The quality of the data and metadata affects the interoperability of the collections and the quality of all processing. Our metadata quality metric helps the metadata harvester collection administrators detecting and improving the weaknesses of their metadata, and harvesters locating the most problematic collections, in terms of metadata quality, and prompt their administrators to improve their metadata. We extended and used an adaptive quantitative metadata quality metric and a tool to implement it. In controlled values, their value distribution is considered, and in free text values the length of their description. Moreover, we also consider additional information in the OAI-PMH XML responces, that is not normally mapped in metadata elements, but still contains metadata information, such as XML attributes. We used the tool to make quality observations, to examine collections for patterns and irregularities and to produce the appropriate advice for the collection administrators. Some of these observations are demonstrated here. We compared the reported quality over a 3-year period, to get a general quantitative and qualitative feeling of the diversity in the record descriptions, and the changes in their quality during their lifetime. We verified the assumption that the quality increases over time: usually by a tiny amount, in every collection, and by a lot on a small number of collections. Also, the lower quality collections are the ones that stop responding and vanish.