Daniel D. Lam

38 papers receiving 2.2k citations

Peers

Daniel D. Lam
Comparison fields: 5 of 122
  • Endocrine and Autonomic Systems 760
  • Physiology 757
  • Nutrition and Dietetics 397
  • Behavioral Neuroscience 60
  • Cellular and Molecular Neuroscience 289
Replace Corinne Leloup with:
Corinne Leloup France
Chen Liu United States
Alexandre Bénani France
Cláudia R. Vianna United States
Clémence Blouet United Kingdom
A. Christine Könner Germany
Julien Castel France
Andrew G. Swick United States
Guo Zhang China
Syann Lee United States
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Citations per field
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Citations per year

Countries citing papers authored by Daniel D. Lam

Since Specialization
Citations

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

Fields of papers citing papers by Daniel D. Lam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008382
2 2007219
3 2010207
4 2020206
5 2007177
6 2007122
7 2009105
8 201585
9 201779
10 201671
11 201663
12 201561
13 201154
14 201153
15 200746
16 201539
17 201435
18 201931
19 201529
20 200827

About Daniel D. Lam

Daniel D. Lam is a scholar working on Endocrine and Autonomic Systems, Nutrition and Dietetics, Issues, ethics and legal aspects, Neurology and Cellular and Molecular Neuroscience, having authored 38 papers that have together received 2.2k indexed citations. Recurring topics across this work include Regulation of Appetite and Obesity (18 papers), Biochemical Analysis and Sensing Techniques (14 papers), Sleep and Wakefulness Research (5 papers), Restless Legs Syndrome Research (4 papers), Parkinson's Disease Mechanisms and Treatments (4 papers), Adipose Tissue and Metabolism (4 papers), Genetics and Neurodevelopmental Disorders (4 papers) and Connective tissue disorders research (2 papers). The work is most often cited by research in Endocrine and Autonomic Systems (760 citations), Physiology (757 citations), Nutrition and Dietetics (397 citations), Behavioral Neuroscience (60 citations) and Cellular and Molecular Neuroscience (289 citations). Daniel D. Lam has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Lora K. Heisler, Alastair S. Garfield, Oliver Marston, Magdalena J. Przydzial, Jill Shaw, Justin J. Rochford, Gregory M. Sutton, Andrew A. Butler, Mark L. Evans and Stephen O’Rahilly. Their work appears in journals such as Cell Metabolism, Behavioural Brain Research, Diabetes, Nature Genetics and Endocrinology.

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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