Daniel Williams

1.5k total citations
58 papers, 939 citations indexed

About

Daniel Williams is a scholar working on Management Science and Operations Research, Economics and Econometrics and Computer Networks and Communications. According to data from OpenAlex, Daniel Williams has authored 58 papers receiving a total of 939 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Management Science and Operations Research, 10 papers in Economics and Econometrics and 8 papers in Computer Networks and Communications. Recurrent topics in Daniel Williams's work include Public Policy and Administration Research (8 papers), Monetary Policy and Economic Impact (7 papers) and Parallel Computing and Optimization Techniques (7 papers). Daniel Williams is often cited by papers focused on Public Policy and Administration Research (8 papers), Monetary Policy and Economic Impact (7 papers) and Parallel Computing and Optimization Techniques (7 papers). Daniel Williams collaborates with scholars based in United States, South Korea and Türkiye. Daniel Williams's co-authors include Don Miller, Wei Wang, Jun Huan, Greg Chen, Jack W. Davidson, Jason D. Hiser, Kim R. Geisinger, Bruce R. Childers, John W. Keyes and James O. Cappellari and has published in prestigious journals such as SHILAP Revista de lepidopterología, Ecology and Clinica Chimica Acta.

In The Last Decade

Daniel Williams

51 papers receiving 858 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 Williams United States 16 184 139 131 124 117 58 939
D. Brent Edwards United States 21 39 0.2× 66 0.5× 285 2.2× 25 0.2× 185 1.6× 107 1.4k
Simon Scerri Germany 12 43 0.2× 234 1.7× 404 3.1× 119 1.0× 61 0.5× 40 898
Enrico Ferro Italy 13 59 0.3× 42 0.3× 458 3.5× 71 0.6× 44 0.4× 30 879
Fabrizio Orlandi Ireland 12 44 0.2× 287 2.1× 457 3.5× 116 0.9× 83 0.7× 54 969
Alex Wright United States 15 32 0.2× 79 0.6× 15 0.1× 160 1.3× 24 0.2× 67 1.3k
Judie Attard Germany 11 45 0.2× 287 2.1× 450 3.4× 150 1.2× 53 0.5× 21 793
Anita Prinzie Belgium 16 29 0.2× 105 0.8× 22 0.2× 82 0.7× 20 0.2× 23 816
Maria A. Wimmer Germany 16 73 0.4× 63 0.5× 589 4.5× 200 1.6× 37 0.3× 113 999
İsmail Ertürk Türkiye 21 24 0.1× 5 0.0× 176 1.3× 75 0.6× 171 1.5× 87 1.3k
Ming Zeng China 22 8 0.0× 56 0.4× 27 0.2× 101 0.8× 76 0.6× 205 2.4k

Countries citing papers authored by Daniel Williams

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Williams

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Williams. A scholar is included among the top collaborators of Daniel Williams 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 Williams. Daniel Williams 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.
Williams, Daniel, et al.. (2025). Using Large Language Models to Forecast Local Government Revenue. 2(2). 85–98.
2.
Porosnicu, Mercedes, Anderson O. Cox, Joshua D. Waltonen, et al.. (2022). Early [18]FDG PET/CT scan predicts tumor response in head and neck squamous cell cancer patients treated with erlotinib adjusted per smoking status. Frontiers in Oncology. 12. 939118–939118. 1 indexed citations
3.
Rodríguez, Luis Alfredo, et al.. (2020). Development of a Motion Control Laboratory Focusing on Control Design and Fluid Power Education. Papers on Engineering Education Repository (American Society for Engineering Education).
4.
Williams, Daniel & Don Waisanen. (2020). Real Money, Real Power?: The Challenges with Participatory Budgeting in New York City. 1 indexed citations
5.
Gopalan, Kartik, et al.. (2017). Multi-hypervisor virtual machines: enabling an ecosystem of hypervisor-level services. USENIX Annual Technical Conference. 235–249. 3 indexed citations
6.
Williams, Daniel. (2016). Cracking The Code On The Hidden Curriculum In The Medical Education Pipeline And Its Contribution To Attrition. SHILAP Revista de lepidopterología. 1 indexed citations
7.
Williams, Daniel & Thad D. Calabrese. (2016). The Status of Budget Forecasting. SHILAP Revista de lepidopterología. 2(2). 127–160. 17 indexed citations
8.
Williams, Daniel, et al.. (2016). Local government revenue forecasting methods: competition and comparison. Journal of Public Budgeting Accounting & Financial Management. 28(4). 488–526. 7 indexed citations
9.
Bianchi, Carmine & Daniel Williams. (2015). Applying System Dynamics Modeling To Foster a Cause-and-Effect Perspective in Dealing with Behavioral Distortions Associated with a City’s Performance Measurement Programs. Public Performance & Management Review. 38(3). 395–425. 16 indexed citations
10.
Williams, Daniel. (2014). The Evolution of the Performance Model from Black Box to the Logic Model Through Systems Thinking. International Journal of Public Administration. 37(13). 932–944. 5 indexed citations
11.
Williams, Daniel. (2012). The Politics of Forecast Bias: Forecaster Effect and Other Effects in New York City Revenue Forecasting. Public Budgeting & Finance. 32(4). 1–18. 12 indexed citations
12.
Williams, Daniel, et al.. (2010). Information Literacy in Public Affairs Curriculum. Journal of Public Affairs Education. 16(2). 279–306. 3 indexed citations
13.
Williams, Daniel, et al.. (2009). A cross-layer approach to heterogeneity and reliability. Formal Methods. 88–97. 2 indexed citations
14.
Williams, Daniel & Mordecai Lee. (2008). Déjà Vu All Over Again. The American Review of Public Administration. 38(2). 203–224. 7 indexed citations
15.
Kumar, Naveen, et al.. (2005). Compile-Time Planning for Overhead Reduction in Software Dynamic Translators. International Journal of Parallel Programming. 33(2-3). 103–114. 11 indexed citations
16.
Williams, Daniel. (2003). Measuring Government in the Early Twentieth Century. Public Administration Review. 63(6). 643–659. 124 indexed citations
17.
Williams, Daniel. (2002). Before Performance Measurement. Administrative Theory & Praxis. 24(3). 457–486. 21 indexed citations
18.
Williams, Daniel, et al.. (1999). Criteria for evaluating revenue options: a comprehensive view. International Journal of Public Administration. 22(11-12). 1507–1533. 1 indexed citations
19.
Ginsberg, Lawrence E., Daniel Williams, & Constance A. Stanton. (1993). MRI of Vertebral Sarcoidosis. Journal of Computer Assisted Tomography. 17(1). 158–158. 25 indexed citations
20.
Werner, Mario, Jack A. Zeller, & Daniel Williams. (1981). Consultation in the Coagulation Laboratory: A Conceptual Analysis. American Journal of Clinical Pathology. 75(4). 504–508. 4 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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