Figshare Curation Service

Reviewing datasets before they get published and guiding researchers through the data sharing process.

File and metadata checks to enhance the discoverability and reusability of data in line with the FAIR principles and ensure compliance with repository standards and policies.

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checked and FAIR stamp figshare curation service

why we are offering this

Over the past 10 years, we have observed a remarkable growth in data sharing coupled with institutional, publisher, and funder mandates for public access to research data. Along with this has come the recognition that making data open is not sufficient to make it reusable. The development of the FAIR principles in 2014 has helped unite global initiatives with a broad, common goal to make open data “Finable, Accessible, Interoperable, and Reusable” according to community standards.

At the same time we have seen researchers' knowledge of FAIR and interest in these data best practices grow year over year. FAIR data has great potential to advance science by supporting discovery, aggregation, and reuse of datasets by both human researchers and artificial intelligence models.

a system with human input

One thing that we have found through our work with institutions, including with a repository pilot with the National Institutes of Health (NIH), is that the involvement of human curators makes a significant difference in metadata completeness, which can affect the discoverability and reusability of open data. We found it was useful for data curation experts to review datasets before they were published and also to guide researchers through the data sharing process, which is a data management skill not often explicitly part of scientific training.

Both the notion of data curation and the FAIR principles have many layers; from checking metadata and policy compliance, to ensuring open file formats for preservation or specific metadata or documentation schemas, all the way to verifying computational reproducibility. As the need for FAIR data review increases, these checks by humans or machines must be designed in a way that is scalable

Figshare Curation Service with a human input

our Curation Service

The Figshare repository infrastructure has encouraged these checks for several years through our review workflow and curation module for Figshare for Institutions portals, which allows data curators and librarians at the institution to review and approve items before they are published and to work directly with researchers submitting the work. 

However, we recognize that not all organizations who are interested in supporting a Figshare portal to host open research have the staffing bandwidth or expertise to conduct this curation themselves. Thus, in 2020, we launched the Figshare Curation Service (FCS), as a service subscription that can be added to a Figshare for Institutions platform subscription.

Figshare Curation Service workflow

Leveraging Figshare’s review workflow and in-house data curation expertise, FCS offers publishers, institutions, governments and funders, and commercial clients alike the option to have all submitted content moderated, reviewed, and refined prior to publication. The goal of FCS is to promote best practices for metadata assignment and naming mechanisms and ensure that all research content published to your branded portal is as discoverable and accessible as possible and working towards interoperability and reusability.

When research is submitted to a Figshare instance that has FCS in place, our deposit review team of Figshare staff with expertise in data curation and scientific 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 complies with repository policies. The deposit review team may contact the submitting author 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, the FCS team will check:

●   Files match the description, can be opened, and are documented.

●   A descriptive title is included.

●   Item is an output type (e.g. dataset, poster, article) appropriate for the specific 

●   Recommended file organization as single or multiple ‘items’ or ‘collections’.

●   Submitter has affirmed that no personally identifiable information (PII) is contained 
     within the files or metadata.

●   Metadata describes the data type or links to resources that further describe it.

●   A license has been applied.

●   Funding information is specified and linked.

●   Related publications are linked.


These checks are also customizable to your portal and the needs of your researchers or organization. This customization might include custom metadata fields for your organization, metadata, file, or documentation standards specific to a discipline or methodology, or adherence to any other organization policies for licenses, links to publications or funding, or other requirements.


With Figshare Curation Service, our Figshare data experts are available to your researchers throughout the deposit process and can provide one-on-one guidance on using Figshare and recommendations for data sharing best practices. Custom training is also included with the service subscription including a custom guide for data deposit and up to 2 remote training sessions per year. A quarterly update from FCS staff will give you details about the items that have been curated and services provided.

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or read in more detail about the Figshare Curation Service Model

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why figshare?

Figshare are the research data specialists. We have been at the forefront in making data publicly available and open access. Giving data more impact has been our mission since we launched our platform in 2012.

FAIR data principles were there right from the start.

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