Veracity is a critical dimension of data quality that directly impacts a wide range of tasks. In entity search scenarios, Knowledge Graphs (KGs) such as DBpedia and Wikidata serve as core resources for accessing factual content. The veracity of these KGs is therefore essential for ensuring the reliability and trustworthiness of retrieved entities -- factors that directly influence user confidence in the search system. However, ensuring the truthfulness of entities remains a major challenge due to the complexities associated with the scale, development, and maintenance of KGs.

In Information Retrieval (IR), ground truth creation is a crucial yet resource-intensive task that relies on human experts to build test collections - essential for training and evaluating retrieval models. Large-scale evaluation campaigns, such as TREC and CLEF, demand significant human effort to produce reliable, high-quality annotations. To ease this process, tailored annotation tools are pivotal to supporting assessors and streamlining their workload. To this end, we introduce Doctron, a web-based, dockerized annotation tool designed to streamline ground truth creation for IR tasks. Doctron enables the annotation of both textual documents and images. It supports annotating textual passages, identifying relationships, tagging and linking entities, evaluating document relevance to a topic with graded labels, and performing object detection. It offers a collaborative environment where teams can work with defined user roles and permissions. The integration of Inter Annotator Agreement (IAA) measures helps to identify inconsistencies between annotators, thereby ensuring the reliability and high quality of the annotated ground truth data.

Datasets play a central role in scholarly communications. However, scholarly graphs are often incomplete, particularly due to the lack of connections between publications and datasets. Therefore, the importance of dataset recommendation—identifying relevant datasets for a scientific paper, an author, or a textual query—is increasing. Although various methods have been proposed for this task, their reproducibility remains unexplored, making it difficult to compare them with new approaches. We reviewed current recommendation methods for scientific datasets, focusing on the most recent and competitive approaches, including an SVM-based model, a bi-encoder retriever, a method leveraging co-authors and citation network embeddings, and a heterogeneous variational graph autoencoder. These approaches underwent a comprehensive analysis under consistent experimental conditions. Our reproducibility efforts show that three methods can be reproduced, while the graph variational autoencoder is challenging due to unavailable code and test datasets. Hence, we re-implemented this method and performed a component-based analysis to examine its strengths and limitations. Furthermore, our study indicated that three out of four considered methods produce subpar results when applied to real-world data instead of specialized datasets with ad-hoc features.