Overview

Coordinator: Hafid Laayouni
Teaching staff: Marius Costache, Ramon Miquel, Borja Requena, Gorka Muñoz, David Romero and Hafid Laayouni
ECTS: 5
Workload: 125 hrs.
Term: 1^st
Location: Online

Teaching guide

Presentation of the course

This course focuses on statistical methods to analyse Research data in Experimental Sciences. The course starts with a crash course in the programming tools needed to complete the subject, and an introduction to useful tools for their research projects. The format is a 4-day workshop, modelled on and taking advantage of open-source online materials.

After a general introduction on probability theory and parameters estimation, an emphasis will be made on statistical inference, along with a general introduction to Bayesian statistics. The course comprises 5 ECTS credits, involving approximately 30 hours of plenary lectures, and 20 hours of exercises and hands-on computer classes. The subject is based on the understanding of key methodological concepts and tools and on the application of Python resources to solve statistical analysis. As this is an intensive course, students are advised of the need for strong interaction with the lecturers and of the need to keep the class material up to date.

The subject focuses on practical implementation of different types of tools for statistical inference. Thus, the methods covered are strongly based on a good understanding of basic principles of probability and programming.

Prerequisites in order to follow the itinerary

Previous programming knowledge and notions of probability are required. A Python Bootcamp is organised for 4 days at the beginning of the course to introduce python language to all students.

Information on online teaching

This course will be delivered on remote fashion. The structure of the course will be based on the following 3 activities:

Theory: Videos will be posted on the theoretical aspects of the course. Students must watch them before the live lecture. A short streaming lecture will also take place.

Exercises: Half of the streaming session will be to perform exercises all the class together. Moreover, several exercises will be available. Doing the exercises is essential for a real learning.

Practicals: we will perform 3 practical sessions. The practicals will be available before the sessions. You must do all the practical before the session, so we can dedicate the whole session to interpret the results.

Associated competences

General competences

Instrumental:

Proficient reading/writing/listening of scientific English related to the subject.

Interpersonal:

Group work

Ability to solve by yourself a given problem

Systemic:

Analysis and synthesis abilities

Ability to search for information

Specific competences

1. To understand the concept of probability.
2. To understand Bayes’ Theorem.
3. To distinguish statistical description from inference.
4. To understand the concept of random variable.
5. To become familiar with central trend and dispersion measures.
6. To understand the concept of probability distribution.
7. To become familiar with the most common kinds of distributions.
8. To understand the implication of large numbers’ use and convergence.
9. To understand the concept of confidence intervals and standard error.
10. To understand the concept and application of Monte Carlo techniques.
11. To understand the concept of estimator and its main properties.
12. To master standard techniques for parameter estimation such as least-squares and maximum likelihood fits.
13. To master standard techniques for error propagation.
14. To understand the concept of hypothesis testing.
15. To understand the concept of Type I and II errors.
16. To master the concept of ANOVA and its different designs.
17. To master the concept of contingency tables and the relevant testing procedures.
18. To master the concept of and procedures for Regression and Correlation Analysis.
19. To understand resampling methods.
20. To understand the concepts of multiple regression and correlation.
21. To understand the concept and procedures for Likelihood ratio tests, Linear tests, Non-linear tests and machine learning.
22. To understand the concept of Bayesian Statistics.
23. To master parameter estimation in a Bayesian framework.
24. To master hypothesis testing (“model selection”) in a Bayesian framework.
25. To become familiar with Markov chain Monte Carlo and its applications in Bayesian statistics.

Learning aims

To understand and apply algorithms and methods currently used in multidisciplinary research in experimental sciences to perform statistical analysis upon data.

Contents

Module 0 (Borja Requena, Gorka Muñoz, David Romero)

Python Bootcamp

Module 1 (Hafid Laayouni)

Introduction. Descriptive statistics

Basic concepts of probability. Distributions and probability density functions

Sampling distribution. Law of large numbers and convergence

Hypothesis testing: t-student, ANOVA, Regression analysis, Categorical data.

Module 2 (Ramón Miquel)

Bayesian statistics

Module 3 (Marius Costache)

Machine learning

Calendar

Assessment

General assessment criteria

The evaluation will consist of three parts:

All assessment and exercises to be delivered are to be individual work, that is, students can and are advised to discuss and work together to resolve assessments, but the final resolution and presentation must be individual. Disciplinary action will be taken against students who breach guidelines (e.g. colluding with other students or copying other students’ work).

Course Materials

Course materials available on GitHub at https://github.com/philipp-germann/BIST-Python-Bootcamp

Preliminary Requirements

Students are requested to bring their own laptop with a working installation of Anaconda Python 3.6. Installation instructions and additional resources are given here.

Recommended Installation

Version: Anaconda Python 3.6

Distribution: Anaconda with Jupyter and Spyder (or another editor)

Packages: NumPy, SciPy, MatplotLib, Pandas, Seaborn, Scikit-learn

Version Control: Git

Online Resources

Learn X in Y minutes where X = Python

Learn Python

10 Minutes to Pandas

Pythonic Perambulations

Subtleties of Colour

Useful Courses

Software Carpentry - Programming with Python

Software Carpentry - Plotting and Programming with Python

Software Carpentry – Version Control with Git

Software Carpentry - Instructor Training

Python - Python Tutorial

Data Carpentry – Python for Ecologists

AstroEd – Python for Physics and Astronomy

SciPy – Lecture Notes, particularly the Statistics in Python chapter

J.R. Johansson – Scientific Computing with Python

Institute of Space Sciences – Python for Astronomy and Particle Physicists.

Teaching Resources

https://www.otexts.org/book/biostat

http://onlinestatbook.com/

http://www.biostathandbook.com/

Other References

Best Practices in Scientific Computing

Good Enough Practices in Scientific Computing

Bibliography

M.L. Samuel, J.A. Witmer, A. Shaffner. Statistics for the Life Sciences.

G. Cowan; "Statistical Data Analysis", 1998, Oxford University Press

Stuart et al., "Kendall's Advanced Theory of Statistics", Vol 2A. Wiley.

F. James, "Monte Carlo Theory and Practice", Rep. Prog. Phys. 43 (1980) 73.

D. Sivia and J. Skilling, "Data Analysis, A Bayesian Tutorial", 2nd ed., 2006, Oxford University Press

E.T. Jaynes, "Probability Theory: The Logic of Science", Cambridge University Press.

W.T. Press et al., "Numerical Recipes: The Art of Scientific Computing", Cambridge University Press.

Overview

Coordinator: Carolina Llorente (UPF) and Núria Bayó (BIST)
Teaching Staff: Carolina Llorente (UPF); Núria Bayo (BIST); Geraint Story (Cambridge Training Associates); Gavin Lucas (The Paper Mill); Maruxa Martínez (PRBB)
ECTS: 5
Workload: 125 hrs.
Term: 1^st 2^nd, 3^rd
Location: Online training, UPF Campus Mar (Dr Aiguader, 80) and BIST Centers

Description of the subject

The course is a developmental training programme which is focused on enhancing the effectiveness of future researchers by providing an opportunity to build their understanding, skills and confidence in basic knowledge of responsible research, project management and effective communication. It also encourages critical discussions and thorough reflection on the wider impact of concrete research and innovation (R&I) aspects and the overall science and technology system. It equips students with knowledge and skills to understand Responsible Research and Innovation (RRI), Open Science (OS) or Quadruple Helix model of innovation (QH) approaches and to promote and facilitate such discussion and reflection processes, and gives them the opportunity to be part of such activities. The course also focuses on different skill sets for scientific communication: how to gather information, and how to communicate science to peers, multidisciplinary peers and to general public. During the course students will learn to gather, manage and summarise scientific information and also they will develop their abilities in three key channels for traditional scientific communication (poster presentations, scientific articles, and oral presentations) and by designing, performing and evaluating a public engagement activity for general public.

The course is divided into three different domains:

BLOCK I: RRI and public communication

BLOCK II: Project management

BLOCK III: Scientific communication

Objectives

On completion of this seminar students will be able to:

• Understand methods to facilitate dialogue on R&I with different actors: multidisciplinary peers, strategic stakeholders (users, consumers, patients, industry representatives, policy makers, CSO representatives), media and the general public
• Develop public communication skills
• Adapt these methods to their specific R&I process or development
• Carry out a dialogue activity to discuss a specific R&I process or development and analyse the participants’ different perspectives on and assessment of the R&I issue under debate
• Develop attitudes and techniques on effective planning and project management
• Develop techniques to effectively communicate with thesis supervisor and relevant people for the success of the thesis
• Develop an individual plan for the coming year and identify the things that need to be done now in order to secure the job they want
• Develop techniques to communicate the outputs of their research projects in different ways: poster, paper and oral presentation

Methodology

Block I: RRI and public communication

In this course, students will have the opportunity to experience both sides of deliberation activities. Thereby it will be possible for them to reflect on different societal aspects of R&I developments (including issues of sustainability, societal equality, gender, open science, open access etc.) applied to their own research. Students will not only be sensitised for the embeddedness of R&I, but also how different actors engage in mutual discussions on these matters, including the challenges and opportunities that such engagement entails.

Students will get to know and discuss different methods to facilitate dialogues on R&I and related developments. In groups they will prepare and conduct presentations on different related methods suggested by the course instructor. Groups of students supervised by the course instructor will design and implement a dialogue “experiment”. Thus, each student will experience both the side of the facilitator and that of a participant. At the end of the course students will assess their own public engagement activity and present it in an oral presentation.

Block I sessions overview:

Block II: Project Management

The focus of the block is project management techniques which create effective interactions and a well-managed research project implementation. This course will build on your existing skills and can be applied directly to your research.

Research requires a display of initiative, commitment and persistence. Project management compliments those skills by providing process, monitoring, communication and risk response.

On this course, students will develop:

• An understanding of the impact project management skills can have on your research project
• An introduction to foundation project management tools and their application in research projects
• An insight into project management responsibilities in maintaining relationships and communication channels

This training course is full of activity and discussion. The class will be working in small groups and applying project management theory onto a research project.

Block II sessions overview:

Block III: Science Communication

Throughout this workshop series, the instructor will introduce basic concepts in written and visual communication, as a common theme for scientific communication, and will expand and build upon these in each successive session. As part of the students’ learning process, the instructor will reinforce the culture of always considering the Why of each scientific task (Why am I doing this? What do I want to achieve?), rather than just applying a formula for how it ‘should’ be done. Thus, the students will develop their scientific skills through autonomous thinking, rather than just applying standard practice.

Block III sessions overview:

Calendar

Assessment

Attendance and active participation in all sessions will be taken into account for the final grade. It can modify up to 10% of the final grade.

This course does not have a final exam; the assessment will be done continuously throughout the course. This will be through several deliveries along the three blocks, oral presentations and self-assessment activities. It is necessary to pass all three blocks (minimum score 5/10) to pass the course.

Overview

Coordinator: Robert Sewell (ICFO)
Teaching staff: Robert Sewell and Rubén Vicente
Total credits: 5 ECTS
Workload: 125 hrs.
Term: 1^st, 2^nd and 3^rd terms
Location: Online, UPF Campus Mar (Dr. Aiguader 80) and BIST Centers + DCEXS

Description of the subject

This course provides broad exposure to multidisciplinary research in experimental sciences. The aim is to give students direct contact with inspirational speakers, introduce cutting-edge challenges in contemporary research, and help prepare students to understand what is involved in pursuing a cutting-edge research career in academia or industry, and specifically for pursuing a PhD at a leading international institution.

There are two main components of the course:

Research Seminars: Designed to complement the hands-on training they will receive in carrying out their research projects. These seminars are presented by PIs from the UPF and BIST research centres during the Initial Training Period, and invited external speakers during the Training Days.

Group Discussion Sessions: During the training days, there will be a student-led group discussion session emphasising critical evaluation of scientific literature. These will involve faculty from the UPF and BIST, and the invited external speakers.

Objectives

• Meet researchers from participating institutions, and outstanding international scientists
• Learn about important contemporary research topics
• Discuss topics relevant to becoming a successful scientist
• Learn about and discuss some of the challenges involved in multidisciplinary research

Methodology

Seminars and discussion sessions with BIST/UPF PIs and invited external speakers, including student-led presentation and discussion of scientific publications. Students will be required to prepare and lead a discussion session based on a research article on the presented topic, which they must choose and research.

Format

For the 2020-21 academic year, all seminars and discussions will take place online via a videoconferencing platform.

Both internal and external seminars will follow the same format. For the internal seminars we ask the speaker to include a few slides to introduce the centre / department, and opportunities for (PhD) students, at the start of their seminar.

Research Seminars

Speakers will be invited to present a seminar, followed by a Q&A about their chosen research topic and general challenges in undertaking multidisciplinary research / pursuing a research career.

There will be 8 seminars from BIST/DCEXS PIs, and 8 seminars from invited external speakers, with a standard format:

• 15-minute introduction to the research field of the speaker and their corresponding research institute / department
• 45-minute research seminar on a topic of their choice, which might include research that the speaker has led, or an open challenge in the field
• 30-minutes discussion, which may open onto more general topics about pursuing a research career

Note that the order is flexible, and for the external seminars the students will be responsible for leading the introductory discussion via a presentation of a research paper from the invited external speaker.

The research seminars will be open to the BIST community, and publicly announced.

Where possible, we will arrange an informal Q&A session over coffee with the speaker and MSc students.

Group Discussion Sessions

For the student-led discussion of a research paper, the group of students will be required to choose and present a research paper on a topic related to the seminar that day in consultation with the invited external speaker, and lead the group in a discussion of the paper and the seminar. A short CV of the speaker and the paper the students are going to present should be uploaded to Aula Global one week before the training day in order to share this information with the rest of the classmates.

Students will undertake this task in small groups. The assignment of students to each Discussion Session will be defined during the initial training period. The format will be:

• 30-minute student-led introduction and discussion of their chosen research paper
• 30-minutes discussion, which may open onto more general topics about pursuing a research career

Calendar

Initial Training Period - Research Seminars

Training Days - Group Discussion Sessions

Assessment

Attendance of at least 80% of the seminars and group discussion sessions is required to pass the subject.

Students are responsible to communicate and justify absences and late arrivals.

Students are expected to participate actively in group discussions.

Overview

Coordinators: Jordi Arbiol (ICREA & ICN2), Maria García-Parajo (ICREA & ICFO)
Lecturers: Aitor Mugarza (ICN2); Belén Ballesteros (ICN2); Cesar Moreno (ICN2); Christian Neissner (PIC); Daniel Kerzberg (IFAE); Emilio Gualda (ICFO); Francisco Belarre (ICN2); Gabriel Gomila (IBEC); Marc Botifoll (ICN2); Jordi Andilla (ICFO); Jordi Fraxedas (ICN2); Jorge Jiménez (IFAE); Julien Colombelli (IRBB); Laurent Ladepeche (ICFO); M. Chiara Spadaro (ICN2); Manel Martínez (IFAE); Marcos Rosado (ICN2); Maria Marsal (ICFO); Neus Domingo (ICN2); Nikos Giakoumakis (IRBB); Pablo Guerra (IBMB); Pablo Loza (ICFO); Santi Serrano (IEEC); Sara Martí-Sánchez (ICN2); Sébastien Tosi (IRBB); Timo Zimmermann (CRG).
ECTS: 5
Workload: 125 hrs.
Term: 1^st and 2^nd
Location: Online / BIST Centers
Prerequisites: None

Description of the subject

Intensive winter school combining theoretical courses and hands-on training in a selected topic in multidisciplinary science. This course will take full advantage of the research and academic facilities at the centres. The topic and location(s) will rotate each year.

The topic for 2020/21 will be Microscopy & Imaging Science, covering the following five topics:

1. Optical microscopy
2. Electron microscopy
3. Scanning probe microscopy
4. Raman imaging and spectroscopy
5. Imaging technology and approaches in astrophysics / cosmology

Objectives

• To acquire knowledge in thematic advanced techniques in experimental science
• To develop the hands-on practical and technical skills in specific experimental and/or theoretical techniques
• To gain experience working in groups

Methodology

Lectures, research seminars, and hands-on training in specific experimental techniques.

Location & Organisation

The theoretical part of the course will be carried out with online sessions during the first week of December.

The hands-on training of the winter school will be hosted by the BIST research centres, with the location rotating depending on the topic. This year ICFO will host the symposium, with input from IRB, CRG, ICN2, IBEC and IFAE researchers. Practical training will be undertaken at each centre to take advantage of their research and training facilities.

Assessment

Overview

Coordinator: Núria Bayó (BIST) and Rubén Vicente (UPF)
Academic Tutors: Rubén Vincente (UPF), Pau Gorostiza (IBEC), Robert Sewell (ICFO), Luciano Di Croce (CRG), Rafel Escribano (IFAE), Mónica Perez Temprano (ICIQ), Arben Merkoçi (ICN2), Roger Gomis (IRB Barcelona)
ECTS: 20
Workload: 500 hrs.
Term: 1^st 2^nd & 3^rd
Location: BIST Centers & DCEXS

Description of the subject

Hands-on, intensive training-through-research. The aim is to provide in-depth training in a specific discipline. The student chooses and develops during five months one of the projects offered by the BIST-DCEXS research groups and re-enforces the training in multidisciplinary science provided in the initial period.

Research Component: Students will join a research team at one of the BIST institutes / DCEXS and develop a research project assigned and supervised by a principal investigator. During this time, the student will perform calculations and/or experiments, analyse data, describe and discuss results, research the literature, and other tasks required to successfully carry out a research project. The aim is to acquire key conceptual knowledge and experimental skills, familiarise the student with the organisation and functioning of a research team, and provide the student with first-hand knowledge of life as a researcher, as a first step towards pursuing a future research career.

Training Component: Under the guidance of their supervisor, students will gain a broad understanding of theoretical concepts and standard research techniques in their field, and a deep understanding of the background to their research topic.

Objectives

• To acquire advanced knowledge in a field of the experimental sciences
• To develop the practical and technical skills required for a specific discipline on experimental sciences
• To learn good practices to design, record and discuss experiments.
• To analyse and communicate properly scientific results.

Methodology

Research Component: Students complete a guided research project, with clear goals in terms of acquiring conceptual knowledge and technical skills, as well as expected research outcomes. Progress is monitored through regular structured reports and research group meetings.

Training Component: The methodology combines guided independent learning through reading textbooks and scientific literature, with regular tutorial sessions with supervisor, and hands-on training in the laboratory.

Assessment

Overview

Coordinator: Núria Bayó (BIST) and Rubén Vicente (UPF)
Academic Tutors: Rubén Vincente (UPF), Pau Gorostiza (IBEC), Robert Sewell (ICFO), Luciano Di Croce (CRG), Rafel Escribano (IFAE), Mónica Perez Temprano (ICIQ), Arben Merkoçi (ICN2), Roger Gomis (IRB Barcelona)
ECTS: 10
Workload: 250 hrs.
Term: 1st & 2nd
Location: BIST Centers & DCEXS

Description of the subject

The aim of this subject is to provide students with complementary training in a different research discipline to that of their major project. Students are required to carry out a 10-week stay in a different research group (the host group). The aim is to gain complementary conceptual knowledge and experimental skills. Students will gain experience working in a different research environment, and an ability to analyse the multidisciplinary component of a research project.

Objectives

To acquire advanced knowledge in a different field of the experimental sciences to that of the main project

To develop the practical and technical skills required for a specific discipline on experimental sciences

To train multidisciplinary approaches to a given research topic

Methodology

Students gain supervised, hands-on training guided by the principal investigator of the host group. Student and supervisor will develop clear goals in terms of acquiring conceptual knowledge and technical skills. Progress is monitored through regular structured reports. Assessment is via these reports, and evaluation by the supervisor and PI of the host group.

Assessment

Overview

Coordinator: Núria Bayó (BIST) and Rubén Vicente (UPF)
Academic Tutors: Rubén Vincente (UPF), Pau Gorostiza (IBEC), Robert Sewell (ICFO), Luciano Di Croce (CRG), Rafel Escribano (IFAE), Mónica Pérez Temprano (ICIQ), Arben Merkoçi (ICN2), Roger Gomis (IRB Barcelona)
ECTS: 10
Contact Hours: 250
Term: 3^rd
Location: BIST Centers & DCEXS

Description of the subject

The student will write a research manuscript in the format of a scientific paper, based on the original results obtained by the student during their research training. In addition, the student will make a public oral presentation and defence of this work to an examining committee.

Objectives

• To elaborate a scientific manuscript with the different sections of a scientific article
• To put in practice the knowledge acquired in data analysis in the results section
• To present in the introduction and discussion sections the aspects related to responsible research and multidisciplinary approach derived from the project
• To practise oral communication of scientific results
• To demonstrate the acquisition of advanced knowledge during the master’s in the discipline of the projects performed

Assessment

Students should attend all the oral presentations within the same evaluation session.