Drfitloggar

Drfitloggar is a cross-platform, open-source software platform designed to collect, store, and analyze drift-related logs produced by autonomous systems, environmental sensors, and maritime equipment. The project aims to standardize data collection around drift measurements, enabling researchers and operators to track drift vectors, speeds, and calibration signals over time.

Its core features include a modular data ingestion layer that supports JSON, CSV, and binary formats; a time-series database backend; visualization dashboards; and a workflow engine for data processing pipelines. The platform emphasizes reproducibility, with versioned plugins and a workflow language that records parameters and software versions alongside data.

Development began in 2023 with a community-driven repository and regular releases. It draws on concepts from drift analysis in navigation, oceanography, and environmental monitoring, and is intended to be used by researchers, hobbyists, and operators who need auditable drift logs.

Data model and architecture: logs are timestamped records with fields such as source, drift vector (x, y, z components), position, velocity, sensor quality, and confidence flags. The system is built on a modular architecture with loosely coupled services for ingestion, storage, processing, and visualization; it supports containerization and cloud deployment.

Reception and usage: Drfitloggar has a small but active community, with several plug-ins for popular hardware and data formats. Community contributions have led to integrations with popular GIS tools and Jupyter-based analysis. The project is known for its focus on transparency and data provenance.