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Compulsory Course descriptions XXXVI cycle - ay 2020 - 2021

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Programming and data manipulation in R *

Data analysis R + RStudio

Biological risk safety

Critical analysis, writing and revision of a scientific article

Criteria for the realisation of a research project

The concept of "principal investigator"

Computational Statistics *

English (Academic writing)

Elements of Intellectual Property - Patents

 

 *  Mandatory  to attend at least one of the two courses 

 

 

 

Programming and data manipulation in R


CFU: 3

Teacher: Federico Mattia Stefanini, Riccardo Bozzi, Luisa Ghelardini

Compulsory attendance: 75% of lessons (signature collection)
Final assessment: yes
Course structure: classroom lesson and exercitation
Teaching material: provided  by the teacher

 

Course description

The course introduces open source resources for data analysis, and in particular the R environment. In the first part the basics of the R environment are introduced, together with the basic principles of the R code and the available resources for help.

The second part narrows down toward R programming, with a bird’s eye view of the software capabilities.

The third part proceeds toward a more practical introduction, where we will learn through examples on dummy datasets to satisfy the most common needs of data analysis one might find in natural sciences.

In the fourth and last part the student will practice what has been learned by analyzing autonomously some data*2.

The final exam will evaluate the capability of the student to analyze autonomously a dataset.


*1: I dati per questa prova pratica / accertamento finale saranno forniti dall’insegnante. Nel caso il tempo a disposizione sia sufficiente e ce ne sia la possibilità pratica, lo studente può concordare con l’insegnante per l’utilizzo di dati propri.

*2: the dataset for this hands-on / final examination will be provided by the teacher. In case there will be enough time and it will be possible, the student can discuss with the teacher the possibility of using his/her own data.

 

 

Data analysis with R + RStudio


Teachers: Riccardo Bozzi, Stefano Biffani

 

Course description

Founded over 20 years ago, the R programming language has profoundly evolved over time to become today an extremely versatile tool that allows not only the management and analysis (more or less advanced) of data of different sources (SQL , MSDB, csv, txt, xls, pdf, html, json, etc) but the ability to create reports (doc, pdf, html), web pages and applications, presentations with a single tool. Over time the limitation on the size of the data to be managed has also disappeared and today there are packages developed ad hoc to manage the so-called big data. Procedures and libraries are available for the most varied statistical analyses but also for simple data management (e.g. transformation of variables).

In the wide range of packages available today, the tidyverse library stands out for its versatility, providing tools for importing files (of any format) and for their subsequent manipulation and display. This library and its use, which will represent the main body of the course, will be accompanied by other tools for the actual statistical analysis and for the so-called 'reproducibility' of the research and routine (e.g. creation of dedicated pipelines with output reporting automated).

Topics covered:

  • Using projects in RStudio - 2h
  • Import of data (formats and problems) - 2h
  • Data management - 8h
    • filter records by a certain value (filter)
    • sort records by one or + keys (arrange)
    • select some variables (select)
    • create new variables (mutate, ifelse, case_when)
    • create new summary data (summarise), grouped by variable (group_by)
    • transpose the data (pivot_longer, pivot_wider)
    • work with dates (lubridate, as.date)
    • merge data.frame (merge, join)
    • write results to an external file (xls, csv, txt etc)
  • Display (ggplot) 6 h
  • Basic statistical analysis (mean, sd, t.test, regression) 8 h
  • Report creation (Rmarkdown) 6 h

The use of the R software is always in combination with a graphic viewer that supports and facilitates its use. Among the most used RStudio stands out for its ease of use and versatility.

In order to reduce downtime, it is advisable to download and install on your PC, following the instructions provided during installation, the latest versions of R and RStudio

R: https://cran.r-project.org/bin/windows/base/

Rstudio: https://www.rstudio.com/products/rstudio/download2/#download

R and RStudio are available for any operating platform but the course will be held using the Windows environment

 

 

Critical analysis, writing and revision of a scientific article


CFU: 1

Teacher: Giovanni Mastrolonardo


Compulsory attendance: 75% of lessons (signature collection)
Final assessment: yes
Course structure: classroom lesson and exercitation
Teaching material: provided by the teacher

 

Course description

The course is divided into two distinct parts, which are treated in separate lessons. The first lesson provides to PhD students useful information to learn how to properly "design" scientific communication.

The second lesson introduces PhD students to the control of “scientific quality” through the process of peer-review, a basic exercise to learn to "evaluate" and "being evaluated."

 

 

Criteria for the realisation of a research project


CFU: 2

Teacher: Cristina Vettori

Compulsory attendance: 75% of lessons (signature collection)
Final assessment: yes
Course structure: classroom lesson and exercitation
Teaching material: provided by the teacher

 

Course description

The purpose of the course is to make PhD students aware of the different types of research projects and to provide the information necessary for the correct drafting of a project proposal. Projects launched on regional, national and European funds will be illustrated.

Among the European projects, the main programs under which research projects (H2020, Life, PRIMA), training and mobility projects (Marie Curie Actions) are announced will be presented.

The scientific and technical aspects (state of the art, planned activity), administrative aspects (budgeting, co-financing), social (impact) and dissemination (dissemination plan) aspects will be considered.

The process that the project proposals follow their presentation and the evaluation criteria adopted by the reviewers will be illustrated. The final evaluation test will consist in the drafting of a research project compiled and evaluated according to the criteria set out during the lessons. The projects prepared by the PhD students will then be discussed to highlight the critical issues.

 

 

The concept of "principal investigator"


Teacher: Elena Paoletti 

Compulsory attendance: 75% of lessons (signature collection)
Final assessment: yes
Course structure: classroom lesson and exercitation
Teaching material: provided by the teacher

 

Course description

The current research policy in Italy, Europe and USA.

Statistics and metrics on the researchers employment in Italy, Europe and USA.

The principles at the roots of the recent measures for young researchers in Italy and Europe.

The post-doc as principal investigator: writing of the personal career development plan, the mobility, the search for a mentor.

The scientific credit: how to increase it, the appropriate measures to use it.

The role of the principal investigator in the research project.

Useful url link for document download

 

 

Computational Statistics


CFU: 3

Teacher: Federico Mattia Stefanini

Compulsory attendance: 75% of lessons (signature collection)
Final assessment: yes
Course structure: classroom lesson and exercitation
Teaching material: provided by the teacher

 

Course description

The  aims of the course  is  to provide the computational elements required to develop Bayesian statistical models, to program Monte Carlo simulations and to create packages for platform R.

 

Course program

The R software. Frequenciesdistributions, moments, quantiles. Graphical univariate and multivariate summaries. Probability calculus and common random variables: Bernoulli, Binomiale, Normal, Poisson, Multinomial, Beta and Gamma families. Introduction to Bayesian subjective methods. Linear and logistic regression models: estimation and testing with qualitative and/or quantitative explanatory variables. Randomized controlled experiments: random sampling, randomization, control, replication, target and baselines variables.
Requirements: only for PhD students who have already successfully attended the courses of Deepening of  statistics and  Programming and data manipulation in R

  

Biological risk safety


CFU: 1

Teacher:

Stefano Biricolti

Compulsory attendance: 75% of lessons (signature collection)
Final assessment: yes
Course structure: classroom lesson and laboratory exercitation
Teaching material: provided by the teacher

 

Course description

Definitions and meaning of biosafety: laboratory hazards and risk assessment in manipulating genetically modified organisms (GMOs): good laboratory practices, use of Biological Safety Cabinets, laboratory protocols, contaminated stuff and waste handling.

Ulteriori informazioni sui corsi sulla sicurezza dal sito della Scuola di Agraria

 

 

English (Academic writing)


CFU: 5

Teacher: Jessica  Thonn

Compulsory attendance:75% of lessons (signature collection)
Final assessment: yes
Course structure: lessons and exercitation
Teaching material: text book

The course costs 50 euro

 

Course description

At the end of the course students will be able to:

  • Write about a range of matters;
  • Describe people, objects and processes in Agriculture;
  • Write clear, smooth-flowing, well-structured paragraphs and essays;
  • Adapt their writing to their readers;
  • Adopt a multiple-step writing process;
  • Edit successfully their own and others’ work;
  • Increase grammatical accuracy.

 

 

Elements of Intellectual Property - Patents


Complementary Skills

Teachers: Tessa Pazzini, Federico Rorini

Corso organizzato da UNIFI-CSAVRI organisation

To be defined by the UNIFI-CSAVRI organisation

Compulsory attendance: 75% of lessons (signature collection)
Final assessment: yes
Course structure: classroom lesson
Teaching material: provided by the teacher

 

Course description

Il corso si propone di portare a conoscenza dei dottorandi le opportunità offerte dalla terza missione delle università, quella della valorizzazione e del trasferimento delle conoscenze.

Il corso descriverà il processo di trasferimento tecnologico verso il settore privato dei risultati della ricerca svolta presso le università sviluppando in particolare i temi della brevettazione e degli spin-off.

Verrà introdotto il concetto di gestione della proprietà intellettuale e verranno illustrati i requisiti e la procedura per il deposito di un brevetto di invenzione Obbligatorio per accesso laboratori e attività di campo

Sarà trattato lo strumento degli spin-off (accademici e universitari), descrivendo il ruolo dei facilitatori e l’importanza del piano di impresa.

Verranno presi ad esempio alcuni spin-off nell’ambito delle scienze agrarie sia dell’ateneo fiorentino che di altri atenei.

Ai fini della verifica dell’apprendimento il dottorando dovrà simulare una proposta di spin-off per valorizzare un’idea nell’ambito della tematica  di ricerca svolta nel dottorato.

 

 

Last update

12.12.2023

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