[okfn-labs] Frictionless Data Vision and Roadmap

[Rufus Pollock]

Hmmm, good question.

- https://github.com/datasets has a good selection of data packages but not
sure any one exploits all of the spec

- Double line breaks: not sure any of the "core" datasets have this but
have certainly  worked with double line breaks (that is more about your CSV
parser than the data package). I'd just suggest creating one for your
testing purposes

Anyway good questions and perhaps we should create an exemplar test data
package along the lines you suggest - we already have some example data
packages on https://github.com/datasets with prefix "ex-" and this would
make a good addition.

Rufus

On Saturday, 22 February 2014, Matthew Fullerton <matt.fullerton at gmail.com>
wrote:

> Hi Rufus,
> Is there a data package available which exploits all aspects of the
> specification and has exotic things in the CSV (double line breaks!?)
> for testing purposes?
>
> Thanks,
> Matt
>
> On 23 January 2014 10:51, Rufus Pollock <rufus.pollock at okfn.org> wrote:
> > Hi Matt,
> >
> > That would be fantastic. I note some initial work has been done so you
> > already have a bit of a start on this. More info in this issue:
> >
> > https://github.com/okfn/data.okfn.org/issues/24
> >
> > Plus you can check out the current appscript "macro"
> >
> > Rufus
> >
> >
> >
> >
> > On 22 January 2014 23:02, Matthew Fullerton <matt.fullerton at gmail.com>
> > wrote:
> >>
> >> I really like the vision. I could work on the Google Spreadsheets
> >> "Integration".
> >>
> >> -Matt
> >>
> >>
> >> On 21 January 2014 15:03, Rufus Pollock <rufus.pollock at okfn.org> wrote:
> >>>
> >>> There is now a short Frictionless Data "vision" doc online at:
> >>>
> >>> http://data.okfn.org/vision
> >>>
> >>> It is based on input from various people and comments would be warmly
> >>> welcome. I've excerpted some of it below for those who prefer info in
> the
> >>> mail client.
> >>>
> >>> Regards,
> >>>
> >>> Rufus
> >>>
> >>> ## Frictionless Data Ecosystem
> >>>
> >>> There's too much friction working with data - friction getting data,
> >>> friction processing data, friction sharing data.
> >>>
> >>> This friction stops people doing stuff: stops them creating, sharing,
> >>> collaborating, and using data - especially amongst more distributed
> >>> communities.
> >>>
> >>> It kills the cycles of find, improve, share that would make for a
> >>> dynamic, productive and attractive (open) data ecosystem.
> >>>
> >>> We need to make an ecosystem that, like open-source for software, is
> >>> useful and attractive to those without any principled interest, the
> vast
> >>> majority who simply want the best tool for the job, the easiest route
> to
> >>> their goal.
> >>>
> >>> We think that by getting a few key pieces in place we can reduce
> friction
> >>> enough to revolutionize how the (open) data ecosystem operates with
> >>> massively improved data quality, utilization and sharing.
> >>>
> >>> We think this because there's a multiplier here that means relatively
> >>> small changes can have big effects. This multiplier is Network
> effects: the
> >>> utility of a particular standard, pattern or even tool depends on how
> many
> >>> other people are using it. This means that creating a critical mass of
> use
> >>> around the tooling and standards will have a huge effect. This isn't
> easy.
> >>> But after working on these issues for nearly a decade we think the
> time is
> >>> right.
> >>>
> >>> ## A Metaphor
> >>>
> >>> Today, when you decide to cook, the ingredients are readily available
> at
> >>> local supermarkets or even already in your kitchen. You don't need to
> travel
> >>> to a farm, collect eggs, mill the corn, cure the bacon etc - as you
> once
> >>> would have done! Instead, thanks to standard systems of measurement,
> >>> packaging, shipping (e.g. containerization) and payment ingredients
> can get
> >>> from the farm direct to my local shop or even my door.
> >>>
> >>> But with data we're still largely stuck at this early stage: every time
> >>> you want to do an analysis or build an app you have to set off around
> the
> >>> internet to dig up data, extract it, clean it and prepare it before
> you can
> >>> even get it into your tool and begin your work proper.
> >>>
> >>> What do we need to do for the working with data to be like cooking
> today
> >>> - where you get to spend your time making the cake (creating insights)
> not
> >>> preparing and collecting the ingredients (digging up and cleaning
> data)?
> >>>
> >>> The answer: radical improvements in the "logistics2 of data associated
> >>> with specialisation and standardisation. In analogy with food we



-- 


*Rufus PollockFounder and Executive Director | skype: rufuspollock |
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