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Presentation . 2021
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Presentation . 2021
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Accelerating Science: Data Discovery and Interoperability

Authors: Heacock, Michelle; Kinkade, Danie; Stall, Shelley;

Accelerating Science: Data Discovery and Interoperability

Abstract

This seminar is part of a series to provide societies and their journals with information and resources to help their communities be more knowledgeable and prepared to share data (and software) in a way that is relevant and meaningful for each discipline. This is a 12-month series. Following the planned presentation, participants will have ~30 minutes of Q&A and discussion specific to society engagement to improve data sharing, credit, and transparency. Accelerating Science: Data Discovery and Interoperability 7 May 2021, 10am ET (1400 UTC) Speakers: Michelle Heacock, Program Officer and Lead for Data Science, NIEHS Superfund Research Program Danie Kinkade, Director of BCO-DMO, Woods Hole Oceanographic Institution Moderator: Shelley Stall, Senior Director – Data Leadership, AGU Description: This seminar focuses on data discovery and interoperability that benefits the researcher by giving more time for analysis and reducing the time needed to clean and harmonize datasets. With intentional investment in data management and curation, along with collaboration and coordination across the research ecosystem, data products that are research-ready become important elements to accelerate science. Seminar Recording: https://youtu.be/8DHHlT7d5nk Resources referenced during the presentation: Session I – Data Sharing Tools, Workflows, and Platforms Monday, May 17, 2021, 1:00 PM-3:00 PM EDT To register, visit the EPA’s CLU-IN Training & Events webpage The first session will introduce tools, strategies, workflows, and platforms developed by SRP researchers to organize existing data obtained from measuring environmental contaminants to facilitate interoperability. We will also hear about the U.S. EPA’s CompTox Chemicals Dashboard, a compilation of information from many sites and databases developed to organize chemical data and address data gaps. Session II – Geospatial Platforms for Analysis and Visualization Across Environmental Data To register, visit the EPA’s CLU-IN Training & Events webpage Thursday, June 3, 2021, 2:00 PM-4:00 PM EDT In the second session, presenters will describe efforts to combine and analyze data sets from SRP Centers and other sources using geospatial platforms. This session will also feature a speaker supported by NSF who will provide discuss Hydroshare, an online system to share hydrologic data and models. Session III – Integrating Omics Data Across Model Organisms and Populations Friday, June 18, 2021, 1:00 PM-3:00 PM EDT To register, visit the EPA’s CLU-IN Training & Events webpage The third and final session will feature SRP-funded researchers collaborating to combine omics (e.g., genomics, proteomics) data within and across model organisms as well as studies in human populations. We will also hear from The Global Alliance for Genomics and Health about their work to incorporate semantic data models for sharing of genomic data. NIH Strategic Plan for Data Science: https://datascience.nih.gov/strategicplan NIEHS Strategic Plan: https://www.niehs.nih.gov/about/strategicplan/strategicplan20182023_508.pdf NIEHS Superfund Research Program Strategic Plan: https://www.niehs.nih.gov/research/supported/centers/srp/assets/docs/srp_strategic_plan_202025_508.pdf Data Management and Sharing, Superfund Research Program: https://tools.niehs.nih.gov/srp/data/index.cfm Biological and Chemical Oceanography Data Management Office: https://www.bco-dmo.org

Special thank you to Laura Lyon of AGU and her support organizing and managing this seminar.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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