Information for Researchers

A toolkit for understanding and using the NIH Figshare instance for your NIH-funded research data

Overview

The purpose of this toolkit is to inform researchers about the purpose and process of uploading NIH-funded research to the generalist NIH Figshare instance - nih.figshare.com​.

This toolkit is comprised of the following tools for understanding and using the NIH Figshare instance:

  • Information about NIH Figshare instance
  • FAQs about NIH Figshare instance
  • Case studies from researchers who have shared their data on NIH Figshare
  • User guides detailing the process of uploading data to the NIH Figshare instance
  • How to get in touch with the NIH Figshare team for questions or further support
  • Webinars (upcoming and recordings of past webinars) on the impact of the NIH Figshare instance on NIH-funded researchers

About the NIH Figshare Instance

For the most up-to-date information about NIH Figshare, visit https://nih.figshare.com/f/about.

The NIH is piloting Figshare as a way to make datasets resulting from NIH-funded research more accessible and compliant with government policies. All NIH-funded researchers can have a place to store and share their research datasets underlying publication figures and tables, as well as data not associated with publications, to enhance the rigor and reproducibility of research results.

The partnership provides a data repository to store any NIH-funded research that does not already have a designated home in a subject-specific repository. The curated NIH data repository is intended to be a supplement to those solutions and not a replacement. The repository is free to use and free for others to reuse.

All NIH-funded researchers can make use of the repository to upload and publish data that underlie publication figures or enhance rigor and reproducible research results.

Data submitted to the NIH Figshare instance will be reviewed to ensure there is no personally identifiable information in the data and metadata prior to being published. Review will also ensure the data and metadata are in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles adopted by Figshare. The NIH Figshare partnership provides NIH-funded researchers with:

  • The ability to self-publish any data type in any file format
  • All data assigned a branded, citable Digital Object Identifier (DOI)
  • All data associated with a license
  • Ability to link grant information to published data
  • Ability to embargo data
  • Open access to all published data
  • Data being indexed in Google and discoverable across search engines
  • Usage metrics - including views, downloads, citations, and Altmetrics - tracked openly

NIH Figshare FAQs

For a full list of FAQs, visit https://nih.figshare.com/f/faq.

What is Figshare?

Figshare is an online repository for making research data citable, shareable, and discoverable. Data published on Figshare is assigned a persistent, citable DOI (Digital Object Identifier) and is discoverable in Google, Google Scholar, Google Dataset Search, and more.

NIH already has numerous data repositories. Does the NIH Figshare instance replace them?

No - Figshare is not designed to replace any existing NIH-related repository. For NIH Institutes and Centers (ICs) with existing repositories, the NIH Figshare instance is a supplemental option for any data that may not fit in an existing repository.

Are there any storage limits per user?

The initial storage quota is 500GB per user. To request more storage, click on the “Request more storage” button at the top of your “My data” page. Large files or large quantities of files can be uploaded using ​Figshare’s API​ or desktop uploader​.

What type of data can I upload to the NIH Figshare instance?

Data resulting from NIH funding can be uploaded as long as it is de-identified and contains no sensitive information. Any data type and file formats can be uploaded. Examples of the data that can be uploaded are datasets and spreadsheet data, video, audio, code, and more. Most file types can also be previewed in the browser, meaning others can see a visualization of the data on the item page without necessarily having to download the file(s).

Is my data secure?

All uploads that are privately stored can only be accessed by the submitter when they are logged in. The NIH Figshare instance is hosted on Amazon Web Services (AWS) S3 to ensure the highest level of security for your research data. AWS utilizes an end-to-end approach to secure and harden its infrastructure, including physical, operational, and software measures and provides authentication mechanisms to ensure that data is kept secure from unauthorized access.

The security and persistence of your files on Figshare make it easy to prevent plagiarism of your research data as all uploads are time-stamped.

How is my data stored?

The NIH Figshare instance is hosted on Amazon Web Services (AWS) S3 to ensure the highest level of stability for your research data. AWS stores multiple, redundant copies of your information so you don't have to worry about ever losing your master copy.

Every part of the Figshare data store is backed up - we don't just rely on the redundancy of Amazon's cloud. We do daily backups of the metadata, and weekly snapshots of the entire data system, including an encrypted one of the S3 file store.

The Figshare server clusters are monitored in real time and the service is able to scale readily to meet traffic spikes that may occur when you release new or exciting datasets. We also use MD5 checksums when storing a file, which are checked against the file regularly to ensure the file is intact.

When I submit my data, what checks are done before publishing?

When you submit data to the NIH Figshare instance, our deposit review team of Figshare employees with expertise in data curation and biomedical research will conduct a file and metadata review before the item is made public. This review includes a file and metadata quality check to ensure the description is accurate and the item can be shared. This check will also ensure the data and metadata are in line with the FAIR (findable, accessible, interoperable, and reusable) principles adopted by Figshare to encourage data reusability. The deposit review team may contact the submitter by email and work with them to make edits to ensure the highest quality and greatest discoverability of the published data. As part of this process, we will check:

  • Files match the description, can be opened, and are documented.
  • A descriptive title is included.
  • Item type is appropriate for the NIH Figshare instance.
  • Submitter has affirmed that no personally identifiable information (PII) is contained within the files or metadata and no obvious PII is observed during review.
  • Metadata sufficiently describes the data or links to resources that further describe it.
  • Embargoes are used appropriately.
  • An appropriate license has been applied.
  • NIH funding is specified and linked.
  • Related publications are linked.

Case studies

There are several case studies covering the rationale researchers had and process researchers experienced when uploading their data to the NIH Figshare instance.

Storing and sharing x-ray scattering data on the NIH Figshare instance

This case study explores the process of James Fraser and Michael Thompson, researchers in the Fraser Lab at the University of California, San Francisco (UCSF), uploading x-ray scattering data to the NIH Figshare instance.

Click​ ​here​ to access this case study.

Using the NIH Figshare instance to make fMRI and eye movement data associated with a publication openly available

This case study explores how Michal Ramot, NIH intramural researcher and visiting fellow at the National Institute of Mental Health (NIMH), and her colleagues published a collection of neuroimaging research in the NIH Figshare instance.

Click​ ​here​ to access this case study.

Making the NIH Figshare instance part of the research lifecycle: a case study of sharing single cell databases in the Carpenter Lab at The Broad Institute of MIT and Harvard

This case study examines how Gregory Way, postdoctoral associate in the Carpenter Lab at The Broad Institute of MIT and Harvard, and his colleagues published single cell databases on the NIH Figshare instance using Figshare’s API.

Click​ ​here​ to access this case study.

User guides

There are several resources available documenting the process of uploading data to NIH Figshare.

For text documentation, including screenshots of the portal throughout the process, click here​.

For video documentation of the process of uploading to the NIH Figshare instance:

  • Click ​here​ for the video on Figshare (download, cite, and embed in your own guides)
  • Click here​ for the video on YouTube

How to get in touch with the NIH Figshare Team

If you would like a one-on-one call or meet with the NIH Figshare team to answer any questions you may have, or provide support for your institution, please email nihsupport@figshare.com.

Webinars

View previous recordings:

Publishing your Datasets in the NIH Figshare Instance: An Introduction for Biomedical Researchers
Access recording here​.

The NIH Figshare Instance - What does it mean for NIH funded researchers?
Access recording here​.