Spring Clean Your Data
6 Steps to Spring Cleaning Your Data
1. Where to start..?
Leave the hard work to us: your metadata will be scrubbed and polished to ensure it is free of spelling or grammatical errors. We’ll even add in the relevant keywords so it can be found again.
2. Crystal clear descriptions of datasets
A neat and tidy description of datasets and the underlying research method is created based on metadata and associated manuscript(s).
3. It’s around here somewhere...
The funder information, requirements and acknowledgements are all visible in the datasets and metadata to ensure compliance. No searching required.
4. Find and cite your data, wherever you’ve hidden it
No more burying data in the back of a drawer (or depths of the web): a DOI (Digital Object Identifier) for each dataset enhances accessibility and creates a persistent link for citation.
Wherever possible, datasets are linked to publication(s) so research can be identified and accessed easily. Your data is placed in a depository which is openly accessible. Usually, it's the Springer Nature Figshare space, but it may be a depository we believe is better suited to your specific research community.
5. But you’ll want to put this on show!
The data note you write will be published with your newly cleaned data. This is published in BMC Research Notes which is a peer-reviewed journal indexed in major databases - making it easier for others to find your work, and cite it.
6. And remember, if you’re not using it…
…someone else will! Now your data is much more visible, and findable, others can find your work easier, use it to help with their own research, and give you credit for it.
1. Baker, M. 1,500 scientists lift the lid on reproducibility. Nature. 2016;532:452–454.
2. Ioannidis, J. PA, et al. Repeatability of published microarray gene expression analyses. Nature Genetics. 2009;41:149–155.
3. Astell, M. et al. Whitepaper: Practical challenges for researchers in data sharing. Springer Nature. 2018.