Countries citing papers authored by Stephan Zednik
Since
Specialization
Citations
This map shows the geographic impact of Stephan Zednik'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 Stephan Zednik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephan Zednik more than expected).
This network shows the impact of papers produced by Stephan Zednik. 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 Stephan Zednik. The network helps show where Stephan Zednik may publish in the future.
Co-authorship network of co-authors of Stephan Zednik
This figure shows the co-authorship network connecting the top 25 collaborators of Stephan Zednik.
A scholar is included among the top collaborators of Stephan Zednik 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 Stephan Zednik. Stephan Zednik 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.
Prabhu, Anirudh, Peter Fox, Hao Zhong, et al.. (2017). Visualizing Complex Environments in the Geo- and BioSciences. AGU Fall Meeting Abstracts. 2017.1 indexed citations
Leadbetter, Adam, Adam Shepherd, R. A. Arko, et al.. (2016). Experiences of a “semantics smackdown”. Earth Science Informatics. 9(3). 355–363.1 indexed citations
6.
Ma, Xiaogang, Patrick West, John Erickson, et al.. (2015). From Data Portal to Knowledge Portal: Leveraging Semantic Technologies to Support Interdisciplinary Studies.. 2–7.1 indexed citations
Gil, Yolanda, Simon Miles, Khalid Belhajjame, et al.. (2013). PROV Model Primer: W3C Working Group Note. Research Explorer (The University of Manchester).12 indexed citations
10.
Belhajjame, Khalid, James Cheney, David Corsar, et al.. (2013). PROV-O: The PROV ontology:W3C recommendation 30 April 2013. Lancaster EPrints (Lancaster University).26 indexed citations
11.
Gil, Yolanda, Simon Miles, Khalid Belhajjame, et al.. (2012). PROV Model Primer.40 indexed citations
12.
Zednik, Stephan, et al.. (2010). A Semantic Provenance-aware Expert Advisory System in a Web-based Science Data Analysis Tool. AGUFM. 2010.
13.
McGuinness, Deborah L., et al.. (2010). Progress toward a Semantic eScience Framework; building on advanced cyberinfrastructure. AGUFM. 2010.2 indexed citations
14.
West, Patrick, et al.. (2009). Developing an Ontology for Ocean Biogeochemistry Data. AGU Fall Meeting Abstracts. 2009.2 indexed citations
15.
Zednik, Stephan, Peter Fox, & Deborah L. McGuinness. (2009). Domain Knowledge and Provenance in Science Data Systems. AGU Fall Meeting Abstracts. 2009.
16.
McGuinness, Deborah L., et al.. (2009). Provenance-Aware Faceted Search. AGU Fall Meeting Abstracts. 2009.1 indexed citations
17.
Michaelis, James, et al.. (2008). Towards Usable and Interoperable Workflow Provenance: Empirical Case Studies using PML. International Semantic Web Conference. 23–28.2 indexed citations
18.
Zednik, Stephan, et al.. (2008). Semantic Provenance for Science Data Products: Application to Image Data Processing. International Semantic Web Conference. 4–10.6 indexed citations
19.
McGuinness, Deborah L., Peter Fox, Stephan Zednik, et al.. (2008). Annotating and embedding provenance in science data repositories to enable next generation science applications. AGU Fall Meeting Abstracts. 2008.6 indexed citations
20.
Politovich, Marcia K., et al.. (2006). Progress in the Development of Practical Remote Detection of Icing Conditions.9 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.