Hey Hunters! Joe from data.world here.
As a special treat for you, we've compiled the most comprehensive PH dataset ever released.(https://data.world/producthunt/p...). 2 years of posts, votes, taglines, and more! Dig in - we can't wait to see what insights you find!
And a bit about the platform...
We're all about helping data people solve problems faster, so we've built a collaboration platform to address a glaring, urgent need...
With hundreds of killer visualization and analysis tools out there, why are we stuck in the stone age when it comes to the most frustrating and time-consuming parts of any data project: finding, understanding, preparing, and sharing data?
data.world tackles this issue by helping you discover, explore, contribute, and share/publish---better, faster, easier, and all in one place.
Discover:
Browse thousands of open datasets contributed by organizations and data people from all over the world.
Explore:
See the data's "story" alongside the data itself. Preview the data before you dive in. Query within and across datasets, and create exploratory visualizations with just a few clicks.
Contribute:
Join the discussion with an international community of data people. Post hunches, share analysis techniques and insights, and find new collaborators.
Share / Publish:
Upload from your computer or pull down from the cloud. Automatically enhance your data, make it instantly queryable and joinable to other datasets. Showcase your work and build out your data work portfolio.
Please don't hesitate to share any questions or feedback right here. We'll be online 😎
Thanks, Hunters, and welcome to the social network for data people!
-Joe Boutros
https://data.world/jboutros
Hey @contextjunkie!
I'm not familiar with SPARQL - How's it different from sql? Do people need to know it to use dataworld?
Also - saw dwsql, which syntax already looks very familiar/identical to sql. What are the differences? What's each used for?
@mscccc@contextjunkie
Hi Mike,
Where SQL is the language of choice for tabular and relational data, SPARQL is more well suited to pattern matching across linked data (RDF, Semantic Web, etc). The languages look somewhat similar, but serve distinct purposes. We believe that linked data is an important part of the future of open data. We've put together an awesome SPARQL tutorial for those who want to learn more: https://docs.data.world/tutorial...
We created dwSQL to make the powerful querying and joining capabilities of linked data accessible to anyone who knows SQL. Our implementation is quite full featured. We support the vast majority of SELECT style queries: including joins, aggregation, sorting, limits, etc. You can learn more here: https://docs.data.world/tutorial...
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Awesome product, this is like a github for data!
Curious how you've thought about the opportunity to accelerate learning for the next generation of people just starting to get into the data world.
@andrewaward This question couldn't be more astute. The key to our ongoing success will heavily rely the next generation of data people. To that end, we are active in forming relationships at universities via speaking engagements, hosted class projects, and capstone sponsorships.
Github provided a great platform for coders to create a portfolio of their work; we are that portfolio and repository for data folks. This is especially important as people start their careers in data.
We are also building partnerships with organizations like Data Society and Coding4TX, which focus on an even younger, emerging data people crowd.
@kevando_ thanks for the kind words! There are a lot of other great platforms approaching these types of 'first mile' data problems, Datazar among them. What became super clear to us last year when we started data.world is that the time is right for data science to undergo the same transformation effect that open source software did with the rise of github.
FB LIVE in 30! @contextjunkie will take us on a wild ride through our top 5 features, and answer your questions. I'm trying to get him to wear a black turtleneck but no promises. See you soon!
https://www.facebook.com/datadot...
Thanks for the question, @jt_singh! At data.world our community is 100% focused around collaboration and the efficiencies that can be unlocked when people and data start working together. Our aim is to get data people of all stripes working together - from journalists and designers to data scientists and researchers. Kaggle was a true pioneer in the area of machine learning competitions, so you can see the major difference in approach - collaboration vs. competition, appeal to all data people vs. those with a specific machine learning focus.
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got it thanks for the clear response. ;)
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This is huge! I second the effort to partner with research universities and other groups tackling tough problems. Using data.world could make a big difference, and the infrastructure to utilize complex data sets is already there (I would have loved to have this on many of the projects I was working on). Thank you!
@renastake Thank you so much, Rena! We have visited many universities to help them with data.world and it feels really good to help in that way as a Public Benefit Corporation and B Corporation.
The post-truth world in which we seem to have found ourselves is pretty scary. So I'm happy we're doing what we're doing to enable evidence-based solutions to the world's problems.
@arlogilbert Thanks so much, Arlo - we appreciate your kind words. We are launching a lot of functionality each and every week, so it will keep getting better and better quickly!
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