B9069 - COMUNICARE LA SCIENZA CON I DATI

Learning outcomes

At the end of the course, the student has the theoretical and practical basis of univariate and multivariate statistics for the processing data obtained from experimental measurements of natural variables. In particular, the student can design experiments and can process data using modern software packages. Finally, the student knows how to apply the acquired skills to the design of didactic and science communication experiences, also by searching for information on the web.

Course contents

COURSE STRUCTURE

6 credits (ECTS), equal to 42 hours, including:
5 credits (ECTS) of classroom lessons, equal to 30 hours
1 credit of computer lab work, equal to 12 hours

 

GENERAL COURSE NOTES

The course is divided into two modules:
Module 1: Chemometrics
Module 2: Data Communication in the Natural Sciences

The specific contents of the two modules are specified below.
The following considerations apply to both modules.

Use of Artificial Intelligence
Generative AI can be a useful tool to support individual study, for example through in-depth analysis, summaries, reformulations, and self-assessment.
Regarding learning assessment, a limited, declared, and non-substantial use of generative AI is permitted for support activities, such as summaries, linguistic revision, or reformulation of texts produced by students.
Substantial use of AI for the uncritical generation of texts, content, bibliography, data interpretations, or communication materials to be presented as a personal paper is not permitted.

Students with Special Learning Disabilities
Students with learning disabilities (LD) or temporary or permanent disabilities are advised to contact the relevant University office promptly (https://site.unibo.it/studenti-con-disabilita-e-dsa/it ).
The office will advise the affected students of any learning and assessment adjustments. These adjustments must be submitted to the instructor for approval 15 days in advance, who will evaluate their suitability, including in relation to the course's learning objectives.

 

CONTENTS OF MODULE 1

Module Structure
4 credits (ECTS), equal to 30 hours, of which:
3 credits (ECTS) of classroom lessons, equal to 18 hours
1 credit of computer lab work, equal to 12 hours

Prerequisites
The student who accesses this course must have a good preparation in the fundamentals of basic laboratory techniques of biology, chemistry and geology.

Learning outcomes
The module aims to provide the student with the ability to design an educational experience or an informative communication concerning the natural science laboratory, starting from the design of experiments to arrive at the correct data processing and presentation of results.
With these objectives, the following mathematical and statistical knowledge are provided: elements of statistical analysis; data exploration methods; modeling methods: classification and regression; Design of experiments (DOE).

Program
Significance tests. Calibration by univariate regression. Quality parameters of the experimental data. Validation of experimental methods. Multivariate data pretreatment. Transformation of variables. Multivariate data exploration: cluster analysis and principal component analysis. Multivariate statistical models: control parameters, classification by discriminant analysis, regression by least squares method and by principal components. Design of experiments: multivariate methods for the choice of standard samples and variables for the construction of the models. Use of software packages (type "Office") for the management of spreadsheets, for the preparation of written documents and for the preparation of informative presentations; use of software packages (type "R") for writing programs for the application of statistics to scientific problems. Use of on-line databases for bibliographic research.

 

CONTENTS OF MODULE 2

Module Structure
2 credits of classroom lessons, equivalent to 12 hours

Learning Outcomes
The module aims to provide students with the ability to transform data, experimental results, and scientific content into effective educational and outreach products, with particular reference to the natural sciences.
To this end, the module provides knowledge and skills related to the principles of science communication, data visualization, data storytelling, the communication of uncertainty and natural variability, and the design of materials intended for different audiences and educational contexts.

Programme
Translation of results obtained through statistical and chemometric analyses into effective and accessible forms of communication for different audiences. Principles of science communication in the natural sciences. Specialist, educational, and outreach communication. Main forms of communication of scientific results: articles, presentations, posters, and outreach materials. Organization of scientific results according to the IMRaD structure. Role of the title, abstract, figures, tables, and captions in the communication of scientific results. Bibliographic research, use of scientific databases, and proper use of sources. Representation and visualization of biological, chemical, geological, and environmental data through graphs, tables, and infographics. Communication of uncertainty and limitations of scientific data. Elements of data storytelling applied to science communication. Design and production of communication products based on real or simulated data.

Readings/Bibliography

MODULE 1 

The notes that students can take by following the lessons in person or by listening to the recording of the lessons are fundamental.
For each lesson, the teacher produces a pdf document corresponding to what the teacher writes on the electronic board used for explanations; this document is published on the platform virtuale.unibo.it
All the contents of the course are present in the handouts that the teacher publishes on the platform virtuale.unibo.it, the reading of which is recommended.

Recommended Readings for Further Study
- J.C. Miller, J.N. Miller, Statistics and Chemometrics for Analytical Chemistry, Pearson Education, 2010.
- Richard G. Brereton, Applied Chemometrics for Scientists, Wiley, 2007.

 

MODULE 2

 Students’ notes taken during in-person lectures or while watching lecture recordings are essential.
For each lecture, the instructor provides teaching materials in PDF format, including outlines, case studies, examples of data visualization, and exercise guidelines; these materials are published on the virtuale.unibo.it platform.

During the module, real or simulated scientific data related to biological, chemical, geological, and environmental topics will be used, with the aim of designing educational and outreach materials based on the correct interpretation and communication of results.

All module contents are included in the lecture notes and materials published by the instructor on virtuale.unibo.it; students are advised to read them.

Recommended Readings for Further Study
- Cole Nussbaumer Knaflic, Storytelling with Data: A Data Visualization Guide for Business Professionals, Wiley, 2015.
- Alberto Cairo, The Truthful Art: Data, Charts, and Maps for Communication, New Riders, 2016.
- Claus O. Wilke, Fundamentals of Data Visualization, O’Reilly, 2019.
- Nancy Baron, Escape from the Ivory Tower: A Guide to Making Your Science Matter, Island Press, 2010.

Teaching methods

MODULE 1

Lecturing and computer exercises.

Teaching material published on virtuale.unibo.it

Attendance in presence is strongly recommended for all teaching activities; please note that it will no longer be possible to follow the teaching activities live on TEAMS.
In any case, all lessons and exercises will be recorded and made visible on virtuale.unibo.it, in order to facilitate students who are unable to attend in person or who want to review the lessons to verify unclear passages.

 

MODULE 2

Theoretical lectures and applied classroom exercises.

The exercises involve the analysis of real or simulated scientific data and the design of educational and outreach materials intended for different audiences and communication contexts.

The teaching material used during the lectures is published on the virtuale.unibo.it platform.

In-person attendance at all teaching activities is strongly recommended. In any case, lectures will be recorded and made available on virtuale.unibo.it, in order to support students who are unable to attend in person or who wish to review the teaching activities.

Assessment methods

MODULE 1

Students produce a report on an assigned experimental problem.

The report is discussed during an oral exam, in which questions about the general theory are also asked.

The evaluation takes into account mastery of the content, clarity of presentation, ability to connect theory and practice, and autonomy in discussion. The judging criteria are structured as follows:

• Sufficient (18–20): essential knowledge; correct, but uncertain and poorly structured presentation.
• Good (21–24): consolidated knowledge; overall adequate presentation, but predominantly memorized and with limited ability to connect topics.
• Very good (25–27): mastery of the content; confident, well-structured presentation with analytical skills.
• Excellent (28–30L): comprehensive and in-depth knowledge; rigorous, independent, and critical presentation. 30 cum laude is awarded for excellent presentations.

MODULE 2

Students prepare an educational or outreach product based on real or simulated scientific data related to natural science topics.

The assignment may consist, for example, of a presentation, a poster, an educational sheet, an infographic, or a short science communication pathway aimed at a specific target audience.

The assignment is discussed during an oral examination, which also includes questions on the principles of science communication, data visualization, and the adaptation of the message to the target audience.

Assessment takes into account the scientific accuracy of the content, clarity of expression, communication effectiveness, quality of data visualization, ability to select and organize relevant information, and autonomy in the discussion.

The assessment criteria are as follows:

Sufficient (18-20): essential knowledge; communication product correct in its main contents, but simple, poorly developed, or with limited communication effectiveness.

Good (21-24): consolidated knowledge; overall adequate communication product, with correct organization of data and information, but with limited originality or limited ability to adapt to the target audience.

Very good (25-27): good mastery of the contents; clear, well-structured, and effective communication product, with appropriate use of graphs, tables, images, or other visualization tools.

Excellent (28-30L): complete and in-depth knowledge; rigorous, autonomous, critical, and particularly effective communication product with respect to the audience and communication objectives. 30 cum laude is awarded for excellent presentations.

FINAL GRADE
The final grade will be the weighted average of the grades obtained in the two modules.

 

Teaching tools

MODULE 1

Blackboard for theoretical lessons. Video projector for explanation of spreadsheets. Informatic laboratory for exercises.

For lectures and exercises the teacher uses the following programs: Microsoft Excel and "R". The software "R" is used in the simplified version CAT, which can be dowloaded from the site http://gruppochemiometria.it/index.php/software.

To carry out individual exercises and calculations for the final report, students can use the PCs of the informatic laboratory or they can use their computers, both in presence and in remote mode

 

MODULE 2

 Whiteboard for theoretical explanations. Video projector for the discussion of examples of science communication, data visualization, presentations, posters, infographics, and educational/outreach materials. Computer laboratory for applied exercises.

For lectures and exercises, the instructor uses software for data management and representation, preparation of presentations, and creation of communication materials. In particular, Microsoft Excel may be used for data organization and graph construction, Microsoft PowerPoint for the preparation of presentations and visual materials, as well as open-source graphic software, e.g. Inkscape, or web platforms and open digital applications dedicated to design and infographic creation, e.g. Canva.

During the exercises, students work on real or simulated scientific data related to natural science topics, with the aim of transforming them into communication materials intended for different audiences.

To carry out individual exercises and prepare the final assignment, students may use the computers in the computer laboratory or their own computers, both in person and remotely.

Office hours

See the website of Dora Melucci

See the website of Alessandro Girolamo Rombolà