Dissemination and exploitation of findings, of intellectual property and of open acces to data and research products

Open Science: basic principles
Open Science and management of research data (Open Access, Fair data). The course, subdivided in three modules, aims at providing information on scientific publications from writing to publishing, on data management, on the identification of a predatory publisher, on open literature search, on the allocation of a license to digital data. Open Science:
Principles of a extensive and comprehensive access to scientific knowledge;  rights and licenses.
Open access :
The production and dissemination of scientific publications. The eractions of the scientific communities.

Preprints, scientific journals, editorial platforms, predatory publishers.

FAIR data:
The crisis of reliability. Whay lack of data management is more expensive that data management.

Acquired skills:

At the end of the course the PhD student will have gained basic digital skills on what Open Science is, on ethic management of data and publications, on production, validation, accessibility of publications and data, on tools for searching open data and publications. Students will be able to: focus information needs, search data and information in digital environments, to access and to navigate within them; to create and to update personal search strategies; to manage digital data, information and contents. The PhD Student, also in the light of ethical and safety aspects, will have received a training on the following contents: correct data management – collection, documentation and storage – data storage or destruction; data conformity to the requirements of the European Commission and to the Open Science policies (FAIR: (FAIR: Findable, Accessible, Interoperable, Reusable).  

Seminars

The intensive seminare in scientific writing is aimed to get familiar  on structuring and drafting scientific papers showing how, regardless of the specific discipline, scientific publications have common aspects and a common writing strategy. The follwing topics will dbe addressed:
Expected results: understanding of revision process and of the role of the editor of a scientific journal, knowledge of different sections of a paper and of their preparation. Ability to draft a scientific paper complete in each part, structured and convincing.
Ability to evaluate findings in the context of the state of the art to chose the scientific journal, ability to organize data for drafting a paper. Communication skills: report strenght and limitations discussing them in relation to the literature; ability to describe the state of the art; ability to make graphical materials of quality and clarity.

Contents:

description of what does it mean scientific publication, scientific method, curiosity-based or problem-solving based research; peer review process, role of the editor of the scientific journal, description and comparison of different types of scientific journals, impact factor, green, gold e diamond open access; preparation of title and abstract; affiliations and author list, crieria to chose co-authors; drafting of introduction, key steps towards results presentation; results and discussion, order and criterion; the importance of preparation of figures and tables; conclusions and future perspectives; preparation of references; perparation of the cover letter; preparation of poster and oral presentation.  

Research management and knowledge of European and international research systems

Funding tools, research management and procedures: the common course for PhD organized by the University includes seminars aimed at presenting different types of national and internazional research projects (Horizon, Life, Prin, etc.), structural funds (FESR, FEAMP, FEASR, etc.), national clusters, regional clusters, reading and interpretation of a call, cofinancing, presentation of experiences of approved and funded programs, acquisition of an updated methodology aimed to prepare new research proposals with relevant funding systems.

Dissemination and exploitation of findings, of intellectual property and of open acces to data and research products

Spin Off and Start up of research: basic concepts and starting points for the creation of a company; the business model and regulations; the innovation ecosystem to support sturt-up. Tthree modules of 4 hours each are included, common to all PhDs. The cours eis focused on principles, policy and strategies of patenting in management and exploitation processes of national and international research activities.
The lessons aim to provide a theoretical background to create practical tools such as modules and procedures to write a patent application; research techniques in patenting databases and classification codes; requirements for patenting an invention.
In order to allow PhD students to acquire the necessary competencies to exploit results obtained in their research career and of the relevant intellectual property, new contexts and areas of application of industry research will be identified as well as smart business models, integrated projects, and innovative solutions.
Lab activities will start for combination-exchange between PhD students of different academic disciplines aimed at structuring new proposals to exploit intellectual property to simulate the creation of start up and spin off.  

Advancement in computer technology  

Machine Learning and neural network course, common to the PhD school Life Sciences.

Introduction to Machine Learning and artificial intelligence.
PRINCIPLES:
- Data and pattern: digital, categorical, sequences.
- Problems of learning: classification, regression, clustering, reduction, dimensionality, representation learning.
- Types of Learning: supervised, non-supervised, batch, incremental, natural, reinforcement learning.
- Training and assessment of performances: objective, parameters, hyperparameter, assessment of performances, training, validation, test, convergence, generalization and overfitting.
CLASSIFICATION:
- Bayes classifier.

- Nearest neighbor.
- SVM.
- Multiple classificators.
REGRESSION:
- Linear.
- Non-linear.
CLUSTERING:
- Hierarchical.
- Centroide-based.
REDUCTION OF DIMENSIONALITY:
- Principal Component Analysis (PCA).
- Linear Discriminant Analysis (LDA)
NEURAL NETWORK:
- Biologici neurons.
- Artificial neurons.
- Types of networks.

- Multilayer Perceptron (MLP).
DEEP LEARNING:
- Why deep? Levels of complexity, types of DNN.
- From MLP to CNN.
- Convolutional Neural Networks (CNN).  

Language advancement  

The English course is aimed at acquiring basic competencies for writing and publishing of scientific papers in English, as well as language skills necessaries during active participation in international academic contexts (congresses, seminars, etc.).
The course is common to the PhD School and is also aimed at providing knowledge in some “soft skills”, such as working in a team and to develop critical thinking and flexibility, required to PhD students in organizational contexts in both academic community and professional world. The course is of inermediate level (access with B1 level) and lasts 50 hours, 30 with the lecturer + 20 hours of independent study. The advanced level will be specific for scientific writing (50 hours: 30 with the teacher, 20 hours of idnependent study). In addition the University by means of the Rosetta Stone platform, provides language courses with the choice between 24 languages.In particular, for a low level of competence in English, Rosetta Stone platform is recommended since the first year.  

Research management and knowledge of European and international research systems 

Courses common to all PhD courses will be organized by the University in collaboration with the Agenzia per la Promozione della Ricerca Europea (APRE) of the University: 1) Open access & science, 1 module of 2 hours; 2) Dissemination, Communication & Exploitation: how to maximize results , 1 module of 2 hours; 3) Possible training pathways post-PhD, 1 module of 2 hours.  

Seminars

The evaluation of research: bibliometric indicators (IF, citation index and H-index). The web and the medicine: tools and resources 2.0. Compariosn between Pubmed and other databases. The major databases relevant to the bibliometric sector and search tools. Resources services of the University library will be showed to the PhD students Training to the use of Scival and Research Professional.
Focus on WoS and JCR of Clarivate. Focus on Scopus and Scopus Journal List of Elsevier.  

Basic principles of ethics, gender equality and integrity

The University, in collaboration with the Agenzia per la Promozione della Ricerca Europea (APRE), organizes trainings on Horizon Europe calls. They include modules on Ethics in Horizon and Europe Gender in Horizon Europe.
males and females PhD students will be encouraged to reflect on (gender) orientation of research and its role in European funding programmes. It will include two modules of of 4 hours (8 hours total) common to all PhD students.

Seminars

"Big data" and national and international databases: cloud computing and parallel computing. It includes a common module of 4 hours on basic concepts and one of 4 hours specific for each curriculum (addressing the specificity of available or to be created databases).

Lab activities

Lab activities and applied research aimed at learning basic methods and lab techniques specific of different curricula. They include the following sectors: biochemistry, molecular biology, genetics, imaging (electrophysiology, structural and functional magnetic resonance imaging), neuropsychology, cell cultures.