Problem
Research questions and friction points this paper is trying to address.
science-software co-evolution
cross-corpus linkage
research impact
software reuse
citation coupling
The Reciprocal Impact of Science and Software: A Cross-Corpus Analysis of How Research Shapes Software and Software Enables Research
🤖 AI Summary
Scientific knowledge and software practice co-evolve, yet existing corpora remain fragmented, lacking systematic cross-domain analysis. This study addresses this gap by constructing, for the first time at a global scale, an integrated cross-corpus knowledge graph linking World of Code, Semantic Scholar, and OpenAlex—comprising 69.8 million edges—and introduces the Science–Software Supply Chain (S3C) analytical framework. Through entity alignment, dependency parsing, multi-source metadata fusion, and manual annotation validation, the work uncovers bidirectional influence mechanisms: scientific research primarily drives the development of reproducibility toolchains, while software underpins scientific advancement via foundational machine learning infrastructure. Furthermore, the analysis reveals only weak correlations between citation and reuse metrics, with such associations highly contingent on pairing methodologies.
📝 Abstract
Software and scientific knowledge co-evolve, yet they are catalogued in separate corpora that rarely speak to one another. We bridge them at global scale by linking World of Code (a near-complete mirror of public version-control history) to Semantic Scholar and OpenAlex through a typed cross-corpus graph of 69.8M edges over eight relation types (paper-to-software mentions, software-to-paper citations, software dependencies, authorship, affiliation, and identity bridges). Anchoring on 18,247 curated science repositories, we ask two reciprocal questions: what is the impact of science on software, and of software on science? To test whether this Science-Software Supply Chain (S3C) view is feasible, we run basic investigations rather than claim a definitive measurement. The two directions appear to illuminate different, complementary strata: the literature's reach into software is dominated by a reproducibility and packaging layer (nf-core, Nextflow, Bioconda) and sequence-analysis tools, whereas software's reach back into science is proxied by a largely invisible machine-learning and data-science infrastructure tier (PyTorch, seaborn, NLTK). The direct paper-names-software channel is too sparse to rank: a human-curated gold benchmark links none of its 65 in-scope cases. Dependency reuse stands in as a proxy and is at most weakly coupled to citation count and to stars (Spearman rho=0.36). Our most cautionary finding is about measurement itself: the reuse-citation coupling flips sign and confidence across two reasonable ways of pairing a repository with a citation count, through papers that name it (n=137, rho=0.05, CI straddling zero) versus DOIs a repository declares for itself (n=1,067, rho=0.13, CI [0.07,0.19]). With linkage this sparse, the sign of a headline correlation depends on which gap one tolerates, so we report both and refrain from a strong decoupling claim.
Innovation
Methods, ideas, or system contributions that make the work stand out.
cross-corpus graph
Science-Software Supply Chain
software citation
dependency reuse
research software
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