Daniel A. Donoho

2.4k total citations
77 papers, 1.7k citations indexed

About

Daniel A. Donoho is a scholar working on Surgery, Endocrinology, Diabetes and Metabolism and Epidemiology. According to data from OpenAlex, Daniel A. Donoho has authored 77 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Surgery, 19 papers in Endocrinology, Diabetes and Metabolism and 13 papers in Epidemiology. Recurrent topics in Daniel A. Donoho's work include Surgical Simulation and Training (19 papers), Pituitary Gland Disorders and Treatments (17 papers) and Head and Neck Surgical Oncology (12 papers). Daniel A. Donoho is often cited by papers focused on Surgical Simulation and Training (19 papers), Pituitary Gland Disorders and Treatments (17 papers) and Head and Neck Surgical Oncology (12 papers). Daniel A. Donoho collaborates with scholars based in United States, India and Canada. Daniel A. Donoho's co-authors include Gabriel Zada, Elizabeth A. Lawson, Madhusmita Misra, Anne Klibanski, Karen K. Miller, Erinne Meenaghan, David B. Herzog, Joseph J. Cavallo, Howard P. Forman and Ian A. Buchanan and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Clinical Endocrinology & Metabolism and Scientific Reports.

In The Last Decade

Daniel A. Donoho

68 papers receiving 1.6k 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 A. Donoho United States 24 546 397 334 223 198 77 1.7k
Ilene Staff United States 27 639 1.2× 112 0.3× 201 0.6× 136 0.6× 480 2.4× 117 2.3k
Sandra L. Wootton‐Gorges United States 27 642 1.2× 326 0.8× 100 0.3× 56 0.3× 292 1.5× 64 2.4k
Giuseppe Servillo Italy 25 708 1.3× 101 0.3× 153 0.5× 148 0.7× 217 1.1× 156 3.1k
Aida Lteif United States 27 536 1.0× 954 2.4× 84 0.3× 148 0.7× 138 0.7× 71 2.1k
Mohamed Boucékine France 26 178 0.3× 46 0.1× 252 0.8× 85 0.4× 270 1.4× 124 2.5k
Donghwi Park South Korea 25 712 1.3× 45 0.1× 159 0.5× 64 0.3× 168 0.8× 166 2.2k
Okan U. Elci United States 24 300 0.5× 75 0.2× 187 0.6× 369 1.7× 309 1.6× 59 1.9k
Noel N. Williams United States 40 3.0k 5.4× 160 0.4× 1.1k 3.2× 865 3.9× 144 0.7× 184 5.2k
Paul Dimitri United Kingdom 20 322 0.6× 293 0.7× 44 0.1× 220 1.0× 241 1.2× 71 1.6k
Anil Nanda United States 24 827 1.5× 96 0.2× 123 0.4× 379 1.7× 540 2.7× 136 2.5k

Countries citing papers authored by Daniel A. Donoho

Since Specialization
Citations

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

Fields of papers citing papers by Daniel A. Donoho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel A. Donoho

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel A. Donoho. A scholar is included among the top collaborators of Daniel A. Donoho 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 A. Donoho. Daniel A. Donoho 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.
Eyre, Harris A., Kerry A. Vaughan, Sameer A. Sheth, et al.. (2025). The Neurosurgeon's Role in Brain Health Diplomacy: The Next Step for Global Neurosurgery. World Neurosurgery. 193. 1165–1167.
2.
Kim, Alexander, et al.. (2025). Evaluating the value of AI-generated questions for USMLE step 1 preparation: A study using ChatGPT-3.5. Medical Teacher. 47(10). 1645–1653.
3.
Rana, Md. Sohel, Suresh N. Magge, Daniel A. Donoho, et al.. (2025). Optimal timing of endoscopic sagittal suturectomy. Journal of Neurosurgery Pediatrics. 35(5). 436–441.
4.
Kilburn, Lindsay, Adriana Fonseca, Roger J. Packer, et al.. (2024). MR-guided focused ultrasound in pediatric neurosurgery: current insights, technical challenges, and lessons learned from 45 treatments at Children’s National Hospital. Neurosurgical FOCUS. 57(3). E6–E6. 5 indexed citations
5.
Yilmaz, Recai, Samuel R. Browd, & Daniel A. Donoho. (2024). Controversies in Artificial Intelligence in Neurosurgery. Neurosurgery Clinics of North America. 36(1). 91–100. 1 indexed citations
6.
Kugener, Guillaume, Dhiraj J. Pangal, Hee Won Lee, et al.. (2024). Simulated outcomes for durotomy repair in minimally invasive spine surgery. Scientific Data. 11(1). 62–62. 4 indexed citations
7.
Liu, James K., et al.. (2024). AI-Based Surgical Tools Detection from Endoscopic Endonasal Pituitary Videos. Journal of Neurological Surgery Part B Skull Base. 85(S 01). S1–S398. 1 indexed citations
8.
Buchanan, Ian A., et al.. (2024). A microdiscectomy surgical video annotation framework for supervised machine learning applications. International Journal of Computer Assisted Radiology and Surgery. 19(10). 1947–1952. 1 indexed citations
9.
Ning, Bo, et al.. (2023). Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier. Bioengineering. 10(10). 1190–1190. 10 indexed citations
10.
11.
12.
Fischer, Elizabeth, et al.. (2023). A methodology for the annotation of surgical videos for supervised machine learning applications. International Journal of Computer Assisted Radiology and Surgery. 18(9). 1673–1678. 7 indexed citations
13.
Kiyasseh, Dani, Runzhuo Ma, Brian J. Miles, et al.. (2023). A vision transformer for decoding surgeon activity from surgical videos. Nature Biomedical Engineering. 7(6). 780–796. 71 indexed citations
14.
15.
Buchanan, Ian A., Michelle Lin, Daniel A. Donoho, et al.. (2019). Venous Thromboembolism After Degenerative Spine Surgery: A Nationwide Readmissions Database Analysis. World Neurosurgery. 125. e165–e174. 14 indexed citations
16.
Chang, Stephanie W., Daniel A. Donoho, & Gabriel Zada. (2018). Use of optical fluorescence agents during surgery for pituitary adenomas: current state of the field. Journal of Neuro-Oncology. 141(3). 585–593. 25 indexed citations
17.
Donoho, Daniel A., Arati Patel, Ian A. Buchanan, et al.. (2018). Treatment at Safety-Net Hospitals Is Associated with Delays in Coil Embolization in Patients with Subarachnoid Hemorrhage. World Neurosurgery. 120. e434–e439. 6 indexed citations
18.
Donoho, Daniel A., et al.. (2016). Management of aggressive growth hormone secreting pituitary adenomas. Pituitary. 20(1). 169–178. 24 indexed citations
19.
Fazeli, Pouneh K., Elizabeth A. Lawson, Rajani Prabhakaran, et al.. (2010). Effects of Recombinant Human Growth Hormone in Anorexia Nervosa: A Randomized, Placebo-Controlled Study. The Journal of Clinical Endocrinology & Metabolism. 95(11). 4889–4897. 80 indexed citations
20.
Lawson, Elizabeth A., Daniel A. Donoho, Karen K. Miller, et al.. (2009). Hypercortisolemia Is Associated with Severity of Bone Loss and Depression in Hypothalamic Amenorrhea and Anorexia Nervosa. The Journal of Clinical Endocrinology & Metabolism. 94(12). 4710–4716. 119 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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