Digital Infrastructures for Scholarly Content Objects

As digital libraries make the dissemination of research publications easier, they also enable the propagation of invalid or unreliable knowledge. Examples of relevant problems include: retraction and inadvertent citation and reuse of retracted papers [1], [2]; propagation of errors in literature and...

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Vydané v:2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) s. 346 - 347
Hlavní autori: Schneider, Jodi, De Waard, Anita, Balke, Wolf-Tilo, Wang, Xiaoguang, Song, Ningyuan, Hua, Bolin, Fu, Yuanxi
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2021
Predmet:
argumentation
Cognition
Data mining
digital infrastructure
fact checking
Information retrieval
innovation in publishing
knowledge updates
Libraries
living documents
metadata
ontologies
quality assessment
quality assurance
reproducibility
robustness
scholarly database curation
scholarly knowledge graphs
semantic publishing
Systematics
Visualization
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Popis
Shrnutí:As digital libraries make the dissemination of research publications easier, they also enable the propagation of invalid or unreliable knowledge. Examples of relevant problems include: retraction and inadvertent citation and reuse of retracted papers [1], [2]; propagation of errors in literature and scientific databases [3], [4]; non-reproducible papers; known domain-specific issues such as cell line contamination [5]; bias in research datasets and publications [6]-[8]; systematic reviews that arrive at different conclusions about the same question at the same time [9], [10]. The digital environment facilitates broad interdisciplinary reuse beyond the originating scientific community; thus, marking known problems and tracing the impact on dependent and follow-on works is particularly important (but still under-addressed). Further, context-specific information inside a paper may not be immediately reusable when extracted by automated processes, leading to apparent contradictions [11]. Current mitigating approaches use the underlying reasoning for information retrieval [12], [13], develop new infrastructures analyzing the reasoning [14]-[16] or certainty [17] of statements, or use visualization to highlight possible discrepancies [10], [15].
DOI:10.1109/JCDL52503.2021.00069