1. NORMAN Databases Collection of data for EMPODAT – key issues Environmental Institute, Koš, Slovakia

2. Norman Databases Process of data collection & upload Data collection - excel Data Collection Templates (matrices water, waste w., sediments, biota, spm, sewage sludge, soil, air)… DCT example online DCTDCT exampleonline DCT Contacting a data provider –To provide data in the DCT Template –To provide data in their format, to be transformed into DCT Template by the database team Checking data completeness –Returning the filled-in template to the data provider for check Preparation of dataset for upload & upload –Technical check – e.g. the database codes, typing mistakes, etc. –Upload via DCT allows to upload also incomplete data, where not all obligatory fields are filled in

3. Data collection - Problems/gaps Provided Data quality – main problem: missing info in the templates -Data source – obligatory fields not filled: (“easy” metadata like: Organisation (data owner), e-mail; Type of data source – monitoring, survey, research) -Analysis – not obligatory, but usefull: coordinates, national codes; sometimes station names are given only as a code -Analytical method - significant gaps, obligatory fields filled-in only partly, missing info related to quality related info Result: Data are clasified in the lowest quality category Not possible for user to follow-up on the data reasons: data provider does not have it either /too tedious to collect information/ analysis was done by third parties – infomation missing /not for public DCT good example

4. Example http://www.normandata.eu/empodat_detail1.php?id=43340www.normandata.eu/empodat_detail1.php?id=43340

5. http://www.normandata.eu/empodat_detail1.php?id=69000www.normandata.eu/empodat_detail1.php?id=69000 Example

6. Data collection - Problems/gaps Solution needs to be find for: -Data already uploaded (in planning process) -Exchanged datasets of insufficient quality -Individual corrections via online forms -Data in pipeline -Requesting data owner to provide as much data as possible -Data provided in the future -discuss in advance with the data provider, what is the Norman database strucutre -Do not accept data without required metadata

7. Data collection – Data in the pipeline MODELKEY data – water & sediment (about 260K data) VEOLIA data – water (about 60K data) BRGM data – water (about 5K data) Missing : Data source/monitoring type Analysis: sampling parameters – geographical/analytical Information about the analytical method (QC/QA information about chemical data) IVL data – water & sediment & biota (about 31K data) Missing : Analysis: sampling parameters – geographical/analytical Information about the analytical method (QC/QA information about chemical data)

8. Data collection - Problems/gaps Data collection – main problems Data providers may found the DCTs too complicated Time consuming to prepare DCT ready for upload (needs several rounds going back to data provider for missing information) Available info do not match exactly with required info in DCT Datasets are too large Solutions: We offer to convert the data provided from any format to DCT (access or other excel form) Clarification of DCTs with the data provider For really large datasets or regularly updated datababses a technical sollution needs to be developed for automated data transfer, IT interfaces can be created if necessary

9. Data collection - Summary Development of a process for improvement of the data quality / rules for data acceptance Development of a strategy/agreement for the data collection: –Who should provide the data –In which form the data will be provided –When the data should be provided – annual basis? –Other consideration?