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arxiv: 2607.00546 · v1 · pith:6P6I5YJ2new · submitted 2026-07-01 · 🌌 astro-ph.IM · physics.soc-ph

Open Science in Astrophysics: Citation Benefits of Open Code, Open Data, and Open Access

Parth Joshi , Rupert Croft This is my paper

Pith reviewed 2026-07-02 06:00 UTC · model grok-4.3

classification 🌌 astro-ph.IM physics.soc-ph
keywords open sciencecitationsastrophysicsopen dataopen accessopen coderegression
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The pith

Astrophysics papers with open data receive 32 percent more citations after statistical controls for other factors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper measures citation effects of openness in a sample of over 53,000 astrophysics papers from 2021 to 2025. It tracks open data, open code, and open access status along with variables such as grants received, author count, and paper length. Multivariate regression, partial correlations, and non-parametric tests are used to separate the role of openness from those other influences. The analysis shows positive citation associations for all three openness types, strongest for open data and present across all six sub-fields examined.

Core claim

After controlling for grants, code size, data repository size, programming language, number of authors, paper length, and publication date, open data is associated with a 32 percent citation increase, open access with 26 percent, and open code with 16 percent; the open-data advantage appears in every sub-field and is largest in Galaxies+Cosmology and ISM.

What carries the argument

Multivariate least-squares regression together with partial correlations and non-parametric tests that isolate the contribution of each openness variable from the other measured quantities.

Load-bearing premise

The regression and partial correlations successfully remove the effects of unmeasured paper quality, subfield citation norms, and selection biases that could link openness to higher citations.

What would settle it

A new analysis of a comparable paper sample that adds direct measures of intrinsic paper quality and still finds no citation difference for open versus closed papers would falsify the reported advantages.

Figures

Figures reproduced from arXiv: 2607.00546 by Parth Joshi, Rupert Croft.

Figure 1
Figure 1. Figure 1: Dataset overview for the 53,194 astrophysics papers in our sample (2021–2025). (a) Number of papers per publication year. (b) Fraction of papers in each astrophysical sub-field (see [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: OLS regression coefficients on log(1+citations) with 95% confidence intervals (HC3 heteroskedasticity-robust standard errors). Orange bars are the three openness variables; blue bars are control variables. (a) Base model without astrophysical sub-field fixed effects (R 2 = 0.370, N = 53,194). (b) Full model including sub-field indicator variables (R 2 = 0.375). Significance markers: ∗∗∗p < 0.001; ∗∗p < 0.0… view at source ↗
Figure 3
Figure 3. Figure 3: Raw citation distributions compared between open and closed papers. All panels use a logarithmic vertical axis. (a) Violin plot comparing citation distributions for papers with and without open code. (b) Same for open data. (c) Same for open access vs. closed access. Horizontal bars mark group medians. Annotation in each panel gives sample sizes and the two-sided Mann–Whitney U test p-value. Neither Code o… view at source ↗
Figure 4
Figure 4. Figure 4: Citation distributions grouped by openness combination and by astrophysical sub-field. (a) Box plots of citation counts for the four code/data combinations: papers with neither open code nor open data, code only, data only, and both. Boxes show the interquartile range; whiskers extend to 1.5 times the IQR. Medians (M) and sample sizes (n) are annotated. (b) Same box-plot format showing citations across the… view at source ↗
Figure 5
Figure 5. Figure 5: Binned median citations (solid line) as a function of each continuous predictor, with the 25th–75th percentile range shaded. The Spearman rank correlation rs and its p-value are given in each panel. For code size and data size, only papers that share code or data are included. larger professional networks that generate citations. In practice, large collaborative papers often describe ma￾jor datasets or sof… view at source ↗
Figure 7
Figure 7. Figure 7: Partial correlations of each predictor with log(1+ citations) for the full dataset, after removing the linear con￾tribution of the remaining control variables (number of au￾thors, number of grants, paper length, paper age). Orange bars are the three openness variables; blue bars are control variables. Significance markers: ∗∗∗p < 0.001; ∗∗p < 0.01; ∗ p < 0.05. Solar System Planets Stellar ISM HEA Galaxies … view at source ↗
Figure 8
Figure 8. Figure 8: Partial correlations of open code (orange) and open data (pink) with log(1 + citations) within each astro￾physical sub-field, after controlling for author count, grant count, paper length, and paper age. Significance markers: ∗∗∗p < 0.001; ∗∗p < 0.01; ∗ p < 0.05. at p < 0.05 in the full sample and in the larger sub￾field groups (Galaxies + Cosmology, Stellar, and HEA). The open-code partial correlation is … view at source ↗
Figure 9
Figure 9. Figure 9: Citation distributions by openness combination within each astrophysical sub-field. Box shows the interquartile range; whiskers extend to 1.5 times the IQR; medians and sample sizes are annotated. The statistical annotation in each panel gives the Mann–Whitney p-value comparing papers with neither open code nor open data against those with open code. estimates, but the direction of the effect is consistent… view at source ↗
Figure 10
Figure 10. Figure 10: Programming language analysis for the 337 papers with a GitHub repository link. Colors are consistent across all three panels: each language has the same color in all panels. Languages with fewer than three papers are omitted. (a) Number of papers per dominant language. (b) Median citation count per language, sorted from highest to lowest. (c) Box-plot citation distributions for the five most common langu… view at source ↗
Figure 11
Figure 11. Figure 11: Difference in median citations between papers with and without open code or open data, shown as a func￾tion of publication year. The dashed line at zero is included for reference. 4.7. Grant Funding and Openness [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Grant-related trends in citation counts and openness. (a) Median citation count as a function of the number of grants acknowledged, with sample sizes (n) annotated above the bars. (b) Fraction of papers with open code, open data, or open access as a function of grant count. Stodden et al. 2018). This mechanism predicts that the data-sharing advantage should be larger in sub-fields where community re-use o… view at source ↗
read the original abstract

We analyze the relationship between open-accessibility in data, code, and paper text in astrophysics using a sample of 53,194 peer reviewed papers published between January 2021 and April 2025, drawn from NASA's Astrophysics Data System (ADS). We measure eleven quantities: open accessibility of text, open-code status, open-data status, number of grants received, code size, programming language, data repository size, citation count, number of authors, paper length, and publication date. We break down citation advantages based on six astrophysical sub-fields: Solar System, Planet, Stellar, ISM, High Energy, and Galaxies+Cosmology, determined by keywords. This is accomplished by tuning a multivariate least-squares regression model with alongside partial correlations and non-parametric tests to isolate the contribution of each facet of openness. After controlling for the aforementioned quantities, we find significant citation advantages associated with all three forms of openness: open data (+32%, p < 10^-24), open access (+26%, p < 10^-67), and open code (+16%, p = 0.003). The open-data citation advantage is present in all six sub-fields, and especially in Galaxies+Cosmology and ISM, which have the strongest cultures of sharing simulation outputs and observational data products. Open-code and open-data sharing rates are highest in Galaxies+Cosmology and HEA (~0.9% and ~2.9%), reflecting their more developed community data infrastructure, and lowest in Solar System and ISM, where data is distributed on platforms not taken into account by this study. Our findings support the long held notion that public access comes with concrete personal incentives for authors in terms of citations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper analyzes citation benefits associated with open access, open data, and open code using a sample of 53,194 peer-reviewed astrophysics papers (2021–2025) from NASA's ADS. It measures eleven variables including openness indicators, grants, authors, length, date, subfield (via keywords: Solar System, Planet, Stellar, ISM, High Energy, Galaxies+Cosmology), code size, language, and repo size. A multivariate least-squares regression is tuned alongside partial correlations and non-parametric tests to isolate openness effects after controls, yielding reported advantages of +32% (open data, p<10^{-24}), +26% (open access, p<10^{-67}), and +16% (open code, p=0.003), with subfield variations and higher sharing rates in Galaxies+Cosmology and HEA.

Significance. If the regression successfully isolates causal effects, the work supplies large-sample quantitative evidence that openness confers measurable citation gains in astrophysics, strongest for data and varying by subfield infrastructure. The combination of regression, partial correlations, and non-parametrics plus the sample size are methodological strengths that could inform open-science policy if robustness to selection is demonstrated.

major comments (2)
  1. [Abstract and Methods (regression specification)] The multivariate least-squares regression (described in the abstract and methods) controls for grants, authors, length, date, subfield, code size, language, and repo size but includes no proxy for intrinsic paper quality, novelty, or rigor. This omission risks confounding the reported +32%/+26%/+16% coefficients, as openness decisions and citation rates may both be driven by unmeasured quality; the central claim that controls isolate the contribution of openness therefore requires additional justification or sensitivity tests.
  2. [Abstract and Results (subfield breakdown)] Subfield classification is performed via keywords, yet the abstract notes that low sharing rates in Solar System and ISM partly reflect platforms not captured by the study. This raises the possibility that subfield-specific citation norms or data practices are incompletely controlled, which could affect the claim that the open-data advantage is present (and strongest) in all six sub-fields.
minor comments (3)
  1. [Methods] Provide explicit definitions and detection criteria for open-code status, open-data status, and open-access status, including any thresholds or external databases used.
  2. [Methods] Report the exact regression specification (functional form, interaction terms, variance inflation factors) and the robustness checks performed against the listed controls.
  3. [Results] Clarify whether the percentage advantages are derived from exponentiated coefficients or marginal effects and how they account for the citation-count distribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive report. We address each major comment below and outline planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and Methods (regression specification)] The multivariate least-squares regression (described in the abstract and methods) controls for grants, authors, length, date, subfield, code size, language, and repo size but includes no proxy for intrinsic paper quality, novelty, or rigor. This omission risks confounding the reported +32%/+26%/+16% coefficients, as openness decisions and citation rates may both be driven by unmeasured quality; the central claim that controls isolate the contribution of openness therefore requires additional justification or sensitivity tests.

    Authors: We agree this is a substantive limitation: no direct measure of intrinsic quality, novelty, or rigor is available in the ADS-derived dataset, and residual confounding remains possible. Number of grants, author count, and paper length provide partial proxies (as these correlate with perceived quality and resources), and the combination of multivariate regression, partial correlations, and non-parametric tests offers some triangulation. However, these do not fully substitute for a quality proxy. In revision we will (1) add an explicit limitations subsection discussing this issue and the direction of potential bias, and (2) report additional sensitivity checks (e.g., subfield-stratified models and robustness to alternative specifications). We will not claim full causal isolation but will qualify the language accordingly. revision: yes

  2. Referee: [Abstract and Results (subfield breakdown)] Subfield classification is performed via keywords, yet the abstract notes that low sharing rates in Solar System and ISM partly reflect platforms not captured by the study. This raises the possibility that subfield-specific citation norms or data practices are incompletely controlled, which could affect the claim that the open-data advantage is present (and strongest) in all six sub-fields.

    Authors: Keyword-based subfield assignment follows standard practice in ADS analyses and is the only scalable approach for 53k papers. The manuscript already flags that Solar System and ISM rates are depressed by external platforms. The regression treats subfield as a categorical control, and the open-data coefficient remains positive in every subfield. To address the referee's concern we will expand the results and discussion sections with (a) more detail on known subfield data practices and (b) explicit caveats about possible residual variation in citation norms. We will also verify that the reported advantage holds after additional interaction terms between openness and subfield. revision: partial

Circularity Check

0 steps flagged

No significant circularity: empirical regression on external citation data

full rationale

The paper's central claim consists of fitted coefficients (+32%, +26%, +16%) obtained via multivariate least-squares regression, partial correlations, and non-parametric tests applied to an external sample of 53,194 ADS papers. Controls (grants, authors, length, date, subfield via keywords, code size, language, repo size) and the target citation counts are all measured quantities independent of the openness indicators. No equations reduce the reported advantages to quantities defined in terms of themselves; no self-citations are invoked as load-bearing premises; no ansatz or uniqueness theorem is smuggled in. The derivation is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the chosen regression specification removes confounding; no free parameters beyond the fitted coefficients are introduced in the abstract, and no new physical entities are postulated.

axioms (1)
  • domain assumption Least-squares regression assumptions hold (linearity, independence, homoscedasticity) for the citation model.
    Invoked by the choice of multivariate least-squares regression to isolate openness effects.

pith-pipeline@v0.9.1-grok · 5847 in / 1302 out tokens · 24334 ms · 2026-07-02T06:00:47.455342+00:00 · methodology

discussion (0)

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