FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services

A Unified Nanopublication Model for Effective and User-Friendly Access to the Elements of Scientific Publishing

Scientific publishing is the means by which we communicate and share scientific knowledge, but this process currently often lacks transparency and machine-interpretable representations. Scientific articles are published in long coarse-grained text with complicated structures, and they are optimized for human readers and not for automated means of organization and access. Peer reviewing is the main method of quality assessment, but these peer reviews are nowadays rarely published and their own complicated structure and linking to the respective articles is not accessible. In order to address these problems and to better align scientific publishing with the principles of the Web and Linked Data, we propose here an approach to use nanopublications as a unifying model to represent in a semantic way the elements of publications, their assessments, as well as the involved processes, actors, and provenance in general. To evaluate our approach, we present a dataset of 627 nanopublications representing an interlinked network of the elements of articles (such as individual paragraphs) and their reviews (such as individual review comments). Focusing on the specific scenario of editors performing a meta-review, we introduce seven competency questions and show how they can be executed as SPARQL queries. We then present a prototype of a user interface for that scenario that shows different views on the set of review comments provided for a given manuscript, and we show in a user study that editors find the interface useful to answer their competency questions. In summary, we demonstrate that a unified and semantic publication model based on nanopublications can make scientific communication more effective and user-friendly

Using nanopublications as a distributed ledger of digital truth

With the increase in volume of research publications, it is very difficult for researchers to keep abreast of all work in their area. Additionally, the claims in classical publications are not machine-readable making it challenging to retrieve, integrate, and link prior work. Several semantic publishing approaches have been proposed to address these challenges, including Research Object, Executable Paper, Micropublications, and Nanopublications. Nanopublications are a granular way of publishing research-based claims, their associated provenance, and publication information (metadata of the nanopublication) in a machine-readable form. To date, over 10 million nanopublications have been published, covering a wide range of topics, predominantly in the life sciences. Nanopublications are immutable, decentralised/distributed, uniformly structured, granular level, and authentic. These features of nanopublications allow them to be used as a Distributed Ledger of Digital Truth. Such a ledger enables detecting conflicting claims and generating the timeline of discussion on a particular topic. However, the inability to identify all nanopublications related to a given topic prevent existing nanopublications forming a ledger. In this dissertation, we make the following contributions: (i) Identify quality issues regarding misuse of authorship properties and linkrot which impact on the quality of the digital ledger. We argue that the Nanopub community needs to be developed a set of guidelines for publishing nanopublications. (ii) Provide a framework for generating a timeline of discourse over a collection of nanopublications by retrieving and combining nanopublications on a particular topic to provide interoperability between them. (iii) Detect contradictory claims between nanopublications automatically highlighting the conflicts and provide explanations based on the provenance information in the nanopublications. Through these contributions, we show that nanopublications can form a distributed ledger of digital truth, providing key benefits such as citability, timelines of discourse, and conflict detection, to users of the ledger

Reliable Granular References to Changing Linked Data

Nanopublications are a concept to represent Linked Data in a granular and provenance-aware manner, which has been successfully applied to a number of scientific datasets. We demonstrated in previous work how we can establish reliable and verifiable identifiers for nanopublications and sets thereof. Further adoption of these techniques, however, was probably hindered by the fact that nanopublications can lead to an explosion in the number of triples due to auxiliary information about the structure of each nanopublication and repetitive provenance and metadata. We demonstrate here that this significant overhead disappears once we take the version history of nanopublication datasets into account, calculate incremental updates, and allow users to deal with the specific subsets they need. We show that the total size and overhead of evolving scientific datasets is reduced, and typical subsets that researchers use for their analyses can be referenced and retrieved efficiently with optimized precision, persistence, and reliability