It\u2019s a common clich\u00e9 that a picture is worth a thousand words. I\u2019ve been trying this week to create a visual that represents the metric environment. Using the data life cycle I\u2019ve been examining where potential metrics can be recorded, and what form that those metrics might take. I\u2019ve been looking at the problems with data citation, and the opportunities for metrics within data management plans and scientific work flows. There\u2019s also a lot of recent material on provenance, including the recent webinar. For data metrics good provenance is way to recognize the often complex data contributions that go into projects. If we\u2019re looking at citations as measure of impact we may also need to use the new DataONE provenance tools to provide a truer picture of the source datasets.<\/p>\n
I\u2019m not visually orientated. I\u2019ll generally skip the image for the paragraphs of text that follows, but I recognize the value of a graphic and how it can neatly and concisely express concepts and relationships. Creating it forces you to focus. It is an exercise in winnowing down what is important, and being precise in what you are saying. It turns out that for me, at least, you have to write those thousands of words first!<\/p>\n","protected":false},"excerpt":{"rendered":"
It\u2019s a common clich\u00e9 that a picture is worth a thousand words. I\u2019ve been trying this week to create a visual that represents the metric environment. Using the data life cycle I\u2019ve been examining where potential metrics can be recorded, and what form that those metrics might take. I\u2019ve been Continue reading A picture takes a thousand words<\/span>