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Good scientific practice for life science research and development requires careful planning and design for the collection of data from any source, such as laboratory experiments, clinical studies, databases and scientific literature. A Data Management Plan (DMP) is an essential element in the process to manage the FAIR data life cycle. It will document the plan for the dataset and associated metadata in specific terms of what, how, who and when. This process will support making the data FAIR by design and is highly relevant to clinical study design. More details on the DMP method can be found in the Pistoia Alliance FAIR Toolkit: https://fairtoolkit.pistoiaalliance.org/methods/data-management-plans/

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However, adhering to these specifications is not enough to achieve a FAIR maturity level that enables full machine actionability (see section 3 of this guide) and computable knowledge still remains out of reach for software agents even when the text description has a standardised structure. To further demonstrate the problem, we considered the following trial as our test bed:  “Effect of Propolis or Metformin Administration on Glycemic Control in Patients With Type 2 Diabetes Mellitus” available from http://clinicaltrials.gov (link).

Glycemic control is naturally the principal topic of diabetes and complications that can be developed as a consequence of loss of sensitivity to perceive insulin signals by the cell. The glycemic control goals established by the ADA are: glycosylated hemoglobin (A1C) <7.0%, fasting plasma glucose 80-130 mg/dL and casual plasma glycemia <180 mg/dL. The first-line treatment in patients of recent diagnosis is metformin, however, studies have shown that propolis, a resinous balsamic material collected by the Apis mellifera bee, from sprouts, exudates of trees and other parts of the plants, represents a very important and promising natural alternative in medicine, which can be considered as an antidiabetic agent.

The aim of this study is to evaluate the effect of propolis or metformin administration on glycemic control in patients with type 2 Diabetes Mellitus without pharmacological treatment. The investigators hypothesis is that propolis or metformin administration, modify the glycemic control in patients with type 2 Diabetes Mellitus without pharmacological treatment.

Figure 2 This study description is taken from https://clinicaltrials.gov/ct2/show/study/NCT03416127

The initial examination unsurprisingly reveals that the “Brief summary” of the trial is human readable and, more interestingly that the same information can be, it can also be accessed programmatically via an API providing a json file to an HTTPS request. Upon closer inspection however, the web pages for human consumption reveals that only provide basic metadata markup by relying on the OpenGraph protocol is provided. Search engine optimisation (SEO) using http://schema.org markup is absent and may somewhat limit discoverability and findability.

We then focused on the data submission process: In order to submit the trial to http://clinicaltrials.gov ,  the (meta)data has to be submitted in a structured way, a set of key/value pairs, which can be presented as a table containing the study protocol and summary results, in addition to the free text (see below).

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Figure 3: Structured descriptive Information (i.e. structured metadata) for https://clinicaltrials.gov/ct2/show/study/NCT03416127 . Structured summary results are available as well in a separate tab.

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With these observations in mind and with the knowledge that FAIRifying data and metadata retrospectively, can be very costly and time consuming (see the Roche use case in the Pistoia Alliance FAIR Toolkit - FAIRification of clinical trial data), the following sections will expand on the notion of prospective FAIRification of data and metadata. This approach, by setting up from the start to implement the FAIR guiding principles to facilitate secondary reuse and by design saves costs, ensures efficiency and supports longevity of data and metadata. We explore the key aspects of prospective FAIRification of data and metadata from clinical studies in the following chapter “Clinical study data process through the FAIR lens”.