Mark Servilla
Impact in
- Ecological Modeling top 10%
- Species Distribution and Climate Change
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- Scientific Computing and Data Management
Papers in
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- Research Data Management Practices 9
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- Scientific Computing and Data Management 9
- Co-authors
- Kristin Vanderbilt (5 shared papers)John H. Porter (1 shared paper)William K. Michener (1 shared paper)K. Dean (1 shared paper)Andrew T. Roach (1 shared paper)Brian L. Foster (1 shared paper)Kevin Engle (1 shared paper)Margaret O’Brien (6 shared papers)
- Journals
- Ecological Informatics (4 papers)Eos (1 paper)BioScience (1 paper)Data Science Journal (1 paper)Ecology and Evolution (1 paper)
- Partner nations
- United States
In The Last Decade
Mark Servilla
15 papers receiving 200 citations
Peers
Comparison fields: 5 of 55
- Ecological Modeling 57
- Information Systems and Management 72
- Information Systems 83
- Developmental Biology 8
- Geology 14
Countries citing papers authored by Mark Servilla
This map shows the geographic impact of Mark Servilla'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 Mark Servilla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Servilla more than expected).
Fields of papers citing papers by Mark Servilla
This network shows the impact of papers produced by Mark Servilla. 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 Mark Servilla. The network helps show where Mark Servilla may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark Servilla, 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 | 2010 | 55 | |
| 2 | 1998 | 53 | |
| 3 | 2009 | 19 | |
| 4 | 2017 | 17 | |
| 5 | 2023 | 13 | |
| 6 | 2016 | 12 | |
| 7 | 2007 | 12 | |
| 8 | 2016 | 9 | |
| 9 | 2018 | 8 | |
| 10 | 2006 | 7 | |
| 11 | 2019 | 7 | |
| 12 | The LTER Network Information System: Improving Data Quality and Synthesis through Community Collaboration | 2011 | 3 |
| 13 | 2022 | 2 | |
| 14 | Pasta: A Network-level Architecture Design for Automating the Creation of Synthetic Products in the LTER Network | 2006 | 1 |
| 15 | Bridging the barriers agriculture remote. | 1998 | 1 |
About Mark Servilla
Mark Servilla is a scholar working on Information Systems, Information Systems and Management, Ecological Modeling, Artificial Intelligence and Management Science and Operations Research, having authored 15 papers that have together received 219 indexed citations. Recurring topics across this work include Scientific Computing and Data Management (9 papers), Research Data Management Practices (9 papers), Species Distribution and Climate Change (5 papers), Data Quality and Management (3 papers), Computational Physics and Python Applications (2 papers), Distributed and Parallel Computing Systems (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper) and Hermeneutics and Narrative Identity (1 paper). The work is most often cited by research in Ecological Modeling (57 citations), Information Systems and Management (72 citations), Information Systems (83 citations), Developmental Biology (8 citations) and Geology (14 citations). Mark Servilla has collaborated with scholars based in United States. Frequent co-authors include Kristin Vanderbilt, John H. Porter, William K. Michener, K. Dean, Andrew T. Roach, Brian L. Foster, Kevin Engle, Margaret O’Brien, Robert B. Waide and J. Brunt. Their work appears in journals such as Ecological Informatics, Eos, BioScience, Data Science Journal and Ecology and Evolution.
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.