Daniel L. Oberski
- Sociology and Political Science top 5%
- Statistics and Probability top 1%
- Clinical Psychology top 5%
- Social Psychology top 5%
- Artificial Intelligence top 5%
- Co-authors
- Jeroen K. VermuntAina GallegoSara van ErpJoris MulderZsuzsa BakkRaoul D. SchramErik–Jan van KesterenLaura Boeschoten
- Topics
- Statistical Methods and Bayesian Inference (18 papers)Survey Methodology and Nonresponse (9 papers)Advanced Causal Inference Techniques (7 papers)
- Journals
- NatureJournal of Personality and Social PsychologySHILAP Revista de lepidopterología
- Partner nations
- NetherlandsUnited KingdomUnited States
In The Last Decade
Daniel L. Oberski
88 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 178
- Sociology and Political Science 534
- Statistics and Probability 339
- Clinical Psychology 319
- Social Psychology 309
- Artificial Intelligence 288
Countries citing papers authored by Daniel L. Oberski
This map shows the geographic impact of Daniel L. Oberski'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 L. Oberski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel L. Oberski more than expected).
Fields of papers citing papers by Daniel L. Oberski
This network shows the impact of papers produced by Daniel L. Oberski. 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 L. Oberski. The network helps show where Daniel L. Oberski may publish in the future.
Co-authorship network of co-authors of Daniel L. Oberski
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel L. Oberski. A scholar is included among the top collaborators of Daniel L. Oberski 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 L. Oberski. Daniel L. Oberski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 6 | |
| 4 | 48 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 31 | |
| 8 | 23 | |
| 9 | 12 | |
| 10 | 15 | |
| 11 | An open source machine learning framework for efficient and transparent systematic reviews | 68 |
| 12 | 7 | |
| 13 | 14 | |
| 14 | 1 | |
| 15 | 4 | |
| 16 | The effect of survey measurement error on clustering algorithms | 1 |
| 17 | 222 | |
| 18 | 13 | |
| 19 | 56 | |
| 20 | Local dependence in latent class models : application to voting in elections | 1 |
About Daniel L. Oberski
Daniel L. Oberski is a scholar working on Statistics and Probability, Health Informatics and Management Science and Operations Research, having authored 93 papers that have together received 2.4k indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (18 papers), Survey Methodology and Nonresponse (9 papers) and Advanced Causal Inference Techniques (7 papers). The work is most often cited by research in Statistics and Probability (339 citations), Health Informatics (48 citations) and Applied Psychology (114 citations). Daniel L. Oberski has collaborated with scholars based in Netherlands, United Kingdom and United States. Frequent co-authors include Jeroen K. Vermunt, Aina Gallego, Sara van Erp, Joris Mulder, Zsuzsa Bakk, Raoul D. Schram, Erik–Jan van Kesteren, Laura Boeschoten, Susan Branje and Ayoub Bagheri. Their work appears in journals such as Nature, Journal of Personality and Social Psychology and SHILAP Revista de lepidopterología.
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.