Launching VITAL DATA SERIES (Video, Image, Text, Audio, and Learning)
The VITAL series, created by the Community Engagement Group concerns the analysis of Video, Image, Text, Audio, or Learning (VITAL) data. In it we will be exploring use cases and techniques across disciplines in each of these fundamental types of data. This is an effort to break down barriers across disciplines and bridge solutions across sectors including industry, academia, government, and non-profits. The VITAL series is being developed by the Community Engagement working group. To participate or become a speaker please contact: Dr. Renata Rawlings-Goss Co-Executive Director of the South Big Data Hub at email@example.com and cc: firstname.lastname@example.org
Video Data Analysis: Thursday, October 27th, 1:00 -2:00 pm EST
Join this open panel discussion if you are a researcher or company working with the analysis of large and small video data or enthusiastic to learn what is happening in this space. We will be discussing topics like crowd-sourced solutions, when to move to algorithmic solutions, video processing with high performing computing and proprietary versus open source solutions. It is a two-way discussion so please register here.
Image Data Analysis: Thursday, November 10th, 1:00 -2:00 pm EST
Join this open panel discussion if you are a researcher or company working with the analysis of image datasets, in any discipline. Also learn about taking the next step in image data analysis. We will be discussing topics like high profile image screening, computer vision, big image data management across verticals which may include astronomy, biology, weather, biodiversity, and industry search. It is a two-way discussion so please register here.
Text Data Analysis: Thursday, December 8th, 2016 1:00 -2:00 pm EST
Join this open panel discussion if you are a researcher or company working on text mining, in any sector. We will be discussing topics like web-scraping, semantic web, analysis tools in R and Python, the benefits of open source search engines such as Solr and elastic search as well as current industry grade search. It is a two-way discussion so please register here.
Audio Data Analysis:Thursday, January 19th, 2017, 1:00 -2:00 pm EST
Join this open panel discussion if you are a researcher or company working on audio data, in any sector. We will be discussing topics like 3D audio, speech recognition, and more. It is a two-way discussion so please register here.
Learning Data Analysis: February 2017 1:00 -2:00 pm EST
Join this open panel discussion if you are a researcher or company working on learning data, in any sector. We will be discussing topics like machine learning, spatial temporal data, time-series data, and more. It is a two-way discussion so please register here.
The Big Data in Materials and Manufacturing theme community is an open forum to discuss data solutions and issues facing modern Material Science and Advanced Manufacturing. This pad text is synchronized as you type, so that everyone viewing this page sees the same text. Please feel free to add to the discussion. To join contact Renata Rawlings-Goss <email@example.com>
This Github repository was created after the Materials and Advanced Manufacturing Workshop, from combined participant input. Courtesy of Andrew Medford, Mark Jack and Jason Hattrick-Simpers.
If you know of resources for materials informatics. Feel free to become a contributor.
Most of big data scholarly literature over 10 years is focused on data mining (9%) and data analytics (7%), while privacy and security are only 2%. See Strang & Sun (2017), Scholarly big data body of knowledge: What is the status of privacy and security? Annals of Data Science, in press.
In 2016, data mining as a publication topic dropped to 1% and social media and cloud computing increased in total percentage.
In 2016, there also was a proliferation of specific topics in big data. There was a high percentage of literature reviews and conceptual frameworks, and limited focus on applications of big data.
In 2016, in the first 6 months of production, research in journals (excluding websites, trade magazines, magazines): 26% focused on literature reviews, 15% focused on taxonomies/frameworks/research design. Total 41%.
Then there was a focus on statistics and machine learning (1%).
Privacy and security studies, however, decreased in pulications with relative production (below 1%) for 2016. It's about half of what it was.
Lea: Is this due to limited NSF funding for privacy & security big data research?
Ken: Ethics of big data is another topic that needs attention.
Ken: Difficult to do big data research in terrorism. Hard to assess accuracy of the infrormation, and sensitivy and layers of security on the data. Need behavioral and predictive research.
Ken: What would be ways we could apply methods to come up with predictive models using big data? Need structure and standards in place for organiations that do have access to the data, like DHS, so can be replicated as a model.
Several forums have identified "Data Science" as an essential element in educating students for the 21st century workforce. Because data literacy at multiple levels is now needed to navigate the deluge of data in almost every scientific disciple and business sector, the United States is suffering from a lack of a trained workforce to meet the current demand and the emerging demands.