Fostering open science in social science

FOSTER_logoOn 10th of June, the Data Library team ran two workshops in association with the EU Horizon 2020 project, FOSTER (Facilitate Open Science Training for European Research), and the Scottish Graduate School of Social Science.

The aim of the morning workshop, “Good practice in data management & data sharing with social research,” was to provide new entrants into the Scottish Graduate School of Social Science with a grounding in research data management using our online interactive training resource MANTRA, which covers good practice in data management and issues associated with data sharing.

The morning started with a brief presentation by Robin Rice on ‘open science’ and its meaning for the social sciences. Pauline Ward then demonstrated the importance of data management plans to ensure work is safeguarded and that data sharing is made possible. I introduced MANTRA briefly, and then Laine Ruus assigned different MANTRA units to participants and asked them to briefly go through the units and extract one or two key messages and report back to the rest of the group. After the coffee break we had another presentation on ethics, informed consent and the barriers for sharing, and we finished the morning session with a ‘Do’s and Dont’s exercise where we asked participants to write in post-it notes the things they remembered, the things they were taking with them from the workshop: green for things they should DO, and pink for those they should NOT. Here are some of the points the learners posted:

DO
– consider your usernames & passwords
– read the Data Protection Act
– check funder/institution regulations/policies
– obtain informed consent
– design a clear consent form
– give participants info about the research
– inform participants of how we will manage data
– confidentiality
– label your data with enough info to retrieve it in future
– develop a data management plan
– follow the certain policies when you re-use dataset[s] created by others
– have a clear data storage plan
– think about how & how long you will store your data
– store data in at least 3 places, in at least 2 separate locations
– backup!
– consider how/where you back up your data
– delete or archive old versions
– data preservation
– keep your data safe and secure with the help of facilities of fund bodies or university
– think about sharing
– consider sharing at all stages. Think about who will use my data next
– share data (responsibly)

DON’T
– unclear informed consent
– a sense of forcing participants to be part of research
– do not store sensitive information unless necessary
– don’t staple consent forms to de-identified data records/store them together
– take information security for granted
– assume all software will be able to handle your data
– don’t assume you will remember stuff. Document your data
– assume people understand
– disclose participants’ identity
– leave computer on
– share confidential data
– leave your laptop on the bus!
– leave your laptop on the train!
– leave your files on a train!
– don’t forget it is not just my data, it is public data
– forget to future proof

Robin Rice presenting at FOSTERing Open Science workshop

Our message was that open science will thrive when researchers:

  • organise and version their data files effectively,
  • provide comprehensive and sufficient documentation for others to understand and replicate results and thus cite the source properly
  • know how to store and transport your data safely and securely (ensuring backup and encryption)
  • understand legal and ethical requirements for managing data about human subjects
  • Recognise the importance of good research data management practice in your own context

The afternoon workshop on “Overcoming obstacles to sharing data about human subjects” built on one of the main themes introduced in the morning, with a large overlap of attendees. The ethical and regulatory issues in this area can appear daunting. However, data created from research with human subjects are valuable, and therefore are worth sharing for all the same reasons as other research data (impact, transparency, validation etc). So it was heartening to find ourselves working with a group of mostly new PhD students, keen to find ways to anonymise, aggregate, or otherwise transform their data appropriately to allow sharing.

Robin Rice introduced the Data Protection Act, as it relates to research with human subjects, and ethical considerations. Naturally, we directed our participants to MANTRA, which has detailed information on the ethical and practical issues, with specific modules on “Data protection, rights & access” and “Sharing, preservation & licensing”. Of course not all data are suitable for sharing, and there are risks to be considered.

In many cases, data can be anonymised effectively, to allow the data to be shared. Richard Welpton from the UK Data Archive shared practical information on anonymisation approaches and tools for ‘statistical disclosure control’, recommending sdcMicroGUI (a graphical interface for carrying out anonymisation techniques, which is an R package, but should require no knowledge of the R language).

DrNiamhMooreFinally Dr Niamh Moore from University of Edinburgh shared her experiences of sharing qualitative data. She spoke about the need to respect the wishes of subjects, her research gathering oral history, and the enthusiasm of many of her human subjects to be named in her research outputs, in a sense to own their own story, their own words.

Links:

Rocio von Jungenfeld & Pauline Ward
EDINA and Data Library

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