## 2 Materials and Methods
### Description of Data  
Increasing human use of marine and coastal areas can affect marine ecosystems through various types of physical, chemical, and biological disturbances and contamination by hazardous substances. In particular, in the Adriatic and Ionian Seas, the general increase in maritime traffic, the growing urbanization of coastal areas, and the likely increase in offshore oil and gas production pose a serious risk of pollution by hazardous substances for several coastal countries [16]. The HarmoNIA [17] project datasets on hazardous substances in sediment, biota and water column were produced with the help of the EU EMODnet initiative for the management and provision of fragmented marine data and within the framework of the HarmoNIA project. The datasets cover the Adriatic and Ionian Seas and span the period 1980-2017. These data come from 10 different institutions. The data were collected at 2149 stations, which were sampled over 4282 times. Sampling was performed by research ships in the frame of various projects and national monitoring programs. This results in a final number of 95,231 data values related to 510 different parameters in 22 groups. Parameters include Metals and metalloids (in biota, sediment and water), Pesticles and biocides (in biota, sediment and water), PAHs (in biota, sediment and water), Antifoulants (in biota and water), Polychlorinated biphenyls (in biota and water) and Halophenols, Hydrocarbons and Radionuclides in water. All data are quality tagged using a common approach. The datasets contain some restricted access data (by negotiation or academic--6010 out of 95,231). These data are not displayed as individual values, but are used for statistical calculations. The dataset was extracted from EMODnet in the form of the files and then bulk inserted into the relational database for the purpose of data visualizations. EMODnet uses SeaDataCloud infrastructure [18] containing a central metadata database and distributed data sources. It was beyond this case study to modify SeaDataCloud infrastructure, but as data are fetched in JSON format, the same code with minor modifications can be used to fetch data from many different sources using JSONP. Environmental data are generally dispersed in space and time. Monitoring stations are, in theory, places where measurements are made regularly as part of a monitoring program. However, there may also be locations  
Figure 4: Schematic view of changed “two way” data flow. where only one-time measurements are made and cannot be used to detect a trend. A set of stations measured at approximately the same time or averaged over a period of time can be used for spatial interpolation of a particular parameter. A set of environmental measurements for a given parameter should provide this information:  
* Where are the hot-spots or locations with increased values;
* What is the trend for each location, especially for hot-spots;
* Are the values, and which ones are over some thresholds or maximum values;
* What is the category (excellent, good, poor, etc.) of each value if categories are defined for this parameter (and area). The easiest and most intuitive way to obtain these answers is through good data visualization. The type of data determines the type of graph [19] used, and we use line, column, and stacked category charts. Simple statistical processing is not good enough. Values should be associated with locations, and the importance of elevated or incidental values or pollution is associated with location preferences. Areas with greater socioeconomic values or protected natural areas have greater importance. Besides the pure value, metadata should also be displayed. We can explain the necessary metadata with the term 4W, which stands for Where, When, What, and Who. These aspects of metadata are considered as minimum metadata to achieve good data management.