Teaching activities scheduled for the 1st year (2021/22)
Compulsory training program common to all PhD students
Bibliographic services for research (part A: Marco Tagliavini, Silvia Muzzi, Goidanich Library - part B: Davide Gori)
Part A
Main on-line tools for bibliographic resources that can be accessed through the library system of the university of Bologna: book and journals catalogues, databases. Practical exercises on the tools presented.
Part B
Goals. Students will achieve the methodology for building evidence based strings for making research in medical literature. Students will discuss together the basic principles for appraising a scientific article.
Contents. How to build evidence based search strings and filters for the identification of all relevant studies to answer clinical questions. Basic principles for appraising and evaluating the quality of a scientific article. A practical example and discussion with the class of literature search in PubMed and/or other medical databases
Set up of a research protocol (Marco Menchetti)
Learning outcomes
At the end of the course, the student has knowledge, as far as he is competent, relating to the field of research methodology and will be able to conduct a critical appraisal of a clinical trial/observational study.
Course contents
- Characteristics of the different study designs
- Selection of subjects for study
- Sources of error and of bias
- Confounding
- Critical appraisal of some research protocols
Research ethics (Marco Alvise Bragadin)
Research ethics govern the standards of conduct for scientific researchers. These standards promote not only the aims of research, such as knowledge, truth, and avoidance of error, but also the values essential to collaborative work such as trust, accountability, mutual respect and fairness (i.e. concerning authorship, copyright, patenting). The lecture will address the European Code of conduct for research integrity, and a list of ethical issue concerning privacy and data protection, the human subjects protection, animal care and use, conflict of interest, environmental impact and other possible scientific misconduct of the proposed research.
Use of statistical and data management software (David Neil Manners)
Course objectives
By the end of the course, the student will be able to organise and prepare data for scientific research, and to visualise and analyse it using R. The course will emphasise applications to real data sets, and focus on "small" data (< 1 Tb).
Course contents
1) Project and data management
Outline of main considerations regarding data management for scientific projects, and brief review of useful software packages
2) Data type and structures
Overview of data types and data structures, with special emphasis on the R language
3) Outline of R
Introduction to the R language, including fundamental elements of grammar including syntax and control structures, and use of the Rstudio GUI
4) Data wrangling
Description of processes needed to prepare data for visualisation and analysis, including importing and exporting data sets, data cleaning, and reformatting for easy processing
5) Data visualisation
Graphical techniques for visualising distributions of and correlations between categorical and continuous variables
6) Data modelling, analysis and statistics
A brief review of techniques to summarise and simplify data using descriptive and inferential statistics
Teaching methods
Conventional lessons and computer laboratories, conducted in a computer room using terminals with required software pre-installed
Big data: data processing and use of statistical software for the management and construction of large databases to support research (Maria Pia Fantini, Davide Gori)
The role of big data in modern epidemiology and as a way to tackle modern epidemics (i.e. chronic diseases and ageing)
Construction of database to manage population based data.
Practical examples of data construction and management using statistical packages.
Students will learn the basis of the utilization of big data in medicine.
Students will undergo practical laboratories for data construction and management using statistical packages.
Big data: applications in the medical and environmental fields (Giulia Menichetti)
Learning outcomes
The student will acquire the bases of big data analysis, familiarizing with the most common techniques for storing, managing and use of unstructured data with a specific attention to the extraction and analysis of information in biological and environmental fields.
Writing a scientific paper (Marco Menchetti)
International mobility guidance (Maria Luisa Dindo)
Learning outcomes
The seminar aims to illustrate to the students of the SSSV course the opportunities for international mobility offered to them, taking into account that a stage abroad (in an appropriate place and with a study / research program well in line with the thesis topic) represents an essential moment in the training of a PhD student. The seminar also aims to briefly illustrate the results of some concrete experiences, with the aim of better directing the PhD students towards the choices they will be able to make.
Contents
The contents will be as follows:
1. Requirements for stays transferred and to obtain the related increase in scholarship
2. Main mobility programs (e.g. Erasmus plus, Marco Polo, Brown University)
3. Other opportunities abroad
4. Reference sites and contacts
5. Previous experiences abroad of PhD students (with the teacher as tutor), with suggestions to better exploit this opportunity
Presentation of research findings (Stefano Benni)
Learning outcomes
At the end of the course the PhD student knows the key aspects of the structure of an effective presentation of research findings and achieves the main communication skills to present a research to the scientific community.
Course contents
• Fundamentals of communication for academic presentations
• Analysis of presentations of research findings
• Process of outlining the structure of a scientific presentation
• Workshop for the application of the theoretical contents
Geographical information systems (Daniele Torreggiani)
Learning outcomes: The student will acquire the theoretical and practical bases of maps and geographic information systems (GIS), and will be able to use both raster and vector layers, perform data spatialization and data analysis and geoprocessing in in GIS environment.
Contents: Fundamentals of cartography, introduction to digital maps and Geographic Information Systems (GIS). ArcGIS software (ArcMap, ArcToolbox, ArcCatalog): main commands and functions, geodata: sources and online services. GIS practicals with ArcGIS software: loading, management and queries on vector and raster maps, reclassification. Reference system definition and transformation. Creating and editing of maps and attribute tables, feature selection based on attributes and location, join and spatial join. Main geoprocessing functions: proximity analysis, buffer analysis, overlay mapping, clipping, merging. Importing GPS data into ArcGIS. Print and exporting: legend, dataframe management.
Academic writing (University Language Center - CLA)
Complementary training program aimed at knowledge alignment and in-depth analysis of various topics (courses to be identified with the support of the supervisors and the academic board)
Click here to see the page with the complementary course offered in the 2021/22 academic year.