Best practice for managing your outputs on Figshare
Figshare is a place to share, showcase and manage your research outputs. Research outputs can include, but are not limited to, tabular data, images, video, presentations, posters, code, book chapters, and more.
Does all of this seem like a lot of extra work? It’ll save you so much time in the long-run and give you peace of mind knowing that you’ve covered all the bases necessary to ensure that your data is re-used appropriately! Remember, really reusable data gets you increased citations – if others can’t understand it, they can’t re-use it, and they won’t cite it.
Things to consider before conducting your research
Data Management Plans (DMPs) are increasingly becoming a part of Research Data Management Policies within institutions and requested in grant application by funders. Depending on the research field, some of the things you’ll need to include are ethics, consent, how and when you’ll share your data and long term preservation.
If you are unsure as to whether you need to create a DMP before starting a research project check with a research data manager – we’d recommend checking your institutions website and library services for more details.
Even if you are not required to do so, creating a DMP or planning just some of its components can still be useful. For more information please visit https://dmponline.dcc.ac.uk/. Another tool for DMPs is DMPTool; their funder templates helpful for complying with funder mandates around data management.
We have provided some guidance and links below to help you get started.
Ethical obligations
It is important to check that you have the right to publish your data openly. For more information see:
Copyright
Useful questions to consider:
- Have you established who owns the copyright in your data? Might there be joint copyright?
- Have you considered what kind of license is appropriate for sharing your data and what, if any, restrictions there might be on re-use?
- If you are purchasing or re-using someone else’s data sources have you considered how that data might be shareable, for example negotiating a new licence with the original supplier?
For more information on licenses, see the section in this user guide.
Suggested Checklist:
- Check consent (see above)
- Check license
- Credit the authors of any data reused in your dataset, according to the original license
- Ensure that any and all sensitive or private information (e.g. human-subjects, rare and endangered species) has been anonymized / de-identified (or separated and uploaded as metadata-only / confidential)
- Ensure that any data pending commercial processes is only published in accordance with the contract or agreement with a commercial partner (e.g. an embargo may be necessary, or you may be required to publish commercial data confidentially)
Things to consider when you’re ready to share your research
Make the most out of your metadata. Metadata is (are) the information about your outputs on Figshare. This includes title, author, categories, keywords, description, and more. The more information you include in your metadata, the more likely it is your research will be found by others and the more likely it is that they will be able to reuse and cite your work.
Make your research outputs FAIR. Research outputs that are FAIR are Findable, Accessible, Interoperable, and Reusable. More on FAIR can be found on GO-FAIR’s website and more on Figshare and FAIR is found in the FAQ section of this guide. Also see this infographic on increasing your research’s exposure on Figshare using the FAIR data principles.
How to make your outputs as FAIR as possible:
- Name your files and folders consistently:
- Choose a naming convention and stick to it
- Avoid punctuation, special characters, and capitalization
- Don’t use full-stops or spaces
- Keep names relevant but as short as possible
- Ensure that your outputs include all files, code, and anything else that is necessary in order for another person to recreate your analysis, or validate your results
- Ensure that your outputs are published with descriptive metadata (information describing your data):
- Fill in the basic metadata fields during upload as thoroughly as possible.
- Choose an appropriate, descriptive, but concise title for your dataset.
- Make use of the “Description” field to describe your data in as much detail as possible.
- Make use of the “Related materials” section to link to any papers, publications, or other outputs that your output informs, is informed by, or is related to.
- Add as many keywords as possible but keep them relevant and consistent.
- Include a README file in your upload that contains all the necessary information to enable somebody else to understand your outputs, re-create your results, and/or reuse it ethically. Consider including:
- Information about the research project and all collaborators
- Author’s contact details
- Description of the research process (steps taken to collect, create, and analyse the data)
- Description of the intended and unintended uses of the data (especially NB for data collected on human subjects)
- List of files contained within the dataset alongside a description of each file (see DCC’s list of disciplinary metadata standards)
- Description of all hardware and software required to run the files, as well as open source alternatives for proprietary items, where necessary
- Description of any code created and/or used to process the files
- Reference list of any files reused from other sources (with links to sources)
- For more information on README files, see 4TU’s and Cornell University’s guidance.
- Choose an appropriate license that allows your data to be published as openly as possible, but to be reused as intended
- Convert proprietary files to open formats whenever possible and upload them along with the original files (your institution might have file format recommendations that you should follow). UK Data Service’ recommended file formats. When formatting and organizing the files, consider the following:
- The recommended file formats (your institution might have file format recommendations that you should follow)
- Open formats (Readme, CSV)
- Project versus fileset (a Figshare item with multiple files) versus Collection
