Daniel B. Forger

5.9k total citations
88 papers, 4.0k citations indexed

About

Daniel B. Forger is a scholar working on Endocrine and Autonomic Systems, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Daniel B. Forger has authored 88 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Endocrine and Autonomic Systems, 29 papers in Cellular and Molecular Neuroscience and 20 papers in Cognitive Neuroscience. Recurrent topics in Daniel B. Forger's work include Circadian rhythm and melatonin (57 papers), Photoreceptor and optogenetics research (20 papers) and Light effects on plants (18 papers). Daniel B. Forger is often cited by papers focused on Circadian rhythm and melatonin (57 papers), Photoreceptor and optogenetics research (20 papers) and Light effects on plants (18 papers). Daniel B. Forger collaborates with scholars based in United States, South Korea and United Kingdom. Daniel B. Forger's co-authors include Charles S. Peskin, Richard E. Kronauer, Megan E. Jewett, Jae Kyoung Kim, Olivia Walch, David M. Virshup, David Paydarfar, Yitong Huang, Amy L. Cochran and Cathy Goldstein and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Daniel B. Forger

85 papers receiving 3.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel B. Forger United States 34 2.5k 945 925 885 797 88 4.0k
Steven M. Reppert United States 25 3.5k 1.4× 884 0.9× 1.7k 1.9× 705 0.8× 246 0.3× 27 4.4k
David K. Welsh United States 42 6.3k 2.6× 1.7k 1.8× 2.4k 2.6× 1.6k 1.8× 930 1.2× 80 7.9k
Johanna H. Meijer Netherlands 48 6.6k 2.7× 3.1k 3.3× 3.3k 3.6× 554 0.6× 999 1.3× 145 8.2k
Diego A. Golombék Argentina 42 4.2k 1.7× 1.6k 1.7× 1.5k 1.7× 339 0.4× 914 1.1× 171 6.0k
Tanya Leise United States 24 1.1k 0.5× 297 0.3× 495 0.5× 264 0.3× 123 0.2× 48 1.7k
Jude F. Mitchell United States 32 1.0k 0.4× 2.9k 3.0× 2.2k 2.3× 136 0.2× 728 0.9× 87 5.5k
Sooyoung Chung South Korea 28 1.1k 0.4× 2.2k 2.3× 1.8k 2.0× 155 0.2× 235 0.3× 63 4.4k
Sean Hill United States 25 642 0.3× 2.6k 2.8× 1.6k 1.7× 55 0.1× 424 0.5× 68 3.8k
Jeffrey C. Smith United States 56 7.8k 3.2× 4.7k 5.0× 1.8k 1.9× 107 0.1× 69 0.1× 131 11.0k
Petra E. Vértes United Kingdom 33 179 0.1× 3.5k 3.7× 476 0.5× 43 0.0× 741 0.9× 62 5.0k

Countries citing papers authored by Daniel B. Forger

Since Specialization
Citations

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

Fields of papers citing papers by Daniel B. Forger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel B. Forger

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

All Works

20 of 20 papers shown
1.
Walch, Olivia, Walter Dempsey, Zachery R. Reichert, et al.. (2025). A circadian and app-based personalized lighting intervention for the reduction of cancer-related fatigue. Cell Reports Medicine. 6(3). 102001–102001. 1 indexed citations
2.
Kim, Dae Wook, et al.. (2024). The Combination of Topological Data Analysis and Mathematical Modeling Improves Sleep Stage Prediction From Consumer-Grade Wearables. Journal of Biological Rhythms. 39(6). 535–553. 5 indexed citations
3.
Kim, Dae Wook, et al.. (2024). The real-world association between digital markers of circadian disruption and mental health risks. npj Digital Medicine. 7(1). 355–355. 6 indexed citations
4.
Kim, Dae Wook, et al.. (2024). Predicting circadian phase in community‐dwelling later‐life adults using actigraphy data. Journal of Sleep Research. 34(4). e14425–e14425. 5 indexed citations
5.
Kim, Dae Wook, et al.. (2023). Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data. Journal of The Royal Society Interface. 20(205). 20230030–20230030. 15 indexed citations
6.
Kim, Dae Wook, et al.. (2023). Wearable Data Assimilation to Estimate the Circadian Phase. SIAM Journal on Applied Mathematics. 84(3). S452–S475. 6 indexed citations
7.
Tyler, Jonathan, Yu Fang, Elena Frank, et al.. (2022). Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression. Cell Reports Medicine. 3(4). 100601–100601. 14 indexed citations
8.
Tyler, Jonathan, Daniel B. Forger, & Jae Kyoung Kim. (2021). Inferring causality in biological oscillators. Bioinformatics. 38(1). 196–203. 5 indexed citations
9.
Fang, Yu, Daniel B. Forger, Elena Frank, Srijan Sen, & Cathy Goldstein. (2021). Day-to-day variability in sleep parameters and depression risk: a prospective cohort study of training physicians. npj Digital Medicine. 4(1). 28–28. 58 indexed citations
10.
Huang, Yitong, Philip Cheng, Helen J. Burgess, et al.. (2021). Predicting circadian phase across populations: a comparison of mathematical models and wearable devices. SLEEP. 44(10). 46 indexed citations
11.
Bradley, Christina, Erin Sandford, Jonathan Tyler, et al.. (2021). Monitoring Beliefs and Physiological Measures Using Wearable Sensors and Smartphone Technology Among Students at Risk of COVID-19: Protocol for a mHealth Study. JMIR Research Protocols. 10(6). e29561–e29561. 7 indexed citations
12.
Sandford, Erin, Jonathan Tyler, Emily Stoneman, et al.. (2021). Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study. JMIR Research Protocols. 10(5). e29562–e29562. 11 indexed citations
13.
Cheng, Philip, Olivia Walch, Yitong Huang, et al.. (2020). Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers. SLEEP. 44(2). 57 indexed citations
14.
Cochran, Amy L., et al.. (2020). Gene-set Enrichment with Mathematical Biology (GEMB). GigaScience. 9(10). 3 indexed citations
15.
Narasimamurthy, Rajesh, Yining Lu, Jean‐Michel Fustin, et al.. (2018). CK1δ/ε protein kinase primes the PER2 circadian phosphoswitch. Proceedings of the National Academy of Sciences. 115(23). 5986–5991. 117 indexed citations
16.
Zhou, Min, et al.. (2015). A Period2 Phosphoswitch Regulates and Temperature Compensates Circadian Period. Molecular Cell. 60(1). 77–88. 130 indexed citations
17.
Ko, Caroline H., Yujiro Yamada, David K. Welsh, et al.. (2010). Correction: Emergence of Noise-Induced Oscillations in the Central Circadian Pacemaker. PLoS Biology. 8(10). 18 indexed citations
18.
Forger, Daniel B.. (2009). Synchronization in Biological Systems. APS.
19.
Gallego, Mónica, et al.. (2006). An opposite role for tau in circadian rhythms revealed by mathematical modeling. Proceedings of the National Academy of Sciences. 103(28). 10618–10623. 140 indexed citations
20.
Paydarfar, David, Daniel B. Forger, & John R. Clay. (2006). Noisy Inputs and the Induction of On–Off Switching Behavior in a Neuronal Pacemaker. Journal of Neurophysiology. 96(6). 3338–3348. 86 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Steven M. Reppert Breakdown of academic impact, for papers by David K. Welsh Breakdown of academic impact, for papers by Johanna H. Meijer Breakdown of academic impact, for papers by Diego A. Golombék Breakdown of academic impact, for papers by Tanya Leise Breakdown of academic impact, for papers by Jude F. Mitchell Breakdown of academic impact, for papers by Sooyoung Chung Breakdown of academic impact, for papers by Sean Hill Breakdown of academic impact, for papers by Jeffrey C. Smith Breakdown of academic impact, for papers by Petra E. Vértes
Rankless by CCL
2026