Fair Trade Data

coffe from the worldThe rise of Fair Trade food and other products has been amazing over the past 4 years. Indeed, it’s great to see how certification for the origins (and production processes) of products  is becoming both prevalent and expected. For me, it’s nice to know where my morning coffee was grown and indeed knowing that lets me figure out the quality of the coffee (is it single origin or a blend?).

I now think it’s time that we do the same for data. As we work in environments where our data is aggregated from multiple sources and processed along complex digital supply chains, we need the same sort of “fair trade” style certificate for our data. I want to know that my data was grown and nurtured and treated with care and it would be great to have a stamp that lets me understand that with a glance without having to a lot of complex digging.

In a just published commentary in IEEE Internet Computing, I go into a bit more detail about how provenance and linked data technologies are laying the ground work for fair trade data. Take a look and let me know what you think.

 

 

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5 comments
  1. The “fair trade” concept for understanding data is great! Associating the complicated supply chain of coffee with that of data is clever and apt. It’s why more “librarians” need to be involved in the semantic web. After all, evaluating resources has always been hallmark of the profession. I suspect that the explosion of Open access publishing has raised a lot of similar issues. Did you come across those discussions at UKSG? Itt never hurts to reinforce the traditional basics of resource evaluation: – authority, accuracy, currency, point of view (objectivity), coverage, relevance, format, as outlined very well in these quickly-found pages…

    http://library.uwb.edu/guides/eval.html

    http://www.noblenet.org/merrimack/FYS_eval.pdf

    Thanks to @kersfors for pointing me to your blog.

  2. paulgroth said:

    I didn’t hear those discussions at UKSG but I was focused on metrics there. I think there were some

    There’s quite a lot of discussion around this see: http://rigourandopenness.org/home/

  3. Alex Hillsberg said:

    I’ve had researched data for popular media for topics ranging from how an iPad is made to a hypothetical look at the coffee supply chain from farm to retail as in this example: http://financesonline.com/heres-how-you-make-coffee-a-billion-dollar-business/ Albeit I would have wanted to solicit untainted, raw data, some of my reference is sourced from second-hand and, hopefully not, sanitized figures. I could only cross my fingers. At the back of my mind though, my objective is to help my readers understand a process more than prove the veracity of absolute figures and facts, and as a writer, I make a judgment call that pass-around data without a “fair trade” seal is still usable as café Americano on a second press. The caffeine still kicks in. However, I recognize that pass-around data is risky, too, especially in matters of scientific research and, yes, news. It’s comforting to know there are projects like fair trade data; that experts in this field are pushing to guard the quality of data. Let me just be honest, the open access publishing has opened thousands of gateways to flood us with data; we can only stamp so much fair trade seal on a number of these gates. Don’t you think we will still need to rely on discernment and fair judgment in using data more responsibly?

  4. paulgroth said:

    I agree that discernment and judgement is critical but I think providing a first level filtering mechanism is important.

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