One of the significant challenges that scientists and laboratories are facing is the barrier that unstructured data creates with regards to productivity. This emphasizes the need to structure data in order to analyze and defend results. Despite recent advances in technology, many laboratories still rely on paper-based notebooks to record experiments, keep track of results, and record data analysis. The volume of data generated by laboratories makes this process very labor-intensive, limiting cross-site collaboration and communication. Being able to review, arrange, and prove results becomes near impossible when vast amounts of data are scattered in various places, rather than in a structured, organized form. Therefore, the need for laboratories to be able to collect and use structured data is of vital importance.
The use of unstructured data, which includes text files (spreadsheets, emails, word files, etc.) and machine-readable data, is an increasingly common part of laboratory research and workflows. Traditional analysis tools struggle to evaluate and digest the vast amounts of data available, and this hinders the evaluation process of any experiments.
Structured Data
Structured data is highly-organized and formatted in a way that is easily searchable in databases - a necessity for many laboratories – and enables scientists to collect data in a methodical form with a precise set of requirements. It forces the experiment performer to have certain information data objects, which in turn will need to have certain properties (that is, data types). Data streams from various instruments and analysis systems can be unified into one final data store that makes it easy to compare results from different experiments.
With the amount of data generated, a workable unified format that enables analysis, insights, and decisions is the most practical and efficient way to use the data. Taking the time to plan and build form-like protocols can help control the input from experiments and create order and efficiency in the lab, saving both time and money. In all experiments, regardless of who is conducting them, it is imperative to set a working process, streamlining procedures, resulting in structured results recorded in the same way so later they can be easily analyzed. Structuring data might require work in advance, but once the format is in place, labs will achieve significant efficiencies.
Maintaining Data Integrity
Maintaining data integrity throughout the entire scientific data lifecycle is a significant concern of biopharmaceutical research and development executives. Complying with data integrity regulations is vital for any industrial laboratory and is crucial for shortening time to market and avoiding unexpected expenses and delays due to regulatory violations.
Virtually all laboratories are governed by rules regarding data integrity, referring to the completeness, consistency, and accuracy of data. The ALCOA principles, known as ALCOA+, and are used by the Food and Drug Administration (FDA), Medicines and Healthcare products Regulatory Agency (MHRA), Good Automated Manufacturing Practice (GAMP), World Health Organization (WHO) and The Pharmaceutical Inspection Convention and Pharmaceutical Inspection Co-operation (PIC/S). The principles ensure that data is attributable, legible, contemporaneously recorded, original or a true copy and that the lab is meeting the regulatory requirements.
Ensuring data is well structured can help avoid data integrity violations. With every individual in an organization benefiting from advanced laboratory technology, the entire workflow can be seamless. Laboratory technicians can document data; team leaders review it, and project managers can oversee activity and relate it to timelines and budgets. Links can be placed digitally between projects and experiments and metadata created to make searches faster and simpler. All this makes communicating feedback faster and easily maintains regulatory compliance.
Electronic Lab Notebooks
Electronic Lab Notebooks (ELNs) allow a lab to structure its data in a more intuitive and user-friendly way by enabling lab managers to focus on specific workflows and directly compare data collected. This also helps to identify potential productivity gaps in workflows and streamline outdated processes, which might be time-consuming and labor-intensive. By doing so, laboratories can change the way they manage research, saving time on searching for samples because all experiments and results are planned, tracked, and recorded.
Using an ELN allows laboratories to create a structured protocol as well as track, manage, and store vast amounts of accumulated data. An ELN collates unstructured data to form a cloud-based record. Where data is unstructured, for example, in early drug discovery, an ELN permits more qualitative data to be recorded (e.g., spectra, sequences, photos, descriptions, annotation of protocols). (2) This allows laboratories to create a streamlined procedure enabling results to be easily analyzed and shared. Developing a carefully designed automated data pipeline will allow consolidation from multiple sources and provide a laboratory with reliable well-structured datasets for analytics and machine learning. The time saved on administrative tasks by implementing an ELN system enables researchers to reach milestones faster while remaining cost-effective.
A paperless laboratory provides interoperability and data exchange regardless of the origin or destination of that data. The goals of the paperless lab are to lower costs, improve throughput, and maximize regulatory compliance by minimizing manual paperwork - objectives which are fully aligned with ELN software.
Structuring Unstructured Data in a Real-World Scenario
BiomX is a microbiome drug discovery company developing customized phage therapies that seek and destroy harmful bacteria in chronic diseases such as inflammatory bowel disease (IBD) and cancer. As research expanded, a key challenge became the need to track, manage, and store the vast amounts of data being accumulated.
BiomX required a platform that enabled it to share and search data within context between the different teams and to generate relevant insights from the data collected. It made sense to make a move to a digital platform in the form of an ELN.
The ELN offered BiomX the ability to plan and track its different projects in a more intuitive and user-friendly way by allowing lab managers to store results and communication within context. By switching to an ELN, BiomX was able to design a structured protocol, adjusted to their company-specific needs, where the scientists were able to document the data and create a formal review process - allowing managers to review all aspects of the projects efficiently and conveniently.
The Laboratory of the Future
Today, tools such as ELNs are challenging the lab's dependence on manual data entry, analog tools, and time-consuming operating procedures. Instead, the modern lab has switched out paper manuals and notebooks for cloud-based systems that maximize throughput and preserve data integrity.
The absence of structure creates inefficiency, with time lost on documenting data and analyzing results in isolation. Structure can be built into the lab with the help of cloud-based ELNs that incorporate features such as Form Element, which allows you to design a uniformed or structured protocol, adjusted to company-specific needs and the results you are looking for. ELNs can provide proactive insights, allow better management of inventories, and help labs manage their collaborations more effectively. By simplifying laboratory workflows and processes, teams can focus their time and efforts towards 'tomorrow's innovations, resulting in a more efficient lab.
References
1. Innovation in the pharmaceutical industry: New estimates of R&D costs - Journal of Health Economics Volume 47, May 2016
2. Choosing the Best Tool for your Data Tasks - https://www.laboratoryequipment.com/article/2019/02/choosing-best-tool-your-data-tasks