Sarah Giessing
Impact in
- Artificial Intelligence top 10%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Internet Traffic Analysis and Secure E-voting
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- Mobile Crowdsensing and Crowdsourcing
Papers in
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- Bayesian Methods and Mixture Models 1
- Data Analysis with R 1
- Bayesian Modeling and Causal Inference 1
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- Data Mining Algorithms and Applications 1
- Co-authors
- Anco Hundepool (2 shared papers)Luisa Franconi (1 shared paper)Keith Spicer (1 shared paper)Peter‐Paul de Wolf (2 shared papers)Josep Domingo‐Ferrer (1 shared paper)Eric Schulte Nordholt (1 shared paper)Jordi Castro (1 shared paper)
- Journals
- Data & Knowledge Engineering (1 paper)LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (1 paper)RECERCAT (Consorci de Serveis Universitaris de Catalunya) (1 paper)
- Partner nations
- NetherlandsGermany
In The Last Decade
Sarah Giessing
4 papers receiving 201 citations
Peers
Comparison fields: 5 of 40
- Artificial Intelligence 165
- Computer Science Applications 20
- Statistics and Probability 27
- Management Science and Operations Research 40
- Sociology and Political Science 65
Countries citing papers authored by Sarah Giessing
This map shows the geographic impact of Sarah Giessing'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 Sarah Giessing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sarah Giessing more than expected).
Fields of papers citing papers by Sarah Giessing
This network shows the impact of papers produced by Sarah Giessing. 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 Sarah Giessing. The network helps show where Sarah Giessing may publish in the future.
Co-authors
The 7 scholars most cited alongside Sarah Giessing, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 207 | |
| 2 | Rounding methods for protecting EU-aggregates | 2007 | 3 |
| 3 | Eliminating small cells from census counts tables: empirical vs. design transition probabilities | 2011 | 1 |
| 4 | 2009 | 1 |
About Sarah Giessing
Sarah Giessing is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Nutrition and Dietetics and Plant Science, having authored 4 papers that have together received 212 indexed citations. Recurring topics across this work include Soybean genetics and cultivation (1 paper), Bayesian Methods and Mixture Models (1 paper), Data Quality and Management (1 paper), Data Analysis with R (1 paper), Data Mining Algorithms and Applications (1 paper), Bayesian Modeling and Causal Inference (1 paper) and Food composition and properties (1 paper). The work is most often cited by research in Artificial Intelligence (165 citations), Computer Science Applications (20 citations), Statistics and Probability (27 citations), Management Science and Operations Research (40 citations) and Sociology and Political Science (65 citations). Sarah Giessing has collaborated with scholars based in Netherlands and Germany. Frequent co-authors include Anco Hundepool, Luisa Franconi, Keith Spicer, Peter‐Paul de Wolf, Josep Domingo‐Ferrer, Eric Schulte Nordholt and Jordi Castro. Their work appears in journals such as Data & Knowledge Engineering, LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) and RECERCAT (Consorci de Serveis Universitaris de Catalunya).
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.