Data Science Community News
Open Science Prize announces nextstrain.org as Grand Prize Winner
Congratulations to the nextstrain.org development team led by Trevor Bedford, PhD, of the Fred Hutchinson Cancer Research Center, Seattle, and Richard Neher, PhD, of Biozentrum at the University of Basel, Switzerland winners of the grand prize of $230,000. Also participating were students from the laboratories of the team leaders; the University of Washington, Seattle; and the University of Auckland in New Zealand.
Read the official NIH press release.
A prototype online platform that uses real-time visualization and viral genome data to track the spread of global pathogens such as Zika and Ebola is the grand prize winner of the Open Science Prize. The international team competition is an initiative by the National Institutes of Health, in collaboration with the Wellcome Trust and the Howard Hughes Medical Institute (HHMI). The winning team, Real-time Evolutionary Tracking for Pathogen Surveillance and Epidemiological Investigation, created its nextstrain.org prototype to pool data from researchers across the globe, perform rapid phylogenetic analysis, and post the results on the platform’s website.
Genome sequences of viral pathogens provide a hugely valuable insight into the spread of an epidemic, but to be useful, samples have to be collected, analyzed and the results disseminated in near real-time. The statistical analyses behind nextstrain.org can be conducted in minutes, and can reveal patterns of geographic spread, timings of introduction events, and can connect cases to aid contact tracing efforts. The phylogenetic analyses are posted on the website as interactive and easy to understand visualizations. They hope that the platform will be of great use to researchers, public health officials and the public who want a snapshot of an epidemic.
Nextstrain.org placed first out of three top finalists, selected from a pool of 96 multinational, interdisciplinary teams including 450 innovators from 45 countries. This award is the culmination of a year-long process which included development and demonstration of working prototypes and multiple stages of rigorous review by panels of expert Open Science advisors and judges from the Wellcome Trust and NIH. All stages of the competition emphasized open science in both form and process, including public input for the award gathered via a global public voting portal. During the public voting phase, which narrowed the six finalists to three top contenders, nearly 4,000 online votes were cast by members of the public from a total of 76 countries on all six inhabited continents.
The Open Science Prize is a global competition designed to foster innovative solutions in public health and biomedicine using open digital content. As increasing amounts of data are produced by scientists around the world and made openly available through publicly-accessible repositories, a major challenge to fully maximize this health information will be the lack of tools, platforms, and services that enable the sharing and synthesizing of disparate data sources. Development in this area is essential to turning diverse types of health data into usable and actionable knowledge.
The prize, which was launched in October 2015, aims to forge new international collaborations that bring together open science innovators to develop services and tools of benefit to the global research community. All six finalist teams were considered exemplary by the funders and are to be commended for their tenacity in developing creative approaches to applying publicly-accessible data to solve complex biomedical and public health challenges. The topics spanned the breadth of biomedical and public challenges, ranging from understanding the genetic basis of rare diseases, mapping the human brain, and enhancing the sharing of clinical trial information. As evidenced from the six Open Science Prize finalists, public health and biomedical solutions are enriched when data are combined from geographically diverse sources. Final prototypes developed by the six finalists can be accessed on the Open Science Prize website.
NLM Director Dr. Patricia Flatley Brennan Appointed NIH Interim Associate Director for Data Science
ON JANUARY 6, 2017, the National Institutes of Health announced that National Library of Medicine Director Patricia Flatley Brennan, RN, PhD will assume an additional role as NIH Interim Associate Director for Data Science.
The NIH Associate Director for Data Science (ADDS) and team provide input to the overall NIH vision and actions undertaken by each of the 27 Institutes and Centers in support of biomedical research as a digital enterprise. Among other duties, the office oversees the Big Data to Knowledge (BD2K) initiative, stimulating the best developments in the data science community.
This year will see the transition of trans-NIH data science initiatives to NLM, with the operational oversight of the BD2K initiatives being housed within the Common Fund programs in the Division of Program Coordination, Planning and Strategic Initiatives. This change builds on the recommendations by the NLM Working Group Report to the NIH Director, makes concrete steps towards the vision of NLM’s future proclaimed in the Advisory Committee to the NIH Director’s report—that the National Library of Medicine become the “epicenter of data science for the NIH.”
“I believe the future of health and health care rests on data—genomic data, environmental sensor-generated data, electronic health records data, patient-generated data, research collected data,” Dr. Brennan observed. “The data originating from research projects is becoming as important as the answers those research projects are providing.”
“NLM must play a key role in preserving data generated in the course of research, whether conducted by professional scientists or citizen scientists,” she continued. “We know how to purposefully create collections of information and organize them for viewing and use by the public. We can extend this skill set to the curation of research data. We also have the utilities in place to protect the data by making sure only those individuals with permission to access data can actually do so.”
“NLM is well positioned to add these new functions to its research portfolio,” the NLM Director observed. “In this new year and the years to follow, we welcome these exciting opportunities and challenges.”
Big Data to Knowledge Multi-Council Working Group - January 2017
Notice is hereby given of a meeting of the Big Data to Knowledge (BD2K) Multi-Council Working Group.
Name of Working Group: Big Data to Knowledge Multi-Council Working Group
Date: January 9, 2017 - Canceled
This portion of the meeting is open to the public and is being held by teleconference. This is a listen ONLY meeting. Please submit any questions or comments via email to the contact person listed below.
Join WebEx Meeting
Meeting number: 627 298 875
Meeting password: 1234
Open Session: 11:00am - 12:00pm ET
Discussion will review current Big Data to Knowledge (BD2K) activities and newly proposed BD2K initiatives.
- Roll Call and Introduction
- Update from the Associate Director for Data Science
- BD2K All Hands Meeting and Open Data Science Symposium Recap
Closed Session: 12:30pm - 3:00pm ET
Agenda: Discussion will focus on review of proposed FY17 Funding Plans for BD2K Funding Opportunity Announcements and Administrative Supplements.
Individuals who plan to attend and need special assistance, such as sign language interpretation or other reasonable accommodations, should notify Tonya Scott, email: Tonya.Scott@nih.gov, phone: 301-402-9817.
Federal Register Meeting Announcement:
National Institutes of Health, Office of the Director - Notice of Meeting
Public Voting Determines Three Finalists for the Open Science Prize
Public voting for the Open Science Prize is now closed. Thank you to everyone who voted. The 3 prototypes which scored highest and will therefore be going forward to the next stage of review are:
MyGene2: Accelerating Gene Discovery with Radically Open Data Sharing
Real-Time Evolutionary Tracking for Pathogen Surveillance and Epidemiological Investigation
We will now be collecting expert reviews of these three prototypes. We anticipate announcing the the Grand Prize winner in early March 2017.
For additional information, contact: Elizabeth.Kittrie@nih.gov.
Need Cloud for Your Research? Calling All NIH Extramural Investigators
The NIH Big Data to Knowledge (BD2K) initiative has partnered with the CMS Alliance to Modernize Healthcare (CAMH), operated by MITRE, to launch and test a new funding paradigm that will provide NIH extramural researchers with access to cloud computing and storage capabilities. This funding model, called the Commons Credits Pilot, will provide extramural biomedical investigators with active NIH grants access to cloud-based environments to network, securely store, and share their work in the form of digital objects.
The first cycle for applications is open now through January 16, 2017.
Successful pilot applicants will receive dollar-denominated “credits” to obtain cloud-based computing and storage resources through an online market environment. Currently, the Commons Credits Pilot environment offers a variety of conformant cloud providers, including IBM, Seven bridges, and resellers of Google and Amazon. This list will grow as more vendors become available. Investigators will have the flexibility to select their preferred cloud provider from the list and provide feedback to NIH on their experiences. The Commons Credits Pilot is not a grants program; it has shorter application requirements and review times, ensuring that the credits are dispensed rapidly to keep pace with novel research.
An active NIH extramural grant is required for participation in the Commons Credits Pilot. Successful applications will likely complement the current grant(s) to enable novel research that may not have been accomplished or funded through other outlets. NIH expects that requests will not typically exceed $50,000 in dollar-denominated credits.
To date, the NIH Commons Credits Pilot has been shared with researchers at various research institutes and conferences, including the BD2K All-Hands Meeting held November 29-30, 2016. NIH encourages active NIH grant holders to take advantage of this new funding mechanism and we hope that you’ll also share this opportunity with your respective institutes.
Interested researchers should register and apply now at: http://www.commons-credit-portal.org. The Commons Credits Pilot team has created a short instructional video describing the application process within the portal to facilitate participation. To stay connected on the latest news regarding the NIH Commons Credit Pilot:
Please share this very exciting announcement with your extramural reasearch communities. For additional information, email the Commons Credits Pilot Team at: firstname.lastname@example.org.
Public Voting for the Open Science Prize is LIVE!
Public voting for the Open Science Prize is LIVE!
Help shape new directions in biomedical research by VOTING HERE.
Voting will be open December 1, 2016 through January 6, 2017 at 11:59pm PST.
In the spirit of Open Science, we invite you to help decide which of the prototypes competing for the Open Science Prize will be considered for the final grand prize. You will be asked to review 6 prototypes developed by the finalist teams and cast your vote for the most novel and impactful ones. The 3 prototypes receiving the highest number of public votes will advance to a final round of review by a panel of science experts and judges. A single, grand prize winner of $230,000 will be announced in March 2017.
In this competition, the teams were challenged to use open, publicly accessible data to improve human health. Each team produced prototypes that demonstrate how the power of Open Data can be harnessed to address a wide array of human health concerns through crowdsourcing or the development of innovative platforms on which to conduct computational modeling. Each team includes at least one U.S. and one international member with the goal of forging new collaborations with health and technology innovators from across the world, benefiting the global research community and the public in the process.
We invite you to watch the video demonstrations and test drive the prototypes before voting at: https://www.openscienceprize.org/. An archive of the NIH Open Data Science Symposium webcast is available here: http://www.tvworldwide.com/events/bd2k/161129/default.cfm?id=16845&type=flv&test=0&live=0, if you would like to watch the onstage prototype demonstrations or any other presentations from the Big Data to Knowledge (BD2K) All Hands Meeting (November 29-30) or Open Data Science Symposium (December 1).
The winning prototype will be selected by the National Institutes of Health and the Wellcome Trust and publically announced in March 2017. For additional information, email: Elizabeth.Kittrie@nih.gov.
The Open Science Prize is a collaboration between the National Institutes of Health (Bethesda, MD, USA) and the Wellcome Trust (London, UK), with additional funding provided by the Howard Hughes Medical Institute (Chevy Chase, MD, USA). This opportunity is being funded in part by the NIH Big Data to Knowledge (BD2K) Initiative.
We appreciate your help with getting the word out to your stakeholder communities about this worldwide public voting opportunity. Thank you for voting and helping to support the Open Science Prize.
bioCADDIE DataMed Version 1.5 Now Live
The bioCADDIE development team announces the release of DataMed Version 1.5, a Data Discovery Index (DDI) prototype
…with enhancements and important code corrections!
Thanks to user feedback, the DDI prototype has many new usability enhancements and code corrections.
New features introduced:
- Increased coverage to twice the number of biomedical data repositories
- Total number of datasets doubled
- Repositories mapped to DATS 2.1 metadata model
- Sorting on publication date of the dataset
- Visualization of results via timeline
- Usability enhancements based on user feedback and user interviews
User-reported issues resolved:
- Search capabilities expanded to include search by dataset IDs, PMIDs
- Compatibility with Google Chrome fixed
- Generate collections from search results
- Ability to view results in different formats
- Links to related datasets
- and Many More Features...!
DataMed is a work in progress and the bioCADDIE development team welcomes your feedback HERE.
Get involved in the bioCADDIE project and DataMed user studies!
For more details, contact: Anupama.E.Gururaj@uth.tmc.edu or email@example.com.
IEEE 2016 International Conference on Data Science and Advanced Analytics (DSAA 2016)
IEEE 2016 International Conference on Data Science and Advanced Analytics (DSAA 2016)
October 17-19, 2016 - Montreal, Canada
Special Session on Health Data Science (HDS)
Aims and Scope:
The health sector has been recently experiencing an increasing accessibility and availability of public and private data from various sources. This wide range of data sources are the result of: 1) the continuing investment in the digitization of health records, 2) the availability of an increasing number of health-related mobile and web-enabled applications, and 3) the use of social media for community-focused health research. This data presents a unique and cost-effective opportunity for knowledge discovery and has the potential to accelerate research while enabling the translation of the research findings to direct benefits to the community. This session brings together scientists, engineers, and researchers from academia and industry in order to discuss:
- The development of algorithms, tools, and techniques that can enhance our understanding of health data
- The use of large data sets to conduct health-focused studies
- The use of social networks to influence community behavior
Contributions that clearly demonstrate the benefits of large scale studies and systems as opposed to traditional studies and systems are highly solicited.
Deadline has been extended to June 12, 2016. For more information, click here.
Exponential Medicine 4-Day Program in San Diego
Exponential Medicine (October 8-11, 2016) is a unique and intensive cross-disciplinary 4-day program that brings together world-class faculty, innovators and organizations from across the biomedical and technology spectrum (from mobile health & 3D printing, to A.I., robotics, synthetic biology, and beyond) to explore and leverage the convergence of fast moving technologies in the reinvention and future of health and medicine. The program will focus on how computing through robotics, big data, and artificial intelligence will cause a disruptive change in medicine.
For more information, visit https://exponential.singularityu.org/medicine/
This program is sponsored by Singularity University. In computing, singularity “is a hypothetical event in which artificial general intelligence would be capable of recursive self-improvement and is the point beyond which events may become unpredictable or even unfathomable to human intelligence.”
Extracting Insights from Healthcare Data with Deep Learning
The Office of the Associate Director for Data Science (ADDS) announces a training opportunity in Deep Learning. This day-long, in-depth workshop is the second session of a two-part series. Part 1 (September 8), is an hour-long overview of deep learning followed by a half hour for questions. Part 2 (September 22), is a day-long, in-depth workshop. Attending or watching the overview first is highly recommended. Registration is required for the September 22 workshop, but not for the September 8 lecture.
Title: Extracting Insights from Healthcare Data with Deep Learning
Date: September 22, 9:00am - 5:00pm ET
Location: Building 31, 6C Room 10, NIH Bethesda campus
This event is a hands-on workshop and will not be videocast.
Open to NIH only; registration required.
Register for this course: https://datascience.nih.gov/deeplearningreg
Information on all upcoming courses: https://datascience.nih.gov/community/workforce/upcoming
Abstract: Recent years have seen a dramatic increase in the amount of healthcare-related data being collected. Now we need powerful analysis tools to extract insights and understanding from these mountains of data. A new approach called Deep Learning - based on neural networks inspired by the brain - is proving to be very effective in a wide range of research, diagnostic, and clinical applications. Join us to learn how advanced deep learning techniques are being applied to these rich data sets to help solve problems in diagnosis of diabetic retinopathy, calculating ventricular ejection fraction, and predicting survival in a Pediatric ICU. We will cover a variety of frameworks, tools, and languages including: DIGITS, Caffe, MXNet, Keras, MATLAB, R, Python, and more. The hands-on exercises will help you get started with applying deep learning to your own work.
For additional information, contact Sonynka Ngosso, 301-402-9816.