Data management best practices

March 2024

Dayane Araújo

www.ebi.ac.uk/training

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Welcome to this virtual course

Course and events organisers

• Dayane Araújo — Scientific Training Officer (dayane@ebi.ac.uk)
• Brenda Stride — Postdoctoral programme Manager (stride@embl.de)
• Sophie Spencer — Events Organiser (sophie@ebi.ac.uk)

After this session you should be able to

• Define open science and explain FAIR principles.�
• Summarise EMBL’s position with regards to open science and access appropriate sources of further information.�
• Locate appropriate data resources, metadata standards and tools to support you in making the outputs of your research both open and FAIR.�
• Participate in cultural change in support of fair research assessment and accurate attribution, for you, your co-authors and those who support your science.

Helpful information

Course handbook

• Includes all relevant information about the course�
• Presentation slides and other relevant materials�
• Accessible for 6 months after the course

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How to ask question

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• How?�
• Raise your hand and speak up�
• Write in the chat box�
• Write in the Q&A document�

The importance of “good” data management

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3 reasons to share your data…

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Key thing!

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Start with the plan……

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“Your primary collaborator is yourself six months from now, and your past self doesn’t answer e-mails.”

Rachael Ainsworth,

astrophysicist at the University of Manchester, UK

Data management plan (DMP)

EMBL requires that all projects have a data management plan………………..

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………….funded by grants, or is part of the PhD and postdoctoral projects, or is intended to support a scientific article

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EMBL Open Science and Open Access Policy page

 Creating a Data Management Plan

• As a default, EMBL researchers should use this EMBL Data Management Plan Template

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How to get started?

EMBL’s Data Management checklist

Data management checklist

Best practice tips

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Organising data

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File names and folder structures

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• Is your folder structure and file naming convention consistent?
• Is it clear to and adopted by everyone in your team? What about collaborators?
• Do you use a README.txt file explaining naming conventions, folder structure and changes?
• Are your files consistently versioned?

Analysing data

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Planning analyses

When planning an analysis, we need to consider and keep track of:

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• The format of the data at all stages
• Any pre-processing steps
• Tools/software used, including which release/version
• Parameters we select when using tools

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Workflows go a step beyond keeping track of these elements by allowing us to build in decisions we make, such as parameters we select, ensuring reproducibility of our analyses.

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Workflows

Computational workflows allow for automation of multi-step analyses and support the reproducibility of analyses.

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Workflows can include tools written in different programming languages. A workflow consists of a set of rules, which each have an input and output.

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Tools for developing workflows

There are many different platforms and tools for creating workflows, such as:

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• NextFlow - used by scientists to write, deploy and share data-intensive workflows. It is scalable and can be integrated with GitHub.

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• SnakeMake - an open source workflow management system used to create reproducible and scalable data analyses.

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• Galaxy - an open-source platform for data analysis that allows users to create and use workflows of tools, including running code, through its graphical web interface.

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Learn more about workflows with this short introduction.

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Storing data

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Storing your data

• Is it backed up?
• Does your university provide storage?
• Is it in a place that you can find and access it? Or collaborators?
• How long will the data be accessible?
• What if someone asks you for your data in 5 or 10 years time?

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Record keeping

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Good record keeping

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Have you tried Electronic Lab Notebooks

• further guidance: https://www.nature.com/articles/d41586-018-05895-3

OR Computational Notebooks:

• Jupyter Notebooks

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Sharing data

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Good data management = good data�

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Choosing a data repository

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Adapted from ‘Managing and making the most of your data’ – Marta Teperek and Yasemin Türkyilmaz- van der Velden

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Ontologies – adding more information

Ontologies make it easier to…

• Understand and interpret data
• Search and find links between data
• Combine datasets

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Finding ontologies

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What if I am only using public data?

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You can still apply these practices!

• What was the dataset identifier?
• Where and when did you access and download it?
• What did you do to the data?
• Have you got the correct citations for the data?
• Don’t forget the tools and software as well!

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How to cite data

Minimum information:

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Check with the database for any specific way to cite them.

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Author(s). Year. Title. Repository. (Version). Identifier

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Any questions?

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