Hello, and welcome to Machine-Centric Science. My name is Donny Winston, and I am here to talk about the FAIR principles in practice for scientists who want to compound to their impacts, not their errors. Today, we're going to be talking about the fifth of the fair principles, A1: metadata and data are retrievable by their identifier using a standardized communication protocol.

So, you've identified a digital resource. Now it's time to retrieve it and/or its metadata record.

You have the identifier. This principle is about how to access, not about who can or should have access.

A great mechanized protocol is something like the Hypertext Transfer Protocol Secure, HTTPS. This works over a low-level protocol, TCP -- transmission control protocol, along with IP, the internet protocol, often used in conjunction with the domain name system, DNS. You generally, don't have to worry about that too much if you stay at the high level of HTTP.

You want to avoid protocols with limited implementation, poor documentation, and, when possible, components involving human intervention. Now, it may not be possible to provide secure access through a fully mechanized protocol like HTTP, for example, for highly sensitive data. However, the protocol must be clear and explicit in the metadata, whether it involves a verbal request, email, telephone number, Slack username, et cetera. The important thing is that the communication protocol for how to access is explicit and clearly defined in the metadata, whether fully mechanized or not.

Here, the emphasis is on ability. There should be no additional barrier to retrieval by some agent when access is permitted, however that permission process works, which will come in later principles. The agent may be a machine working behind a firewall, so it already has access.

Another way to put "standardized communication protocol" is that it's a predictable way to get it, separate from access being granted or not.

That'll be it for today. I'm Donny Winston, and I hope you join me again next time for Machine-Centric Science.