We present here a provenance management system adapted to astronomical projects needs. We collected use cases from various astronomy projects and defined a data model in the ecosystem developed by the IVOA (International Virtual Observatory Alliance)
. From those use cases, we observed that some projects already have data collections generated and archived, from which the provenance has to be extracted (provenance on top), and some projects are building complex pipelines that automatically capture provenance information during the data processing (capture inside). Different tools and prototypes have been developed and tested to capture, store, access and visualize the provenance information, which participate to the shaping of a full provenance management system able to handle detailed provenance information.
Recently the International Virtual Observatory Alliance (IVOA) released a standard to structure provenance metadata, and several implementations are in development in order to capture, store, access and visualize the provenance of astronomy data prod
ucts. This BoF will be focused on practical needs for provenance in astronomy. A growing number of projects express the requirement to propose FAIR data (Findable, Accessible, Interoperable and Reusable) and thus manage provenance information to ensure the quality, reliability and trustworthiness of this data. The concepts are in place, but now, applied specifications and practical tools are needed to answer concrete use cases. During this session we discussed which strategies are considered by projects (observatories or data providers) to capture provenance in their context and how a end-user might query the provenance information to enhance her/his data selection and retrieval. The objective was to identify the development of tools and formats now needed to make provenance more practical needed to increase provenance take-up in the astronomical domain.
The Simple Image Access protocol (SIA) provides capabilities for the discovery, description, access, and retrieval of multi-dimensional image datasets, including 2-D images as well as datacubes of three or more dimensions. SIA data discovery is based
on the ObsCore Data Model (ObsCoreDM), which primarily describes data products by the physical axes (spatial, spectral, time, and polarization). Image datasets with dimension greater than 2 are often referred to as datacubes, cube or image cube datasets and may be considered examples of hypercube or n-cube data. In this document the term image refers to general multi-dimensional datasets and is synonymous with these other terms unless the image dimensionality is otherwise specified. SIA provides capabilities for image discovery and access. Data discovery and metadata access (using ObsCoreDM) are defined here. The capabilities for drilling down to data files (and related resources) and services for remote access are defined elsewhere, but SIA also allows for direct access to retrieval.
This document describes the linking of data discovery metadata to access to the data itself, further detailed metadata, related resources, and to services that perform operations on the data. The web service capability supports a drill-down into the
details of a specific dataset and provides a set of links to the dataset file(s) and related resources. This specification also includes a VOTable-specific method of providing descriptions of one or more services and their input(s), usually using parameter values from elsewhere in the VOTable document. Providers are able to describe services that are relevant to the records (usually datasets with identifiers) by including service descriptors in a result document.
