University of Maryland

The Nature and Impact of Hidden Data Errors on Information Risk and Data Science

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.

Distance in Spatial Analysis: Challenges Related to Spatial Data Aggregation, Scale, and Computation

Distance is one of the most critical concepts in geography and has been widely used to quantify spatial separation between geographical entities. While measuring the distance between two points is straightforward, assessing the spatial separation between non-point objects can be challenging. This study investigates distance measurement between a location (point) and an area (polygon).

Bias Propensity to Inform Responsive and Adaptive Survey Design

Responsive and adaptive survey designs can be used to reduce the risk of nonresponse bias through data collection. In responsive design, different protocols that appeal to prior nonrespondents can be introduced in phases. In adaptive survey design, particular nonrespondents can be targets in these subsequent phases, based on predefined criteria. In the case of nonresponse bias, the criteria can be propensity models. Key, however, it the specification of these models.

Large-Scale Infrastructure for Social Data Science

Gathering, managing, and using social data to address critical questions requires the development of large-scale data infrastructures. In this Social Data Science Center (SoDa) panel, the distinguished speakers will share their experiences with the challenges and opportunities presented by efforts to create social data science infrastructure at the state, national, and international level.

MIS Quarterly Special Issue Showcase

Next-Generation Information Systems Theory January 27, January 29, and February 4th, 2021 Accelerating change, increasing complexity, and the unprecedented availability of data and algorithms for pattern identification have led some to argue for a reduced emphasis on theory in IS research.  However, it is our contention that theorizing is now more critical than ever. Rather […]

Data on Economic Anxiety Offer New Opportunities for Insights on the Global Effects of the COVID-19 Pandemic

Frauke Kreuter, Esther Kim, Sarah LaRocca, Katherine Morris, Christoph Kern, Andres Garcia December 21, 2020 Context The Global COVID-19 Trends and Impact Survey, which launched in April, 2020, is currently the largest ongoing public health data collection effort related to COVID-19. The survey is led by the University of Maryland and Carnegie Mellon University in […]

Measuring Emotion, Conflict and Disagreement – Watch Event Video

Social Data Science Center presents: Measuring Emotion, Conflict and Disagreement A Panel Discussion with Q&ATuesday, November 10, 2020 Panelists Annotation of Emotions and DisagreementPresented By: Dr. Susannah B. F. PaletzResearch Professor, College of Information Studies (iSchool), University of Maryland.Affiliate, Applied Research Lab for Intelligence and Security (ARLIS).Associate, Social Data Science Center (SoDa). When Does Disagreement […]

Map

Global Trends of Mask Usage in 19 Million Adults

October 12, 2020 By Ting-Hsuan Chang1, Elena Badillo Goicoechea1, Elizabeth Stuart1, Esther Kim2, Katherine Morris2, Sarah LaRocca2, Curtiss Cobb2, Xiaoyi Deng3, Samantha Chiu3, Adrianne Bradford3,Frauke Kreuter3,4 [The Global COVID-19 Trends and Impact Survey is a partnership between The University of Maryland, Carnegie Mellon University, and Facebook Data for Good. Data represented in the map are […]

JPSM Faculty, Alumni Receive Census Bureau Grants

Faculty and alumni of the Joint Program in Survey Methodology (JPSM) recently received awards from the U.S. Census Bureau in support of innovative work to improve the science of data collection. The first award was to RTI International to build a new Ask US Panel. This project will design, build and maintain a probability-based online research […]

3-Day Virtual Event

3-Day Virtual Event: UMD Launches the new Social Data Science Center (SoDa)

What does the future of collaborative efforts look like when research methods and strategies of academics are combined with industry help? The powerful information available in large social science data sets is critical to understanding and addressing many of our nation and world’s most pressing challenges: from Covid-19 to racial, social and economic injustice; and […]


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