The rapid growth of social media has inevitably led to a wealth of data into all aspects of our everyday lives. At the same time, this has been accompanied by concerns about how we use data harvested from social media sources. There is already a growing literature out there on the ethical implications of using social media and so this FAQ will aim to briefly summarise some of key arguments that researchers will need to consider.

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A format is essentially the form your data will take once you collect and archive it. Researchers are strongly advised to think very carefully about the final format their data will take so that it can be preserved for future use.

There are two main two main categories of files – proprietary and non-proprietary formats. Proprietary formats are more limited as they only work with the software provided by the creator of that data. On the other hand, non-proprietary formats can be used by anyone, are usually free of charge and therefore have more utility for future researchers. Plus open formats provide instant and easy access to data. In most cases, you should aim for your data to take the following formats:

1. Non-proprietary
2. Unencrypted
3. Uncompressed
4. Open, documented standard
5. Commonly used by your research community
6. Use common character encodings – ASCII, Unicode, UTF-8

There will always be cases where you will inevitably need to change the format of your data during the course of your research. This is why it is important that you provide further details about the format your data will take in your DMP and any features that may be lost once you convert it for archiving. Open formats may not support all of the original functionality of proprietary formats so you must take steps to hold on to both your raw and converted data sets. Some funders may also have specific requirements surrounding the final form your data should take so be sure to check their policies before committing to any set format.

Further links

• Scientific Data Formats: http://justsolve.archiveteam.org/index.php/Scientific_Data_formats
• LOC: http://www.loc.gov/preservation/resources/rfs/
• Digital Preservation Coalition: https://www.dpconline.org/handbook/technical-solutions-and-tools/file-formats-and-standards

SES Open:  Harnessing FAIR Data Symposium

Harnessing FAIR Data (3 September 2018, 13:00 – 17:00) focuses on researchers who employ or are seeking to use data in their work. FAIR is a set of guiding principles to make data Findable, Accessible, Interoperable, and Reusable. In this thematic context we aim to better understand the cross-disciplinary practice of sourcing, using and managing data and its associated implications, such as ownership, standards and metadata, and access and licensing. Experienced speakers from around the UK will come together to explore FAIR data and services, and a panel session will help to identify key questions that researchers face when considering using data in research.

Register online

Programme highlights:

Realising the Potential: Final Report of the Open Data Task Force. 
Prof. Pam Thomas, University of Warwick
The Re-use of Consumer Data for the Social Good
Prof. Paul Longley, Department of Geography, UCL
Using socio-ecological simulation models to make the most of hard-won paleoecological data 
Andrew Lane, Department of Geography, King’s College London
The CLOSER consortium of longitudinal studies: Opportunities and obstacles in harmonising data from diverse sources
Dr. Dara O’Neill, CLOSER, UCL Institute of Education
Publishing FAIR Data in Chemistry
Dr. Charles Romain, Department of Chemistry, Imperial College London

Panel members: Prof. Pam Thomas, Dr. Paul Ayris (UCL), Prof. Henry Rzepa (Imperial College London)

This event is hosted by QMUL, UCL eResearch Domain and the SES Consortium.

Free event for UCL researchers and staff.

This one day workshop will explore the facets of Open Science and how these are/could be pursued by UCL researcher. In the morning speakers will discuss different aspects of and perspectives on Open Science with afternoon workshops offering practical advice. There will also be opportunity to discuss the steps UCL should take to support Open Science. This free event will be open to all UCL staff and is delivered by UCL Library Services with support from UCL Organisational Development.

Speakers include:

• Prof. David Price, Vice-Provost (Research), UCL
• Dr Paul Ayris, Pro-Vice-Provost, UCL Library Services
• Catriona MacCallum, Director of Open Science, Hindawi
• Emily Sena, University of Edinburgh
• James Wilsdon, University of Sheffield
• Simon Hettrick, Sustainable Software Institute

Afternoon workshops will cover:

• How do we make Open the default at UCL
• How to make your data open (and FAIR)?
• Citizen Science
• Open Peer review
• Open Education

Registration available via Eventbrite. Please contact lib-researchsupport@ucl.ac.uk with any questions.

In 2017 we conducted a series of short interviews as part of the LEARN project. We asked several UCL researchers about data management and data sharing in their disciplines.

Dr Martin Zaltz Austwick is a Senior Lecturer at the Centre for Advanced Spatial Analysis (Bartlett). Martin’s research and teaching makes extensive use of a wide variety of data sources including government data, business data and textual data.

Our interview with Martin provides a clear example of how researchers are already making extensive use of open data and have a desire to share their own data with broad audiences. The interview also emphasized some of the challenges faced by researchers who want to make their data available as a services through interactive websites or APIs. A particular challenge is that whilst funders cover some of the costs associated with Research Data Management usually these don’t include funding after projects have finished to maintain ‘live’ data services. Supporting researchers in their efforts to present data in new and innovative ways is an area university support services may want to continue investigating.

Watch Martin’s 5-minute interview (opens a new window in Youtube).

In 2017 we conducted a series of short interviews as part of the LEARN project. We asked several UCL researchers about data management and data sharing in their disciplines.

Dr Jenny Bunn is a lecturer in UCL Department of Information Studies; she is the Program Director for the MA/Diplomate/Certificate in Archive and Records Management. She previously worked as an archivist at the V&A Museum, The Royal Bank of Scotland Archives, Glasgow University Archives and The National Archives before moving to teaching and research at UCL.

Alongside Jenny’s insights as a researcher, her professional experience as an archivist also informs her approach to Research Data Management (RDM). She emphasised the importance for researchers to reflect on three questions: what data could potentially be useful for other researchers; where to share this data most effectively; and whether some data should not be kept. A central message of the interview was the need for researchers not to view RDM as ‘keeping everything’ but instead as viewing RDM as a key component of research integrity.

Watch Jenny’s 7-minute interview (opens a new window in Youtube).

In 2017 we conducted a series of short interviews as part of the LEARN project. We asked several UCL researchers about data management and data sharing in their disciplines.

Dr Josep Grau-Bove is a Lecturer in Science and Engineering in Arts, Heritage and Archaeology; he works at the Institute for Sustainable Heritage (Bartlett School Environment, Energy & Resources). He is the Assistant Director of one of the Institute’s Programmes for MRes students.

In our interview he reflects on the benefits of Data Management Plans for research students, the technical challenges of managing data and the importance of data sharing within his Institute. He also highlights the need to raise students’ awareness of good practices in data management.

Watch Josep’s 4-minute interview (opens a new window in Youtube).

To celebrate the 3rd international Love Data Week, several Research Data Management teams from London universities have joined forces. More than 15 free events are taking place across Bloomsbury from Monday 12th to Friday 16th of February.

Most events are open to all UCL research staff and research students; here is an overview:

• Get Ready for GDPR: Monday, Tuesday, Wednesday, Thursday and Friday, 1pm
• What’s my Research Data?: Monday, 2pm
• UCL Research IT & Data Management drop-in: Tuesday, 10am
• Get Your Data In Shape (Organizing and Documenting): Tuesday, 2pm
• Working With Audio Visual Data: Wednesday, 11am
• Consumer Data Research Centre drop-in: Wednesday, 11am-2pm
• Working With Personal Data: Wednesday, 2pm
• Making the Most of Your Research Data: Thursday, 11am
• UCL Introduction to Digital Scholarship and Open Research: Thursday, 1.30pm
• Data Management Planning: Thursday, 3pm
• Introduction to UK Data Service: Friday, 2-3.3opm

A full listing of events is available to share.

For any UCL-specific questions, please contact the UCL Research Data Support officers at lib-researchsupport@ucl.ac.uk.

Two upcoming events organised by Open Research London exploring open science/research are open for booking:

The first event is taking place on the 21st of November and is a satellite event of the OpenCon 2017 conference. During the day there will be a ‘hackathon’ focusing on the themes of ‘Open for reproducibility’ and ‘Open for collaborative-coding’. You can find more information and book here. In the evening of the 21st, there will be a number of talks on open access and open science. Further information and booking here.

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The FAIR data principles aim to provide a framework to ensure that research data can be effectively reused. The principles are outlined below alongside recommendations for practically achieving these principles.

Why FAIR data? 

The FAIR Data Principles were developed by a FORCE11 group and originally published in Nature Scientific Data in 2016. The authors argue that ‘Good data management is not a goal in itself, but rather is the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse by the community after the data publication process.’

What are the fair data principles?

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