Make your data FAIR

Evaluate your metadata with community established FAIR principles.
FAIR products

Researchers increasingly rely on
computational tools to find, access & use data.
Are your data ready?

At a glance, see how your data score in categories of Findability, Accessibility, Interoperability, and Reusability (“FAIR”). With assessments based on the community established FAIR data principles, you can guide your community toward maximizing the value of their digital assets.
Available withDataONE Plus

DataONE Plus Portals

A FAIR scores chart is included in each DataONE Plus portal with scores that summarize the dataset collection. Additionally, access assessments for each and each dataset in your portal.

Aggregate FAIR scores for your portal -
Learn about aggregated scores

Individual FAIR scores for each dataset in your portal -
Read more about individual reports

An example line chart with one line for each of the four FAIR metrics (Findability, Accessibility, Interoperability, and Reusability) showing changes in scores per month
Available withHosted Repository

Hosted Repositories

In addition to FAIR scores for portals, each Hosted Repository includes a FAIR scores chart for the entire repository holdings, plus individual assessments for each and every dataset

Aggregate FAIR scores for your repository -
Learn about aggregated scores

Individual FAIR scores for each dataset in your repo -
Read more about individual reports

Representation of FAIR products available to Hosted Repository users. A time series chart of aggregated FAIR metrics over time, and a stack of individual metadata assessment for each dataset. The individual assessment shows 38 metadata checks and an overall score for each of the four FAIR metrics (Findability, Accessibility, Interoperability, and Reusability)
Available withDataONE Member

Free

A FAIR scores chart is included in each DataONE Plus portal with scores that summarize the dataset collection. Additionally, access assessments for each and each dataset in your portal.

Individual FAIR scores for your dataset -
Read more about individual reports

A example metadata report for one dataset. There is a percentage score for each of the four FAIR metrics, a donut chart giving a summary of the 38 metadata checks completed, and section headings showing which checks passed or failed. These sections indicate 31 checks out of 37 passed, 1 check had a warning, 3 checks failed, and there were 4 informational checks. Text at the top of the report says 'After running your metadata against our standard set of metadata, data, and congruency checks, we have found the following potential issues. Please assist us in improving the discoverability and reusability of your research data by addressing the issues below.'
Available withDataONE Plus
Available withHosted Repository
Aggregated FAIR reports

Quantify your metadata improvement efforts

Get aggretated FAIR scores for all data within your Hosted Repository or DataONE Plus portal

Changes by month - Aggregated FAIR score charts show how your FAIR scores have changed month-to-month

Broken down into FAIR categories - Aggregated scores are divided into the four FAIR categories, so you can pinpoint areas that need improvement and see what your metadata strengths are.

A colorful timeseries chart with a legend. The line chart includes one line for each of the four FAIR metrics (Findability, Accessibility, Interoperability, and Reusability) showing changes in scores per month. The legend indicates the current score for each metric as a percentage.

Preview aggregated FAIR reports with a DataONE Plus portal

Create a portal for free during the preview period. No credit card required.
Available withDataONE Member
Available withDataONE Plus
Available withHosted Repository
Individual FAIR reports

Guide your community toward creating better metadata

Provide detailed FAIR reports for each dataset and give researchers the power to discover exactly which metadata fields are missing or incomplete.

Get instant assessments - Whenever a dataset is updated in your Hosted Repository, the metadata is automatically assessed.

Check metadata automatically - Each assessment is made of a number of individual checks that assess specific fields in the metadata, such as geographic coverage, start and end dates, data attributes, or publication date (among many more).

A example metadata report for one dataset. There is a percentage score for each of the four FAIR metrics, a donut chart giving a summary of the 38 metadata checks completed, and section headings showing which checks passed or failed. These sections indicate 31 checks out of 37 passed, 1 check had a warning, 3 checks failed, and there were 4 informational checks. Text at the top of the report says 'After running your metadata against our standard set of metadata, data, and congruency checks, we have found the following potential issues. Please assist us in improving the discoverability and reusability of your research data by addressing the issues below.'

What is FAIR?

FAIR is a community-led set of principles for data and metadata, whose ultimate goal is to enhance the reusability of data.
Wilkinson et al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3:160018. https://doi.org/10.1038/sdata.2016.18

Need a custom metadata assessment?

Let us help. Use the DataONE Consulting services to create a custom metadata assessment report built specifically for your data management requirements.

Ready to make your data FAIR?

Enhanced DataONE services are currently available on a limited basis as part of a beta program. Please provide the information below and we’ll get in touch when these services are ready for your organization.