Machine Readable Semantics in a Data Science Workflow
IEEE Palouse Section, Computer Society Chapter, Technical Presentation, In-Person and Online.
Abstract: FAIR data principles (findable, accessible, interoperable, reusable) have been well received in the data science community. In recent years, many best practices of open data have demonstrated their value to facilitate data-driven discoveries in scientific research. Among those studies, semantics remains as a key topic of wide interest, which is relevant to terminology, data models, formats, metadata, ontology, vocabulary, knowledge graph, and many other subjects. In particular, with the thriving of Web-based data sharing and discovery activities and the extension of FAIR principles to data analysis software and other objects in open science, it is worth to have a reflection on the role of semantics in the FAIR principles and make recommendations for future works. In this presentation, we will review a few recent projects that have worked on semantics of data and implemented it in different steps of the data science workflow. We will analyze the pros and cons of the current practices, and will also present a vision for potential future improvements.
Date and Time
Location
Hosts
Registration
- Date: 14 Oct 2022
- Time: 12:30 AM UTC to 02:00 AM UTC
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- University of Idaho - Moscow Campus
- Moscow, Idaho
- United States 83844
- Building: Janssen Engineering Building (JEB)
- Room Number: JEB thinkTANK (First Floor)
- Click here for Map
Speakers
Xiaogang (Marshall) Ma, Ph.D. of University of Idaho
Machine Readable Semantics in a Data Science Workflow
Biography:
Bio: Xiaogang (Marshall) Ma is an associate professor of computer science at the University of Idaho. He received his Ph.D. degree of Earth Systems Science and GIScience from University of Twente, Netherlands in 2011, and then completed postdoctoral training of Data Science at Rensselaer Polytechnic Institute. His research focuses on deploying data science in the Semantic Web to support cross-disciplinary collaboration and scientific discovery, with broad interests in complex systems in Earth and environmental sciences, data interoperability and provenance, and visualized exploratory analysis of Big and Small Data. Ma was one of the four invited early-career panelists at the 2016 International Data Week. He is active in international societies of data science and geoinformatics, including ACM SIGWEB, CODATA, ESIP, RDA, GSA, AGU and IAMG. Ma received the Science of Team Science (SciTS) Meritorious Contribution Award in 2018, the IAMG A.B. Vistelius Research Award in 2015, and the inaugural ICSU-WDS Data Stewardship Award in 2014.
Agenda
Social event at 5:30 PM, Local only.
Techical presentation and Q & A session 6 to 7 PM. Local and online.