What Is ASAM MDF? The Definitive Guide | Page: 1 of 4 | Next: Page 2: How ASAM MDF works.

ASAM MDF (Measurement Data Format) is the industry standard binary file format (aka. the mdf4 file format, MDF4 file, MF4) for recording measurement data in automotive testing and beyond. Published and maintained by ASAM e.V. (Association for Standardization of Automation and Measuring Systems), the ASAM MDF standard defines how test benches, data loggers, ECU calibration tools, and vehicle bus recorders store acquired signals. The current version, ASAM MDF 4.3.0, was released on September 23, 2025. This four-page guide provides a comprehensive introduction to the ASAM MDF standard for test engineers who work with measurement data daily and data architects evaluating how MDF fits into a broader test data management strategy, covering what MDF defines, how it works technically, what MDF 4.3 adds for ADAS, and who uses it.

The ASAM MDF standard defines a binary file format for persistent storage of recorded or calculated measurement data, covering the complete chain from raw acquisition through post-processing to long-term archival. The table below summarizes what the ASAM MDF standard specifies and why each component matters to engineers working with measurement data.

The self-describing nature of ASAM MDF is a defining characteristic of the standard. Each MDF file contains all the metadata needed to decode and interpret its data, including the conversion formulas that transform raw measurement values into physical quantities. An engineer can open an MDF file from an unfamiliar test bench, an unfamiliar tool, or even an unfamiliar decade, and read the signals correctly because the interpretation rules travel with the data. This property makes ASAM MDF suitable both as a data exchange format between teams and tools, and as a long-term storage format for regulatory or quality archives.

ASAM MDF has no inherent limit on the type of data it can store, but the ASAM MDF standard is best suited for generalized time-series data recorded from physical measurements. The format provides out-of-the-box mappings for common automotive data sources, and its extensible architecture accommodates new data types as they emerge. The testing domains below represent the primary areas where ASAM MDF is used in production today.

Recording measurement data across all these domains is only the first step. The greater challenge is organizing, finding, and governing that data over time. HighQSoft already manages battery test data for organizations including TÜV SÜD Battery Testing GmbH through the dacore partnership, combining LIMS and measurement data management. ASAM MDF 4.3 extends this breadth further by adding native support for ADAS sensor data types (camera, radar, LiDAR) and dynamic data structures, which previously required proprietary solutions or alternative formats such as ros2bag.

ASAM MDF relates to ASAM ODS as the acquisition half of a complete measurement data lifecycle. ASAM MDF is optimized for fast writing during data acquisition. ASAM ODS (Open Data Services) is optimized for fast reading, searching, and governance of managed data. ASAM MDF captures data at the point of measurement; ASAM ODS organizes, governs, and makes it searchable for the long term. Together, ASAM MDF and ASAM ODS form the two halves of a complete measurement data lifecycle.

From the ASAM ODS perspective, MDF files represent mass data: the raw signals and time-series recordings from a test run. To become fully managed, this mass data must be combined with metadata from other sources. Test order systems provide context about what was being tested and why. Lab management systems (LIMS) contribute equipment calibration records and technician assignments. Project management tools supply project codes and approval workflows. JSON files, APIs, and manual annotations add further context such as test conditions, ambient temperature, and firmware versions. ASAM MDF delivers the measurements; ASAM ODS integrates them with the metadata that makes those measurements findable, traceable, and compliant with quality and regulatory requirements.

HighQSoft's ModelMapper (MoMa), a rule-driven ETL engine, bridges ASAM MDF and ASAM ODS by importing MDF data and combining it with metadata from these external sources to create fully contextualized, searchable ODS records. Three integration patterns connect MDF files to ASAM ODS systems: import (full conversion into ODS entities), reference (ODS metadata pointing to external MDF files), and storage (MDF as a managed attachment). The details of ModelMapper's rule architecture and HighQSoft's integration approach are covered on Page 4 of this guide. HighQSoft's Merlin Analysis Server can trigger these imports automatically, creating fully automated pipelines from MDF recording to governed, analyzed data, running thousands of jobs daily at major automotive OEMs including BMW, Ford, and Bosch.

The next page examines what makes ASAM MDF the right format choice by comparing its capabilities against TDMS, CSV, HDF5, and Parquet.

HighQSoft provides test data management solutions for automotive OEMs with over 25 years of production deployments at BMW, Ford, Volkswagen, Bosch, Cummins, and Honda. As a contributor to ASAM MDF 4.3, HighQSoft bridges the gap between measurement data acquisition and enterprise data management.