J. Baird‐Lambert

494 citations
16 papers · 364 · h-index 11

Impact in

Papers in

J. Baird‐Lambert

16 papers receiving 331 citations

Peers

J. Baird‐Lambert
Comparison fields: 5 of 89
  • Physiology 40
  • Clinical Biochemistry 38
  • Pediatrics, Perinatology and Child Health 96
  • Biochemistry 32
  • Pharmacology 71
Replace Carol J. Gross with:
Carol J. Gross United States
O. Heidenreich Germany
P P Sokol United States
N. B. Higgs United Kingdom
J.G. Spenney United States
Mark Morales United States
R. Heipertz Germany
Colin Dalton United States
Mark Kao United States
Elizabeth M. Novak Canada
J. Baird‐Lambert relative to Carol J. Gross United States Carol J. Gross's profile →
Citations per field
00.5×3.9×
Carol J. Gross · 1×
Citations per year

Countries citing papers authored by J. Baird‐Lambert

Since Specialization
Citations

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

Fields of papers citing papers by J. Baird‐Lambert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Baird‐Lambert. 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 J. Baird‐Lambert. The network helps show where J. Baird‐Lambert may publish in the future.

Co-authors

The 20 scholars most cited alongside J. Baird‐Lambert, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with J. Baird‐Lambert Line = papers co-authored together J. Baird‐Lambert links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 197899
2 198044
3 198638
4 198231
5 198427
6 198221
7 198720
8 199218
9 198118
10 198315
11 198511
12 19829
13 19848
14 19873
15 19851
16 19861

About J. Baird‐Lambert

J. Baird‐Lambert is a scholar working on Pediatrics, Perinatology and Child Health, Molecular Biology, Physiology, Pharmacology and Pharmacology, having authored 16 papers that have together received 364 indexed citations. Recurring topics across this work include Pharmaceutical studies and practices (4 papers), Pharmacological Effects and Toxicity Studies (3 papers), Neonatal Health and Biochemistry (3 papers), Adenosine and Purinergic Signaling (3 papers), Metabolism and Genetic Disorders (2 papers), Pharmacogenetics and Drug Metabolism (2 papers), Epilepsy research and treatment (2 papers) and Neonatal Respiratory Health Research (2 papers). The work is most often cited by research in Physiology (40 citations), Clinical Biochemistry (38 citations), Pediatrics, Perinatology and Child Health (96 citations), Biochemistry (32 citations) and Pharmacology (71 citations). J. Baird‐Lambert has collaborated with scholars based in Australia and United States. Frequent co-authors include N. Buchanan, Les P. Davies, George J. Cardinale, Sidney Udenfriend, J. Thomas Hjelle, Pat Doyle, KM Taylor, Elke Jäger‐Roman, Dana Jamieson and Paul Davis. Their work appears in journals such as Clinical and Experimental Pharmacology and Physiology, The Journal of Pediatrics, Life Sciences, Neuropharmacology and Journal of Medicinal Chemistry.

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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