Daniel Barr

679 citations
16 papers · 393 · h-index 10

Impact in

Papers in

    • Peroxisome Proliferator-Activated Receptors 3
    • Protein Structure and Dynamics 2
    • Protein Kinase Regulation and GTPase Signaling 2
    • RNA and protein synthesis mechanisms 2
    • Cholesterol and Lipid Metabolism 2

Daniel Barr

13 papers receiving 376 citations

Peers

Daniel Barr
Comparison fields: 5 of 91
  • Biochemistry 68
  • Nutrition and Dietetics 135
  • Endocrinology, Diabetes and Metabolism 35
  • Molecular Biology 141
  • Physiology 47
Replace Veronika Paluchová with:
Veronika Paluchová Czechia
Thi Thu Trang Tran France
Antwi‐Boasiako Oteng Netherlands
Jofre Jacob da Silva Freitas Brazil
Anna Tang United States
C. Austin Pickens United States
Weisheng Xie United States
Agnès André France
Tanja Winter Canada
Magdalena Franczyk‐Żarów Poland
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Citations per field
00.5×8.3×
Veronika Paluchová · 1×
Citations per year

Countries citing papers authored by Daniel Barr

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Barr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Barr, 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 Daniel Barr Line = papers co-authored together Daniel Barr links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1
Prostaglandins, Leukotrienes and Essential Fatty Acids
2009124
2 200980
3 201150
4 201330
5 201125
6 202019
7 202019
8 201914
9
Determination of trans fatty acid levels by FTIR in processed foods in Australia.
200812
10 20219
11 20117
12 20231
13 20221
14 20221
15 20141
16 20240

About Daniel Barr

Daniel Barr is a scholar working on Molecular Biology, Surgery, Nutrition and Dietetics, Organic Chemistry and Rheumatology, having authored 16 papers that have together received 393 indexed citations. Recurring topics across this work include Fatty Acid Research and Health (3 papers), Peroxisome Proliferator-Activated Receptors (3 papers), Protein Structure and Dynamics (2 papers), Computational Drug Discovery Methods (2 papers), Cholesterol and Lipid Metabolism (2 papers), Protein Kinase Regulation and GTPase Signaling (2 papers), Carbohydrate Chemistry and Synthesis (2 papers) and RNA and protein synthesis mechanisms (2 papers). The work is most often cited by research in Biochemistry (68 citations), Nutrition and Dietetics (135 citations), Endocrinology, Diabetes and Metabolism (35 citations), Molecular Biology (141 citations) and Physiology (47 citations). Daniel Barr has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Andrew J. Sinclair, Gunveen Kaur, David Cameron‐Smith, Nicky Konstantopoulos, Denovan P. Begg, Manohar L. Garg, Arjan van der Vaart, Charles Antzelevitch, Taiji Oashi and Paul Shapiro. Their work appears in journals such as Biophysical Journal, ACS Chemical Biology, Physical Chemistry Chemical Physics, British Journal Of Nutrition and Biochemistry.

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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