Sarah E. Heaps

783 total citations
18 papers, 504 citations indexed

About

Sarah E. Heaps is a scholar working on Artificial Intelligence, Molecular Biology and Paleontology. According to data from OpenAlex, Sarah E. Heaps has authored 18 papers receiving a total of 504 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Molecular Biology and 3 papers in Paleontology. Recurrent topics in Sarah E. Heaps's work include Genomics and Phylogenetic Studies (5 papers), Bayesian Methods and Mixture Models (4 papers) and Evolution and Paleontology Studies (3 papers). Sarah E. Heaps is often cited by papers focused on Genomics and Phylogenetic Studies (5 papers), Bayesian Methods and Mixture Models (4 papers) and Evolution and Paleontology Studies (3 papers). Sarah E. Heaps collaborates with scholars based in United Kingdom, Australia and Sweden. Sarah E. Heaps's co-authors include Tom A. Williams, T. Martin Embley, Peter G. Foster, Thijs J. G. Ettema, Gergely J. Szöllősi, Anja Spang, Bastien Boussau, Richard J. Boys, Paul Thaw and Tom M. W. Nye and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Philosophical Transactions of the Royal Society B Biological Sciences.

In The Last Decade

Sarah E. Heaps

16 papers receiving 502 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sarah E. Heaps United Kingdom	9	246	121	71	68	66	18	504
Artémis Kosta France	17	537 2.2×	76 0.6×	28 0.4×	30 0.4×	68 1.0×	35	859
Arturo Becerra Mexico	18	629 2.6×	181 1.5×	78 1.1×	5 0.1×	92 1.4×	49	981
Eugenia M. Clérico United States	14	915 3.7×	62 0.5×	174 2.5×	13 0.2×	53 0.8×	24	1.1k
S. Krishnaswamy India	18	553 2.2×	296 2.4×	70 1.0×	5 0.1×	61 0.9×	96	1.0k
Matteo P. Ferla United Kingdom	11	534 2.2×	124 1.0×	65 0.9×	8 0.1×	38 0.6×	19	780
Shen Jean Lim United States	11	364 1.5×	137 1.1×	21 0.3×	5 0.1×	48 0.7×	27	667
Gabriel Gelius‐Dietrich Germany	10	457 1.9×	57 0.5×	19 0.3×	43 0.6×	36 0.5×	13	567
Jonathan Lombard France	11	1.1k 4.4×	400 3.3×	99 1.4×	16 0.2×	106 1.6×	14	1.4k
Peter Hufnagel Germany	11	600 2.4×	123 1.0×	49 0.7×	14 0.2×	124 1.9×	13	1.0k
Aaron D. Goldman United States	15	858 3.5×	193 1.6×	71 1.0×	14 0.2×	153 2.3×	32	1.1k

Countries citing papers authored by Sarah E. Heaps

Since Specialization

Citations

This map shows the geographic impact of Sarah E. Heaps's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sarah E. Heaps with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarah E. Heaps more than expected).

Fields of papers citing papers by Sarah E. Heaps

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sarah E. Heaps. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sarah E. Heaps. The network helps show where Sarah E. Heaps may publish in the future.

Co-authorship network of co-authors of Sarah E. Heaps

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah E. Heaps. A scholar is included among the top collaborators of Sarah E. Heaps based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sarah E. Heaps. Sarah E. Heaps is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Heaps, Sarah E. & Ian H. Jermyn. (2024). Structured prior distributions for the covariance matrix in latent factor models. Statistics and Computing. 34(4).

Heaps, Sarah E., et al.. (2024). Bayesian Inference on the Order of Stationary Vector Autoregressions. Bayesian Analysis. -1(-1).

Heaps, Sarah E., et al.. (2023). A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields. Environmetrics. 34(8). 1 indexed citations

Heaps, Sarah E., Tom M. W. Nye, Thomas P. Curtis, et al.. (2022). A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant. Computational Statistics & Data Analysis. 179. 107659–107659. 8 indexed citations

Heaps, Sarah E.. (2022). Enforcing Stationarity through the Prior in Vector Autoregressions. Journal of Computational and Graphical Statistics. 32(1). 74–83. 9 indexed citations

Heaps, Sarah E., et al.. (2020). Generalizing rate heterogeneity across sites in statistical phylogenetics. Statistical Modelling. 20(4). 410–436. 1 indexed citations

Barr, Stuart, Xiaodong Ming, M. V. Peppa, et al.. (2020). FLOOD-PREPARED: A NOWCASTING SYSTEM FOR REAL-TIME IMPACT ADAPTION TO SURFACE WATER FLOODING IN CITIES. SHILAP Revista de lepidopterología. VI-4/W2-2020. 9–15. 6 indexed citations

McMonagle, Charles J., Paul G. Waddell, Sarah E. Heaps, et al.. (2020). Encapsulated Nanodroplet Crystallization of Organic-Soluble Small Molecules. Chem. 6(7). 1755–1765. 115 indexed citations

Heaps, Sarah E., et al.. (2018). Automating the Placement of Time Series Models for IoT Healthcare Applications. Newcastle University ePrints (Newcastle Univesity). 290–291. 7 indexed citations

10.

Houghton, David, Kate Hallsworth, Christian Thoma, et al.. (2017). Effects of Exercise on Liver Fat and Metabolism in Alcohol Drinkers. Clinical Gastroenterology and Hepatology. 15(10). 1596–1603.e3. 13 indexed citations

11.

Williams, Tom A., Gergely J. Szöllősi, Anja Spang, et al.. (2017). Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proceedings of the National Academy of Sciences. 114(23). E4602–E4611. 155 indexed citations

12.

Heaps, Sarah E., et al.. (2017). The Effect of Nonreversibility on Inferring Rooted Phylogenies. Molecular Biology and Evolution. 35(4). 984–1002. 9 indexed citations

13.

Heaps, Sarah E., et al.. (2016). Automating computational placement in IoT environments. 434–437. 4 indexed citations

14.

Heaps, Sarah E., Richard J. Boys, & Malcolm Farrow. (2015). Bayesian Modelling of Rainfall Data by Using Non-Homogeneous Hidden Markov Models and Latent Gaussian Variables. Journal of the Royal Statistical Society Series C (Applied Statistics). 64(3). 543–568. 9 indexed citations

15.

Williams, Tom A., et al.. (2015). New substitution models for rooting phylogenetic trees. Philosophical Transactions of the Royal Society B Biological Sciences. 370(1678). 20140336–20140336. 42 indexed citations

16.

Heaps, Sarah E., Tom M. W. Nye, Richard J. Boys, Tom A. Williams, & T. Martin Embley. (2014). Bayesian modelling of compositional heterogeneity in molecular phylogenetics. Statistical Applications in Genetics and Molecular Biology. 13(5). 589–609. 14 indexed citations

17.

Nakjang, Sirintra, Tom A. Williams, Eva Heinz, et al.. (2013). Reduction and Expansion in Microsporidian Genome Evolution: New Insights from Comparative Genomics. Genome Biology and Evolution. 5(12). 2285–2303. 110 indexed citations

18.

Heaps, Sarah E., Richard J. Boys, & Malcolm Farrow. (2013). Computation of marginal likelihoods with data-dependent support for latent variables. Computational Statistics & Data Analysis. 71. 392–401. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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