Effective Research Publishing Handbook

Go to the Research Data Support Services Research Library Guide for extensive support and information. The guide is provided by the Research Data Team.

Topics include: RDM explained; writing a DMP; costing data management; working with data; archiving research data; sharing and re-using data.

The FAIR principles are intended to improve the findability, accessibility, interoperability, and reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
 

1. Findability -The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers.
2. Accessibility -Once the user finds the required data, she/he needs to know how can they be accessed, possibly including authentication and authorisation.
3. Interoperability -The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
4. Reproducibility -The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

If you collect or create primary data that support your research findings, make them FAIR by depositing them in a data repository under an open licence, in usable formats and with appropriate documentation and metadata, and cite the data using the DOI or other unique identifier in your publications.  

You may have heard of the 'Reproducibility Crisis' before, it is the problem of the majority of scientific studies whose results are unable to be replicated by others. This could result in wasting vast amounts of time and money. 

This is partly due to the current systems' preference to flashy, new discoveries over confirmatory studies, but there are now efforts to address the issue; for example Nature has provided a 'Reproducibility checklist' for submitting authors. 

Swansea University has a local branch of the UK Reproducibility Network promoting Open and Reproducible Science which currently runs a journal club and can provide guidance on ensuring your work is reproducible. You can contact them via email: ukrnswansea@swansea.ac.uk or Twitter; @UKRNSwansea

Further Reading 

Munafò, M.,(2018) How do you deal with a problem like reproducibility? Available at: https://www.jisc.ac.uk/blog/how-do-you-deal-with-a-problem-like-reproducibility-29-nov-2018

Munafò, M., Nosek, B., Bishop, D. et al. A manifesto for reproducible science. Nat Hum Behav 1, 0021 (2017). https://doi.org/10.1038/s41562-016-0021

Feilden, T., (2017) Most scientists 'can't replicate studies by their peers' Available at: https://www.bbc.co.uk/news/science-environment-39054778

Where to archive data?

General lists of data repositories that can be browsed by subject and you can check trustworthy data repository requirements via CORE TRUST SEAL website.

Some funders mandate their own repositories for research data. Check their websites for details.

The Digital Curation Centre (DDC) has a “Where to keep research data checklist” which looks at how to approach the following questions:

1.     is a reputable repository available?

2.     will it take the data you want to deposit?

3.     will it be safe in legal terms?

4.     will the repository sustain the data value?

5.     will it support analysis and track data usage?

If no suitable subject or funder data repository is available, Swansea University has a community on the Zenodo service. This service is run by CERN who are expert at dealing with large datasets and guarantee to migrate the service to other repositories if it discontinued so it is robust enough to satisfy funder requirements. It will also provide a free DOI for your data so that it is easier to find and promote.

Pre-registration is a process by which a researcher can publish a data analysis plan before the projects' outcome, in order to increase research transparency, quality of research in general, and curb elements of publication bias. A further exploration of the pros of pre-registering can be found here: https://blog.oup.com/2014/09/pro-con-research-preregistration/ 

If you are interested in pre-registering your project, you may want to use the pre-registration template provided by the Open Science Framework: https://osf.io/k5wns/ 

Registry of Research Data Repositories

Wikidata

FAIRsharing data and metadata standards, databases and policies

Swansea University Zenodo Community

The SAIL Databank (based at Swansea University) is a world-class flagship for the robust secure storage and use of anonymised person-based data for research to improve health, well-being and services. Its databank of anonymised data about the population of Wales is internationally recognised: website