The growing popularity of digital libraries as a medium for dissemination of scientific knowledge and discoveries has provided easy access to a voluminous amount of research articles, editorials, systematic reviews, thesis and other relevant scientific discoveries. However, with the exponential growth of research articles, researchers face the challenge of efficiently navigating overwhelming amounts of information. This often constitutes the problem of information overload and retrieving information relevant to one’s requirement becomes a daunting and cumbersome task. The current study emphasizes the importance of mining and organizing information driven by information-seeking needs of the researcher in a digital library ecosystem. Bibliometric analysis and traditional scientific recommendation systems are essential methods in providing a necessary starting point amidst overwhelming amount of online information. We employ bibliometric analysis to provide insights into key papers, authors, venues and themes in a particular research area. Further, we develop an algorithm, SPACE-R that amalgamates popularity, semantic similarity and communities in a citation network to recommend scholarly articles. These methods are extremely useful in facilitating the discovery of relevant scholarly works for novice researchers by addressing cold start issues in addition to the information overload problem. While adept at meeting the requirements at a primitive level of inquiry, they are however prone to criticisms surrounding biases and being vulnerable to the concept drift phenomenon. When the objective is to obtain a deeper understanding of the field or when the target user is an experienced researcher with a sufficiently large publication record, these issues become more pressing. To address common biases and ensure all papers get an equal opportunity to garner attention on such platforms, we propose a novel metric to identify “sleeping beauties” in science and differentiate them from other citation distributions. Furthermore, we propose to develop a recommendation protocol that integrates the dynamic interests of researchers and fairness considerations to foster diversity and inclusion in digital libraries. Developing the digital library ecosystem with the robust implementation of these systems could thus provide a platform for researchers to leverage the full potential of these online repositories to make significant strides in their research fields.