John Wilbanks

The mPower study, Parkinson disease mobile data collected using ResearchKit

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.

What the semantic web could do for the life sciences

Abstract Scientific research is predicated on the effective exchange of knowledge. The effective exchange of data and accompanying interpretation underpin new hypotheses and experimental designs, typically followed by a community-based process of debate and rebuttal. This community-driven process clarifies and strengthens the elements of facts and hypothesis. Within the life sciences, the result of this process is a collective understanding of emerging biological viewpoints. The methodologies for community debate and knowledge transfer have changed little over the past twenty years, although both scientific instrumentation and publishing technologies have undergone revolutionary change. It is proposed that newly published recommendations from the World Wide Web Consortium (W3C), which handle the domain and process-specific semantics of life sciences, would better support the application of peer-reviewed knowledge in discovery research. W3C semantic web technologies support flexible, extensible and evolvable knowledge transfer and reuse, enabling scientists and their organizations to increase efficiency across the scientific process.

First, design for data sharing

To upend current barriers to sharing clinical data and insights, we need a framework that not only accounts for choices made by trial participants but also qualifies researchers wishing to access and analyze the data.

Stop the privatization of health data

Over the past year, technology titans including Google, Apple, Microsoft and IBM have been hiring leaders in biomedical research to bolster their efforts to change medicine. In September 2015, Tom Insel announced that he would quit his position as head of the US National Institute of Mental Health to join Google Life Sciences (now Verily). Three months later, Michael McConnell took a leave of absence from directing major cardiovascular research programmes at California’s Stanford University to join him. And last month, Stephen Friend took a senior position with Apple. He is co-founder and former president of Sage Bionetworks, a non-profit organization that promotes open science and patient engagement in research (where one of us, J.T.W, works). In many ways, the migration of clinical scientists into technology corporations

Why Open Drug Discovery Needs Four Simple Rules for Licensing Data and Models

When we look at the rapid growth of scientific databases on the Internet in the past decade, we tend to take the accessibility and provenance of the data for granted. As we see a future of increased database integration, the licensing of the data may be a hurdle that hampers progress and usability. We have formulated four rules for licensing data for open drug discovery, which we propose as a starting point for consideration by databases and for their ultimate adoption. This work could also be extended to the computational models derived from such data. We suggest that scientists in the future will need to consider data licensing before they embark upon re-using such content in databases they construct themselves.

Openness as infrastructure

The advent of open access to peer reviewed scholarly literature in the biomedical sciences creates the opening to examine scholarship in general, and chemistry in particular, to see where and how novel forms of network technology can accelerate the scientific method. This paper examines broad trends in information access and openness with an eye towards their applications in chemistry.

Citizen Science on Your Smartphone: An ELSI Research Agenda

The prospect of newly-emerging, technology-enabled, unregulated citizen science health research poses a substantial challenge for traditional research ethics. Unquestionably, a significant amount of research ethics study is needed to prepare for the inevitable, widespread introduction of citizen science health research. Using the case study of mobile health (mHealth) research, this article provides an ethical, legal, and social implications (ELSI) research agenda for citizen science health research conducted outside conventional research institutions. The issues for detailed analysis include the role of IRBs, recruitment, inclusion and exclusion criteria, informed consent, confidentiality and security, vulnerable participants, incidental findings, and publication and data sharing.

Licence restrictions: A fool's errand

Objections to the Creative Commons attribution licence are straw men raised by parties who want open access to be as closed as possible, warns John Wilbanks.

Creating a data resource: what will it take to build a medical information commons?

National and international public–private partnerships, consortia, and government initiatives are underway to collect and share genomic, personal, and healthcare data on a massive scale. Ideally, these efforts will contribute to the creation of a medical information commons (MIC), a comprehensive data resource that is widely available for both research and clinical uses. Stakeholder participation is essential in clarifying goals, deepening understanding of areas of complexity, and addressing long-standing policy concerns such as privacy and security and data ownership. This article describes eight core principles proposed by a diverse group of expert stakeholders to guide the formation of a successful, sustainable MIC. These principles promote formation of an ethically sound, inclusive, participant-centric MIC and provide a framework for advancing the policy response to data-sharing opportunities and challenges.

Responsible sharing of biomedical data and biospecimens via the “Automatable Discovery and Access Matrix” (ADA-M)

Given the data-rich nature of modern biomedical research, there is a pressing need for a systematic, structured, computer-readable way to capture, communicate, and manage sharing rules that apply to biomedical resources. This is essential for responsible recording, versioning, communication, querying, and actioning of resource sharing plans. However, lack of a common “information model” for rules and conditions that govern the sharing of materials, methods, software, data, and knowledge creates a fundamental barrier. Without this, it can be virtually impossible for Research Ethics Committees (RECs), Institutional Review Boards (IRBs), Data Access Committees (DACs), biobanks, and end users to confidently track, manage, and interpret applicable legal and ethical requirements. This raises costs and burdens of data stewardship and decreases efficient and responsible access to data, biospecimens, and other resources. To address this, the GA4GH and IRDiRC organizations sponsored the creation of the Automatable Discovery and Access Matrix (ADA-M, read simply as “Adam”). ADA-M is a comprehensive information model that provides the basis for producing structured metadata “Profiles” of regulatory conditions, thereby enabling efficient application of those conditions across regulatory spheres. Widespread use of ADA-M will aid researchers in globally searching and prescreening potential data and/or biospecimen resources for compatibility with their research plans in a responsible and efficient manner, increasing likelihood of timely DAC approvals while also significantly reducing time and effort DACs, RECs, and IRBs spend evaluating resource requests and research proposals. Extensive online documentation, software support, video guides, and an Application Programming Interface (API) for ADA-M have been made available.