FAIR and the notion of metadata



Recipe Overview
Reading Time
10 minutes
Executable Code
No
Difficulty
FAIR and the notion of metadata
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Recipe Type
Guidance
Audience
Everyone
Maturity Level & Indicator
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Main Objectives

The main purpose of this recipe is:

To introduce the notion of metadata and detail why their matter in the context of FAIR. This recipe aims to provide insights into the different types of metadata, why they differ, and how they relate to each other. Finally, we also highlight the various models and vocabularies available as well as explain what it means to make metadata machine actionable.


Introduction

Upon reading the FAIR principles, one can’t help but notice that everything rests on the availability of machine readable metadata. For a number of newcomers to FAIR or Data Management for that matter, the first hurdle is to grasp the notion of ‘metadata’ and this alone can be a challenge. Below are a number of commonly found definitions of metadata:

  • the recursive definition “data about the data”, which provides the idea that to understand data, you need extra data describing it 4.

  • the formal definition “set of agreed-upon descriptors to denote an entity”, which indicates that a social contract is needed to ensure we all mean the same thing when adding a descriptor.

  • the poetic definition “a love note to the future about data”, which conveys the notion that providing descriptors about data is an altruistic and caring action.

  • the fractal definition “your metadata is my data”, which conveys the notion that quantity has a quality all of its own when it comes to metadata, and by accumulating enough metadata, however small, statistical handling may reveal patterns and provide insights. This in fact can have unforeseen and possibly untowards consequences, such as loss of privacy and risks of reidentification of supposedly anonymized data.

So, already we have the feeling that metadata is indeed important and above all pervasive in any modern society which relies on information technology.

In the following sections, we will delve further into the possibly controversial typology of metadata 5.


Graphical Overview


Different shades of metadata

In this section, we will simply build on the illustration presented above and provide more details and attempt to frame the context of each of these definitions. Remember that for each of these definitions, we are actually talking about tags used to provide information about an entity, be it a physical object (e.g. a chair in your favourite furniture supplier) or a digital object (e.g, a file on a server).

1. Descriptive metadata

This is really the fundamental of metadata, things should at least be described by a label, or name, which is a probably the simplest form of metadata: a small string denoting a thing. Depending on the domain of knowledge, descriptive metadata complexity can increase. Taking an example, let’s consider the domain of a bibliographic record and let’s look at a bibtex 2 record:

@Article{Alter2020DATS,
   Author="Alter, G.  and Gonzalez-Beltran, A.  and Ohno-Machado, L.  and Rocca-Serra, P. ",
   Title="{{T}he {D}ata {T}ags {S}uite ({D}{A}{T}{S}) model for discovering data access and use requirements}",
   Journal="Gigascience",
   Year="2020",
   Volume="9",
   Number="2",
   Month="02"
}

In this object, seven metadata element are provided, each of them meant to receive a specific category of information about a journal article. Each key in this json object corresponds to a metadata element the definition of which can be found in 2.

2. Structural metadata

Structural metadata concerns itself with providing descriptors allowing agents to understand how data is organised so this type of metadata could provide information about the layout of a table, the relations between elements, and their types. Here, there are historical two important models to know about:

  • OAI-PMH OAI-PMH stands for the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and is a low-barrier mechanism for repository interoperability.

  • Portland Common Data Model

3. Administrative metadata

Administrative metadata covers things like record identifier and can also include specialization such as:

3.1 Provenance metadata

This subtype of metadata is mainly concerned with capturing information about the agents and processes which produced the entity. So processes such as creation, modification, conversion should be described as well as the dates on which these have been executing and by whom. Versioning information also falls into this category of metadata information 1. This used to be known as “Audit and Trail” information as it allows understanding how information was generated and therefore provides tokens of trust and evidence which can be used to establish validity, reliability, and trust-worthiness of data.

4. Quality metadata

This is possibly a more recent layer adding to the set of metadata. It provides specific elements to define indicators providing insights into the quality of a dataset or piece of information. It can range from quantitative metrics such as the variance or standard error of a variable or more qualitative aspects such as a discrete ranking 3.

Note

This typology can easily be critisized as the boundaries between to different domains can be something blurred by underlying semantic models, granularity levels so we advise the reader to be mindful of this. Volumes have been written on the topics and this is an area in constant evolution and where shape shifting happens.

Semantic frameworks for metadata

We have given an overview of the basic definitions of what metadata is and its various possible flavours. This raises an unanswered key question: How does it work in real life? For this, we need to look at some technical details and since we are dealing with things FAIR, we inevitably slide back into the notion of Linked Data and Resource Description Framework. This essentially means that we can not talk about machine actionable metadata (referred to in some quarters as ‘active metadata’) without talking about semantics and controlled terminologies on the one hand, and on the other hand about syntax and serialization format. This is simply because for computational methods to work, there needs to be agreements, social-contracts, and protocols in places to define things to enable interoperability. As with all human things, there will be redundant, overlapping, competing efforts vying for a domain of knowledge. Semantic models for metadata do not escape this.


Conclusion

In this recipe, we introduced the concept of metadata and presented a possible typology for it. We also showed the close relation between metadata and semantic models used to represent information as well as how, with the reliance on the Resource Description Framework, the boundary with syntax and semantics somehow becomes more fuzzy. We therefore encourage readers to delve into these other sections of the FAIR Cookbook.


References


Authors