Parton distributions for the LHC
Parton distributions for the LHC is a dataset published in The European Physical Journal C (2009). On theSindex it has a DataRank of 15.2, placing it in the top 12.2% of the data-sharing corpus. It has been cited 3,932 times, with 190 citing works in its 1-hop citation network. Its calibrated FAIR score is 43/100.
Abstract
We present updated leading-order, next-to-leading order and next-to-next-to-leading order parton distribution functions (“MSTW 2008”) determined from global analysis of hard-scattering data within the standard framework of leading-twist fixed-order collinear factorisation in the MS scheme. These parton distributions supersede the previously available “MRST ” sets and should be used for the first LHC data-taking and for the associated theoretical calculations. New data sets fitted include CCFR/NuTeV dimuon cross sections, which constrain the strange quark and antiquark distributions, and Tevatron Run II data on inclusive jet production, the lepton charge asymmetry from W decays and the Z rapidity distribution. Uncertainties are propagated from the experimental errors on the fitted data points using a new dynamic procedure for each eigenvector of the covariance matrix. We discuss the major changes compared to previous MRST fits, briefly compare to parton distributions obtained by other fitting groups, and give predictions for the W and Z total
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
1.2
From this paper's citation signal
Citation Network Contribution
14.0
From 190 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- An Algorithm for Least-Squares Estimation of Nonlinear ParametersJournal of the Society for Industrial and Applied Mathematics196330,320 citationsDataRank 1.5
- A method for the solution of certain non-linear problems in least squaresQuarterly of Applied Mathematics194412,141 citationsDataRank 1.4
- Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHCPhysics Letters B201210,654 citationsDataRank 1.4
- Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHCPhysics Letters B20129,917 citationsDataRank 1.4
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulationsJournal of High Energy Physics20147,377 citationsDataRank 1.3
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 8% comes from its base citations and 92% from the citation network (190 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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