History

Digital Assets Management (DAM) Software

Digital Assets Management (DAM) Software

NOTE: Apparently Queen Mary has an institutional repository (although only for papers at the moment), called QMRO (Queen Mary Research Online). It is built on DSpace . In DSpace, one can create "Communities", which can be for example a Department (e.g. C4DM). It might be a possible option, but it will probably require some customisation of the DSpace installation to accommodate the kind of data produced at C4DM.

Requirements: OpenSource, free, extensible, widely used and actively maintained, versioning
Plus: easy to integrate with Soundsoftware

http://www.opensourcedigitalassetmanagement.org/
http://www.rsp.ac.uk/

  1. Fedora Commons
    • Pros: Store all types of content and its metadata, Multiple, customer driven front-ends (e.g. Drupal), Provide RDF search (SPARQL), Access data via Web APIs (REST/SOAP), Many storage options (database and file systems), Versioning support
    • Cons: relatively complex implementation and deployment process when compared with other alternatives, Java
    • Examples: Ensemble , https://wiki.duraspace.org/display/FCCommReg/Fedora+Commons+Registry
  2. DSpace
    • Pros: developed by HP/MIT, Supports Communities, highly configurable and includes a flexible workflow for applying metadata to assets that will suit complex metadata, used in other projects (DRYAD, Edinburgh DataShare)
    • Cons: no versioning support (planed for the next major release), complex implementation, no support for Drupal front-end
    • Examples: QMRO aka Queen Mary Research Online , DRYAD":http://datadryad.org/about , Edinburgh DataShare , http://www.dspace.org/whos-using-dspace
  3. NotreDAM
    • Pros: easier to set up than Fedora and Dspace, supports many formats and metadata
    • Cons: seems not very advanced in the development, versioning planed for next releases
    • Examples: ?
  4. ResourceSpace
    • Pros: powerful, extensible, PHP based, widely used
    • Cons: not sure if supports versioning, more media-oriented
    • Examples: ?
  5. Mercurial LargeFilesextension
    • Pros: already part of Mercurial, can be easily integrated with sound software.co.uk
    • Cons: not a specific DAM, does not support metadata, searches etc. explicitly
    • Examples: soundsoftware?
  6. Artifactory
    • Pros: Easy to set up
    • Cons: Not a DAM, just a repository
    • Examples: ?
  7. EPrints
    • Pros: apparently easy to set up and manage, integrated front-end interface, extensible (Perl), used by more than 200 organisations, long development (since 2001), University of Southampton
    • Cons: very strong focus on scientific publications, although it can be used for research data
    • Examples: Essex-RDR , http://www.eprints.org/exemplar.php
  8. Zentity
    • Pros: strong support (Microsoft), seems to be flexible, supports open standards
    • Cons: .NET, Powershell, not many users
    • Examples: Homecoming Scotland
  9. DataVerse
    • Pros: made specifically for managing datasets, linking them to publications, and sharing them; developed by Harvard University, supports conversion to open formats, versioning, persistent URI, different levels of access to data, can link to other dataverse networks, integrates with OpenScholar for website creation (based on Drupal)
    • Cons: ?
    • Examples: IQSS Harvard University
Other options to make datasets available online:
  • Public Data Sets on Amazon AWS :Public Data Sets on AWS provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community, and like all AWS services, users pay only for the compute and storage they use for their own applications. The Million Song Dataset can be found here.
  • Infochimps : offers datasets (free and payed) and API to access datasets for applications. The Million Song Dataset is also available here hosted on AWS)
  • UCI Machine Learning Repository : a repository for datasets from the University of California Irvine. Again, the Million Song Dataset (divided into subsets) will be soon available here.
  • MARL (Music and Audio Research Lab at NYU) uses git to distribute their new chord transcription ground truth datasets.