Suchergebnisse

The Parkinson's Disease Digital Biomarker Challenge was funded by the Robert Wood Johnson Foundation and the Michael J. Fox Foundation. Data were contributed by users of the Parkinson mPower mobile application as part of the mPower study developed by Sage Bionetworks and described in Synapse [https://doi.org/10.7303/syn4993293].Resources and support for J.S. were provided by Elder Research, an AI and Data Science consulting agency. M.D. was supported by NIH NIGMS Bioinformatics Training Grant (5T32GM070449-12). J.F.D. was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research. L.L.E. reports grants from the European Research Council ERC (677943), European Union's Horizon 2020 research and innovation programme (675395), Academy of Finland (296801, 304995, 310561 and 314443), and Sigrid Juselius Foundation, during the conduct of the study. EG1 acknowledges the funding support by the Fonds Nationale de la Recherche (FNR) Luxembourg, through the National Centre of Excellence in Research (NCER) on Parkinson's disease (I1R-BIC-PFN-15NCER), and as part of the grant project PD-Strat (INTER/11651464). M.K.J. was supported by Alfred Kordelin Foundation. J.J. is supported by UNSW Sydney Electronic Practice Based Research Network (ePBRN) and Translational Cancer Research Network (TCRN) programs. D.L. is supported in part by the University of Rochester CTSA award number UL1 TR002001 from the National Center for Advancing Translational Sciences of the National Institutes of Health. PS2 is supported by the Swiss National Science Foundation (SNSF) project No. 167302 within the National Research Program (NRP) 75 "Big Data". P.S. is an affiliated PhD fellow at the Max Planck ETH Center for Learning Systems. Y.G. is supported by NIH R35GM133346, NSF#1452656, Michael J. Fox Foundation #17373, American Parkinson Disease Association AWD007950. Cohen Veterans Bioscience contributed financial support to Early Signal Foundation's costs (U.R., C.E., and D.B.). ; Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95). ; Robert Wood Johnson Foundation (RWJF) Michael J. Fox Foundation NIH NIGMS Bioinformatics Training Grant 5T32GM070449-12 Canadian Institutes of Health Research (CIHR) European Research Council (ERC) European Commission 677943 European Union's Horizon 2020 research and innovation programme 675395 Academy of Finland 296801 304995 310561 314443 Sigrid Juselius Foundation Fonds Nationale de la Recherche (FNR) Luxembourg, through the National Centre of Excellence in Research (NCER) on Parkinson's disease I1R-BIC-PFN-15NCER project PD-Strat INTER/11651464 Alfred Kordelin Foundation UNSW Sydney Electronic Practice Based Research Network (ePBRN) program UNSW Translational Cancer Research Network (TCRN) program United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Center for Advancing Translational Sciences (NCATS) UL1 TR002001 Swiss National Science Foundation (SNSF) 167302 United States Department of Health & Human Services National Institutes of Health (NIH) - USA R35GM133346 National Science Foundation (NSF) 1452656 Michael J. Fox Foundation 17373 American Parkinson Disease Association AWD007950 Cohen Veterans Bioscience

Serious concerns about the way research is organized collectively are increasingly being raised. They include the escalating costs of research and lower research productivity, low public trust in researchers to report the truth, lack of diversity, poor community engagement, ethical concerns over research practices, and irreproducibility. Open science (OS) collaborations comprise of a set of practices including open access publication, open data sharing and the absence of restrictive intellectual property rights with which institutions, firms, governments and communities are experimenting in order to overcome these concerns. We gathered two groups of international representatives from a large variety of stakeholders to construct a toolkit to guide and facilitate data collection about OS and non-OS collaborations. Ultimately, the toolkit will be used to assess and study the impact of OS collaborations on research and innovation. The toolkit contains the following four elements: 1) an annual report form of quantitative data to be completed by OS partnership administrators; 2) a series of semi-structured interview guides of stakeholders; 3) a survey form of participants in OS collaborations; and 4) a set of other quantitative measures best collected by other organizations, such as research foundations and governmental or intergovernmental agencies. We opened our toolkit to community comment and input. We present the resulting toolkit for use by government and philanthropic grantors, institutions, researchers and community organizations with the aim of measuring the implementation and impact of OS partnership across these organizations. We invite these and other stakeholders to not only measure, but to share the resulting data so that social scientists and policy makers can analyse the data across projects.