Open Humans: A platform for participant-centered research and personal data exploration.
Bastian Greshake Tzovaras,Misha Angrist,Kevin J. Arvai,Mairi Dulaney,Vero Estrada-Galinanes,Vero Estrada-Galinanes,Beau Gunderson,T. Head,Dana Lewis,Oded Nov,Orit Shaer,Athina Tzovara,Athina Tzovara,Jason Bobe,Mad Price Ball +14 more
TLDR
Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be use by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.Abstract:
Background Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals). Results To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects. Conclusions Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.read more
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Glycaemic control in individuals with type 1 diabetes using an open source artificial pancreas system (OpenAPS)
TL;DR: G Glycaemic control using OpenAPS was comparable with results of more rigorously developed and tested AP systems, however, OpenAP was used by a highly selective, motivated and technology‐adept cohort, despite not being approved for the treatment of individuals with T1D.
Journal ArticleDOI
Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed
TL;DR: The many options and obstacles for data sharing are discussed, from fully open, to federated learning, to fully closed, and importantly, the intersection of data sharing, privacy, and data ownership is highlighted.
Journal ArticleDOI
Addressing bias in big data and AI for health care: A call for open science.
Natalia Norori,Natalia Norori,Qiyang Hu,Florence Marcelle Aellen,Francesca Dalia Faraci,Athina Tzovara,Athina Tzovara +6 more
TL;DR: In this paper, the authors describe the challenges in rendering AI algorithms fairer, and propose concrete steps for addressing bias using tools from the field of open science, which can be found in our previous work.
Patient Preferences in Controlling Access to Their Electronic Health Records: a Prospective Cohort Study in Primary Care
Peter H. Schwartz,Kelly Caine,Sheri A. Alpert,Eric M. Meslin,Aaron E. Carroll,William M. Tierney,William M. Tierney +6 more
TL;DR: In this paper, the authors measured preferences among patients when they are allowed to determine the parameters of provider access to electronic health records (EHRs) and found that the majority of the patients agreed to share personal information stored in EHRs with the provider.
Journal ArticleDOI
Ultradian rhythms in heart rate variability and distal body temperature anticipate onset of the luteinizing hormone surge.
Abstract: The menstrual cycle is characterized by predictable patterns of physiological change across timescales. Although patterns of reproductive hormones across the menstrual cycle, particularly ultradian rhythms, are well described, monitoring these measures repeatedly to predict the preovulatory luteinizing hormone (LH) surge is not practical. In the present study, we explored whether non-invasive measures coupled to the reproductive system: high frequency distal body temperature (DBT), sleeping heart rate (HR), sleeping heart rate variability (HRV), and sleep timing, could be used to anticipate the preovulatory LH surge in women. To test this possibility, we used signal processing to examine these measures in 45 premenopausal and 10 perimenopausal cycles alongside dates of supra-surge threshold LH and menstruation. Additionally, urinary estradiol and progesterone metabolites were measured daily surrounding the LH surge in 20 cycles. Wavelet analysis revealed a consistent pattern of DBT and HRV ultradian rhythm (2-5 h) power that uniquely enabled anticipation of the LH surge at least 2 days prior to its onset in 100% of individuals. Together, the present findings reveal fluctuations in distal body temperature and heart rate variability that consistently anticipate the LH surge, suggesting that automated ultradian rhythm monitoring may provide a novel and convenient method for non-invasive fertility assessment.
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Grant,Carla Gallo,Giovanni Poletti,Danish Saleheen,Asif Rasheed,Lisa D. Brooks,Adam Felsenfeld,Jean E. McEwen,Yekaterina Vaydylevich,Audrey Duncanson,Michael Dunn,Jeffery A. Schloss +517 more
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Journal ArticleDOI
The FAIR Guiding Principles for scientific data management and stewardship
Mark Wilkinson,Michel Dumontier,IJsbrand Jan Aalbersberg,Gabrielle Appleton,Myles Axton,Arie Baak,Niklas Blomberg,Jan-Willem Boiten,Luiz Olavo Bonino da Silva Santos,Philip E. Bourne,Jildau Bouwman,Anthony J. Brookes,Timothy Clark,Mercè Crosas,Ingrid Dillo,Olivier G. Dumon,Scott C. Edmunds,Chris T. Evelo,Richard Finkers,Alejandra Gonzalez-Beltran,Alasdair J. G. Gray,Paul Groth,Carole Goble,Jeffrey S. Grethe,Jaap Heringa,Peter A C 't Hoen,Rob Hooft,Tobias Kuhn,Ruben Kok,Joost N. Kok,Scott J. Lusher,Maryann E. Martone,Albert Mons,Abel L. Packer,Bengt Persson,Philippe Rocca-Serra,Marco Roos,Rene van Schaik,Susanna-Assunta Sansone,Erik Anthony Schultes,Thierry Sengstag,Ted Slater,George Strawn,Morris A. Swertz,Mark Thompson,Johan van der Lei,Erik M. van Mulligen,Jan Velterop,Andra Waagmeester,Peter Wittenburg,Katherine Wolstencroft,Jun Zhao,Barend Mons,Barend Mons +53 more
TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
Journal ArticleDOI
A New Initiative on Precision Medicine
TL;DR: A research initiative that aims to accelerate progress toward a new era of precision medicine, with a near-term focus on cancers and a longer-term aim to generate knowledge applicable to the whole range of health and disease.
Journal ArticleDOI
From core referencing to data re-use: two French national initiatives to reinforce paleodata stewardship (National Cyber Core Repository and LTER France Retro-Observatory)
Fabien Arnaud,Cécile Pignol,Pierre Stéphan,Anne-Lise Develle,Pierre Sabatier,Olivier Evrard,Brice Mourier,Maxime Debret,Cécile Grobois,Laurent Millet,Damien Rius,Dominique Marguerie,Mathias Rouan,Elodie Godinho,Bruno Galabertier,Arnaud Caillo +15 more
TL;DR: ROZA was developed under the umbrella of LTER-France (Long Term Ecological Research) in order to facilitate the re-use of data and samples and will favor to use of paleodata by non-paleodata scientists, in particular ecologists.
Journal ArticleDOI
Next-generation genotype imputation service and methods.
Sayantan Das,Lukas Forer,Sebastian Schönherr,Carlo Sidore,Carlo Sidore,Adam E. Locke,Alan Kwong,Scott I. Vrieze,Emily Y. Chew,Shawn Levy,Matt McGue,David Schlessinger,Dwight Stambolian,Po-Ru Loh,William G. Iacono,Anand Swaroop,Laura J. Scott,Francesco Cucca,Florian Kronenberg,Michael Boehnke,Gonçalo R. Abecasis,Christian Fuchsberger,Christian Fuchsberger,Christian Fuchsberger +23 more
TL;DR: Improvements to imputation machinery are described that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools.