Daniel Stafford

853 total citations
10 papers, 764 citations indexed

About

Daniel Stafford is a scholar working on Molecular Biology, Pharmacology and Cell Biology. According to data from OpenAlex, Daniel Stafford has authored 10 papers receiving a total of 764 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 3 papers in Pharmacology and 2 papers in Cell Biology. Recurrent topics in Daniel Stafford's work include Microbial Metabolic Engineering and Bioproduction (4 papers), Enzyme Catalysis and Immobilization (3 papers) and Pharmacogenetics and Drug Metabolism (3 papers). Daniel Stafford is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (4 papers), Enzyme Catalysis and Immobilization (3 papers) and Pharmacogenetics and Drug Metabolism (3 papers). Daniel Stafford collaborates with scholars based in United States. Daniel Stafford's co-authors include Kadee J. Raser, Rathna Nath, Kevin Wang, Po‐wai Yuen, Avigail Posner, Richard B. Gilbertsen, Hamish Allen, Iradj Hajimohammadreza, Robert V. Talanian and Gregory Stephanopoulos and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Biochemical Journal and Journal of Medicinal Chemistry.

In The Last Decade

Daniel Stafford

10 papers receiving 741 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel Stafford United States	8	546	257	150	143	70	10	764
Joanna Kamińska Poland	15	491 0.9×	213 0.8×	62 0.4×	32 0.2×	50 0.7×	47	628
Cristina Florean Italy	15	590 1.1×	83 0.3×	109 0.7×	50 0.3×	154 2.2×	17	861
Alban Girault France	16	648 1.2×	95 0.4×	150 1.0×	55 0.4×	42 0.6×	28	949
Chin‐Chun Hung Taiwan	14	378 0.7×	134 0.5×	41 0.3×	70 0.5×	41 0.6×	25	791
Alfonso J. Sarría Spain	12	292 0.5×	271 1.1×	30 0.2×	85 0.6×	52 0.7×	15	647
Kenneth D. Bromberg United States	14	746 1.4×	65 0.3×	69 0.5×	80 0.6×	26 0.4×	17	926
N Kajikawa Japan	9	610 1.1×	284 1.1×	122 0.8×	71 0.5×	96 1.4×	14	909
Gareth Wayne United Kingdom	12	291 0.5×	106 0.4×	48 0.3×	70 0.5×	182 2.6×	13	605
Kuniaki Sano Japan	15	493 0.9×	62 0.2×	105 0.7×	52 0.4×	87 1.2×	22	731
M. Pabion France	9	566 1.0×	141 0.5×	51 0.3×	26 0.2×	40 0.6×	18	725

Countries citing papers authored by Daniel Stafford

Since Specialization

Citations

This map shows the geographic impact of Daniel Stafford'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 Daniel Stafford with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Stafford more than expected).

Fields of papers citing papers by Daniel Stafford

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Stafford. 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 Daniel Stafford. The network helps show where Daniel Stafford may publish in the future.

Co-authorship network of co-authors of Daniel Stafford

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Stafford. A scholar is included among the top collaborators of Daniel Stafford 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 Daniel Stafford. Daniel Stafford is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Stafford, Daniel. (2019). Machine learning in option pricing. 3 indexed citations

2.

Stafford, Daniel, et al.. (2018). OpenStax. The Charleston Advisor. 20(1). 48–51. 2 indexed citations

3.

Stafford, Daniel, et al.. (2002). Optimizing bioconversion pathways through systems analysis and metabolic engineering. Proceedings of the National Academy of Sciences. 99(4). 1801–1806. 24 indexed citations

4.

Stephanopoulos, Gregory & Daniel Stafford. (2002). Metabolic engineering: a new frontier of chemical reaction engineering. Chemical Engineering Science. 57(14). 2595–2602. 25 indexed citations

5.

Stafford, Daniel & Gregory Stephanopoulos. (2001). Metabolic engineering as an integrating platform for strain development. Current Opinion in Microbiology. 4(3). 336–340. 31 indexed citations

6.

Stafford, Daniel, et al.. (2001). Application of radiolabeled tracers to biocatalytic flux analysis. European Journal of Biochemistry. 268(18). 4950–4960. 18 indexed citations

7.

Stafford, Daniel, et al.. (2001). Metabolic Engineering of Indene Bioconversion in Rhodococcus sp.. Advances in biochemical engineering, biotechnology. 73. 85–101. 10 indexed citations

8.

Hays, Sheryl J., Bradley W. Caprathe, John L. Gilmore, et al.. (1998). 2-Amino-4H-3,1-benzoxazin-4-ones as Inhibitors of C1r Serine Protease. Journal of Medicinal Chemistry. 41(7). 1060–1067. 146 indexed citations

9.

Nath, Rathna, Kadee J. Raser, Kim McGinnis, et al.. (1996). Effects of ICE-like protease and calpain inhibitors on neuronal apoptosis. Neuroreport. 8(1). 249–255. 109 indexed citations

10.

Nath, Rathna, Kadee J. Raser, Daniel Stafford, et al.. (1996). Non-erythroid α-spectrin breakdown by calpain and interleukin 1 β-converting-enzyme-like protease(s) in apoptotic cells: contributory roles of both protease families in neuronal apoptosis. Biochemical Journal. 319(3). 683–690. 396 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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