Information For Librarians

The Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) is a fully open access, peer-reviewed scholarly journal. Academic librarians are encouraged to include the journal in electronic collections, institutional repositories, and open access discovery systems.


Open Access & Licensing

All content published in Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Articles may be freely accessed, downloaded, shared, archived, or linked, provided appropriate attribution is given.


No Subscription or Access Fees

Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) does not charge subscription fees or article processing charges (APCs). Libraries and institutions may provide unrestricted access to all published content.


Archiving & Digital Preservation

Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) is committed to long-term digital preservation. The journal plans to implement recognized preservation services such as LOCKSS or CLOCKSS as publication volume grows.


Indexing & Metadata

The journal supports OAI-PMH metadata harvesting and provides structured metadata compatible with library discovery systems.

OAI-PMH Base URL : OAI-PMH Access Endpoint


Repository Inclusion

Librarians may include articles from Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) in institutional repositories, subject repositories, open access databases, and reference services. No additional permission is required, provided proper citation is maintained.


Library Recommendation

Interdisciplinary Journal of AI, Machine Learning & Data Science (IJAIMLDS) may be of interest to faculty, researchers, and students in:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Contact for Librarians

For metadata, indexing, or library-related enquiries, please contact:
Email:  [email protected]