Data access and regulation

A.Y. 2019/2020
Lesson for
9
Max ECTS
60
Overall hours
SSD
INF/01 IUS/09
Language
English
Learning objectives
The objective of the course is to give students a multidisciplinary appoach to data processing.
According with this objective, each module is focused on a specific aspect.
The aim of the first module is to provide students with the essenatial elements of data protection law, making them familiars with principles, rights and duties set by the General Data Protection Regulation (GDPR)
The aim of the second module is to provide students with the essential elements of data access, making them familiar with the tools and techniques that enable gathering social science data from the Internet. In this setting, students will develop their data literacy and analysis skills, and reinforce their ability to develop code to produce applications or to achieve analytical goals.
The aim of the third module is to give students a clear understanding of the data storage, access and sharing options which are available to them - ranging from simple flat file structures to commercial managed cloud databases - so that they will be able to effectively choose the correct data approach for their research projects. A well-chosen data methodology allows your data management tools to do a lot of laborious work for you, enables smooth collaboration with your colleagues and ensures that you will be able to share your research data when the time comes to publish your work.

Course structure and Syllabus

Active edition
Yes
Responsible
INF/01 - INFORMATICS - University credits: 3
IUS/09 - PUBLIC LAW - University credits: 6
Lessons: 60 hours
Professor: Orofino Marco
ATTENDING STUDENTS
Syllabus
I (first module): Introduction. The European concept of privacy between EU and ECHR; The relevant data subjects; Territorial and material scope; Principles and conditions relating to processing of personal data; Rights of the data subject; The Member States' Independent Supervisory Authorities and the European Data Protection Board; Competence, tasks and powers, Remedies and penalties; Transfers of personal data to third countries (non-EU countries); IA and Data protection.

II (second module): Introduction (How to get help when using R,- Data wrangling with dplyr, Automated and reproducible reporting using rmarkdown, Tidy data principles, Project-oriented workflow, Version control, Git, GitHub) Web scraping ( HTML tags, robots.txt; Web scraping, the rvest package, the RCrawler package; XPath, HTTP protocol and URLs (libcurl, RCurl); XML and JSON; Regular expressions). Dealing with APIs (Introduction to (web-service, RESTful) APIs; Basic API flow; Social media APIs; Cloud computing in AWS.

III (third module): The module will begin with a discussion of the challenges presented to social science researchers by Big Data and the increasingly rigorous demands for transparency and open sharing of research data, and outline the considerations which researchers need to keep in mind when designing a data management plan for their project. Next we will introduce a variety of different data management tools, including flat files, local databases, and large-scale cloud storage systems. For each of these we will discuss strengths and weaknesses, and the kinds of data or analysis for which they might be suitable, and students will have an opportunity to try working with them (adding, manipulating and exporting data) in lab sessions, where they will learn the basics of the data specification and query syntaxes (SQL and NoSQL) that are commonly used for these systems. Finally, we will discuss best practices for creating a data management plan that will address considerations such as collaborating with other researchers, ensuring that your valuable data is securely backed up, and allowing you to easily share it publicly when your research is published. The module will conclude with a project in which students will design a data management plan for a hypothetical research project and write a short report justifying the choices they made.
NON-ATTENDING STUDENTS
Syllabus
I (first module): Introduction. The European concept of privacy between EU and ECHR; The relevant data subjects; Territorial and material scope; Principles and conditions relating to processing of personal data; Rights of the data subject; The Member States' Independent Supervisory Authorities and the European Data Protection Board; Competence, tasks and powers, Remedies and penalties; Transfers of personal data to third countries (non-EU countries); IA and Data protection.

II (second module): Introduction (How to get help when using R,- Data wrangling with dplyr, Automated and reproducible reporting using rmarkdown, Tidy data principles, Project-oriented workflow, Version control, Git, GitHub) Web scraping ( HTML tags, robots.txt; Web scraping, the rvest package, the RCrawler package; XPath, HTTP protocol and URLs (libcurl, RCurl); XML and JSON; Regular expressions). Dealing with APIs (Introduction to (web-service, RESTful) APIs; Basic API flow; Social media APIs; Cloud computing in AWS.

III (third module): The module will begin with a discussion of the challenges presented to social science researchers by Big Data and the increasingly rigorous demands for transparency and open sharing of research data, and outline the considerations which researchers need to keep in mind when designing a data management plan for their project. Next we will introduce a variety of different data management tools, including flat files, local databases, and large-scale cloud storage systems. For each of these we will discuss strengths and weaknesses, and the kinds of data or analysis for which they might be suitable, and students will have an opportunity to try working with them (adding, manipulating and exporting data) in lab sessions, where they will learn the basics of the data specification and query syntaxes (SQL and NoSQL) that are commonly used for these systems. Finally, we will discuss best practices for creating a data management plan that will address considerations such as collaborating with other researchers, ensuring that your valuable data is securely backed up, and allowing you to easily share it publicly when your research is published. The module will conclude with a project in which students will design a data management plan for a hypothetical research project and write a short report justifying the choices they made.
Lesson period
First trimester
Lesson period
First trimester
Assessment methods
Esame
Assessment result
voto verbalizzato in trentesimi
Professor(s)