Enhancement and dissemination of results, intellectual property, and open access to research data and products 

Open Science: basic principles
Open science and research data management (Open Access, Fair data). The objective of the course, divided into three modules, is to provide guidance on the scientific publication cycle from writing to publication, research data management, how to recognize a predatory publisher, how to search for open scientific literature, and how to license one's digital content. Open
science :
- The principles of broad and widespread access to scientific knowledge -
Open access rights and licenses :
- The system of production and circulation of scientific publications. The reactions of the scientific communities
- Preprints, scientific journals, publishing platforms, predatory publishers FAIR
data :
- The reproducibility crisis
- Why not managing data costs more than managing it
Skills :
At the end of the course the doctoral student will have acquired basic digital competence on what Open science is, on the ethical management of publications and data, on the production, validation, accessibility of publications and data, on the tools for searching open publications and data. Therefore, he or she will be able to: articulate information needs; search for data, information, and content in digital environments; access and navigate within them; create and update personal search strategies; and manage data, information, and digital content.
The doctoral student, also taking into account ethical and security-related aspects, will have received training on the following content:
proper data management from data collection, documentation and archiving to data access, use and preservation (or destruction); data responsiveness to European Commission expectations and policies on Open Science (FAIR: Findable, Accessible, Interoperable, Reusable). 

Seminars 

The intensive seminar in scientific writing aims to familiarize on the structure and writing of scientific articles by highlighting how, regardless of the specific scientific discipline, scientific publications show common characters and a common writing strategy. the following aspects will be covered. Expected
outcomes: understanding how peer review works and the role of the scientific journal editor, knowledge of the sections of an article and their preparation.
Ability to write a scientific article that is complete in all its parts, structured and convincing.
Ability to evaluate experimental results in the context of the state of the art for scientific journal selection, ability to organize data for article writing. Communication
skills: Highlighting the strengths of research results, without omitting the weaknesses, discussing them in the context of comparison with the literature; ability to describe the state of the art; ability to prepare graphic material of quality and clarity.
 Content:
description of what is meant by scientific publication, experimental scientific method, research driven by curiosity or problem solving; peer review process, role of the scientific journal editor descriptions of different types of scientific journals and comparison, impact factor, green, gold and diamond open access; title preparation, abstract preparation; affiliation and list of authors, by what criterion to choose co-authors of a scientific paper; writing the introduction, key steps toward presentation of results; results and discussion, order and criterion; importance of preparing figures, tables; conclusions and future prospects; preparation of bibliography; preparation of cover letter; preparation of poster and oral format communication. 

Research and knowledge management of European and international research systems 

Funding instruments, research management and procedures: the joint course for the Doctoral School organized by the University includes seminars aimed at presenting the different types of national and international research projects (Horizon, Life, Prin, etc.), structural funds (ERDF, EMFF, EAFRD, etc.), national clusters, regional clusters, reading and interpreting a call for proposals, co-funding, illustrating experiences of approved and funded programs, and acquiring an updated methodology aimed at writing new research proposals with the respective funding systems. 

Enhancement and dissemination of results, intellectual property, and open access to research data and products 

Research Spin Offs and Start-ups: introductory concepts and prerequisites for start-ups; business model and regulations; innovation ecosystem supporting start-ups. There are 3 modules of 4 hours, common to all doctoral programs. The course common to the Ph.D. School focuses on patenting principles, practices and strategies in the processes of managing and exploiting research activity nationally and internationally. The lectures aim to transfer a theoretical background on which to graft tools of practical use, such as: forms and procedural steps for structuring patent applications; techniques for searching patent databases and classification codes; and requirements for patentability of an invention.
In order to enable doctoral students to acquire the necessary skills to exploit the results achieved in their research and related intellectual property, new contexts and areas of application of industrial research, smart business models, integrated projects and innovative solutions will be identified. Laboratory activities will be initiated for pairing-exchange between doctoral students belonging to different scientific disciplinary fields aimed at structuring new proposals for the valorization of intellectual property to simulate the creation of start-ups and spin-offs. 

Computer enhancement 

Machine Learning and Neural Networks course common to the Doctoral School of Life Sciences.
Introduction to Machine Learning and artificial intelligence.
FUNDS:
- Data and patterns: numerical, categorical, sequences. - Learning problems: classification, regression, clustering, reduction, dimensionality, representation learning. - Types of Learning: supervised, unsupervised, batch, incremental, natural, reinforcement learning. - Training and performance evaluation: objective function, parameters, hyperparameters, performance measurement, training, validation, testing, convergence, generalization and overfitting.
CLASSIFICATION:
- Bayes classifier. - Nearest neighbor. - SVM. - Multi classifiers.
REGRESSION:
- Linear. - Nonlinear.
CLUSTERING:
- Hierarchical. - Centroid-based. DIMENSIONALITY
REDUCTION:
- Principal Component Analysis (PCA). - Linear Discriminant Analysis (LDA) NEURAL
NETWORKS:
- Biological neurons. - Artificial neurons. - Types of networks. - Multilayer Perceptron (MLP).
DEEP LEARNING:
- Why deep? Levels and complexity, types of DNNs. - MLP to CNN. - Convolutional Neural Networks (CNN). 

Language improvement 

The course in English is aimed at acquiring the fundamental skills for writing and publishing scientific articles in English, as well as the language skills needed during interactive participation in international academic contexts (conferences, seminars, etc.).
The course common to the Doctoral School also aims to provide knowledge of some "soft skills" such as working in teams and developing critical thinking and flexibility, which are requirements for doctoral students in the organizational contexts that characterize both the academic community and the world of work. The Intermediate Level Course (access with B1 level) is 50 hours, 30 hours with the lecturer + 20 hours of self-study. The advanced level will be specific to scientific writing (50 hours: 30 with lecturer 20 of self-study). In addition, the University, through the Rosetta Stone platform, offers language courses with a choice of 24 languages. Especially for the lower level of English proficiency, the Rosetta Stone platform will be strongly recommended from the first year. 

Research and knowledge management of European and international research systems 

Organized courses common to all PhD students organized in collaboration with the Agency for the Promotion of European Research (APRE) by the Athenaeum: 1) Open access & science, there is 1 module of 2 hours; 2) Dissemination, Communication & Exploitation: how to maximize results, there is 1 module of 2 hours; 3) Possible post PhD training paths, there is 1 module of 2 hours. 

Seminars 

Research evaluation: bibliometric indicators (IF, citation index and H-index). The web and medicine: tools and resources 2.0. Comparison of Pubmed and other databases. Major databases for the biomedical field and on research tools will be analyzed. PhD students will be introduced to the services of the University library
resources. Tutorials with Scival and Research Professional.
Focus on WoS and JCR from Clarivate. Focus on Scopus and Scopus Journal List of Elsevier. 

Basic principles of ethics, gender equality and integrity 

The University, in collaboration with the Agency for the Promotion of European Research (APRE), organizes information tracks on Horizon Europe calls. There are modules on Ethics in Horizon and Europe Gender in Horizon Europe. Ph.D. and Ph.D. students will be stimulated to reflect on the (gender) orientation of research and the role in European funding programs. There are two 4-hour modules (total 8 hours) common to all PhDs. 

Seminars 

"Big data" and national and international databases: cloud computing and parallel computing. There is a common 4-hour module on introductory concepts and a curriculum-specific 4-hour module (for the specificity of databases available or to be created) 

Laboratory activities 

Laboratory and applied research activities are planned for learning basic methodologies and laboratory techniques specific to the different curricula. These include the following areas: biochemistry, molecular biology, genetics, imaging (electrophysiology, structural and functional nuclear magnetic resonance), neuropsychology, and cell culture.