FAIR and CARE Data Principles
August 24, 2020
Technology has helped facilitate the growth of data sharing and the rise of open data – a movement that DataStream is proud to be part of. When water data is open and accessible it can be used to better inform decision-making and stewardship efforts.
But how data is managed has a big impact on how useful it can ultimately be. In this post we take a look at two important and complementary sets of guiding principles that underpin best practices when it comes to data stewardship and access.
The FAIR Guiding Principles for scientific data management and stewardship were first published in Scientific Data in 2016. They were developed to help address common obstacles to data discovery and reuse – long recognized as an issue within scholarly research and beyond.
The principles provide guidance for making data F indable, A ccessible, I nteroperable, and R eusable.
The first step in (re)using data is being able to find it. Data and metadata (the data about the data) should be easy to find for both humans and computers. This includes assigning datasets a globally unique persistent identifier (like a DOI) and having it indexed in a searchable resource.
Once someone finds the data, they need to know how to access it. Data should be retrievable by their identifier (e.g. DOI) using a standardized communications protocol that is open and free.
Use of common (meta)data standards (like DataStream’s open data schema for water quality data) allow data to be integrated with other data. In addition, data need to interoperate with applications or workflows for analysis, storage, and processing.
The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, data should be released with a clear and accessible data usage license and sufficient metadata to understand the data being accessed.
Find out more about the FAIR Principles from the GO FAIR Initiative, which aims to support implementation of the FAIR principles.
The CARE Principles for Indigenous Data Governance were developed by the Global Indigenous Data Alliance (GIDA) in 2019 to complement the FAIR principles and other movements towards Open Data. As GIDA outlines, this is because a focus on FAIR principles and open data alone do not fully engage with Indigenous Peoples’ rights and interests, including the right to create value from Indigenous data in ways that are grounded in Indigenous world views, and to advance Indigenous innovation and self-determination.
Whereas the FAIR principles are data focused, the CARE principles are people and purpose oriented.
Data ecosystems should be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.
- For inclusive development and innovation
- For improved government and citizen engagement
- For equitable outcomes
AUTHORITY TO CONTROL
Indigenous Peoples’ rights and interests in Indigenous data must be recognized and their authority to control such data be empowered.
- Recognizing rights and interests
- Data for governance
- Governance of data.
Those working with Indigenous data have a responsibility to share how data is used to support Indigenous Peoples’ self-determination and collective benefit.
- For positive relationships
- For expanding capability and capacity
- For Indigenous languages and worldviews.
Indigenous Peoples’ rights and well-being should be the primary concern at all stages of the data life cycle and across the data ecosystem.
- For minimizing harm and maximizing benefit
- For justice
- For future use.
Visit GIDA to find out more and read the full text of the principles.
DataStream and the FAIR and CARE principles
Our work at DataStream reflects an ongoing commitment to upholding the FAIR and CARE principles. These principles inform how we’ve built the technology behind DataStream, how we connect with other data systems and tools, and how we work with data stewards (those collecting and managing data) to ensure clarity around data ownership, licensing, and control over what is published on DataStream.
DataStream’s Data Policy, Open Data Schema , implementation of dataset DOIs, and integration with other tools and repositories (like R, Google Dataset Search, and Canada’s Federated Research Data Repository ) are just some examples of how we are putting these principles into practice to enhance water data management in Canada.
Smart technologies have great potential to improve our understanding, management, and use of the Great Lakes.