Quilt Data

#FAIR data was a hot topic @bioitworld in Boston. But FAIR is a destination, not a map. The Quilt platform is a means to make FAIR data practical, actionable, and real.

Still time to join PTP, @quiltdata, @awscloud at the #AWS StartUp Loft San Francisco for a deep dive on Connected Labs. Happy Hour following the event at Barcha Restaurant, 28 Fremont Street aws-startup-lofts.com/amer/e/874b8/c… #lifesciences @AWS_Partners

There's a bit of collaborative and technical magic brewing in booth 947 at #AWS #reInvent.

@ypotier @schwessinger @OmicsOmicsBlog So we created Quilt to fill in those gaps. Bioinformatics teams rarely think about data version control apart from source control—but when you do it makes compliance and reporting much easier. Your data are only as good as the number of people that can act upon them.

@ypotier @schwessinger @OmicsOmicsBlog Key requirements for bio data: * Compliance ready * Scientist-friendly * API-accessible * Integrated with private cloud (as opposed to another storage silo) S3 does some of this but lacks time travel, metadata, collection-level version control, and business-user-friendliness.

@patrickmineault We've solved these hub-level problems in the open source. So S3 is to Quilt as git is to GitHub. github.com/quiltdata/quilt

@patrickmineault S3 is already git for data (object versioning, hashes, data already there). It's missing a few things: * Support for versioned collections and time travel * Metadata and schemas for collections * Visual browser (esp for business users) * Data quality lifecycle ("branches")

I'm going to add data tracking (i.e. git for data) to the Good Research handbook. What are people currently using? git-lfs, git-annex, datalad or dvc? Where do you store data, s3, gcs, Azure, or local infra? How has your experience been?

@me_beber @patrickmineault @luispedrocoelho You can run `quilt3 catalog` with the open source CLI to get a feel for the full stack #catalog" target="_blank" rel="nofollow noopener">docs.quiltdata.com/api-reference/…

@patrickmineault @luispedrocoelho I played a bit with @quiltdata which sounds really good but I never ran the full setup.

We're also very pleased to have Aneesh Karve (@akarve) from @quiltdata as a #NextflowSummit speaker! Aneesh will talk about "Nextflow + Quilt: Label, Query and Visualize Pipeline data" 👇 summit.nextflow.io/speakers/anees…

Correction: we're meeting at 6PM. Finish work, party on, and see you there.

Join us for fun, food, and best practices on scientific data management, from discovery to clinical and beyond. Lab automation creates the need to capture, organize, and reuse datasets across wet science and dry science. We'll show you how. Sign up here buff.ly/3yHto5G

Had a great time working on this article and giving input on what labs can do to prepare themselves for a successful digital transformation journey! #Biotech #LabAutomation #ELN #MachineLearning #ELN #LIMS @TetraScience @quiltdata @clustermarket

Special shout out to our partners @PTPCloud for hosting the soiree. We'll see you all next year.

Warm thanks to the customers, friends, and guests who visited our booth and filled our soiree with brilliant conversation. It's a pleasure to work and learn with all of you in #biopharma.

#FAIR data was a hot topic @bioitworld in Boston. But FAIR is a destination, not a map. The Quilt platform is a means to make FAIR data practical, actionable, and real.

@bhadrasandeep @nikillinit @akarve And now you can write stable pipelines against fast-moving buckets and datasets because your pipeline interacts with an immutable, logical view, and not fast-moving files. You can do all of this with the open source quilt3: docs.quiltdata.com

@bhadrasandeep @nikillinit @akarve 💯 we already have terabytes of open bio data on open.quiltdata.com. What else would you like to see? The idea of a "quilt" is to stitch as many data sources as you want into a logical view without copying = infinite integrated datasets.

i get a lot of requests of the same datasets from early stage healthcare companies and many of them end up just rebuilding the dataset themselves or spending lots of $ to buy it (payer segmentation by market, list of early stage investors, comp bands, biotechs by indication, etc)

quilt3 integrates data, metadata, charts, & docs into reusable datasets called packages. The open-source quilt3 catalog allows you to browse files and datasets in your S3 buckets. Learn more > buff.ly/3IdnLxx #datamanagement #datascience

Get bench science & informatics on the same page, looking at the same data in Amazon #S3—all while maintaining usability & convenience. New webinar for growing life science teams with Jim Davis from #AWS, @akarve from Quilt, @ajeskey from @PTPCloud. blog.quiltdata.com/from-zero-to-s…