contribute and when & how much they program.

In addition to the data import itself, Karolina demonstrated how this data can be visualized and what can be learned from it, by running a Personal Data Notebook on her own data. This notebook is already publicly available, so if you are using GitHub and want to give it a try, you can start right away.

Data Types & Uploading files

Mad & Bastian shared some ongoing work on the Open Humans end. While data sets are currently organized by the data source that has uploaded them, this sometimes makes sharing the data complex. Either because multiple projects upload the same or very similar data types (e.g. genetic data from different sources), or because a single data source uploads multiple kinds of data (e.g. activity tracking data that contains both step counts and GPS records). As Ben Carr noticed on the call, this can make granular sharing hard.

To adjust for this, Mad has been working on a data type system, which allows individual data files be classified according to the kind of data in them, instead of just relying on the source. They presented a new uploader tool for Open Humans, that can assign the data type to each file upload. Based on this, Bastian presented how this data can be used to upload environmental noise data, as recorded from an Apple Watch and how it can be explored through a Personal Data Notebook. One thing Bastian learned was that some of his noisiest times were at home; when he looked into it more closely, it was when he was in the shower!

Further discussions

Additionally, the participants of the community call discussed their experiences with tracking blood sugar through Continuous Glucose Monitors, how to make the Personal Data Notebooks more user friendly and whether it is possible to allow access to data without sharing the data sets themselves by allowing analyses being run in the cloud. Exchanging community member experiences and what they are working on was inspiring.

If you want to participate in the next community call please see our Community Call information & notes document for event details, it will be 14th of January at 10am PST / 6pm GMT.

In the last few weeks our community has launched a plethora of new projects that you can join to collect more data about yourself as well as new research opportunities covering topics from the genetics of personality over blood pressure tracking to cluster headaches. Find out more about those projects below:

QCycle: Tracking ovulatory cycles

QCycle is a participatory research study that follows the spirit of the Quantified Self. As a collaboration between Azure Grant at the University of California, Berkeley and all participants, the study is interested in mapping the diversity of biological rhythms such as the ovulatory cycle through different wearable devices such as the Oura Ring. In the long-term one of the aims could be creating open-source ovulation predictions.

As the study is a collaborative project, you can also bring along your own research questions as a participant. Visit the study website to learn more on the aims and how it works.

Genetics of Personality Type

If you have your own genetic data from 23andMe, AncestryDNA or FamiliyTreeDNA you might be interested in the Genetics of Personality Type study of Dr. Denise Cook of the Ronin Institute. In her study, Denise wants to find out whether it is possible to find genetic variants that are correlated with the personality type as defined by different questionnaires that make use of the Myers-Briggs Type Indicator.

After joining the study you will be asked to fill out 3 surveys, the results of which will be deposited in your Open Humans account as well. You can learn more about the study and join it on the study website.

Nobism: Tracking cluster headaches

The cluster headache patient community around Nobism has been particularly active in the last few weeks. Under the lead of Rogier Koning , the community was awarded one of the Open Humans project grants. The grant allowed them to integrate a data synchronization into their mobile application for tracking symptoms and interventions. Check out their app for iOS and Android.

Thanks to a collaboration with the Ubiqum Code Academy you can already use the data collected by the mobile apps to get personalized reports and data visualizations. In the Nobism Ubiqum Cluster Headache Project a team of data science students will create evolving, monthly reports based on the data you collect.

Last but not least you can also decide to share those individualized reports with the larger Nobism community through the nobism reports for Advocating project. By sharing those reports you allow the larger community to use those reports in outreach materials like presentations.

snps is a new open source Python package that aims to help users interact with genetic data from a variety of sources, including direct-to-consumer (DTC) DNA testing companies and whole genome sequencing (WGS) services. Specifically, snps provides tools to help with reading, writing, merging, and remapping SNPs.

The initial snps capability was developed by Andrew Riha as part of lineage, which joined the Open Humans ecosystem as a project in early 2019. Soon thereafter, numerous members from the Open Humans community, including Bastian Greshake Tzovaras, Mad Price Ball, James Turner, Ben Carr, and Beau Gunderson, requested support for VCF files. So, in May 2019, Kevin Arvai (with his VCF experience from Imputer) and Andrew teamed up to add the VCF capability, and wanting to share the work with others, snps began as an open source project to further enable citizen science.

Some of snps capabilities are detailed below, and a Notebook demonstrating usage of `snps` is available on Open Humans has been developed by Kevin & Bastian.

Reading

snps supports reading VCF (variant call format) files, in addition to files from 23andMe, Ancestry, Family Tree DNA, and MyHeritage.

Moreover, snps attempts to detect the assembly, or build, of the data. Commonly, Builds 36, 37, and 38 are used today, and these represent the “version” of the reference genome.

Writing

snps supports writing SNPs to CSV and VCF files for Builds 36, 37, and 38. This also means that snps can be used to essentially convert files from DTC DNA tests to VCF format.

Merging

snps supports merging datasets, e.g., if test results are available from more than one source. When SNPs are merged, any discrepancies are identified.

Remapping

snps supports remapping SNPs from one assembly to another. SNPs can be remapped between Builds 36, 37, and 38.

This guest post was written by Andrew Riha and Kevin Arvai. Andrew & Kevin have both launched projects that make use of genetic data on Open Humans before.

Two of our project grant awardees – Kevin Arvai and Andrew Riha – have been working tirelessly to build two new web tools that can make use of your genetic data that’s stored in Open Humans in interesting ways. And their hard work has paid off: Kevin’s Imputer and Andrew’s Lineage are now available!

Imputer is designed to fill the gaps in your genetic testing data. Direct-To-Consumer companies like 23andMe usually genotype just a small fraction of your genome, focusing on generating a low-resolution snapshot across your whole genome. Genotype imputation fills in those gaps by looking at reference populations of many individuals who have been fully sequenced in a high resolution, using this data to predict how to fill the gaps in your own data set. Imputer is using the reference data from the 1000 Genomes Project to perform this gap-filling and deposits the filled-up data in your Open Humans account. Kevin also provides two Personal Data Notebooks that you can use to explore your newly imputed data set. If you want to explore the quality of the newly identified variants, you can use this quality control notebook. And if you’re interested to see where your genome falls within a two-dimensional graph of different populations from around the globe, this notebook allows you to explore how closely you relate to other people in the 1000 Genomes data.

Andrew’s Lineage brings some further tools and genetic genealogy methods to Open Humans.  If you have been tested by more than one Direct-To-Consumer genetic testing company, Lineage allows you to merge those different datasets into one large file, while also highlighting the variants that came out as different between those tests. You can also lift your files to a newer version of the human reference genome, which might be needed for using your data with other tools. Furthermore, Lineage brings a lot of interesting genetic genealogy tools: It allows you to compute how much shared DNA can be found between your own data and the genetic data of other individuals, using a genetic map. You can then create plots of the shared DNA between those two data sets, determine which genes are shared between them and even find discordant SNPs between the data sets.

Enjoy exploring your DNA!

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.

I’m thrilled to announce the results of our 2019 elections for the Open Humans Foundation Board of Directors!

Our community seat winner is Marja Pirttivaara, and our board-elected seats are Gary Wolf and Sasha Wait Zaranek.

Marja Pirttivaara: When I first met Marja at the MyData conference in 2018, it was wonderful to find a like-minded soul — between her interests in genetics and in empowering individuals with their personal data. Marja generously agreed to our EU representative for GDPR, and it’s been exciting to see our project become more global.

Gary Wolf: As co-founder and director of Quantified Self Labs, Gary has supported numerous citizen scientists in their quest to use their personal data to understand themselves, and to collectively create new knowledge. His work is strongly aligned with that of Open Humans, and we very much looking forward to his contribution and leadership.

Sasha Wait Zaranek: Sasha is one of the founders of the Harvard Personal Genome Project and continues to lead in this area. Their focus is on genome data: they want to see that data managed by the people it came from, more understandable, and more re-usable for new projects — and they want to help Open Humans make those things happen.

Marja, Gary, and Sasha join our ongoing board members: Mad Price Ball, Karien Bezuidenhout, Steven Keating, Dana Lewis, and James Turner.

We must bid farewell to Misha Angrist and Michelle Meyer — their terms have ended. Both have been involved with the organization for many years, and we hope this is not the last we see of them! We must also bid farewell to Chris Gorgolewski, who has resigned; his 2018 seat is being left vacant for now. Also, we’ve made the voting results from the 2019 Community Seat election available here: http://openhumansfoundation.org/2019-ohf-election-votes.csv

We’re honored by the contribution of every board member, and their collective stewardship of our project. And we’re honored by all candidates for these positions. Not everyone can win — indeed, it would be a poor election if we didn’t have people to choose between. We very much hope other candidates remains involved — there are so many things to do together!