All-provider course timetable

This course will take a complete look at the final year of your PhD. From the core elements of the thesis and viva and the often forgotten administrative tasks that must get done, on to looking at who you have become and what career path you may take.

This compulsory session introduces Research Data Management (RDM) to Chemistry PhD students. It is highly interactive and utilises practical activities throughout.

Key topics covered are:

• Research Data Management (RDM) - what it is and what problems can occur with managing and sharing your data.
• Data backup and file sharing - possible consequences of not backing up your data, strategies for backing up your data and sharing your data safely.
• Data organisation - how to organise your files and folders, what is best practice.
• Data sharing - obstacles to sharing your data, benefits and importance of sharing your data, the funder policy landscape, resources available in the University to help you share your data.
• Data management planning - creating a roadmap for how not to get lost in your data!

There are hundreds of databases available to us in Cambridge. How confident do you feel navigating them and recommending them to end users? For this session we have four faculty librarians who will demonstrate Scopus, ArtSTOR, Lexis and Westlaw and statistics and markets business databases. There will be plenty of time for questions.

Following the UK government’s announcement of a ‘new’ visa route, known as the Global Talent Visa, Graeme Ross, the University’s Head of Immigration for staff members, will conduct a presentation on what this means for postdocs and how to apply for this visa. There will also be a short presentation on other relevant visa routes, such as dependant visas and Indefinite Leave to Remain/Settlement, and an opportunity to ask questions about other immigration matters.

Cambridge Admissions Office has organised a series of briefing sessions in the coming weeks aimed at Schools Liaison Officers, Faculty/Department Outreach Staff, and any academics and staff members who will communicate with potential students.

Aimed at all outreach practitioners at the University of Cambridge, this briefing will provide an overview of the recently revised WP Participant Targeting/Selection criteria for use on outreach initiatives. The authors of the paper will explain the rationale behind the paper's revised weighting of WP measures and some of the research underpinning the paper. There will also be some discussion on why targeting/selection is important as opposed to using first come/first serve or relying exclusively on teacher nominations.

This session is accompanied by a skills-based session in April aimed at those with less experience implementing targeting/selection criteria.

• Nervous or intimidated about visiting the UL for the first time?
• Don’t know where to begin with a Literature search?
• Can never find the books you need on the open shelves?
• Want to learn what extra support the UL can offer?

Book a buddy!

Email disability@cam.ac.uk to arrange a session. Tell us what you need help with and we’ll match you with a member of library staff who can show you what you need to know, whether it’s searching the catalogue, using Electronic Legal Deposit, finding open shelf books or something else entirely. You are welcome to attend by yourself or alongside the student that you assist.

PLEASE NOTE that until further notice, due to the evolving situation with Coronavirus no courses will be offered as classroom based at the Training Facility. The Bioinformatics Team are investigating a workable solution to offer some of the courses on a remote basis and will be in contact with updates as soon as possible.

R is one of the leading programming languages in Data Science. It is widely used to perform statistics, machine learning, visualisations and data analyses. It is an open source programming language so all the software we will use in the course is free. This course is an introduction to R designed for participants with no programming experience. We will start from scratch by introducing how to start programming in R and progress our way and learn how to read and write to files, manipulate data and visualise it by creating different plots - all the fundamental tasks you need to get you started analysing your data. During the course we will be working with one of the most popular packages in R; tidyverse that will allow you to manipulate your data effectively and visualise it to a publication level standard.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

Fire Warden training (0900-1030) will give clear instruction of the roles of designated fire wardens and assist fire safety managers to comply with current fire safety legislation. Use of Fire Extinguisher training (1045-1145) provides hands on experience in the correct use of portable fire extinguishers.

Fire Warden training (0900-1030) will give clear instruction of the roles of designated fire wardens and assist fire safety managers to comply with current fire safety legislation. Use of Fire Extinguisher training (1045-1145) provides hands on experience in the correct use of portable fire extinguishers.

Fiji/ImageJ is a popular open-source image analysis software application. This course will briefly cover introductory aspects of image processing and analysis theory, but will focus on practical sessions where participants will gain hands on experience with Fiji.

This course is run by the CRUK CI Light microscopy core facility.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

The course will outlay bioinformatic analysis of cell populations from single-cell RNA including visualisation, clustering and functional analysis of genes. This will be using the programming language R and packages such as Seurat. Participants are encouraged to bring their own laptop to follow along.

Lesson 1

• 4.00 - 4.45pm = Setting up
• 4.45 - 5.00pm = Break, questions
• 5.00 - 6.00pm = Introduction to scRNA-Seq

Lesson 2

• 1.00 - 1.45pm = QC, Normalising, Feature Selection
• 1.45 - 2.00pm = Break, questions
• 2.00 - 3.00pm = Scaling, Dimensionality reduction, Determining dimensionality of dataset

Lesson 3

• 1.00 - 1.45pm = Clustering, UMAP/t-SNE
• 1.45 - 2.00pm = Break, questions
• 2.00 - 3.00pm = Cluster biomarkers, Assigning cell type identity, Differential expression, Enrichment

Lesson 4

• 1.00 - 1.45pm = Work on dataset from Stanford/literature/own dataset
• 1.45 - 2.00pm = Break, questions
• 2.00 - 3.00pm = Work on dataset from Stanford/literature/own dataset

This laptop only course is intended to provide a strong foundation in practical statistics and data analysis using the R or Python software environments. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

1. Use R or Python confidently for statistics and data analysis
2. Be able to analyse datasets using standard statistical techniques
3. Know which tests are and are not appropriate

Both R and Python are free software environments that are suitable for statistical and data analysis.

In this course, we explore classical statistical analysis techniques starting with simple hypothesis testing and building up to linear models and power analyses. The focus of the course is on practical implementation of these techniques and developing robust statistical analysis skills rather than on the underlying statistical theory

After the course you should feel confident to be able to select and implement common statistical techniques using R or Python and moreover know when, and when not, to apply these techniques.