Ever heard of the FAIR and ALCOA++ principles? In this blog, we delve into what these principles are and how they act as a guide to ensure data integrity and accuracy of research.
Global collaboration between laboratories and the implementation of worldwide studies or clinical trials create a need for a wide range of data to be collected, collated, and analyzed both securely and efficiently. This is in part facilitated by the FAIR and ALCOA++ guidelines, which ensure that the research is carried out compliant, accurate, and could be replicated.
FAIR principles were introduced in 2014 in response to the rapidly changing technological environment that stimulated a shift in both the rate and volume of the production of research data. In 2016, the FAIR principles were published to set a standard for scientific research to increase the reusability of research data by making it more readily accessible for both humans and machines:
> Both people and computers should be able to find data or metadata related to your research with ease. This is one of the most important components of the process, as this ensures the automatic findability of datasets.
> Data must be stored in a manner where it is openly available. This does not implicitly mean that in all cases data must be openly displayed, it rather outlines the conditions under which the data is accessible. This includes an explanation of where the data, associated metadata, documentation, and code are deposited and how this can be accessed with potential authentication or authorization.
> Data must be structured in a manner that allows it to be combined with other data sets. Also, data must be described in a standard way, using accepted metadata standards, and needs to interoperate with applications or workflows for storage, processing, and analysis. This allows the data to be ‘machine-actionable’ in order for values of attributes to be scrutinized across a range of data sets to ensure they are being measured and represented in the same way. Ultimately, the interoperability component of FAIR is an essential feature that upholds the value and usability of data.
> The researcher should make sure that the data is reusable by detailing the quality assurance procedures alongside documenting the data licensed. Data and metadata should therefore be described thoroughly so they can be replicated or used in different settings.
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Medicine regulatory systems across the world depend on the knowledge of the organizations that develop, manufacture and package, test, distribute and monitor pharmaceutical products. Therefore there is a certain degree of trust between the regulatory bodies and the pharmaceutical companies that the information submitted and used in decision making processes is both complete and reliable. Ultimately, the data that forms the basis of decisions should therefore be attributable, legible, contemporaneous, original, and accurate. These quality attributes were put together and formed the acronym for the five main principles of data integrity, commonly referred to as “ALCOA”:
> Information must be attributable to the person and/or system (e.g. computer) generating the data.
> Information must be recorded in such a way that it can be easily deciphered and understood, allowing a complete and clear picture of the sequencing of steps or events in the record.
> Data must be recorded right at the time of generation or observation.
> The first or source capture of the data or information must be documented as well as all subsequent data required in order to fully reconstruct actions performed on the data.
> Data must be correct, truthful, complete, valid and reliable.
These principles were later updated however to include four new additions, changing the term to ALCOA ++.
Whilst the FAIR principles focus predominantly on the infrastructure for data, placing a large emphasis on metadata, conversely the ALCOA++ principles focus on data integrity issues, making it especially important for benchwork scientists. If the ALCOA++ principles are adhered to, it increases the trustworthiness of the data and subsequently makes research integrity easier to uphold. However, crucially, managing these attributes within electronic systems requires FAIR principles to be considered and implemented, to ensure that data and metadata are stored appropriately and readily accessible to uphold Horizon Europe’s open data aims.
Electronic Lab Notebooks (ELN’s) like Labfolder are designed to encourage both the ALCOA++ and FAIR principles. With features that facilitate data integrity and management, the digitization of scientific processes through software has helped researchers follow both the ALCOA++ and FAIR guidelines. This is because ELNs contribute to both reproducibility and reusability of data, alongside ensuring that research data can be easily accessed and retrieved. Digital tools also make it easier to ensure data integrity with the oversight of metadata in an ELN; the entire experiment can be recorded from the initial design stages to the analyzed results. More and more researchers are embracing digital solutions, not only to establish a strong foundation for long-term laboratory connectivity but also to simplify complex processes and boost efficiency in research and development.
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