Quality of data

Data’s quality assurance

The main objective of ORACLE is to put at the disposal of scientists and operational, data collected during the period of observation. These data require the installation of a quality procedure. This work falls under Irstea policy of quality, established on the whole of its Observation sites, and based on two networks, “Measures and Instrumentation” and “Database”. The quality approach has three essential steps.

1- Maintenance and metrology of ORACLE device:

The metrological aspect with establishment of rigorous protocols for device installation, systematization of the procedures, rigorous and regular maintenance is the first step in our quality approach.

We have set up a database of the ORACLE metrological park with the software installation of maintenance of measurement equipment(SPLIT4®). The technical data of each device, their precise localization on ORACLE, the date of each calibration and the events of maintenance are taken into account by the software. This allows us a better traceability of the equipment and its maintenance, a control of nonconformities and an improvement of the performances of measurement devices.

2- Data processing

The data processing stage includes frequent repatriation of the data, verification of the consistency of the series, as well as verification of the consistency of the variables and parameters with respect to each other. This step is all the more important as it must allow rapid validation of Oracle’s 34,000 monthly data.

1. Repatriation of data

Rainfall and Limnimetric records are automated and directly remotely transmitted on an internal database, every hour.

Limnimetric data are stored after validation on the HYDRO bank, managed by the MEEDDAT.

Other data are repatriated at regular frequency on an internal database (every two months on average).

2. Data validation

All data are validated by the research engineer, manager of the Observatory. Semi-automated routines were set up to allow a faster validation, but also a certain repeatability of the validation.

The use of statistical tools (e.g., average, quartiles…) makes it possible:

I) to carry out an inter-comparison on data set over the full period of observation

II) to check series coherence.

3- Reconstitution of data :

The objective is to bring in work a number of provisions at the managerial and technical level to lead to reliable results and to be able to prove this reliability (concept of confidence). This work is based in particular on recommendations for quality in research, such as the AFNOR document, FDX50-551.

Tools and methods of validation, but also data reconstitution, are based on the experiment and expertise of Irstea on data management, developed for nearly 50 years. In each validated file the validated data and the reconstituted data are labelled.