Labs generate a significant amount of experimental data from a variety of sources: instruments, software, and human input. Scientists/lab technicians have spent a lot of time maintaining experiment data documents for centuries and can seem very productive with these documents. Laboratories must be organized and maintained for multiple purposes, such as data retention guidelines for regulatory compliance. The biological link and its traceability back to the origin is of fundamental importance for any scientist. Data is generated at every stage of an experiment, for example from an ELN, during sequencing, from bioregistries, during primary and secondary screening, etc. This data must be immediately accessible for analysis as soon as the experiment is finished. Every observation made is crucial as it can help in breakthrough innovation one step at a time. There are many companies that produce data entry software, but for any scientist the value of that data lies in its output. The proprietary data formats of each instrument have made it more difficult to interchange data and integrate different systems. There is no holistic option to connect all information, including metadata. Therefore, scientists do not want to abandon paper for two main reasons: paper-based procedures and lack of well-integrated systems. As we move towards paperless labs, what should ideally happen is to change processes and ensure less reliance on paper. However, changing paper-based procedures is not the only solution to achieving a paperless laboratory. Organizations also need to address the other important root cause: the lack of an integrated system. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay Today, most laboratories are more or less automated, in the form of instrumentation and instrumental data systems with Laboratory Information Management Systems (LIMS) being at the core. Typically, labs use many different types of software in addition to LIMS. While LIMS is used to track the sample life cycle and related data management, the result of sample analysis is performed via instrument interfacing. In an effort to achieve “paperless flow” in the laboratory, LIMS must be integrated with other enterprise software such as enterprise resource planning (ERP), electronic laboratory notebook (ELN), scientific data management systems (SDMS ), chromatographic data systems (CDS), inventory management system, training management system, statistical package and so on. While the intention is to have seamless interconnectivity between all these systems, in reality a lot of manual operations still prevail. Many times the workflow/data entry is performed by non-technical or non-scientist personnel. People working at the aggregator layer of the umbrella don't notice that there's a lack of query access to the data and metadata generated by the supporting processes. Therefore there is a disconnect between the data entry process and the data mining process. Most organizations are now trying to reduce the scope of manual operations and thus move closer to the ideal paperless laboratory. If you look at the instrumentation and analytical technologies on the market, they all come with built-in software. Technologies in the pharmaceutical industry are increasingly being networked. Food and Drug Administration regulationsUS, for example, require these tools to be monitored and verified very carefully. Therefore, the software part of the instrumentation has become increasingly important as the hardware part. When research goes global, interconnection, collaboration and analytics at your fingertips become a necessity. Therefore regulatory compliance and business transformation goals are the two drivers of the paperless laboratory. This must be ensured by effective and efficient data stores, as well as effective integration and data transfer between applications that constitute the paperless laboratory for a single organization. The issue of standardization of scientific data and integration of laboratory elements has become a key concern for industry players. There are various initiatives such as the SiLA consortium (Standardization in Lab Automation), AnIML (Analytical Information Markup Language), Allotrope Foundation (ADF Framework) and Pistoia Alliance (HELM – single notation standard capable of codifying the structure of all biomolecules) to develop these common standards for the community. Allotrope Foundation is an international consortium of pharmaceutical and biopharmaceutical companies with a common vision to develop innovative new standards and technologies for data management in research and development, with an initial focus on analytical chemistry. The Allotrope Foundation's effort to create a common laboratory data format that is “instrument and vendor agnostic,” enabling more efficient and compliant analytical and manufacturing control processes, is closely aligned with the FDA's laboratory regulatory goals, as the senior industry players involved point out. The Allotrope Framework includes the Allotrope Data Format (ADF), taxonomies to provide a controlled vocabulary for metadata, and a software toolkit. ADF is a vendor-neutral format that stores datasets of unlimited size in a single file, organized as n-dimensional arrays in a data cube, and stores metadata describing the context of the equipment, process, materials and the results. The Framework enables cross-platform data transfer and sharing and significantly increases ease of use. This effort is fully funded by Allotrope Foundation members such as Amgen, Bayer, Biogen, Pfizer, Baxter, etc. and is rapidly progressing towards achieving common goals to reduce wasted efforts, improve data integrity while enabling the value of analytics data to be realized. The Framework is a toolkit that enables the consistent use of standards and metadata in software development, currently composed of three components and is designed to evolve as science and technology evolves, maintaining access and interoperability with legacy data while reducing barriers to innovation by removing barriers dependencies on legacy data formats.ADF: Allotrope Data Format (ADF) is a versatile data format capable of storing datasets of unlimited size in a single file in a vendor-independent way that can handle any laboratory technique. This data can be easily stored, shared and used between operating systems. The ADF comprises a data cube for storing numeric data in n-dimensional arrays, a data description layer for storing contextual metadata in a Resource Description Framework (RDF) data model, and a data package which acts as a virtual file system to store auxiliary files associated with an experiment. Class libraries are included in the Allotrope Framework to ensure consistent adoption of standards. The Foundation also provides an ADFFree Explorer, an application that can open any ADF file to view the data (data description, data cubes, data package) stored within it. Details of an ADF file: Why the data was collected (sample, study, purpose) How the data was generated (tool, method) How the data was processed (analysis method) The shape of the data (dimensions, measurements, structure) The ADF is intended to enable rapid real-time access and long-term stability of stored analytical data. It was designed to meet the performance requirements of advanced instrumentation and be extensible allowing the incorporation of new techniques and technologies while maintaining backward compatibility. AFO: Allotropic taxonomies and ontologies form the basis of a controlled vocabulary for the context metadata needed to describe and perform a test or measurement and subsequently interpret the data. Drawing from thought leaders from member companies and APN, the standard language for describing equipment, processes, materials and results is being developed to cover a wide range of techniques and tools, driven by real-world use cases, in an extendable design. ADM: Allotrope Data Models provides a mechanism for defining data structures (schemas, models) that describe how to use ontologies for a given purpose in a standardized (i.e. reproducible, predictable, verifiable) way. Data Accessibility: The need for vendor-to-vendor technology integration is eliminated by creating an extensible data representation that facilitates easy access and sharing of data output from any vendor's software or lab equipment. This allows you to instantly share and access metadata, data in incompatible proprietary formats, and siled data. Data Integration: Allotrope Framework's standard format for data and metadata enables compatibility within the laboratory infrastructure by reducing the effort and cost required to integrate applications and workflows. This will ensure greater automation of the system and processes. Data Integrity: Allotrope Framework addresses data integrity at the source by eliminating the need to convert between file formats or manually rewriting data and prevent manual errors before they can occur. Regulatory Compliance: Interoperability within the laboratory infrastructure enables connection of quality control (QC) data and full traceability of data throughout the entire lifecycle. Adopting the Allotrope Framework results in easily readable, searchable and shared data, effectively addressing data integrity and regulatory compliance issues. Scientific Reproducibility: The Framework enables complete and accurate representation of the critical metadata needed to document experiments (methods, materials, conditions, results, algorithms) enabling reproducibility of the original work in just a few clicks. Improved data analysis: Allotrope Framework significantly improves the quality and completeness of metadata and reduces the time needed to convert data between data sources. This enables the successful implementation of big data and analytics strategy. Additionally, the ADF data description layer uses an RDF data model that provides the ability to integrate business rules and other analytics in addition to standardized vocabularies. Reduced costs: Ease of integration between laboratory equipment and software systems will reduce IT expenditure by eliminating the need for custom solutions and software patches. Interoperability of software and tools will also reduce support and maintenance efforts and expenses. Furthermore, the adoption of the Allotrope Frameworkenables greater laboratory automation that will improve overall operational efficiency, leading to further cost savings while laying the foundation for innovations and new solutions in the data lifecycle. SMEs and Technology Partners Member companies, working with vendor partners, have begun to demonstrate how the framework enables cross-platform data transfer, facilitates data search, access and sharing, and enables greater automation in the data flow of laboratory with reduced need for error-prone manuals. entrance. The Allotrope Foundation has released the first phase of a framework for commercial use and was awarded the 2017 Bio-IT World Best Practice Award. As part of the Allotrope Foundation, member companies are active in Allotrope working groups and teams, with a particular role, including teams defining technique-specific taxonomies and data models, technical and ontology working groups, and defining governance and support processes. This collaboration between more than 100 diverse experts from the fields of pharmaceuticals, biopharmaceuticals, crop sciences, tools and software in analytical sciences (discovery, development and production), regulatory and quality, data sciences, information technologies at sector and cross-sector, it allows you to monitor a wide range of technological trends and business needs. The companies in the partner network such as Abbott Informatics, Perkin Elmer, Agilent, Bio via, Labware, Metler Toledo, Terra science, Thermo Scientific, Waters, Persistent Systems, Shimadzu, etc. they not only understand the holistic framework and broader standardization proposition that they will be able to offer their customers, but they will also play a role in developing the standardized framework that can be implemented practically. The value of a particular type of data or its application is significantly greater when shared than the same data in a silo. Agilent is a member of the Allotrope Framework. Allotrope member companies have been committed to the Allotrope Framework since 2012. How Agilent Helps Organize the Allotrope Foundation Agilent's chromatography software, including Chemstation and MassHunter, have data generated in their proprietary format. There is a strict need to standardize the data format for integration, when migrating from Chemstation to MassHunter. Agilent's single quadrupole instrument SIM ion has moved from a binary format (Chemstation) to an INI file format (Chemstation and MassHunter) and more recently to an XML format (OpenLab 2). The concise format does not clearly indicate which number represents the SIM ion and which number is the residence time. Furthermore, the unit of dwell time is not stated. Ultimately, the ADF must be written and read in a commercially available environment for Allotrope Foundation supporters. To demonstrate this, prototype software was developed that supports LC and LC/MS Single Quadrupole Instruments on the ChemStation edition of OpenLAB. The prototype consists of two components. The first component, the ChemStation2ADF converter, writes the ADF format with the method, raw data, results, instrument traces and other metadata. Once created, the ADF is automatically uploaded to an ECM (OpenLAB Enterprise Content Management) system by the Scheduler. The second component, the ADF filter, reads the data description from the ADF and inserts the information into a relational database that is immediately available to all users through the ECM search and retrieval mechanisms. Future work Supports other types of mass spectrometers Includes qualitative results Contributes to the standard ADF for MSRead ADFs produced by other vendors For.