STATUS: IN DEVELOPMENT
Short description
‘… selecting a relevant subset of the data and defining driving user questions(s) are highly relying on being familiar with the data’ (Generic)
In this step, the aim is to gain more insight into the existing data, or the data that you aim to collect. Clearly defining the meaning (semantics) of the data is an important step for creating the semantic model, as well as for data collection via, for example, electronic case report forms (eCRFs).
To understand 'semantics', different aspects of the data elements/variables should be analysed:
the definition/description of data elements
values that are allowed to chose (e.g., in system A, sex allows for male and female, while in system B, sex also allows for intersex. Such difference reflects the gap of their semantics)
relationship between data elements (e.g., ‘sex’ variable is one attribute of ‘patient’ profile, which may imply that the semantics of this ‘sex’ variable is ‘sex of patient’)
The outcome of this step should be a set of data elements (variables) with clear and unambiguous semantics, which reflect the information you want to collect or share. Be aware that finding machine-actionable items from ontologies for the data elements is not yet part of this step, but is described in creating the semantic model.
Why is this step important
Several of the steps that follow rely on being familiar with your data. For example, in order to create or reuse your semantic (meta)data model, it is crucial to understand the meaning and relationships of variables.
For example, in a dataset a variable to collect sex-related data might be called ‘sex’. If the semantics of such variable is not provided or not analyzed, it would be unclear if it means ‘biological sex at birth', ‘phenotypic sex’, or ‘gender’.
How to
While performing this step, keep your
Let’s say we have a dataset containing patient information with the following metadata:
Metadata Field | Value |
|---|---|
Dataset Name | Health Data |
Date of Upload | 01/02/2023 |
Keywords | BP, HR, Conditions |
Creator | Dr. Smith |
Description | Patient health data including BP and HR |
Format | CSV |
Source | Hospital A |
Rights | Open |
Step 1
Compile all the information of data elements, data values, and data structure. Examine the data in whatever format and structure it is available. This step helps to identify inconsistencies, ambiguities, and errors in the data.
In case you are FAIRifying existing/already collected data, locate all relevant sources in which the data is stored. Compile information about the following:
Which variables are present in the data (i.e. in the eCRFs)?
What are the value ranges for each variable?
In our example, we are working on FAIRifying an already existing metadata of a dataset. Let’s compile and examine the information we have.
Variables and Ranges of our metadata are as follows:
Dataset Name: The name of the dataset. Range: Text.
Date of Upload: The date on which the dataset was uploaded. Range: Date values in the format mm/dd/yyyy.
Keywords: Terms that describe the main topics of the dataset. Range: Text.
Creator: The person or organisation that created the dataset. Range: Text, in our example title and last name.
Desription: A brief summary of the dataset. Range: Text.
Format: A file format of the dataset. Range: Text, in our example a short string indicating the file format.
Source: The origin of the dataset. Range: Text, in our case the name of the institution.
Rights: The usage rights or licence of the dataset. Range: Text.
In case you are aiming at collecting FAIR data from the start:
Which data elements/variables are you planning to collect? For this, the driving user’s question might provide some guidance.
If possible, determine the value range for each data element (e.g. for ‘biological sex at birth', values could be ‘male’, ‘female’; while for 'age’, the value range might be 0-110)
Step 2
Check the data semantics. Is the meaning of the data elements clear and unambiguous? For data elements with ambiguous meaning, try to improve their definition. For this, it might help to examine the value range of a variable to find out if next to the intended value range, other values could be filled in, too.
In the example of collecting data on a patient’s 'sex', it might be unclear if it means ‘biological sex at birth' or ‘gender’.
Step 3
Compile information about the relationships between data elements. For example, if the dataset is in a relational database, the relational schema provides information about the dataset structure, the types involved (the field names), cardinality, etc.
In the example of 'biological sex at birth', this variable is an attribute of ‘patient’.
Step 4
Check whether the data representation is clear and unambiguous. For data elements with ambiguous format, try to improve their definition. For this, it might help to examine the value range of a variable to find out if next to the intended value range, other values could be filled in, too.
For example, 'age' of a subject can be expressed in years, but in some cases (i.e. studies with small children) could also be expressed in months. It should therefore be clearly stated of age should be captured in years or months.
Step 5
In addition, check whether the data already contains FAIR features, such as persistent unique identifiers for data elements (for more information, see pre-FAIR assessment).
Step 6
Define or align common data elements (CDEs). For a new data collection: define CDEs whose semantics are clear and unambiguous; for an existing data set, existing data elements can be aligned to CDEs.
Expertise requirements for this step
Below are experts that may need to be involved, as described in Metroline Step: Build the Team.
Semantic data / modelling specialists: Can help understanding the data’s structure and ambiguity.
Domain expert: Can help understanding the data’s elements and their potential ambiguity.
FAIR data steward: Can help identifying FAIR features in the dataset, or data to be collected, as well as ambiguity in the data elements.
Practical examples from the community
Examples of how this step is applied in a project (link to demonstrator projects).
Tools and resources on this page
None.
Training
Relevant training will be added in the future if available.
Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.