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Toward a Standardized Semantic Representation of Research Datasets in the Open Research Knowledge Graph

Core Concepts
This work proposes the ORKG-Dataset content type, a specialized branch of the Open Research Knowledge Graph (ORKG) platform, to provide a standardized framework for recording and reporting research datasets in a structured, semantic manner, integrating them with their accompanying scholarly publications.
The paper presents the design principles and implementation of the ORKG-Dataset content type, which aims to improve the discoverability and reusability of research datasets. Key aspects include: Standardized Nomenclature: Establishing a controlled vocabulary and ontology for research datasets, reusing concepts from existing metadata ontologies like Use of Templates: Defining a form-based template with a set of relevant predicates to maintain consistent formatting when recording new research datasets. FAIR Standards Compliance: Ensuring the ORKG-Dataset model adheres to the FAIR (Findable, Accessible, Interoperable, Reusable) principles for scientific data. The authors demonstrate the application of the ORKG-Dataset content type on 40 research datasets in the field of natural language processing for scientific information extraction. This allows for structured representation of key facets like research problems, statistical attributes, quality indicators, performance benchmarks, and metadata. The structured data enables advanced search and querying capabilities, such as bibliometric views, dataset-specific searches, and state-of-the-art model comparisons.
The 40 research datasets span the years 2011 to 2022. The datasets cover various sub-problems in scientific information extraction, including citation classification, sentence classification, relation extraction, and knowledge graph construction. The ORKG-Dataset content type models 9 relevant statistical properties and allows specifying evaluation scores and metrics using standardized QUDT vocabulary. The ORKG-Dataset content type reuses 19 relevant properties from the ontology.
"Search engines these days can serve datasets as search results. Datasets get picked up by search technologies based on structured descriptions on their official web pages, informed by metadata ontologies such as the Dataset content type of" "Studies show that, in academia, the predominant search pattern for research datasets is either a serendipitous event of finding a dataset when reading scholarly publications or actively searching for datasets in publications." "The ORKG-Dataset publishing model presents a next-generation skimming device of scholarly contributions, that permits viewing their semantic representations in a similar way to comparisons of products on e-commerce websites."

Deeper Inquiries

How can the ORKG-Dataset content type be extended to capture additional contextual information about research datasets, such as their provenance, licensing, and ethical considerations?

To enhance the ORKG-Dataset content type to encompass more contextual information about research datasets, several extensions can be implemented. Provenance: Including provenance information can be achieved by incorporating properties that detail the origin, history, and ownership of the dataset. This can involve capturing metadata on data creators, data collection methods, data processing steps, and versioning information. Licensing: To address licensing aspects, additional properties can be introduced to specify the type of license under which the dataset is released. This can involve linking to standard licensing schemas or providing a structured description of the usage rights associated with the dataset. Ethical Considerations: Incorporating ethical considerations can involve defining properties that outline any ethical guidelines followed during data collection, processing, and sharing. This can include information on data privacy, consent, anonymization techniques, and compliance with ethical standards or regulations.

What are the potential challenges in incentivizing researchers and dataset publishers to adopt the ORKG-Dataset standard, and how can these be addressed?

Several challenges may arise in incentivizing researchers and dataset publishers to adopt the ORKG-Dataset standard: Awareness and Education: Many researchers may not be familiar with semantic publishing or the benefits of structured dataset representations. Providing training, workshops, and educational resources can help raise awareness and encourage adoption. Integration with Existing Workflows: Researchers may be hesitant to adopt new standards if they disrupt their existing workflows. Seamless integration tools, plugins, and documentation can facilitate the transition to the ORKG-Dataset standard. Time and Effort: Creating structured representations of datasets can be time-consuming and require additional effort. Providing tools, templates, and automated processes to assist in metadata creation can mitigate this challenge. Incentives and Recognition: Researchers may need incentives to invest time in adopting new standards. Providing recognition, citations, and visibility for datasets published in the ORKG can motivate researchers to comply.

How can the structured dataset representations in the ORKG be leveraged to enable novel data-driven applications and services in the scholarly ecosystem?

The structured dataset representations in the ORKG offer a wealth of opportunities for enabling innovative data-driven applications and services in the scholarly ecosystem: Advanced Search and Discovery: The structured metadata can power advanced search functionalities, allowing researchers to discover relevant datasets based on specific criteria, such as research problems, statistical attributes, and quality indicators. Bibliometric Analysis: Researchers can conduct bibliometric analyses by leveraging the rich metadata to track citation statistics, identify highly cited datasets, and analyze trends in dataset usage over time. Model Training and Evaluation: The structured representations can facilitate model training by providing detailed information on ground-truth datasets, performance benchmarks, and evaluation metrics. Researchers can use this data to train and evaluate machine learning models effectively. Ethical Data Usage: By including ethical considerations in the dataset representations, data-driven applications can ensure compliance with ethical guidelines, privacy regulations, and data protection laws, promoting responsible data usage in research and innovation.