, Building Big Mobile Social Sensor dataset a crowdfunding campaign to generate the largest mobile and sensor dataset available to the Data Science community for use in research and product development.

By Evan Welbourne,

Seattle Start-Up AlgoSnap launches, a crowdfunding campaign to generate the largest mobile and sensor dataset available to the Data Science community for use in research and product development. will generate a series of datasets ranging from 100GB to 100TB of sensor, social, system, physiological, and interaction data from smartphone and smartwatch owners across the United States. The goal is to enable 1000’s of data scientists and researchers worldwide and across many disciplines with the Big Data they need to design the next intelligent service or to study macroscale behaviors and trends. Numerous leading scientists at top institutions (e.g., MIT, Carnegie Mellon, Cornell, Intel) have endorsed


Smart device and sensor data sets are increasingly the largest driver of innovation in computing, geography, health, mobile, social science, urban planning, and many other areas. Technical and legal challenges make data collection extremely rare and expensive. Product groups and Universities that need data often spend 70% of their project budget on data collection, this can be upwards of $1M USD for data from just 200 people. Over the last year we built the Platform and Legal Framework, a “Large Hadron Collider for Mobile and IoT Data Science”, it automates robust, low-cost, and ethical data collection campaigns using a crowd of informed, consenting, and paid smart device owners across the United States.


Phase 1 of the campaign launched on Indiegogo Tuesday March 22: This pilot study jump-starts the project by collecting 40+ types of rich data streams from smartphones and smartwatches belonging to 30-50 consenting volunteers across the United States. If Phase 1 succeeds, Phases 2 and 3 will launch later in 2016 to collect TB of data from 100s or 1000s of participants.


Base rate for access to the all data: $2 per academic researcher, $5 per commercial data scientist.

Who is organized and run by AlgoSnap, a bootstrapped start-up in Seattle with roots in the research community. The project is advised by top experts in academia and industry, including Deborah Estrin (Cornell), Henry Tirri (Aalto U, former Nokia CTO), Jason Hong (CMU), Andrew Campbell (Dartmouth). The Lead Organizer is Evan Welbourne, Founder and CEO of AlgoSnap. Since the early 2000s Evan has led research and product development for machine learning and intelligent devices at Amazon, Samsung, Nokia, and the University of Washington.


“We organized to address the critical scarcity of shared mobile data sets. By combining insights, expertise, and small contributions from the community we’re creating terabytes of high-quality data that will drive research in a diversity of fields. Data from our devices will eventually help us to understand ourselves as individuals and as a society.” – Evan Welbourne, Lead Organizer,

Links: Project website:, Indiegogo Page:

Videos: Crowdfunding video:, Endorsements reel: