The Lens MetaRecord and LensID: An open identifier system for aggregated metadata and versioning of knowledge artefacts

Jefferson, Osmat Azzam; Koellhofer, Deniz; Warren, Ben; Jefferson, R.

doi:10.31229/osf.io/t56yh

Cited by 20 publications

(6 citation statements)

References 1 publication

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“…Lens [41] or OpenAlex [42], it is possible to compose the full meta-data via DOI-matching. Note that storing the DOI is also not perfect since the metadata associated with a DOI can change, for example, an updated mesh term or keyword, but the object it refers to is persistent.…”

Section: Input Datamentioning

confidence: 99%

Reproducibility and Data storage Checklist for Active Learning-Aided Systematic Reviews

Lombaers¹,

Bruin²,

Schoot³

2023

Preprint

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In the screening phase of a systematic review, screening prioritization via active learning effectively reduces the workload. However, the PRISMA guidelines are not sufficient for reporting the screening phase in a reproducible manner. Text screening with active learning is an iterative process, but the labeling decisions and the training of the active learning model can happen independently of each other in time. So it is not trivial to store the data from both events so that you can still know which iteration of the model was used for each labeling decision. Moreover, many iterations of the active learning model will be trained throughout the screening process, producing an enormous amount of data (think of many gigabytes or even terabytes of data), and machine learning models are continually becoming larger. Together this can add up to an undesirable amount of data when naively storing all the data produced at every iteration of the active learning pipeline. This article clarifies the steps in an active learning-aided screening process and what data is produced at every step. We show how this data can be stored efficiently in terms of size. Most notably, the data produced by the model is where we need to strike a balance between reproducibility and storage size. Finally, we created the RDAL-Checklist (Reproducibility and Data storage for Active Learning-aided systematic reviews – checklist) that helps users and creators of active learning software make their screening process reproducible.

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Section: Input Datamentioning

confidence: 99%

Reproducibility and Data storage Checklist for Active Learning-Aided Systematic Reviews

Lombaers¹,

Bruin²,

Schoot³

2023

Preprint

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“…In addition to these 'classic' products, national solutions also exist such as Dialnet (Mateo, 2015) or Latin American alternative such as Scielo and Redalyc. and Lens (Jefferson et al, 2019) which compiles records retrieved from MAS, PubMed, CrossRef, OpenAlex, UnPaywall, and ORCID, among others. Table 1 offers an overview of the content and size of these third-generation bibliometric sources.…”

Section: The Explosion Of Databasesmentioning

confidence: 99%

The bibliometric journey towards technological and social change: A review of current challenges and issues

Torres‐Salinas

Robinson-García

Jiménez‐Contreras

2023

EPI

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The current trends and challenges in the field of bibliometrics are reviewed. To do so, we take the reader along a bibliometric route with six stations: the explosion of databases, the inflation of metrics, its relationship to Data Science, searching for meaning, evaluative bibliometrics, and diversity and profession. This evaluation encompasses three dimensions of the bibliometrics field regarding research evaluation: the technological, the theoretical, and the social. Finally, we advocate for the principles of an evaluative bibliometrics, balancing the power of metrics with expert judgment and science policy.

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“…However, it does not aggregate information on conference series. Lens.org [13] is another web application that integrates data from MAG, Crossref 31 , CORE [14], and PubMed 32 . It enables the analysis of several entities (e.g., authors, institutions, countries, journals, conferences, topics), but it is built on top of MAG and therefore shares the same limitations of Microsoft Academic Search.…”

Section: Related Workmentioning

confidence: 99%

“…The AIDA Schemahttps://w3id.org/aida/ontology12 The CSO Schemahttps://w3id.org/cso/schema/cso13 The MAKG Schemahttps://makg.org/ontology.owl14 The GRID ontologyhttp://www.grid.ac/ontology/…”

mentioning

confidence: 99%

Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature

Angioni

Salatino

Osborne

et al. 2022

Lecture Notes in Computer Science

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Research publishing companies need to constantly monitor and compare scientific journals and conferences in order to inform critical business and editorial decisions. Semantic Web and Knowledge Graph technologies are natural solutions since they allow these companies to integrate, represent, and analyse a large quantity of information from heterogeneous sources. In this paper, we present the AIDA Dashboard 2.0, an innovative system developed in collaboration with Springer Nature to analyse and compare scientific venues, now also available to the public. This tool builds on a knowledge graph which includes over 1.5B RDF triples and was produced by integrating information about 25M research articles from Microsoft Academic Graph, Dimensions, DBpedia, GRID, CSO, and INDUSO. It can produce sophisticated analytics and rankings that are not available in alternative systems. We discuss the advantages of this solution for the Springer Nature editorial process and present a user study involving 5 editors and 5 researchers, which yielded excellent results in terms of quality of the analytics and usability.

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The Lens MetaRecord and LensID: An open identifier system for aggregated metadata and versioning of knowledge artefacts

Cited by 20 publications

References 1 publication

Reproducibility and Data storage Checklist for Active Learning-Aided Systematic Reviews

Reproducibility and Data storage Checklist for Active Learning-Aided Systematic Reviews

The bibliometric journey towards technological and social change: A review of current challenges and issues

Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature

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