Jarka Glassey

2.3k total citations
82 papers, 1.1k citations indexed

About

Jarka Glassey is a scholar working on Molecular Biology, Control and Systems Engineering and Analytical Chemistry. According to data from OpenAlex, Jarka Glassey has authored 82 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 19 papers in Control and Systems Engineering and 13 papers in Analytical Chemistry. Recurrent topics in Jarka Glassey's work include Viral Infectious Diseases and Gene Expression in Insects (18 papers), Fault Detection and Control Systems (14 papers) and Spectroscopy and Chemometric Analyses (12 papers). Jarka Glassey is often cited by papers focused on Viral Infectious Diseases and Gene Expression in Insects (18 papers), Fault Detection and Control Systems (14 papers) and Spectroscopy and Chemometric Analyses (12 papers). Jarka Glassey collaborates with scholars based in United Kingdom, Germany and Belgium. Jarka Glassey's co-authors include Fernando Russo Abegão, Alan C. Ward, G.A. Montague, Simon Wilkinson, Fernão D. Magalhães, Kristel Bernaerts, Graham A. McCreath, Leon P. Pybus, Amy Green and Moritz von Stosch and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Bioresource Technology.

In The Last Decade

Jarka Glassey

79 papers receiving 1.1k citations

Peers

Jarka Glassey

CSE

EDUCA

Paloma Dı́az Spain

Xia Sun China

Yifan Gao China

Shuwei Zhang China

Jin Li China

Dana M. Barry United States

Chi N. Thai United States

Yanhui Zhang China

Guoying Zhang China

Chen Qiao Hong Kong

Paloma Dı́az Spain

Jarka Glassey

72 ×0.2

72 ×0.4

22 ×0.1

CSE

83 ×0.6

EDUCA

351 ×3.0

Citations per year, relative to Jarka Glassey Jarka Glassey (= 1×) peers Paloma Dı́az

Countries citing papers authored by Jarka Glassey

Since Specialization

Citations

This map shows the geographic impact of Jarka Glassey'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 Jarka Glassey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jarka Glassey more than expected).

Fields of papers citing papers by Jarka Glassey

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jarka Glassey. 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 Jarka Glassey. The network helps show where Jarka Glassey may publish in the future.

Co-authorship network of co-authors of Jarka Glassey

This figure shows the co-authorship network connecting the top 25 collaborators of Jarka Glassey. A scholar is included among the top collaborators of Jarka Glassey 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 Jarka Glassey. Jarka Glassey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ravi, Manoj, Nigel V. Russell, Andrew Hoadley, & Jarka Glassey. (2025). Sustainability − The core of responsible engineering practice and education: Reality or still just utopia? A comparative study between China and the Rest of the World. Education for Chemical Engineers. 51. 43–52.

Azevedo‐Alanis, Luciana Reis, Ida Chapaval Pimentel, Ana Maria Trindade Grégio, et al.. (2024). Cunninghamella echinulata DSM1905 biofilm-based L-asparaginase production in pneumatically-driven bioreactors. PLoS ONE. 19(9). e0308847–e0308847. 1 indexed citations

Abegão, Fernando Russo, et al.. (2023). Game-based assessment framework for virtual reality, augmented reality and digital game-based learning. International Journal of Educational Technology in Higher Education. 20(1). 21 indexed citations

Udugama, Isuru A., Martin J. Atkins, Christoph Bayer, et al.. (2023). Digital tools in chemical engineering education: The needs and the desires. Education for Chemical Engineers. 44. 63–70. 13 indexed citations

Abegão, Fernando Russo, et al.. (2023). Perceptions and factors affecting the adoption of digital games for engineering education: a mixed-method research. International Journal of Educational Technology in Higher Education. 20(1). 2–2. 25 indexed citations

Abegão, Fernando Russo, et al.. (2022). Digital games in engineering education: systematic review and future trends. European Journal of Engineering Education. 48(2). 321–339. 44 indexed citations

Abegão, Fernando Russo, et al.. (2021). Perceptions of the use of virtual reality games for chemical engineering education and professional training. SHILAP Revista de lepidopterología. 6(1). 175–194. 38 indexed citations

Gallagher, Timothy, Kristel Bernaerts, Jarka Glassey, et al.. (2021). Towards design guidelines for virtual reality training for the chemical industry. Education for Chemical Engineers. 36. 12–23. 62 indexed citations

Glassey, Jarka, et al.. (2018). Machine learning in biopharmaceutical manufacturing. 23(4). 62–65. 5 indexed citations

10.

Glassey, Jarka, et al.. (2017). The importance of critical quality attributes in Quality by Design for rapid bioprocess development strategies. 22(6). 48–50. 2 indexed citations

11.

Wilkinson, Simon, et al.. (2016). Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope. Archives of Toxicology. 91(4). 1595–1612. 17 indexed citations

12.

Ward, Alan C., et al.. (2013). Polyunsaturated fatty acid production by marine bacteria. Bioprocess and Biosystems Engineering. 36(11). 1641–1652. 25 indexed citations

13.

Ward, Alan C., et al.. (2013). Screening of Marine Bacterial Producers of Polyunsaturated Fatty Acids and Optimisation of Production. Microbial Ecology. 67(2). 454–464. 26 indexed citations

14.

Ward, Alan C., et al.. (2012). Use of physiological information and process optimisation enhances production of extracellular nuclease by a marine strain of Bacillus licheniformis. Bioresource Technology. 130. 552–558. 9 indexed citations

15.

Carrondo, Manuel J.T., Paula M. Alves, Nuno Carinhas, et al.. (2012). How can measurement, monitoring, modeling and control advance cell culture in industrial biotechnology?. Biotechnology Journal. 7(12). 1522–1529. 31 indexed citations

16.

Sani, Yahaya Muhammad, et al.. (2010). Effect of growth media modifications on cell biomass and polyunsaturated fatty acids (PUFAs) production from Shewanella frigidimarina. AFRICAN JOURNAL OF BIOTECHNOLOGY. 9(52). 8928–8933. 1 indexed citations

17.

Bracewell, Daniel G., Krist V. Gernaey, Jarka Glassey, et al.. (2010). Report and recommendation of a workshop on education and training for measurement, monitoring, modelling and control (M³C) in biochemical engineering. Biotechnology Journal. 5(4). 359–367. 2 indexed citations

18.

Glassey, Jarka, et al.. (2007). Normalisation of Multicondition cDNA Macroarray Data. Comparative and Functional Genomics. 2007. 1–12. 2 indexed citations

19.

Willis, Mark J., et al.. (2003). Predictive scheduling of a bioprocess plant. international conference on Modelling and simulation. 296–301. 1 indexed citations

20.

Cunha, César, Jarka Glassey, G.A. Montague, Steven M. Albert, & Pankaj Mohan. (2002). An assessment of seed quality and its influence on productivity estimation in an industrial antibiotic fermentation. Biotechnology and Bioengineering. 78(6). 658–669. 32 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.

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