Documenting Laboratory Workflows Using the Analytical Information Markup Language

Abstract

T he Analytical Information Markup Language (AnIML) is a standardization effort of ASTM (formerly American Society for Testing and Materials) Subcommittee E13.15 on Analytical Data. AnIML provides an XML-based format for analytical data. It is designed specifically for spectroscopy and chromatography data, but is suitable for use with many different analytical measurement techniques. AnIML consists of a generic core structure that permits the storage of arbitrary analytical data. These include multi-dimensional data, name-value pairs, and hierarchies. The concept of technique definitions permits the formal specification of constraints for usage of the core. This way, a definition can prescribe how the data for specific measurement techniques should be captured in the data file. To address changing requirements, AnIML supports an extension concept that allows vendors or end users to specify additional data that should be stored for a technique. These extensions can also be formally documented so that they do not break compatibility with existing software. This article presents an overview of AnIML and demonstrates how AnIML can be used to record data from everyday experimental workflows in a laboratory environment. Issues related to the usage of AnIML in regulated environments are also discussed, including the use of digital signatures and audit trail functionality to ensure data integrity. ( JALA 2004;9:375-81) This project is funded (in part) by NIST's Systems Integration for Manufacturing Applications (SIMA) Program.

FAIR digital objects in environmental and life sciences should comprise workflow operation design data and method information for repeatability of study setups and reproducibility of results

“…Furthermore, the solutions are in general fixed by structure and not flexible enough to scope the variations of hypothesis-driven research study design. Holistic approaches to model a database software solution appropriate to store the study design data and data of all steps of research workflows in a generic form are therefore scarce ( 58 , 59 ). Summarizing, such software solutions for all-inclusive-data management are recommendable particularly for long-term monitoring projects with an agreed study design.…”

Enabling systematic, harmonised and large-scale biofilms data computation: The Biofilms Experiment Workbench

“…This would also be the case for specific techniques, such as fluorescence in situ hybridisation or flow cytometry, for which minimum information guidelines have also been reported [16]. While existing XML-based laboratory standards, such as the AnIML (the Analytical Instrumentation Markup Language) [17], support the documentation of laboratory workflows, high-throughput and statistical methods are not conveniently linked. As a result, BML was created with the purpose to provide a whole-experiment data representation of the biofilm study, encompassing the corresponding experimental setup, assays and methods of analysis ( Fig.…”

Improving interoperability by incorporating UnitsML into markup languages

“…Extensions of ATID files are possible for vendor-specific, institutional-specific, and user-specific parameters. The goal of AnIML is to interchange and store analytical results and their meta data [ 6 ].…”