GEOSS 4: content discovery through inter-institutional common use of keywords
This time we linked institutions through keywords. No need to go any further, this is what we got:
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This time we linked institutions through keywords. No need to go any further, this is what we got:
What if the patterns of institution network, where metadata cite all contributing institutions, can actually lead us to knowledge of what the GEOSS actually contains? Therefore we created a graph that included institutions as well as individual authors. The optimised network layout was hard to read first but we observed several import patterns. The resulting … Read moreGEOSS 3: content discovery through inter-institutional network patterns
When you have millions of datasets from ten thousands data providers, you may wonder a bit what is the overall picture after all. Below you can find two pictures describing the whole architecture using just keywords and keyword co-location. The first picture shows how the keywords cluster and what difficulties we have to discover any … Read moreGEOSS 2: Bird’s eye perspective of the whole GEOSS content
Sometimes we can discover more information in metadata abstracts than in all other fields, especially when we have so many records as GEOSS can provide. This global data sharing architecture boasting having 300 million metadata records on datasets and services is pretty much operational and delivering data on daily basis. Yet, nobody knows really what … Read moreGEOSS post 1: Semantic spaces of textual metadata content