gapsfree

Gapsfree is a software framework designed to identify and fill missing values in large datasets while preserving data integrity and auditability. The project focuses on data gap analysis, interpolation, and imputation across time-series and spatial data, with an emphasis on transparent methods and reproducible results.

Core features include automatic detection of missingness patterns, a library of imputation techniques ranging from simple interpolation to model-based and machine learning approaches, and uncertainty quantification for imputed values. The toolkit supports data provenance tracking, versioning of imputations, and pluggable backends for common data formats such as CSV, Parquet, NetCDF, and SQL databases. It provides a Python API and a command-line interface, enabling integration into data pipelines and research workflows.

Applications include environmental science for climate and weather datasets, sensor networks, finance time-series with gaps, healthcare analytics where records are incomplete, and other domains requiring gap-free datasets for analysis or modeling.

Development and reception: Gapsfree is maintained by an open-source community and hosted on a public repository. It is released under a permissive license and welcomes contributions, bug reports, and documentation improvements. The project emphasizes transparency, reproducibility, and careful handling of uncertainty, and it provides tutorials and example notebooks.

Limitations: No method can guarantee perfect reconstruction of missing data, and imputations introduce uncertainties that must be considered in analysis. Users are advised to validate imputations against ground-truth data when available and to review the assumptions behind chosen methods.