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Advantages
The Allotrope™ Framework is setting a new standard for the way we leverage data and provides crucial advantages that speed up Research and Development, expand data mining capabilities and, more importantly, drive innovation.
Increased Data Integrity
The manual transcription or conversion of data between incompatible formats or software systems presents a significant challenge to data integrity by introducing the potential for human error.
Address data integrity at the source by eliminating the need to convert between file formats or manually retype data, and prevent errors before they can occur. The adoption of the Allotrope Framework significantly improves data integrity by providing a single, common data format for any analytical technique, the controlled vocabulary, as well as the software components to adapt existing software applications or create new solutions that work with the ADF, and ensure the consistent adoption across our informatics environment.
Scientific Reproducibility
The need to reproduce an experiment or a measurement is fundamental. In the life sciences, issues in the reproducibility of published research have created a credibility problem that has jumped from the scientific to the political arena and popular press, highlighting the impact of inaccurate or incomplete descriptions of methodology, and seemingly minor mistakes in manual data capture or transcription. In many environments critical metadata needed to document experiments (methods, materials, conditions, results, algorithms) is often incomplete, incorrect due to manual entry, or distributed among multiple software applications, databases or even documents. This fragmented context makes reproducing an experiment difficult, even impossible at times.
The semantic capabilities of the Allotrope Framework enable a complete and unambiguous description of the experimental methodologies, conditions and processes leveraging a controlled vocabulary provided by the Allotrope Ontologies and data models. By leveraging the Allotrope ontologies and data models in the
software used to plan and execute an experiment and analyze the results, the context of the data and results can be captured completely and unambiguously. In a fully integrated implementation, the experimental method can be reused directly as an explicit set of instructions to repeat the measurement or experiment- a paradigm shift from free text entry via keyboard and personal decisions as to the relevant details, to the means to capture, transfer and repeat a process via standardized output that a computer can execute reproducibility. This means others will know exactly what was done and will be able to reproduce the original work with a few clicks. Think about how much more efficient and effective collaboration with external partners would be if sharing methods and results was seamless and unambiguously clear.
Simplified IT and Systems Integration
Simply a condition of a constantly changing, science driven business with new and evolving technologies, the broad landscape of instrument & software providers has led to equally diverse data formats & systems to consume them. This means without parsing and translating all those formats, our instruments & software effectively speak different languages or dialects. The result is a large and diverse portfolio of software that’s includes custom, one-off solutions & patches to enable data capture and integration.
One language and one format for data greatly simplifies integration between applications and provides the kind of interoperability realized in other technologies through formats like the MP3 file or standards like Bluetooth and those the Internet Engineering Task Force (IETF) use to make the Internet work. With adoption of the Allotrope Framework by the vendor community, the companies generating and consuming data will see a decreased cost of integrating new software and instruments, as well as significantly lower effort and expense for support and maintenance due to the reduction of custom integrations and work-arounds. Besides reducing the effort and cost of integration, an informatics architecture based on the Framework opens the door to a much higher level of system and process automation and efficiency gains that yield improvements in speed and/or capacity.
Reduction in Manual Paper and Processes
Scientists and lab technicians spend far too much time on tedious typing to describe experiments and capture results, searching across systems and file shares or databases to find data, or manually transcribing data from one document or system to another. This increases non-value added costs, adds delays to the data or product life cycle, and keeps highly technical staff from more value added activities.
By eliminating manual data translation, copy/paste activities, and enabling someone to find data in seconds, we remove significant manual effort from the workflow, which speeds up delivery of medicines to patients or products to market, and allows that effort to be focused on more important or innovative work.
Streamlined Data Access and Integration
Data silos, incompatible and proprietary data formats, and lack of key contextual metadata all impede our ability to access, share and integrate data. While the systems for a particular process or workflow generally allow for the data to be leveraged for that particular purpose locally, finding it later, sharing it with a colleague or partner, or aggregating data over a range of experiments, to look for broader trends or insights can be really challenging and time consuming -
or require expensive custom solutions. By capturing data and the contextual metadata in the ADF - using the Allotrope Ontologies for the metadata, the indexing of data repositories, archiving systems and data lakes will provide a much richer and harmonized index, making it possible to find data in seconds. The adoption of the ADF and elimination of all the diverse file formats currently used means that data from any instrument, group, department or partner can be read or integrated seamlessly. This significantly reduces the friction and overhead of external partnerships, and improves the quality and detail of the data sharing, replacing the documents and PDFs in typical in today’s environment.
The Foundation for Data Science and Next Generation Analytics
Two of the major challenges to implementing a successful big data or analytics strategy remain the quality and completeness of the metadata, and time it takes to interconvert data from a variety of sources. Adoption of the Allotrope Framework not only addresses both of these issues, but actually puts an enterprise in a position to leverage the data in ways that are inconceivable in our current environments.
The architecture of the data description layer of the ADF is based on semantic web and linked data concepts using an RDF Data Model, a World Wide Web (W3C) consortium standard. This provides the capability to build in business rules and other analytics on top of the standardized vocabularies, giving companies enhanced abilities to classify and manage their data. Legacy systems can be maintained more easily and new technologies including cloud databases, Big Data Analytics, or reasoning engines can be employed to allow researchers unprecedented access to important contextualized data, because the foundational class structure is common and highly extensible to new and expanding domains.
Consolidated Requirements across Industries and Shift in Focus to Innovation and New Market Opportunities
In the current laboratory ecosystem, different customers can present a widely diverse set of requirements for how they want to configure and integrate their instruments and software. The Allotrope standards and APIs represent a consolidated set of requirements. So, as a vendor of instruments and/or software, or provider of professional services, supporting customers who want to adopt the standards implemented by the Allotrope Framework means a reduction in the complexity and diversity of customer requirements both now and in the future.
Legacy data formats can create dependencies on software that would otherwise be obsolete. This adds cost and increases friction to introducing innovation if it requires an updated or new file format to capture additional or new types of data. A consolidated set of standards flexible enough to accommodate more dimensionality, detail or complexity to the data, while still supporting legacy data, removes that cost and reduces the friction of introducing new innovations to a mature market.
The reduction in non-value added time and labor in the data lifecycle, via standardization, coupled with the opportunities the semantic foundation offers, will open the door to a whole new world of need and opportunity for innovation and new solutions in the data lifecycle- which means opportunities for new products, new verticals, new partnerships.
