more on fake news, data privacy in this IMS blog
more on fake news, data privacy in this IMS blog
#FakeNews #DigitalRecommendationEngines interpretation of data, market dependency “stupid smart recommendation engines” monopolistic structure, keep competitiveness, big data, market concentration
Reinventing Capitalism in the Age of Big Data (Basic Books / Hachette, 2018) by Viktor Mayer-Schönberger and Thomas Ramge.
more on this broad topic in this IMS blog:
and in the LIB 290 blog:
DuckDuckGo privacy free service
A large global change in data protection law is about to hit the tech industry, thanks to the EU’s General Data Protection Regulations (GDPR). GDPR affects any company, wherever they are in the world, that handles data about European citizens. It becomes law on 25 May 2018, and as such includes UK citizens, since it precedes Brexit. It’s no surprise the EU has chosen to tighten the data protection belt: Europe has long opposed the tech industry’s expansionist tendencies, particularly through antitrust suits, and is perhaps the only regulatory body with the inclination and power to challenge Silicon Valley in the coming years.
So, no more harvesting data for unplanned analytics, future experimentation, or unspecified research. Teams must have specific uses for specific data.
February 9, 201811:37 AM ET ADAM FRANK
Combine the superfast calculational capacities of Big Compute with the oceans of specific personal information comprising Big Data — and the fertile ground for computational propaganda emerges. That’s how the small AI programs called bots can be unleashed into cyberspace to target and deliver misinformation exactly to the people who will be most vulnerable to it. These messages can be refined over and over again based on how well they perform (again in terms of clicks, likes and so on). Worst of all, all this can be done semiautonomously, allowing the targeted propaganda (like fake news stories or faked images) to spread like viruses through communities most vulnerable to their misinformation.
According to Bolsover and Howard, viewing computational propaganda only from a technical perspective would be a grave mistake. As they explain, seeing it just in terms of variables and algorithms “plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it.”
Computational propaganda is a new thing. People just invented it. And they did so by realizing possibilities emerging from the intersection of new technologies (Big Compute, Big Data) and new behaviors those technologies allowed (social media). But the emphasis on behavior can’t be lost.
People are not machines. We do things for a whole lot of reasons including emotions of loss, anger, fear and longing. To combat computational propaganda’s potentially dangerous effects on democracy in a digital age, we will need to focus on both its howand its why.
more on big data in this IMS blog
more on bots in this IMS blog
more on fake news in this IMS blog
Organiser: Fatima Batool (The Alan Turing Institute and WiDS Ambassador)
Date: 6 April 2018
Venue: The Alan Turing Institute
The Stanford Women in Data Science conference (WiDS) is a one day global conference that will bring data scientists together to share cutting edge research. The conference aim is to inspire and encourage data scientists worldwide and exclusively support women in the field.
We will proudly host WiDS at The Alan Turing Institute. The conference will feature eminent female speakers through technical talks, lunchtime discussions on data science (topics to be announced shortly), a panel discussion and networking event.
The conference programme and speaker information will be soon available through the conference website. The event will be available worldwide via live streaming and the conference talks will be broadcast online.
The event will provide great opportunities to connect with potential mentors, collaborators and peers; hear about recent advancements in data science and explore new research dimensions.
Mihaela van der Schaar
We welcome all regardless of gender to join us on Friday 6 April 2018 for an excellent learning experience.
For more information email: email@example.com
Support analytics initiatives with data integration and governance. The changing landscape of enterprise IT is characterized by an expanding set of services, systems, and sourcing strategies. Data governance, cross-enterprise partnerships, and data integration are key ingredients in supporting higher education’s growing need for reliable information.
In this set of EDUCAUSE Review case studies, see how Drake University, the University of Tennessee, and the University of Montana improved their analytics initiatives through data integrations and governance.
more on analytics in this IMS blog
Here are things that can help you build a bridge from your current methods to effective data storytelling–
A few bonus tips to make your data visualizations really pop–
more on big data in education in this IMS blog
Finch, J. f., & Flenner, A. (2016). Using Data Visualization to Examine an Academic Library Collection. College & Research Libraries, 77(6), 765-778.
Visualizations of library data have been used to: • reveal relationships among subject areas for users. • illuminate circulation patterns. • suggest titles for weeding. • analyze citations and map scholarly communications
Each unit of data analyzed can be described as topical, asking “what.”6 • What is the number of courses offered in each major and minor? • What is expended in each subject area? • What is the size of the physical collection in each subject area? • What is student enrollment in each area? • What is the circulation in specific areas for one year?
libraries, if they are to survive, must rethink their collecting and service strategies in radical and possibly scary ways and to do so sooner rather than later. Anderson predicts that, in the next ten years, the “idea of collection” will be overhauled in favor of “dynamic access to a virtually unlimited flow of information products.” My note: in essence, the fight between Mark Vargas and the Acquisition/Cataloguing people
The library collection of today is changing, affected by many factors, such as demanddriven acquisitions, access, streaming media, interdisciplinary coursework, ordering enthusiasm, new areas of study, political pressures, vendor changes, and the individual faculty member following a focused line of research.
subject librarians may see opportunities in looking more closely at the relatively unexplored “intersection of circulation, interlibrary loan, and holdings.”
Using Visualizations to Address Library Problems
the difference between graphical representations of environments and knowledge visualization, which generates graphical representations of meaningful relationships among retrieved files or objects.
Exhaustive lists of data visualization tools include: • the DIRT Directory (http://dirtdirectory.org/categories/visualization) • Kathy Schrock’s educating through infographics (www.schrockguide.net/ infographics-as-an-assessment.html) • Dataviz list of online tools (www.improving-visualisation.org/case-studies/id=5)
Eugene O’Loughlin, National College of Ireland, is very helpful in composing the charts and is found here: https://youtu.be/4FyImh2G7N0.
p. 771 By looking at the data (my note – by visualizing the data), more questions are revealed, The visualizations provide greater comprehension than the two-dimensional “flatland” of the spreadsheets, in which valuable questions and insights are lost in the columns and rows of data.
By looking at data visualized in different combinations, library collection development teams can clearly compare important considerations in collection management: expenditures and purchases, circulation, student enrollment, and course hours. Library staff and administrators can make funding decisions or begin dialog based on data free from political pressure or from the influence of the squeakiest wheel in a department.
more on data visualization for the academic library in this IMS blog
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