SoDa Research RoundtablesMonday, May 24, 20211:00pm EST Talk 1: Introduction to Brainhacks : a Bottom-Up Approach to Science.Junaid Merchant, UMD Neuroscience and Cognitive Science program.Link to this paper: https://www.cell.com/neuron/pdfExtended/S0896-6273(21)00231-2 Talk 2: Models vs. MachinesKeaton Hyuckmin Kweon Ellis, UMD Department of Economics. Join Zoom: https://umd.zoom.us/j/97464475234 Social Data Science CenterUniversity of Maryland1218 Lefrak HallCollege Park, MD, 20742
Gathering and Sampling Social Media Streams Social media are central to many civic, commercial, and public health activities. For this reason, social media streams are seen as rich windows into many aspects of social behavior. However, as with any data source, how the data is gathered and sampled is critical for ensuring the integrity of the information,…
Social Data Science and Collective Resilience: Opportunities and ChallengesSocial data science can both strengthen and damage the fabric of civil society. Predictive models allow us to respond as situations develop and either manage or magnify them. Social data enables quick responses to unrest and conflict, active engagement with developing narratives, and identification of individuals and…
Friday, April 30,20211:00pm – 3:00pm EST Data Defect Correlation: A Unified Quality Metric for Probabilistic and Non-probabilistic Samples. Xiao-Li Meng, P.hD.Editor-in-ChiefWhipple V. N. Jones Professor of Statistics, and Founding Editor-in-Chief of Harvard Data Science Review Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known…
Monday, March 22, 20211:00pm EST Talk 1: Offering Participant Control and Tailored Introductions in Smartphone-based Passive Data Collections to Avoid RefusalsPresented by doctoral student Alex Brown Breslin, JPSM Talk 2: Trends in Trust: Measuring Americans’ Expectations for Trustworthy Research Use of Social Media DataPresented by Sarah Gilbert, Postdoc, iSchool. Upcoming Roundtables April 26, 1 pm Youngho Kim,…
Event Date and Time : Friday, April 9, 2021 – 12:00 am to Sunday, April 11, 2021 – 11:59 pm Location : Washington DC About DataFest The 2021 DC Datafest is a 48-hour competition where teams of undergraduate students from the DC metro area compete virtually to analyze rich, complex data sets from a major organization. Students are…
MONDAY SYMPOSIUM IN MEASUREMENT AND STATISTICS (MSMS) UNIVERSITY OF MARYLAND together with OHIO STATE UNIVERSITY, UNIVERSITY OF NORTH CAROLINA AT CHAPEL HILL* UNIVERSITY OF NOTRE DAME (* Organizer of this talk) This presentation describes a benchmark method to validate statistical methods from the analysis of data on a known or established empirical effect. There are aspects to benchmark validation…
SoDa Associate and UMD Faculty Katie Shilton quoted in the New Yorker In computer science, the main outlets for peer-reviewed research are not journals but conferences, where accepted papers are presented in the form of talks or posters. In June, 2019, at a large artificial-intelligence conference in Long Beach, California, called Computer Vision and Pattern…
Mixed-mode contact and recruitment approaches have become increasingly critical in reaching people to participate in surveys and research panels and improving their likelihood of response and often, the representation of the sample, bringing groups more traditionally represented. Nielsen has designed a phased sequential multi-mode methodology to recruit homes through a combination of web, phone, and in-person/proximity methods for its core TV panels that have relied exclusively on in-person recruitment. This work has been accelerated due to the COVID-19 pandemic. Nielsen will present the results of a “push to web” recruitment test conducted in five markets from October 2020 to January 2021.
Information Risk is an important field that encompasses multiple existing disciplines and overcomes the boundaries among them that has impeded knowledge sharing and effective management of integrated business projects. These projects often span multiple organizational groups and use different structured frameworks of information, data, computing, and security management. The practical realities intrinsic to performing this work leads to gaps in complying with regulations, ensuring secure operations, satisfying auditors, and even meeting program objectives for data analysis and business uses. This talk describes how deeply embedded data disparities that remain hidden to typical data methods lead to high error rates in project results. Lessons learned from assessing and correcting these situations is presented with examples of the problems and methods to detect and fix them.
