Ateneo - UPO

The course introduces the basic concepts of data management in the biomedical field, focusing on the use of Excel for handling, cleaning, and transforming datasets from experimental or observational studies. Students will learn how to import, explore, modify, and document databases, perform data quality checks, and produce simple descriptive reports. Practical examples will be based on real epidemiological and biological datasets.

Lecture materials (slides, exercises, sample datasets).

Provide basic knowledge and skills for biomedical data management, including data organization, cleaning, transformation, and documentation. Foster the ability to use digital tools (Excel) and understand best practices in data management according to FAIR principles.

To successfully follow the course, students should have a basic understanding of computer science.

Lectures, Classroom exercises

The Course material is available in the Moodle platform. Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/services-students-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

The final examination, conducted jointly with the Informatics module, consists of a compulsory written test held on the official examination date.

The test includes a practical activity involving the management and analysis of a dataset provided by the instructor, together with a series of questions, some closed-ended (single- or multiple-choice) and some open-ended. Open-ended questions may also require the completion of simple applied exercises.

The examination is designed to assess the acquisition of knowledge and skills related to the organization, management, cleaning, modification, and documentation of biomedical data, as well as the ability to use Excel to perform basic data management and analysis tasks. Understanding of good data management practices, including the FAIR principles, and the ability to document the procedures adopted in a clear and reproducible manner will also be assessed.

The final assessment will take into account the level of knowledge of the topics covered, the ability to correctly apply data management procedures, the accuracy of data processing, and the clarity of the documentation of the operations performed.

Assessment criteria:
18–21: Essential knowledge of the topics and ability to apply basic data management and organization procedures, although with some inaccuracies.
22–24: Fair knowledge of data management principles and ability to correctly apply the main procedures for data management, cleaning, and documentation.
25–27: Good knowledge of the topics and good ability to appropriately apply data management tools and principles, providing adequate justification and documentation of the procedures adopted.
28–30: Very good or in-depth knowledge of the topics and ability to manage and analyze data correctly, independently, and in a methodologically appropriate manner, clearly documenting the procedures adopted.
30 with honours: Excellent command of the knowledge and skills covered by the course, combined with full autonomy in data management and analysis, a high level of methodological accuracy, and the ability to critically document and justify the choices made.

The final grade for the course is determined on the basis of the joint assessment of the examinations for the Informatics and Data Management modules.

Introduction to data management, data formats, importing/exporting in Excel, data cleaning, variable transformation, filters and pivot tables, introduction to R and RStudio, dataset handling, derived variable creation, descriptive analysis and basic plots, practical exercises with biomedical datasets.

Knowledge and understanding: understand the basic principles of data management and variable transformation. Applying knowledge and understanding: perform data cleaning and data transformation on biomedical datasets. Making judgements: assess data quality and select appropriate handling procedures. Communication skills: clearly present results and document analytical steps reproducibly. Learning skills: gain autonomy in using digital tools for managing complex databases.