It’s time to recognize F.A.I.R. not only applies to data. It also applies to the systems that generate or use FAIR data. In this article, we explore how the concepts of Findable, Accessible, Interoperable, and Reusable apply to build FAIR Robots.
What is FAIR Data Anyway?
In 2016, Wilkinson et al. published an article in the journal Scientific Data titled “The FAIR Guiding Principles for scientific data management and stewardship” (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792175). The authors defined the acronym FAIR as standing for Findable, Accessible, Interoperable, and Reusable. The FAIR Principles ensure that both people and machines can use the data effectively.
The article has received a global response from the informatics world. The general consensus is that all institutions should strive to attain FAIR data if they want to fully leverage the power of artificial intelligence and machine learning on the data.
In addition to the original article referenced above, there are multiple online resources discussing FAIR data, including https://www.go-fair.org/fair-principles/ and https://en.wikipedia.org/wiki/FAIR_data. You should check them out.
FAIR: Findable
Findable asserts that data should be easily discoverable and identifiable by humans or computers. Anyone should be able to easily locate the data they need, with a minimum time and effort searching for it. All data is accompanied by a list of descriptive metadata tags describe the data’s function, purpose, and context.
An expansive list of appropriate metadata, identifies data and uses it for follow-up analyses or other processes. Evolving technology and research practices result in new metadata on old data.
FAIR: Accessible
In the context of FAIR data, accessibility is the second principle, which is crucial for ensuring that data is easily accessible and available to users. The principle asserts that once someone finds the required data, they need to know how they can access it. This does not mean that all data needs to be open to all, jut that there needs to be a way to request and gain access to data described by the metadata which may include specific additional authentication or authorization.
By adhering to the accessibility principle, researchers can ensure that the data they need for their work is readily available.
FAIR: Interoperable
Interoperability is the third of the four principles of FAIR data. FAIR data integrates with other data easily and is useful for multiple applications or workflows.
Interoperable data is easily combined with other data for many applications. This is particularly important in the age of big data, where large datasets from different sources need to be combined and processed to gain meaningful insights.
It is crucial for institutions to ensure that their data is interoperable by adhering to common standards and protocols. If a new standard or protocol is needed then it should be developed kepping prior standards in accont to assure backwards compatibility as well as transparency of communication when that is not possible. These standards should also be included in metadata tags associated with the data.
FAIR: Reusable
The fourth principle of FAIR data is reusability, which focuses on the ability of data to be used again in a variety of contexts and applications. The ultimate goal is to optimize the reuse of data, and this can be achieved by ensuring that both metadata and data are well-described so that they can be replicated and/or combined in different settings.
In order for data to be reusable, it needs to be well-documented and structured in a way that makes it easy to understand and use. This includes providing context for the data, such as the purpose, origin, and intended use, as well as including detailed descriptions of the data itself, such as its format, quality, and limitations.
Data can be used again and again by providing this information, without the need for additional processing or cleaning. This can save time and resources, and can also improve the accuracy and reliability of the data.
In addition to well-described data, reusability leverages the values imbued by assuring interoperability.
Overall, the principle of reusability ensures that data is used to its full potential. By optimizing the reuse of data, we can accelerate scientific progress and improve our understanding of the world around us.
How does FAIR Data apply to Robots?
FAIR data standards are designed to enable “machine-driven activities” – that is, machines finding and accessing data to initiate actions. This capability is also known as “machine actionable”.
In the context of robotics, FAIR data is crucial for the following reasons.
(1) FAIR data enables robots to access and load the data they require to function. By adhering to the FAIR principles, robot developers can ensure that their robots can find and access the data they need to perform their tasks, and that the data is accurate and reliable. (2) FAIR principles need to apply to data and the robots that use and generate FAIR data.
If you are asking yourself, “what is a robot?” Fear not, you can check out our What is a Liquid Handling Robot? or Autonomous mobile robots have arrived… Are you ready? links to get a little background on robots or you could also visit the wikipedia robot page if you want an even more basic overview.
FAIR Robots will produce FAIR data by design
FAIR robots are essential for advancing research and innovation. As the use of robots becomes more widespread, it is important to ensure that they are able to access and process data in a way that is consistent and reliable. By following the FAIR principles, robots can ensure that they are able to effectively interact with data and make informed decisions.
Moreover, FAIR robots can help to promote transparency and reproducibility in research. This is because FAIR robots and robotic methods are easy to share and validate by others. This will enable researchers to build upon each other’s work and accelerate scientific progress.
Findable Robots:
Just like Finding Data, the key to Findable Robots is descriptive metadata tags that accurately describe the robot’s function, purpose, and capabilities.
Making Robots Findable by People
Making robots findable by people requires that the metadata is meaningful to the people who will be using the robot. This is achieved by having metadata that provides descriptions of the robot’s capabilities and functions that is expansive and intuitive. It is also helpful to include images or videos of the robot in action to better illustrate its functionality. In addition, it is important to provide documentation on how to use the robot, including user manuals and tutorials.
Making Robots Findable by Software, other Robots or Artificial Intelligence
The metadata required for findable robots is actually very similar to data. There is the additional need to have details the physical capabilities of the robot. This includes information such as the type of sensors used or the types of tasks it can perform.
Accessible Robots:
Robot Accessibility involves ensuring that robots are easily accessible for use by people, software, other robots or artificial intelligence.
Making Robots Accessible by People:
When it comes to making robots accessible for people, physical design plays a critical role. Accessible robots are designed to easily integrate into any laboratory and research environments. If a robot fails to meet the laboratory needs, it fails to meet the accessibility standard.
In addition to physical design, user-friendliness is another essential aspect of scientist accessibility. If the user interface is not intuitive or easy to use, the robot’s accessibility is compromised. Therefore, a robot’s usability is as important as its physical design.
However, designing robots that meet both the physical design and usability criteria can be challenging. This requires developers to understand (1) the scientific field, (2) the researchers’ needs, (3) the technical requirements of the robot. It also requires collaboration between robot designers and the scientific community to ensure that the robots meet the desired specifications.
By taking all of these factors into account, it is possible to create robots that are truly accessible and useful for scientists from a wide range of disciplines and backgrounds. A robot that is both physically accessible and user-friendly can significantly enhance the researchers’ productivity, allowing them to focus on their research instead of struggling with robot interfaces.
In conclusion, while physical design is undoubtedly essential for making robots accessible, user-friendliness is equally important. An accessible robot integrates into the laboratory and research environments easily with a user-friendly interface. Therefore, robot designers should focus on both aspects to create truly accessible and useful robots for scientists.
Making Robots Accessible by Software, other Robots or Artificial Intelligence:
In order to enhance the accessibility of robots and enable them to integrate more effectively with other machines, it is essential to provide APIs and other tools that facilitate communication between them. Make robots more accessible and easier to control, it will increase their usefulness and value.
By standardizing communication protocols and interfaces, robots can become more accessible for use with other machines, which can greatly expand their range of applications. Adopt common standards and protocols to enable the exchange of data and information with other machines.
Ensuring remote control and monitoring of robots can help to improve their accessibility. It enables scientists and researchers to control and monitor robots from a distance. By doing so, they can use robots more effectively and efficiently in a wide range of applications.
Prioritize ease of access for robots for other machines by standardizing communication protocols and interfaces and ensuring remote control and monitoring. By doing so, robots can become more accessible and useful, which can help to advance research and innovation in a variety of fields.
Interoperable Robots:
This requires adherence to common standards and protocols, and the ability to exchange data and information with other machines. Making robots interoperable enables use in a wider range of applications and integration into complex systems.
Making Robots Interoperable for People
In order to make robots interoperable for people a common communication standard is a good start. Design robotics to be a modular components and swap them out or replace them. The goal is to to create a more cohesive and effective ecosystem of robots that people can leverage.
Making Robots Interoperable for Software, other Robots or Artificial Intelligence
We make robots interoperable for software, robots, or artificial intelligence by in two ways.
- Integrate them with standard communication protocols like ROS (Robot Operating System) and OPC UA (Open Platform Communications Unified Architecture).
- Assure have have middle tier connectivity via application programming interfaces (APIs) or drivers that enable connectivity with centralized schedulers (Preferred).
Interoperable robots are modular and easily swapped or substituted. The goal is a network of robots that can share FAIR data, leading to more efficient and effective automation.
Reusable Robots:
Reusability ensures that robots have a large variety of contexts and applications. This means they should be modular and flexible, with the ability to adapt to new tasks and environments as needed.
Variable driven robot methods is a great way to assure reusability of robotics. Write methods that are reconfigurable for the needs by just modifying an input variable creates the opportuity to ensure plasticity of robotic methods. Deploy enhanced error handling that reconfigures the system or method to accomplish the task.
FAIR Robots for all, but that’s not enough by itself.
In conclusion, FAIR robots are essential for advancing research and innovation. FAIR robots ensure that they are able to access and produce data in ways that are both consistent and reliable. While we should all aim to create FAIR robotic systems, our goals should not end there. In reality all agents that use or create FAIR data should themselves stick to the FAIR principles that includes all data generating equipment as well as machine learning algorithms.
