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DataUniverse

Visual NASA Data Universe

Problem to be solved

The huge amounts of awesome open NASA data currently is not easily discoverable in a visual way in one place. As a user, I need to know pretty clearly what I am looking for, when browsing e.g. data.nasa.gov. The categories on the page are limited (Aerospace, Applied Science, Earth Science, Management/Operations, Space Science)

The data on data.nasa.gov is not complete. The current way of finding data is either by using text search or by asking people directly.

Objectives:

  • Create a(n impressive ;)) visual representation of the data which can easily be explored via a website
  • Structure the data by topics (e.g. Earth, orbit, solar system, deep space, missions, general)
  • Be able to access each piece of API/data in 3 clicks max (3 levels)
  • Possibly use machine learning/crowdsourcing to classify data sources into topics
  • Enable datanauts to easily add newly found APIs and data sources

Use case: I want to play with ISS data: I can select „Orbit“ and find the „ISS“ there. When selecting the ISS, I am seeing different kinds of data I can use: A 3D model of the ISS, experiments that have been performed there, 360 VR videos, the location API.

I have found another interesting link to all missions that have been run so far, which I can add to the data (by drag&drop).

Implementation: This is a first pass at a visual exploration for the 31k NASA APIs.

What it does:

  1. getdata.py gets tags of API data from socrata and saves to data.csv (you can limit the number of APIs that are being requested) //TODO: later on, this should also save the description and URLs so that Datanauts can get to the API of interest fast //TODO: the same info can basically be retrieved from the data.json file, so need to rewrite code to parse it

  2. create_json_files.py creates the nodes and links json files which can be viewed in the data_universe.html //TODO: I wasn't able to delete the last comma in the json files yet, you need to do this manually ;)

Check out the example: 200 data points pulled and written to two json files (nodes and links)