Tag: collections

Notes on Orbital v0.2.1

Paul Stainthorp / / Development

A few notes on some of the new features in the latest version of Orbital: these were presented to Dr Bingo Wing-Kuen Ling on 15 June 2012.

  1. ‘Your Projects’ now includes an Activity Timeline of comments and file changes aggregated across all projects in Orbital; each project page also displays a timeline for that project.
    Screenshot of the Orbital timeline
  2. Files from the File Archives can be organised using Collections (which are ‘tag-like’ rather than ‘folder-like’: i.e. a file can belong to more than one Collection).
    Screenshot of Orbital project
  3. You can now edit project information and add new members to a Project. To do this, go to the Project within Orbital, click on the ‘edit’ button, and scroll down to Project Members.
    Screenshot of the Orbital project page
    Screenshot of the Orbital add members section
  4. Finally, a bug which was preventing the upload of files using Internet Explorer has now been fixed.

Orbital v0.2 release

Joss Winn / / Announcements, Development

Today, we released Orbital v0.2, about a month after our v0.1 release. As per the roadmap, Nick and Harry have made good progress on project activity data, user role management, dynamic datasets and, based on user feedback, we’ve added the ability to organise data into collections. You can read the high-level change log or trawl through the project tracker, if you feel inclined. Paul has also made some notes with screenshots on some of the new features.

You’ll notice that there are now APIs for loading data into Orbital’s MongoDB store and querying it, too. This is now in use on a daily basis, retrieving turbine data from Siemens, loading it into Orbital and then running queries on it. It’s very fast. I might add that updates to the data are being versioned, too, so a researcher can query data as it was stored in the past. There’s much more to be done to make Orbital a versatile platform for data analysis during the research process, but the groundwork is in place.

As we identified in our Implementation Plan, we see a workflow whereby data can be selected from a project workspace (e.g. a network drive), loaded into the dynamic datastore, analysed, and then eventually selected for archiving alongside published research papers.