Author : Ibraheem Mohammed Sultan Al Sadi
Within nearly two decades after the open access movement emerged, its community has drawn attention to understanding its development, coverage, obstacles and motivations. To do so, they depend on data-centric analytics of open access publishing activities, using Web information space as their data sources for these analytical activities.
Open access repositories are one such data source that nurtures open access publishing activities and are a valuable source for analytics. Therefore, the open access community utilises open access repository infrastructure to develop and operate analytics, harnessing the widely adopted Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) interoperability layer to develop value-added services with an analytics agenda.
However,this layer presents its limitations and challenges regarding the support of analytical value-added services. To address these practices, this research has taken the step to consolidate these practices into the ‘open access analytics’ notion of drawing attention to its significance and bridge it with data analytics literature.
As part of this, an explanatory case study demonstrate show the OAI-PMH service provider approach supports open access analytics and also presents its limitations using Registry of Open Access Repositories (ROAR) analytics as a case study.
The case study reflects the limitation of open access registries to enable a single point of discovery due to the quality of their records and complexity of open access repositories taxonomy, the complexity of operationalising the unit of analysis in particular analytics due to the limitations in the OAI-PMH metadata schemes, the complex and resource-intensive harvesting process due to the large volume of data and the low quality of OAI-PMH standards adoptions and the issue of service provider suitability due to a single point of failure.
Also, this doctoral thesis proposes the use of Open Access Analytics using Open Access Repository Data with a Social Machine (OAA-OARD-SM) as a conceptual frame work to deliver open access analytics by using the open access repository infrastructure in acollaborative manner with social machines.
Furthermore, it takes advantage of the web observatory infrastructure as a form of web-based mediated technology to coordinate the open access analytics process. The conceptual framework re-frames the open access analytics process into four layers: the open access repository layer, the open access registry layer, the data analytics layer and open access analytics layer.
It also conceptualises analytics practices carried out within individual repository boundaries as core practices for the realisation of open access analytics and examines how the repository management team can participate in the open access analytics process.
To understand this, expert interviews were carried out to investigate and understand the analytics practices within the repository boundaries and the repository management teams’ interactions with analytics applications that are fed by the open access repository or used by repository management to operate open access analytics.
The interviews provide insight into the variations in the types of analytic practices and highlight the active role played by the repository management team in these practices. Thus, it provides an understanding of the analytics practices within open access repositories by classifying them into two main categories: the distributed analytical applications and locally operated analytics.
The distributed analytics application includes cross repository OAI-based analytics, cross-repository usage data aggregators, solo-repository content-centric analytics and solo-repository centric analytics.
On the other hand, the locally operated analytics take forms of Current Research Information System (CRIS),repository embedded functionalities and in-house developed analytics. It also classifies the repository management interactions with analytics into four roles: data analyst, administrative, data and system management, and system development and support.
Lastly, it raises concerns associated with the application of analytics on open access repositories, including data-related, cost-related and analytical concerns.