The recent developments in the research area of metadata and electronic resource management highlight a significant shift towards enhancing the accessibility, interoperability, and reusability of scientific documents and data. A common theme across the studies is the exploration of innovative methods to improve the accuracy and efficiency of metadata extraction and usage assessment. This includes the application of natural language processing, computer vision, and multimodal approaches for metadata extraction from scholarly documents with high template variance. Additionally, there is a growing emphasis on the importance of well-documented metadata in a FAIR manner, with efforts to standardize metadata collection and annotation processes through the use of ontologies and semantic web technologies. The studies also address the challenges of metadata completeness and the integration of publication and funding metadata across different data systems, suggesting necessary actions for authors and publishers to ensure comprehensive metadata capture. Furthermore, the detection of metadata manipulations and the analysis of library metrics for ebook usage underscore the need for reliable and transparent metadata practices in the scholarly communication ecosystem.

Noteworthy Papers

• Detection of metadata manipulations: Finding sneaked references in the scholarly literature: Introduces methods to identify sneaked references in metadata, highlighting a significant issue in scholarly communication integrity.
• Comparison of Feature Learning Methods for Metadata Extraction from PDF Scholarly Documents: Evaluates various feature learning methods for metadata extraction, offering insights into improving document accessibility and adherence to FAIR principles.
• An ontology-based description of nano computed tomography measurements in electronic laboratory notebooks: Presents a novel approach to metadata collection and annotation, demonstrating the potential for standardized, FAIR-compliant data management in scientific research.