Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2009.12500

Pandemics are catalysts of scientific novelty: Evidence from COVID-19

Meijun Liu·Yi Bu·Chongyan Chen·Jian Xu·Daifeng Li·Yan Leng·Richard Barry Freeman·Eric Meyer·Wonjin Yoon·Mujeen Sung·Minbyul Jeong·Jinhyuk Lee·Jaewoo Kang·Chao Min·Min Song·Yujia Zhai·Ying Ding
View original source ↗Read source document ↗Export metadata ↓

Abstract

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pre-trained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

Explore related subjects

cs.DL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Meijun Liu, Yi Bu, Chongyan Chen, Jian Xu, Daifeng Li, Yan Leng, Richard Barry Freeman, Eric Meyer, Wonjin Yoon, Mujeen Sung, Minbyul Jeong, Jinhyuk Lee, Jaewoo Kang, Chao Min, Min Song, Yujia Zhai, Ying Ding. 2021-11-15. Pandemics are catalysts of scientific novelty: Evidence from COVID-19. https://doi.org/10.1002/asi.24612

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The illusion of neutrality in metric-based research evaluation

Venue prestige and citation counts are two widely used, albeit imperfect, signals of research quality. When the two signals conflict, evaluators must decide how much weight to assign each. Yet, it remains unknown how researchers across disciplines trade off these two signals when evaluating research outcome. To fill this gap, we surveyed 869 researchers using paired choices between hypothetical departmental hiring rules that assigned different weights to venue prestige and citation counts, asking which would produce better science. Among 795 respondents whose choices were largely internally consistent, choices placed nearly equal aggregate weight on the two signals. This apparent balance concealed substantial individual heterogeneity: nearly two in five respondents occupied the most venue-heavy or citation-heavy intervals. Moreover, respondents on average chose more citation-heavy rules than they believed their departments used in hiring. By revealing the subjective judgments that arise when research indicators conflict, our findings reinforce calls for greater caution when quantitative indicators are used to evaluate research.

cs.DL↗
arXiv2026

Reading the Data Back: Enriching Variable-Level Metadata for Model-Data Consistency Checks

Purpose: Research data repositories often lack variable-level metadata. Model-choice screening, checking whether a reported model suits the values of its outcome variable, also requires summaries of those values and links to the analyses that use them. We ask how repositories can represent and acquire them as metadata with evidence and review histories. Methods: We propose a metadata application profile compatible with the Data Documentation Initiative Cross-Domain Integration (DDI-CDI) model, linking versioned variables and empirical profiles to reported analyses, estimators, and analysis roles. Extraction provenance and review decisions are recorded separately. We evaluate streaming data profiling, deterministic extraction from Stata and R code, and language-model extraction of analysis records from papers. Results: A worked export of one replication package illustrates the profile: it passes shape and interchange checks and answers four queries, one of which returns the analyses that raise no alert. Screening 4,868 replication datasets from six political-science journals links outcomes to profiled columns in 1,440 deposits and flags 965, including 14 of the 21 deposits with a hand-verified linear model on a count, proportion, binary, or ordinal outcome. AI-assisted adjudication yields 55% precision on a near-balanced sample of 119 count and proportion candidates; screening rules were developed on the same corpus. Conclusion: The profile makes analysis-variable relationships queryable while preserving evidence and review history; screening yields review candidates with context. Code, metadata, and measurements are released.

cs.DL↗
arXiv2026

From Code Archival to Knowledge Graph: Bridging Software Heritage, COAR Notify and Wikidata

Software is a first-class scientific object, yet validated links between source code and the scholarly record remain largely absent from the Linked Open Data (LOD) cloud, isolating archived artefacts from semantic discovery. This paper presents an end-to-end reconciliation pipeline that harvests, validates, and models publication-to-repository pairs from sources where the link between a paper and its source code is explicit and editorially verified: the software-centric journals JOSS, SoftwareX, and IPOL, together with the reproducibility reports of the SIGMOD Availability and Reproducibility Initiative (ARI). This yields a curated corpus of 4,397 $\langle$DOI, repository-URL$\rangle$ pairs. We design two distinct application profiles grounded in Wikidata classes (one for scholarly articles, one for software instances) aligned with the schema.org and CodeMeta vocabularies. This architectural separation enables rule-based reconciliation at two granularities: lightweight, inline publication references or standalone, first-class Wikidata software nodes equipped with SWHIDs, Software Heritage's content-addressed identifiers. A read-only lookup against Wikidata shows that only 82 of the harvested repositories were already modelled there; human-reviewed batches have since created 4{,}182 new software items cross-linked to their articles. We further show that payloads of the emerging COAR Notify protocol, an external effort we do not develop, map natively onto our input format, so the same backend could later serve a live enrichment stream. Our core contribution is a pair of application profiles that turn Wikidata into a connector between the scholarly record and archived source code; we openly release all code, application profiles, and harvested datasets.

cs.DL↗