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Project: National Database for Autism Research (NDAR) Informatics Platform
Abstract
The autism community defined the goal to share 90% of all human subjects research data. Through a Subject Identifier, harmonized data definition, data federation with other public and private funders, and progressive data sharing policies/techniques, research data on 77,500 subjects related to autism are now shared. Analyzed results, specifically associated with the outcome measures, and research subjects defined by cohorts is supported. In our lightning talk I will present the NDAR model for data sharing focusing on the scientific and computational results (see data from papers), which is likely relevant to the creation of a National Data Service.
Friday
Making Science Data Infrastructure a First Class Citizen
Erin Robinson, Foundation for Earth Science
Project: Federation of Earth Science Information Partners (ESIP), EarthCube
Abstract
We live in a world rich with data, where use and reuse would benefit not just science but also serve national security and society-at-large. However, our scientific data enterprise is evolving and maturing in an unmanaged fashion and due to insufficient coordination across planning, management, and resources, the potential benefits of all these data are not realized. Reliable, long term funding as well as cultural changes including financial incentives and rewards are needed to turn Science Data Infrastructure into a first class citizen equal to Science. The National Data Service has the potential opportunity to provide this overarching national coordination.
Individual Development Through Data
Matthew Turk, NCSA
Project: yt-project.org
Abstract
The most powerful phase transition in a research career occurs when an individual begins to drive their own scientific inquiry. In this
talk, I will describe how with the yt project (yt-project.org) our community has attempted to develop an enabling technology for individuals to ask questions of data, and how this philosophy can be applied elsewhere.
Is All Big Data ‘Messy’? What Questions Must Researchers Ask Before, During, and After Crunching the Numbers?
Cathy N. Davidson, Duke University
Project: Humanities, Arts, Science, and Technology Alliance and Collaboratory (HASTAC)
Abstract
This talk brings the interdisciplinary perspective of the social sciences, humanities, and digital humanities to data science and is a follow-up to our May 28 “Big (and Messy) Data” workshop as part of a two-year NSF EAGER grant on data and cross-disciplinary collaboration and mentoring. A key concern from this workshop that needs to be applied to our National Data Service is what my colleague and collaborator Richard Marciano has termed the “forensics” of understanding and interpreting big data. If we are going to provide a national data service for researchers, we must include in that service useful questions that any researcher, in any field, must pose in order to fully understand the biases, histories, and ambiguities of data, including the way that the inputs can distort the outputs an that all data requires interpretation and context.