Open Data and the Democratization of Science

Data has become a key enabler of innovation and discovery in the 21st Century. Researchers today have unprecedented amounts of data from diverse sources, including sensors, instruments, and computational simulations, as well as an equally unprecedented need for computing to allow them to extract meaningful insights from the data to drive understanding, predictions, and decision making. It is there imperative, now more than ever, that all researchers benefit from the opportunities for scientific exploration enabled by data. As a result, ensuring broad, open, fair, and equitable access data is essential to democratizing science. This was recently highlighted by the US Whitehouse Office of Science and Technology Policy (OSTP) along with its new guidance to ensure that Federally Funded Research Data Equitably Benefits All of America. And similar policies and directive exist in other countries. This panel will explore the challenges and opportunities of open and equitable data and its role in democratizing science. Specifically, the panelists will address one or more of the following questions:

  1. Technology pushes and pulls: What technical challenges need to be addressed to manage the growing scales, heterogeneity and complexity of data, the resources and expertise needed to process this data, and the question being asked of this data (including the increasing use of AI/ML)?
  2. Open equitable and fair access: What policy, financial, social, and technical challenges prevent data from being truly open, and ensure fair and equitable access to data by all communities?
  3. Privacy, Civil Rights, Civil Liberties: What policies and mechanism are needed ensure that privacy, civil rights, and civil liberties are not violated by the data as well as the research that uses of the data?
  4. Reproducibility and replicability: How can open data be leveraged to enable reproducibility and replicability across all areas of computational and data-enable science and engineering research, and what incentives and tools and mechanisms can further reproducible/replicable research practices?


  • Manish Parashar, University of Utah, USA


  • David Abramson, University of Melbourne, Australia
  • Ilkay Altintas, University of California, San Diego, USA
  • Drew Mingl, State Data Coordinator, State of Utah
  • Federica Legger, Ludwig-Maximilians-Universit√§t M√ľnchen, Germany
  • Valerio Pascucci, University of Utah, USA