Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

Phase annealing in SHELX-90: direct methods for larger structures

Acta Crystallographica Section A Foundations of Crystallography(1990)10.1107/s0108767390000277Source: DataRank Database

Phase annealing in SHELX-90: direct methods for larger structures is a research paper published in Acta Crystallographica Section A Foundations of Crystallography (1990). On theSindex it has a DataRank of 1.5. It has been cited 16,297 times.

N/A

1.5DataRank · unranked

1.5

16297 citations · base score 9.7

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

A number of extensions to the multisolution approach to the crystallographic phase problem are discussed in which the negative quartet relations play an important role. A phase annealing method, related to the simulated annealing approach in other optimization problems, is proposed and it is shown that it can result in an improvement of up to an order of magnitude in the chances of solving large structures at atomic resolution. The ideas presented here are incorporated in the program system SHELX-90; the philosophical and mathematical background to the direct-methods part (SHELXS) of this system is described.

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

1.5

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

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(C_q) ÷ max(outdegree_q, 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.

Read the full methodology →