datatogether / learning Goto Github PK
View Code? Open in Web Editor NEWLearning materials for Data Together
Home Page: https://datatogether.github.io/learning/
License: Creative Commons Attribution Share Alike 4.0 International
Learning materials for Data Together
Home Page: https://datatogether.github.io/learning/
License: Creative Commons Attribution Share Alike 4.0 International
Currently the materials are active at:
It doesn't look like adding a CNAME with archivers.co
into gh-pages
branch helps us maintain the /learning/ path... I'm not too sure but I think we fairly soon want to change where we serve content, and rethink the redirect structure
TODOs to mark as complete:
datatogether.org/learning
?)Prepare advance packet for workshop attendees and send it out.
Draft of Pre-materials to send out:
Supplies to bring:
Create a chapter introducing custom crawls on Data Together
Sections:
cc @ebenp
The gitbook lesson we're putting together suggests that people can optionally register on Data Together, but doesn't explain why they would want to do that. What are the benefits of signing up?
Adding a dataset means [...]
Harvesting a dataset means [...]
Storing a dataset means [...]
Public Record means [...]
Data Together Nodes means [...]
Distributed Data Stewardship means [...]
In the short term we should just add these definitions inline into #17 in steps 4 & 5.
Write a tutorial, based on the style of the dweb primer, showing how to replicate a dataset.
General steps
Follow-up info:
We know there are some Boston-based Data Together people (cc @jeffreyliu) or those that are interested in participating/learning more (@titaniumbones).
We should reach out directly about attending the event
Write a tutorial, modeled on the dweb primer, showing how to add a dataset to Data Together
Things to address in addition to the how:
Post a survey for workshop attendees to fill out in advance. When ready, send out an email inviting attendees to fill it out.
Questions:
What datasets do you rely on? keywords + 2 or 3 URLs
If someone signs up on archivers.co, there is currently no explanation of a privacy and security policy. We should probably have one.
There are probably plenty of starting points. Here's one I wrote for something else, to give us some ideas about the topics that probably should be discussed: http://sbml.org/Facilities/Documentation/Privacy_notice_and_terms_of_service_for_the_Online_SBML_Validator
Also, such things typically need to be vetted by lawyers (or at least that's a requirement whenever I put up an online service at my institution – might be different in this context).
Write a tutorial, based on the style of the dweb primer, showing how to browse through the backed up datsets and how to make sense of the information you see.
This tutorial will need to be updated as the tools and UI evolve...
Identify people to be "station" leads for the hands-on portion of the workshop.
Must make sure we have people who can help participants use command line on Windows
Note: We know that some people won't get past station 1. That's ok. Getting exposure to the command line in an encouraging, supportive environment is an extremely valuable and empowering learning experience.
Pre-Harvest URLs identified in RSVP responses, so people have something to find & replicate in the tutorials.
On guest networks, the IPFS ports are likely to be locked down. This makes "Replicate a Dataset" hard to do. Should we figure out a workaround?
The "Replicate a dataset" tutorial presents an essential part of the DT platform -- a mechanism that allows an individual or entity to assume direct responsibility fr the health of a dataset or collection.
This is a conceptually important and without it we can't give a complete account of the DT vision. However, the current implementation is difficult to work with, for at least the following reasons:
Proposal: let's keep this tutorial around but only break it out when we're talking to people who are directly concerned with computing infrastructure. This means people like sysadmins, data managers, and maybe digital project archivists & librarians. This audience can really benefit from a more technical introduction.
Meanwhile, for other audiences, let's craft a new tutorial as soon as the app-internal ipfs node is implemented. We can walk through similar tasks and invite participants to start participants to start contributing to the distributed web via DT, and point the enthusiastic to the command-line version for an in-depth look.
Design icebreaker / interactive performance around an abstract concept like "where the data goes," "distributed hashtables," or other.
Takeaway for participants: ability to see your own role in a larger ecosystem where people hold the data that they care about rather than relying on current institutions and structures
prior art by @dcwalk : https://github.com/dcwalk/performingmesh
Make sure this repo has the following files:
README.md
LICENSE
.github/CONTRIBUTING.md
.github/ISSUE_TEMPLATE.md
This issue forms part of a project-wide meta-issue
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.