Information extraction and transparency in big data processing

  Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
DOI
10.7287/peerj.preprints.2546v1
Published
Accepted
Subject Areas
Clinical Trials, Evidence Based Medicine, Translational Medicine, Computational Science
Keywords
Big Data, Physical Activity, Accelerometry, ActiBelt, Data Flow, Gait
Copyright
© 2016 Clay
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Clay I. 2016. Information extraction and transparency in big data processing. PeerJ Preprints 4:e2546v1

Abstract

Developing new endpoints for mobility is an important strategic aim for many groups both in industry and academia and the focus of a growing field. Bringing those novel endpoints to health authority acceptance for clinical decision making will require a concerted effort from this research community. This in turn will require openness and transparency; sharing data, methods and findings. Here we discuss challenges within the field to such an open approach and give examples of how they might be overcome.

Author Comment

This presentation was a contribution to the 2nd Winter Symposium of the "Human Motion Project" and is part of PeerJ Human Motion Project collection.