What is Provenance?¶
Provenance is information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness. [W3C_PROV]
In a seismological context provenance can be seen as information about the processes that created a particular piece of data. For synthetic waveforms the provenance information describes which solver and settings therein were used to generate it. When looking at processed seismograms the provenance has knowledge about the different time series analysis steps that led to it.
Provenance information can be derived from different perspectives. Agent-centered provenance describes what people where involved in the creation of a particular piece of data. Object-centered provenance traces the origins of data by tracking the different pieces of information that assembled it. Process-centered provenance finally captures the actions that were taken to generate that particular piece of data.
For the following we will take the process-centered viewpoint as essentially all data in seismology can be described by a succession of different processing steps that created it.
Provenance is a kind of metainformation but there is metainformation that is not considered to be provenance. For example the physical location of a seismic data recording is metadata, but not provenance.
Why it matters¶
Provenance is a key step towards the goal of reproducible research. The final result of many research projects are some papers describing methodology and results. Due to many subjective choices greatly influencing the final result many papers are essentially one off studies that cannot be reproduced. Scientists need to be very disciplined if they aim for reproducible results. This problem only intensifies with increasing amounts of data common in modern research.
Provenance is in theory able to solve this by capturing all information that went into producing a particular result. If we want to advance our science we have to become better at tracking, storing, and exchanging it.
Goal of SEIS-PROV¶
Our goal here is not full reproducibility as too many variables affect the final result. Effects we do not aim to capture are for example floating point math differences on different machines and compilers, errors in CPU operations, and similar, hard to describe effects.
What we strive for with our provenance description is simple:
A scientists looking at data described by our provenance information should be able to tell what steps where taken to generate this particular piece of data.