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FAIR for leaders

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Better research by better sharing

Overview

Teaching: 20 min
Exercises: 20 min
Questions

  • Who are today’s trainers?

  • Why should you know about Open Science and Data Management?

  • Why should you adopt good data management practices?

Objectives

  • Introduce ourselves and the course

  • Understand how you shape the future of research

  • Understand your responsibilities to your subordinates

  • Understand the changes triggered by the DORA agreement

Introductions

(3 minutes)

Introductions set the stage for learning.

— Tracy Teal, Former Executive Director, The Carpentries

Hello everyone, and welcome to the FAIR for leaders workshop. We are very pleased to have you with us.

Today’s Trainers

To begin the class, each Trainer should briefly introduce themself.

Online workshop specifics

Our learning tools

Before we begin let’s explain how to use the tools:

  • Raising hands
  • Yes/No sticker
  • Chatroom for sharing info such as links
  • Breakout rooms, leaving and rejoining
  • using Etherpad, answering questions in the Etherpad
  • where to find things If needed, check the pre workshop setup, ask to report problems and help at a break or after the session.

Now we would like to get to learn something about you.

Who are you and what are your expectations from the workshop (4.5 min)

Please, Introduce yourselves telling each other why you have joined this course. Then, try to find one professional/academic thing that your group has in common. For example:

  • we all had our latest grant proposals accepted by MRC
  • we are all desperately searching for an experienced lab technician

You and data sharing (3min)

Thinking of how you and your group make data or code available to others and how your group uses others’ data, write “+1” next to any statements that match your own experience:

  • We do not really share data, we only publish the results as part of a publication:
  • We have made our data available only as Supporting Information for a paper:
  • We have made our data available as both Supporting Information and as a dataset in a repository:
  • We have made our data/code available without having it published in a paper:
  • We share the code in GitHub or another code repository:
  • We make the code available on demand:
  • We have used a dataset from a public repository:

( 7 min teaching )

Better research by better sharing

For many of us, data management or output sharing in general are considered a burden rather than a useful activity. Part of the problem is our bad timing and lack of planning.

We will demonstrate to you:

Evolution of research and Open Science

Academic research works best by exchanging ideas and building on them. This idea underpins the Open Research movement, of which Open Science is a part. The aim is to make science more reproducible, transparent and accessible by making knowledge and data freely available. Open Science improves research by contributing to sharing of data and information and thereby leading to better-informed formulation of research questions and better-informed experimental design. In the long-run, and even in the short-term, this should lead to better, faster, more successful science. As science becomes more open, the way we conduct and communicate science changes continuously.

Digital technologies, especially the internet, have accelerated the diffusion of knowledge and made it possible to make research findings and data available and accessible to the whole of society:

Figure 1. Open Science Building Blocks

After Gema Bueno de la Fuente

Open Science Building Blocks

Exercise 3: Why we are not doing Open Science / Data Sharing already (3 + 2 min)

Discuss Open Science / Data Sharing barriers - what are some of the reasons for not already being open?:

Solution

  • sensitive data (eg anonymising data from administrative health records can be difficult)
  • IP
  • I don’t know where to deposit my data
  • misuse (fake news)
  • lack of confidence (the fear of critics)
  • lack of expertise
  • the costs in £ and in time
  • novelty of data
  • it is not mandatory
  • I don’t see value in it
  • lack of credit (eg publishing negative results is of little benefit to you)

(teaching 3min) Data management is a continuous process

Figure 2. Sharing as part of the workflow Figure credits: Tomasz Zielinski and Andrés Romanowski

Writing a Data Management Plan well can help you to share your data efficiently by identifying the stages of the research data lifecycle where you should document your data, describe your variables, capture your protocols, compile metadata, select suitable file formats. Doing this work while your research is taking place will make it far less time-consuming and more accurate. A stitch in time saves nine!

When should you engage in data sharing and open practices?

In this workshop we will show you how you can plan and do your research in a way that makes your outputs readily available for re-use by others.

Why you should know it - power figures

As a Principal Investigator you wield a certain amount of power through the decisions you make, the expectations you set for your team and the example you set.

Exercise 4. Your presence and influence (5 with Ex5)

Consider some of the ways you exert academic influence by your presence and your activity in the academic community. Which of the following reflect your own experience?

  • I currently supervise at least 2 postdocs
  • I have promoted at least 3 PhDs
  • I revise at least 4 articles a year
  • I have been a member of a grant panel
  • I have been a member of a school/college/university committee
  • I have contributed to development of an institutional/community policy
  • I have been involved in the selection process for fellows / lecturers / readers
  • I am a member of a Research Council
  • Any other activities through which you exert academic influence in the research community?

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Exercise 5. Your minions(!)

Think about your research group. Have they done or been involved in any of the following Open Science activities?

  • releasing software;
  • making data available under an open licence;
  • public engagement activities;
  • participating in scientific community groups (eg journal club, Carpentries, ReproducibiliTea)

(teaching 8) As power figures, you all have the power to bring about a better research culture by inspiring and supporting your teams to do Open Science, and leading by example through your own actions.

Research assessment

To change the way we do science we need to change the way we measure success, and change our incentives.

San Francisco Declaration On Research Assessment (DORA)

Thousands of organisations around the world have signed DORA, including funders such as the Wellcome Trust, BBSRC and over two hundred other UK organisations. The declaration sets out a commitment to assessing research outputs based on their quality (‘intrinsic merit’). While DORA famously calls for a reduced reliance on journal-based metrics it doesn’t just cover papers, it applies to data too.

https://sfdora.org

The Wellcome Trust guidance on implementing DORA states that Wellcome requires institutions it funds to “commit to assessing research outputs and other research contributions based on their intrinsic merit”. They distil the principles of DORA as follows:

“We believe that research assessment processes used by research organisations and funders in making recruitment, promotion and funding decisions should embody two core principles (‘the principles’) as set out in the San Francisco Declaration on Research Assessment (DORA):

  • be explicit about the criteria used to evaluate scientific productivity, and clearly highlight that the scientific content of a paper is more important than publication metrics or the identity of the journal in which it is published

  • recognise the value of all relevant research outputs (for example publications, datasets and software), as well as other types of contributions, such as training early-career researchers and influencing policy and practice.” — Wellcome Trust “Guidance for research organisations on how to implement responsible and fair approaches for research assessment” https://wellcome.org/grant-funding/guidance/open-access-guidance/research-organisations-how-implement-responsible-and-fair-approaches-research

Funders like Cancer Research UK and Wellcome have updated their application processes to include not just papers but more broadly-defined achievements, public engagement and mentoring.

Narrative CVs

What is a narrative CV, and how does it relate to making data FAIR?

“…a CV format in an application that prompts written descriptions of contributions and achievements that reflect a broad range of skills and experiences. This is a change from metrics-based CVs that are primarily publication lists with employment and education history with little context.” Imperial College London

There is a growing recognition of the benefit of ‘narrative CVs’ to shifting the culture towards Open Science, even among the research funding establishment, for example see this UKRI event trailer “Supporting the community adoption of R4R-like narrative CVs” .

Prepare for FAIR, because it is easier to be well-prepared than to fake it!

It takes time to produce open outputs, but the rewards come in time saved down the line, and greater quality and findability of data in the long-run.

The quality of open outputs is easy to assess.

Timestamps on the digital systems that are now used for sharing data, code etc will show very clearly how long you’ve been doing FAIR :-)

Why you should do it

Exercise 6. Lottery winner (5)

Imagine a situation in which you suddenly lose a postdoc because she/he has won the National Lottery and won’t be coming to work any more (or more realistically, they were hit by a bus). Which of the following scenarios can you relate to?

  • everything should be recorded in their notebook, which you hope is in the office.
    But frankly, you have never checked how good their lab notes are.
  • everything should be in the team’s Electronic Lab Notebook, and you can quickly check if that is the case.
  • all data, excel, presentations and paper drafts are in a shared network drive.
  • some data and documents may only be in the postdoc’s PC/laptop.
  • every now and then, you check people’s data and notes, so you are fairly confident they follow good practices and you know where you can find what is needed.
  • your group has a “data management” policy/plan to which all members are introduced as part of their induction, so at least in principle all should be fine.
  • you have left it to your group to organise such trivial matters and you’re hoping they did it well.
  • your lab manager should know it all.
  • there was the old postdoc who knew it all but they left last year.
  • you are getting nervous!!!

Conclusion

Good research data management, FAIR data, Open Science are an integral part of scientific leadership in the modern world.

Agenda

Our agenda:

Attribution

Content of this episode was adapted from:

Key Points

  • You are a power figure

  • You need to give your ‘minions’ both practical support and leadership to help them to be equipped for Open Science going forward

  • Remember, your group can do better research if you plan to share your outputs!

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