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DataScience@NIH Managing Digital Research Objects in an Expanding Science Ecosystem will be held on Wednesday, November 15th, 2017 from 9:00 am - 5:00 pm EST in the Lister Hill National Center for Biomedical Communications at U.S. National Library of Medicine/NIH (8600 Rockville Pike, Bethesda, MD 20894). This event is being co-sponsored by CENDI, National Federation of Advanced Information Services (NFAIS), Research Data Alliance/US (RDA/US) and the National Academies Board on Research Data and Information. Hosted by The National Library of Medicine/NIH. Sessions will include:
Call for Papers for the NIPS Workshop on Machine Learning for Health (NIPS ML4H 2017) entitled What parts of Healthcare are Ripe for Disruption by Machine Learning Right Now? A workshop at the Thirty-First Annual Conference on Neural Information Processing Systems (NIPS 2017) will take place Friday, December 8, 2017: Long Beach Convention Center, Long Beach, CA, USA. https://ml4health.github.io/ Please direct questions to: ml4h.workshop.nips.2017@gmail. Reminders You May Need The KnowEnG BD2K center is pleased to announce the initial public release of the Knowledge Engine for Genomics (KnowEnG). The KnowEnG platform is a scalable cloud-based computing platform that enables knowledge-guided machine learning and graph mining analyses and exploration of results with interactive visualizations. In this first public release, KnowEnG is offered for free and we encourage researchers to try our platform. We welcome your feedback!
A Request for Information (RFI): Next-Generation Data Science Challenges in Health and Biomedicine was announced on Friday, September 29th by the NIH and National Library of Medicine. NLM requests information on the three focal areas listed below:
Within these general topic areas, or others related to data science in health and biomedicine, NLM invites researchers, clinicians, organizations, industry representatives and other interested parties to provide input on:
Responses to this RFI must be submitted by November 1, 2017. Responses should be provided in a narrative form of up to 3 pages per topic, with links to pertinent supplemental information if needed. No attachments will be accepted. No proprietary, classified, confidential, or sensitive information should be included in your response. For all details, guidelines, and further information, please refer to the RFI. Summer Research Program in Biomedical Big Data Science June - August 2018 – The Summer Research Training Program in Biomedical Big Data Science sponsored by the BD2K-LINCS Data Coordination and Integration Center (DCIC) is a research-intensive ten-week training program for undergraduate and graduate students interested in participating in cutting edge research projects aimed at solving data-intensive biomedical problems. Applications are being accepted now, and are due February 1, 2018. New RNA-Seq Data Resource Now Available. BD2K-grantee, Dr. Avi Ma'ayan, and colleagues, recently developed all RNA-seq and ChIP-seq sample and signature search (ARCHS4), a web resource that makes the majority of previously published RNA-seq data from human and mouse freely available at the gene count level. ARCHS4 is freely accessible from: http://amp.pharm.mssm. NIAID is offering an exciting fellowship opportunity in data science for recent graduates (past 5 years) who are interested in acquiring a unique training experience involving rotations throughout the Institute to either intramural or extramural programs engaged in data-intensive science. Please see the following link for more information: https://www. The NIH Data Science Mentoring program is now accepting applications from NIH-affiliated individuals interested in participating in the program as either a mentor or a learner. Skills in the area broadly defined as data science, like programming in languages like R and Python, machine learning, and data visualization, are increasingly important in many areas of biomedical research. This program provides an opportunity for mentors with experience in these areas to help learners acquire the skills they need. We suggest mentors and learners meet for up to one hour every other week at a place and time convenient to both of them, but mentoring pairs can establish a mutually-agreed-upon approach that works best for them. There is no required ongoing commitment; either mentor or learner can opt out at any time. Mentors and learners will receive a guidelines document to help them make the most of the mentoring experience and the program committee welcomes questions and feedback. The program committee will make an effort to pair everyone who applies, but because we typically receive more applications from learners than from mentors, we cannot guarantee that we will be able to pair every learner with a mentor at this time. To sign up as either a mentor or a learner, please complete the form at https://goo.gl/forms/ The BD2K Training Coordinating Center has been creating and populating the Educational Resource Discovery Index (ERuDIte), a database of 10,000+ data science educational resources from collective BD2K activities and from around the web.
The bioCADDIE DDICC Core Team is pleased to announce the release of DataMed v3.0! DataMed is designed to be for data what PubMed has been for scientific literature. This version of the Data Discovery Index (DDI) prototype includes many additional datasets and reflects considerable user/stakeholder input. The user interface has also been updated to reflect this input.
Data Science Events: National Library of Medicine Biomedical Informatics and Data Science Lectures Abstract: Clinical informatics research, and before that, medical informatics research, has made great strides in developing tools to help clinicians improve clinical decision-making and patient care. Yet, electronic health records (EHR) systems today show little aptitude for even simple tasks, like retrieving relevant patient information, while suppressing that which is irrelevant. When bringing artificial intelligence to bear, the best EHRs seem to do is to overwhelm us with alerts that a clinician must override to take action. When the "learning health system" attempts to use data from these systems, it must rely on indirect methods, such as machine learning and natural language processing, to figure out what was actually going on with the patient. The advances that have been made to bring decision support into EHRs rely on formally represented – that is structured and coded – data, such as problem lists, laboratory results and medication lists. What's missing is a formal representation of the clinical cognition of the patient's situation: what we think is going on, what our goals are, what we are trying to do about it, and why we have chosen to do it that way. Adding such information to the EHR would enable informaticians to enhance their tools in ways that will improve situational awareness, reduce information overload, make decision support systems provide more relevant knowledge to clinicians, and enable clinical researchers to draw more solid inferences from observational data. Informatics research is needed to understand what needs to be captured, determine how it should be represented, design user interfaces to minimize the effort required, and develop tools that ultimately reduce the work of clinical documentation by reducing redundant data entry, anticipating and executing work plans, and improve the quality and efficiency of patient care. Dr. Cimino will provide illustrations of the formal representation and use of clinical cognition and present a roadmap for research, development and education toward that goal. This talk will be broadcast live and archived at http://videocast.nih.gov/. Sign Language Interpreters will be provided. Individuals with disabilities who need reasonable accommodation to participate in this lecture should contact Ebony Hughes 301-451-8038 Ebony.Hughes@nih.gov or the Federal Relay (1-800-877-8339). Announcing the Speakers for the 2nd Year of the Big Data to Knowledge Guide to the Fundamentals of Data Science Webinar Series. This virtual lecture series on data science features presentations from experts across the country covering the basics of data management, representation, computation, statistical inference, data modeling, and other topics relevant to "big data" in biomedicine. Learn more and register. Georgetown University will hold a Biomedical Big Data Resources and Analysis Tools Training Workshop November 3-4, 2017. This two-day, in-person workshop for academic faculty at any level, will cover major concepts, methods, and tools of translational bioinformatics. It will feature hands-on computer based exercises using web-based bioinformatics resources. Workshop registration and information. Studying Systems Biology by Cellular Perturbations – The BD2K-LINCS Data Coordination and Integration Center (DCIC) and the University of Miami will host the third annual BD2K-LINCS Data Science Symposium (DSS 2018) on January 31 – February 2, 2018. This symposium brings together experts in systems biology, data science, drug discovery and translational medicine from academia, industry and government to present their latest research and exchange new ideas in data-driven biomedical research. Submissions: We welcome your input! Submissions must be received prior to 12:00 noon ET on Monday to be included in that same week's edition. If you are requesting attendance at an event by Federal employees, it is recommended that you submit your event information a minimum of one month in advance. To submit a news item, contact: Grace.Middleton@nih.gov. Follow DataScience@NIH on This service is provided to you at no charge by the Office of Communication and Public Liaison.
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Tuesday, October 24, 2017
DataScience@NIH Updates from the NIH Interim Associate Director for Data Science
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