Any data format.
One friendly AnIML
Analytical Information Markup Language (AnIML) is an open, standardized data format for storing and sharing experimental lab data. Although its origins are in analytical chemistry, the format now also covers biological data, and it’s suitable for a wide range of other scientific disciplines.
WHAT IS AN OPEN DATA STANDARD?
“MPEG is defined as an open standard. The specification is available (for a fee) to everybody interested in implementing the standard. […] No single company ‘owns’ the standard.”
Karlheinz Brandenburg, Fraunhofer Institute for Integrated
Circuits (FhG-IIS A), in “MP3 and AAC Explained”
Data standards make it easier to create, share and integrate data by ensuring they’re represented and interpreted correctly. Examples you’re familiar with are American Standard Code for Information Interchange (ASCII) text (.txt), comma-separated values (.csv) for transfer of table data, HyperText Markup Language (HTML) for web browser text, JPEG (.jpg) for compression of digital images and MP3 (.mp3) for digital audio.
Scientific progress heavily depends on collaboration, both within and between laboratories. Open data standards support collaboration much better than proprietary standards. An open standard makes it easier for your scientists to share, analyze and work together on data—and easier for you to integrate your instruments and systems with one another.
The “I” in “FAIR”
One of the most promising and widely accepted means of unleashing the full value of data is by making it FAIR:
Discoverable, identifiable and locatable through the use of machine-readable metadata and standard identification mechanisms
Available and obtainable to both humans and machines
Both syntactically parseable and semantically understandable
Sufficiently described and shared, under the least restrictive licenses, and integrated with other data sources in the least cumbersome way
How do you make data FAIR? FAIRification of data involves many different measures. Standardizing your data on an open source standard that's both human readable and machine actionable—like AnIML—can be a crucial step in this direction.
“Embracing accessible, community-supported, interoperable data standards is key to delivering on the promise of Open Science.”
Dr. Frauke Leitner, Product Manager, Data Suite at Connected Lab, in The Analytical Scientist
How does AnIML work?
AnIML documents capture lab workflows and results, no matter which instruments or measurement techniques were used, by providing a generic data container for storage of arbitrary analytical data.
Also included is workflow information that ties experiments and samples together.
Technique Definitions let you formally constrain the use of the data container—in other words, how the data for a specific measurement technique should be captured in an AnIML document. This means you can use AnIML with a wide range of analytical techniques. The AnIML document grows gracefully as new techniques are developed, so you can keep using your existing software components.
How can AnIML benefit my lab?
AnIML is based on XML, which yields several benefits:
- Efforts are ongoing to apply the data format to many scientific domains
beyond analytical chemistry and biology. AnIML is under development by
the ASTM E13.15 Subcommittee on Analytical Data, which is made up of
volunteers from the industrial, academic, governmental and vendor communities.
- Many tools for XML manipulation are readily available off the shelf, making
- Because XML is a text-based format, AnIML documents are human readable,
which is critical to long-term storage.
How can I use AnIML?
Visualize Your Data
...across platforms, devices and operating systems. We provide tools for many measurement techniques. For analysis and processing, we offer peak finding, integration and chemometrics.
Decouple processing from vendor software and benefit from the unique capability of analyzing and processing data from different techniques, as well as pluggable-data functions.
Automate Your Workflows
Support data acquisition and execution for better data management and cost effectiveness.
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