Daniel F. Schmidt
- Molecular Biology
- Artificial Intelligence top 10%
- Pediatrics, Perinatology and Child Health top 10%
- Oncology
- Genetics
- Co-authors
- Enes MakalicJohn L. HopperGraham G. GilesMelissa C. SoutheyPierre‐Antoine DuguéDallas R. EnglishRoger L. MilneJihoon E. Joo
- Topics
- Epigenetics and DNA Methylation (12 papers)Genetic Associations and Epidemiology (8 papers)Global Cancer Incidence and Screening (8 papers)
In The Last Decade
Daniel F. Schmidt
53 papers receiving 965 citations
Peers
Comparison fields: 5 of 125
- Molecular Biology 462
- Artificial Intelligence 147
- Pediatrics, Perinatology and Child Health 139
- Oncology 129
- Genetics 125
Countries citing papers authored by Daniel F. Schmidt
This map shows the geographic impact of Daniel F. Schmidt'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 F. Schmidt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel F. Schmidt more than expected).
Fields of papers citing papers by Daniel F. Schmidt
This network shows the impact of papers produced by Daniel F. Schmidt. 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 F. Schmidt. The network helps show where Daniel F. Schmidt may publish in the future.
Co-authorship network of co-authors of Daniel F. Schmidt
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel F. Schmidt. A scholar is included among the top collaborators of Daniel F. Schmidt 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 F. Schmidt. Daniel F. Schmidt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 36 | |
| 3 | 7 | |
| 4 | 2 | |
| 5 | 7 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 5 | |
| 10 | 0 | |
| 11 | 47 | |
| 12 | 55 | |
| 13 | 35 | |
| 14 | Bayesian Sparse Global-Local Shrinkage Regression for Grouped Variables | 1 |
| 15 | 3 | |
| 16 | 21 | |
| 17 | 6 | |
| 18 | 4 | |
| 19 | 12 | |
| 20 | Using Gaussian spatial processes to model and predict interests in museum exhibits | 3 |
About Daniel F. Schmidt
Daniel F. Schmidt is a scholar working on Statistics and Probability, Aging and Signal Processing, having authored 56 papers that have together received 981 indexed citations. Recurring topics across this work include Epigenetics and DNA Methylation (12 papers), Genetic Associations and Epidemiology (8 papers) and Global Cancer Incidence and Screening (8 papers). The work is most often cited by research in Statistics and Probability (76 citations), Aging (16 citations) and Pediatrics, Perinatology and Child Health (139 citations). Daniel F. Schmidt has collaborated with scholars based in Australia, France and Italy. Frequent co-authors include Enes Makalic, John L. Hopper, Graham G. Giles, Melissa C. Southey, Pierre‐Antoine Dugué, Dallas R. English, Roger L. Milne, Jihoon E. Joo, Ee Ming Wong and Daniel D. Buchanan. Their work appears in journals such as Journal of Personality and Social Psychology, American Journal of Clinical Nutrition and Cancer Research.
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.