1.  Introduction
2. Definitions
3. The different colors of open access: examples
4. Editorial workflow

1.  Open science policies at the University of Milan
2. Focus on the available resources:  AIR (Research Institutional Archive)
3. Funders’ requirement

1. A deep view: Predatory journals, ethics aspects, evaluation, management of rights and licences etc
2. Tools: Sherpa; DOAJ, CORE; Unpaywall etc.

1. Meeting with a researcher (Federico Zambelli, prof. @UNIMI and Technical Coordinator @ EliXir IT)

1. Aim of the Data management session
2. Introduction: The Data Management Plan (DMP)
3. Data management: who is involved?

1. Presentation of a DMP (Dorina Dobi, phd @UNIMI)
2. Analysis of a DPM, focus and relevant sections
3. Data

1. Open science & FAIR data
2. Data storage
3. Documenting your data

1. Data management good practices
2. Research ethics and data protection
3. Writing a Data Management Plan, Conclusions

Introductive lesson – Welcome. Technology transfer in the biomedical sector: an overview

Module 0 – Introduction to Entrepreneurship

9.00 - 10.00 Lecture
Coffee Break
10.15 - 12.15 Assignment  & time for team work
Lunch together
13.30 - 15.30 Workshop & discussion (groups presenting the assignment)

Module 1 – Basics of Innovation 

9.00 - 10.00 Lecture
Coffee Break
10.15 - 12.15 Assignment  & time for team work
Lunch together
13.30 - 15.30 Workshop & discussion (groups presenting the assignment)

Module 2 – Product Development: customer value proposition, analysis and strategy

9.00 - 10.00 Lecture
Coffee Break
10.15 - 12.15 Assignment  & time for team work
Lunch together
13.30 - 15.30 Workshop & discussion (groups presenting the assignment)

Module 3 – Intellectual Property.Management & Strategy 

9.00 - 10.00 Lecture
Coffee Break
10.15 - 12.15 Assignment  & time for team work
Lunch together
13.30 - 15.30 Workshop & discussion (groups presenting the assignment)

Module 4 – Organisation. Team development, Key partners, Funding & Financials

9.00 - 10.00 Lecture
Coffee Break
10.15 - 12.15 Assignment  & time for team work
Lunch together
13.30 - 15.30 Workshop & discussion (groups presenting the assignment)

Module 5 - Pitch & Reflection. Case Studies/Real World

9.00 - 10.00 Lecture
Coffee Break
10.15 - 12.15 Assignment  & time for team work
Lunch together
13.30 - 15.30 Workshop & discussion (groups presenting the assignment)

Welcome

Critical reading of the literature

How to do a Journal club

Experimental design - From hypothesis to conclusions

Case Study

Gender diversity in scientific experiments

Research integrity module:

• Shared values in scientific research;
• Running and recording experiments (labnotebook);
• Generating and manipulating data (scientific misconduct - falsification; fabrication and plagiarism);
• Time management

Famous cases of alleged scientific misconduct

Expression of coding and non-coding genes in mammalian genome

Challenges in measuring RNA expression

Working with mammalian genomic DNA

Visualization of DNA structures in Electron Microscopy

DNA Cloning strategies

CRISPR technology and its applications in drug discovery (streamed online)

Fundamentals of gene targeting

Protein expression and purification

Biochemical and biophysical protein characterization

Measuring protein interactions "in vitro"

Measuring protein interactions "in vivo"

Working with Antibodies

Capturing specific protein–DNA interactions in biological systems by ChIP

Protein-DNA interactions involved in DNA repair and DNA replication

Principles of Enzymology

Liquid-liquid phase separation

Protein-RNA interactions

Case studies

Introduction

Sample preparation

Widefield Microscopy

Flow Cytometry/Cell sorting

Confocal Microscopy

Sectioning:
- confocal
- TIRF

Multiplexing

Big Sample

Electron Microscopy

Super resolution and Mechanobiology

Usable R fundamentals for biologists

 - Developing a scripting attitude: from GUIs to CLIs
 - IDEs, RStudio, packages and good practices
 - Anatomy of a command, standard output/error, batch & interactive mode
 - R data structures, basic operators, coercion, objects, classes
 - Reading data into R objects
 - The Tidyverse. When data are “tidy”?
 - Basic tabular manipulation with dplyr
 - Transformations between wide and long
 - Get quick summarizations with pipes and “group_by"

The grammar of graphics with ggplot & data visualization

 - The graphical packages of R
 - The generic R graphic devices, plot command, colours
 - Colours in R, R colour brewer and palettes
 - The grammar of graphics
 - The ggplot2 levels and hierarchy
 - Different types of geoms for different types of data (histograms, bar plots, pie charts, density plots, scatter plots, box plots)
 - Piping summarized data into ggplot
 - Special purpose graphics: volcano, heatmap
 - (optional) Other graphical packages

Fundamentals of descriptive statistics

 - Different kind of data, how to distinguish them
 - Basic descriptive indices
 - How to evaluate data position, variability and symmetry
 - How to interpret data position, variability and symmetry
 - Correlation and covariance
 - Missing data, their patterns and treatment

Bases of statistical inferences

 - Research hypotheses and tests
 - Choosing the most fitting test to answer a question
 - Differences between parametric and non-parametric tests
 - Differences between independent and paired tests
 - What’s in a p-value?
 - (optional) Type I and II errors and power of a test
 - How to choose a test
 - Meaning of parametric/non-parametric and independent/paired tests
 - What’s in a p-value?
 - (optional) Missing data, their patterns and treatment

Wrap up, Q&A session

Course Introduction: description and objectives

Programming Languages: origins, similarities and differences

R basics: language structure, data types, object-oriented programming

Programming techniques

Data manipulation

Code writing and performance testing

Bioconductor for genomic & transcriptomic data

Writing R Extensions

Data Visualization

Data Communication

Implementing a data analysis pipeline using Bioconductor and R Markdown

Build interactive web app: Shiny

Genomics sequence search and alignment

• Mutations calling (including copy number variations and structural variants)
• Cancer genomics resources and databases (e.g. cancer genome portal, TCGA, ICGC resources and datasets)

Bulk transcriptomic data analysis (RNA-seq - including also expression quantification and splicing)

Single cell transcriptomic data analysis

Bioinformatics data analysis for ChIP-seq and other techniques to study 1D chromatin organization, transcription factors binding sites, epigenomic profiles, chromatin states

Bioinformatics data analysis for Hi-C and other techniques to study 3D chromatin organization

Computational analysis of large cohorts towards the identification of disease variants (GWAS, polygenic risk scores etc)

Journal Club Genomics

Journal Club Genomics

Introduction to MS proteomics

Quantitative Proteomics

Targeted Proteomics

Single Cell proteomics

Proteomics in clinical research and Drug Discovery + Debriefing on the RT "Clinical Proteomics"

Interaction and modification proteomics

MALDI imaging for spatial proteomics profiling

Conformational/structural proteomics: basic principles

Working Group (WG) assembly and discussion for project preparation

Data Analysis tutorial

WG for project preparation

Project presentation by WGs

Genomics and big data, from science to medicine and back

Genetic screens

Modeling cancer genetics

Aneuploidy and cancer

Alternative splicing in cancer

Viral oncogenesis

Harnessing genetic alterations in cancer therapy

Cancer genetics in the clinic: implications and applications

The impact of cancer genetics on precision oncology

Preparing and Delivering an Effective Presentation

Preparing a good poster

Principles of Effective Writing

Grammar in Scientific English

Exercises (performed individually by students)

Writing an original manuscript

Plagiarism and Copyrights

Discussion of Abstracts and Presentations before/after

Introduction to digital images
•    How an image is obtained, sampling, monochromatic and color images, Fourier transforms

Introduction to Fiji and objects segmentation:

• Introduction to Fiji’s key functionalities: installation, basic options, opening of images (Bio-Formats Import), management of simple images, export of analysed images
• Measurements of images: image calibration, set measurements parameters, creation of selections and Region Of Interest
• Processing images: geometric transformations, filters, mathematical operations
• Binarization of images: image thresholding methods, processing of binary images
• Objects segmentation and detection: Analyse particles/Find Maxima functionalities

Hands-on: Nuclear segmentation and spots detection:

• Step-by-step creation of an image analysis workflow for nuclear segmentation and nuclear spots detection
• Step-by-step recording of macro commands within the analysis workflow

Introduction to Macro Language

• Introduction to macro language: What is a macro? Why do we need macros?
• How can move from a set of recorded commands to a macro for batch processing?
• Built-in functions in FIJI: basics imaging analysis functions
• Basics of programming in Fiji macro language with examples: variables, arrays, loops, conditional statements
• Import and export of files: bio-formats and bio-formats macros extensions
• Hands-on: Generalization of a series of recorded commands to create a macro for batch nuclear segmentation

Colocalization:

• Introduction to the concept of colocalization
• Co-occurrence
• Hands-on: co-occurence batch analysis
• Intensity correlation coefficient-based analyses object-based colocalization
• Hands-on: the JACoP plugin

Images for publication and guidelines for best practices in image processing

Tracking in Biology:

• Introduction to the concepts of tracking and biological motivation: tracking of cells and intra-cellular objects (chromatin, proteins, exogenous probes)
• Introduction to TrackMate Software
• Technical details for tracking: objects detection & trajectories reconstruction
• Evaluation of parameters from trajectories: displacement, velocity, directionality, etc
• Mention of advanced features in tracking: mean square displacement to calculate the motion type (free-diffusion, confined, active motion) and environment material properties
• Hands-on: Tracking of nuclei using Trackmate

Collective Motion:

• Introduction to the concepts of collective cellular mobility and motivation
• Different ways and parameters to study collective cell motility:
  Wound Healing, Cell Streaming, Particle Image Velocimetry to evaluate speed of wound closure, velocity and coordination of movement
• Hands-on: Wound Healing speed of closure extraction using Fiji

Wrap-up and Discussion

Crystallization theory and practical

Basis of crystallography

Structure based drug design

Pymol tutorial

Principles of Cryo-EM SPA

Anatomy of the microscopes and sample preparation

Challenges of a SPA project “How to recognize and package mRNA?"

Working with Cryo-EM microscopes

Group 1: Visit to microscopes

Group-2 will alternate with Group-1 for the microscopes’ visit and negative staining tutorial

Group 1: Negative staining tutorial

Group-2 will alternate with Group-1 for the microscopes’ visit and negative staining tutorial

Model building tutorial: Coot for model building and structure interpretation of cryoEM maps

CLEM: correlative light and electron microscopy

Principles of Cryo-ET: sample prep and theory

A Cryo-ET story: “The Molecular Structure of Anterograde Intraflagellar transport trains”

Cryo-ET workshop and tutorial

Principles of NMR

Mass Spectrometry for Structural Biology: XL-MS

Principles of Alpha-fold

Alpha-fold tutorial

Students’ presentation of articles

Speakers’ introduction and ice breaking

- Proposal writing as a transferable skill and a career tool;
- Proposal writing vocabulary;
- Differences respect to a scientific paper.

Proposal development plan: from scientific hypothesis development to proposal submission

CV/personal statement preparation

Funding opportunities for early stage researchers

Decompression break

Let's try: group work

What is Machine Learning?

Building an Infectious Disease Model: SIR and Beyond

Practical Bayesian Networks for Clinical Data (part 1)

Practical Bayesian Networks for Clinical Data (part 2)

Basic Machine Learning for single-cells data analysis

Introduction to the course

Innate and adaptive immunity

Immunological Tolerance

Imaging Immunity

Monoclonal antibodies and immunotherapies

Transposable elements dynamics regulate T-cell exhaustion in tumor microenvinroment

CAR-T

T Cells

B cells

COVID-19: immune response and vaccines

Genome editing in T cells for cancer immunotherapy

Introduction to network theory and its application to biomedicine

Building up small molecular networks step by step

Network biology approaches to study cancer genes

Praticals on gene expression networks

Introduction to Python/Jython programming languages:

• From ImageJ macro language to Jython programming language in Fiji
• Python basics: syntax, statements, expressions, list, loops, conditional statements, functions
• From Python to Jython: object oriented programming
• Jython main APIs and Classes for Fiji
• Python vs Jython: Pros and cons

Hands-on: practice on a nuclei segmentation macro and comparison with Jython language

• Implementation of nuclei segmentation
• Focus on Regions Of Interest Manager
• Creation of customized Results table

More complex image analysis and Analysis of Multiple Images

Hands-on: nuclei segmentation with spots count and intensity measurement

• Bio-formats handling
• User interaction
• Multiple channel handling
• Parent-children objects handling
• Multiple Results Table management
• Multiple images analysis

Introduction to machine learning and deep learning in image analysis

• Theoretical introduction about machine learning and deep learning
• Machine learning as tool for semantic segmentation/pixel classification
• Deep Learning as tool for image restoration and instance segmentation

Hands-on: Semantic segmentation/pixel classification with Labkit

Image restoration and instance segmentation using Deep Learning in Python

• Introduction to Jupyter (Python) notebooks and their use in cloud with ZeroCostDL4Mic
• Deep Learning algorithms for image restoration and instance

Hands-on: Image restoration and nuclear segmentation with Deep Learning algorithms

• Image restoration using Noise2Void algorithm; model training and application using Jupiter notebooks in ZeroCostDL4Mic
• Nuclear segmentation using 2D and 3D Stardist using Jupyter notebooks in ZeroCostDL4Mic

Segmentation in tissues using Stardist with QuPath software:

• Introduction to QuPath for tissue sections analysis
• Cell segmentation in tissues

Hands-on: Cell segmentation in tissues using Stardist and cell-type classification in different populations

Cell segmentation using Cellpose software in Python:

• Introduction to conda environments and their utility for using local Jupyter (Python) notebooks
• CellPose Deep Learning software for cell and nuclear segmentation

Hands-on: Segmentation of cells and nuclei using CellPose and evaluation of cytoplasmic signal using local Jupyter notebooks