Tag: Smartphone

Boost CrowdSignals.io! 100+GB Mobile-Social-Sensor-System Data Guaranteed

News:

NYT logo
CrowdSignals.io most emailed in NY Times Technology this week:
CrowdSignals Aims to Create a Marketplace for Smartphone Sensor Data
kdnuggets
CrowdSignals.io in KDNuggets:
CrowdSignals.io, Building Big Mobile Social Sensor Dataset

Help Us Boost The Dataset!

At our current level of funding we’re guaranteeing 100+GB of data from 30 volunteers for 30 days. This includes sensor, social, system, and interaction data in addition to ground truth on contact relationships, places visited, and 2 additional phenomena to be selected by Backers. But we can do better! With your support we can boost the diversity and density of ground truth labels, making the data more useful for an even broader spectrum of researchers and data scientists!

  • It only costs $2 per academic researcher or $5 per data scientist to contribute
  • Visit the Campaign!

Help us prove the concept and receive Big Data at a tiny fraction of the cost. Support the campaign at any level and share or tweet the news!
Contact us directly with any questions or feedback: organizers@crowdsignals.io

Phase 1: 100+GB of Data for Research and Products

Dear Colleagues,

Today we launch Phase 1 of CrowdSignals.io on Indiegogo!  We’re collecting 100+ GB (over 20K hours!) of rich sensor, social, system, interaction, and ground truth data from smartphones and smartwatches. We’re confident we can create an excellent dataset: the real experiment is in crowdfunding and community.

CrowdSignals.io Infographic Med

We’re asking for your help to generate funds that will pay volunteers and administrative staff. In return, we’ll share the all collected data, sample code, and a direct connection to a community of 1,000s of researchers and developers.

More about CrowdSignals.io:

  • Donations are just $2 per academic researcher and $5 per data scientist or engineer!
  • 100+ GB of sensor, social, system, and interaction data
  • Precise ground truth labels
  • Executed by AlgoSnap, a bootstrapped, Seattle-based start-up
  • Advised by:
    – Andrew Campbell (Dartmouth)
    – Deborah Estrin (Cornell)
    – Henry Tirri (Aalto U)
    – Jason Hong (CMU)

Please support this crowdfunded dataset and/or forward to any lists or colleagues you think may be interested!