A self-organizing, living library of time-series data

Time-series data are measured across the sciences, from astronomy to biomedicine, but meaningful cross-disciplinary interactions are limited by the challenge of identifying fruitful connections. Here we introduce the web platform, CompEngine , a self-organizing, living library of time-series data, t...

Full description

Saved in:
Bibliographic Details
Published in:Scientific data 2020-07, Vol.7 (1), p.213-213, Article 213
Main Authors: Fulcher, Ben D., Lubba, Carl H., Sethi, Sarab S., Jones, Nick S.
Format: Article
Language:English
Subjects:
631/114/2164
706/648/697/129
Humanities and Social Sciences
Interdisciplinary aspects
Libraries
multidisciplinary
Science
Science (multidisciplinary)
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Time-series data are measured across the sciences, from astronomy to biomedicine, but meaningful cross-disciplinary interactions are limited by the challenge of identifying fruitful connections. Here we introduce the web platform, CompEngine , a self-organizing, living library of time-series data, that lowers the barrier to forming meaningful interdisciplinary connections between time series. Using a canonical feature-based representation, CompEngine places all time series in a common feature space, regardless of their origin, allowing users to upload their data and immediately explore diverse data with similar properties, and be alerted when similar data is uploaded in future. In contrast to conventional databases which are organized by assigned metadata, CompEngine incentivizes data sharing by automatically connecting experimental and theoretical scientists across disciplines based on the empirical structure of the data they measure. CompEngine ’s growing library of interdisciplinary time-series data also enables the comprehensive characterization of time-series analysis algorithms across diverse types of empirical data.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-020-0553-0