Hosted by the Chair of Frauke Kreuter and the Munich Center for Machine Learning (MCML), this 2-month full-time program joins forces of aspiring talents in the area of Data Science in small groups to work on projects with a positive societal impact.
The program is designed for two teams of 4-5 fellows, each working on a separate project for the social good. Both teams will be assisted by a Technical Mentor and a Project Manager. The participants receive a fellowship that covers living expenses for the time of the program from August 1st – September 30th.
The program is aimed at students, recent graduates or PhD students from diverse scientific, as well as geographical backgrounds. We therefore encourage applications from the fields of data science, computer science, statistics, but also in general the social and natural sciences.
In addition to in-app survey data, the IAB-SMART project collected 1.3 million locations observations from GPS and mobile network data on participants recruited among Android users in the Panel Study Labor Market and Social Security (PASS). Financed by BERD@NFDI, these collected raw geolocations were aggregated and published as IAB-SMART-Activity at the Research Data Center of the Institute for Employment Research (IAB), containing geolocation indicators that can be used by researchers and linked to PASS survey data and PASS-ADIAB administrative employment data.
In this webinar, we will provide you with an overview of the preparation and anonymization tasks necessary to edit raw geolocations to meaningful indicators and to publish these indicators as a Scientific Use File (SUF) . Using R code examples, we will guide you step-by-step through the data editing process and provide you with tips & tricks for preparing smartphone location data. We will also highlight helpful packages, papers and tutorials.
The webinar’s analytical challenges, questions and open discussions support you directly in your current and/or future work with (smartphone) geolocation data.
This webinar is part of the IAB-SMART Webinar Series with three sessions. To get the whole picture, we recommend participating in the whole series, but the sessions can also be attended individually.
Smartphones are ubiquitous in everyday life, and researchers capture behavioral data using sensors built into smartphones, such as geolocations. IAB-SMART-Activity is a new dataset with 398 participants and activity indicators based on geolocation data. This data was collected from early January 2018 to the end of August 2018. The data from IAB-SMART-Activity can be linked to the Panel Study Labor Market and Social Security (PASS) survey and administrative employment histories (PASS-ADIAB), providing a unique opportunity to study activity and labor market participation.
This webinar describes the IAB-SMART-Activity dataset and its research potential. In addition to describing the variables in the IAB-SMART-Activity data module, possible applications are outlined. Finally, the possibilities for accessing the data via Research Data Center at the Institute for Employment Research (IAB) are discussed.
This webinar is part of the IAB-SMART Webinar Series with three sessions. To get the whole picture, we recommend participating in the whole series, but the sessions can also be attended individually.
Do you own a smartphone? Probably. Smartphones have become an ubiquitous tool of our daily life, always with us and hard to imagine without. You are probably aware that smartphones collect various kinds of personal information about you, such as geolocations, call and text message log data, and app usage data. While your smartphone collects sensitive data about you, it has an unknown potential for understanding human behavior and society in social sciences. However, to use this data researchers have to design data collections that are in line with the current legal regulation such as, GDPR, transparent as possible and protect the data of participants.
In this webinar, I will give you an overview on how to collect smartphone data ethically and transparently with an Android app. The app we used is called IAB-SMART and collected various sensor data over a period of six months. Besides from showing how to design the recruitment process, I will provide an overview of the data collected and its various hidden error sources along the Total Survey Error Framework (TSE).
This webinar is part of the IAB-SMART Webinar Series with three sessions. To get the whole picture, we recommend participating in the whole series, but the sessions can also be attended individually.
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