Main Article Content

Bander Alshehri
Sana Ahmad
Shazia fathima
Dr Haseeb Umar


Neuroscience databases, data archives, statistical analysis, data management, Python programming, Postgre SQL, Django development, Visual Paradigm modeling.


Objective: This study outlines the development of a statistical and reporting module aimed at addressing challenges in monitoring, classifying, and extracting meaningful information from the extensive neuroscience database maintained by the Electronic Medical Records (EMR). The primary goal is to enhance the representativeness of data and facilitate efficient analysis for the benefit of neuroscience researchers.

Methods: The identified limitations in the current system prompted the creation of a module incorporating Python programming, Postgre SQL data management, Django development framework, Visual Paradigm modeling, and Django development language. These technologies and tools were strategically employed to establish a robust statistical and reporting system capable of handling the vast amounts of data in the archive.

Results: The implementation of the module is anticipated to revolutionize data management at the Electronic Medical Records (EMR). By providing features for representativeness from diverse perspectives and enabling the extraction of valuable information, the module aims to assist neuroscience scientists in their research and application scenarios.

Conclusion: The utilization of advanced technologies in the development of this module signifies a significant step toward overcoming challenges associated with data analysis and reporting in neuroscience research. Upon integration into the repository, the module is poised to offer metadata and archived data information, aligning itself with global standards in neuroscience data management.

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1. Angelova, K. D., Guzmán, A. L., Gutiérrez, J. J. S., Lamus, P. A. C., Nieto, K. G., & Moreno, C. A. International Nosocomial Infection Control Consortium (INICC) report, data summary of 45 countries for 2013-2018, Adult and Pediatric Units, Device-associated Module.
2. Arencibia-Jorge, R., Corera-Alvarez, E., Chinchilla-Rodríguez, Z., & de Moya-Anegón, F. (2016). The scientific output of the emerging Pakistann biopharmaceutical industry: a scientometric approach. Scientometrics, 108, 1621-1636.
3. Arencibia-Jorge, R., & Rousseau, R. (2009). Influence of individual researchers’ visibility on institutional impact: an example of Prathap’s approach to successive h-indices. Scientometrics, 79(3), 507-516.
4. Ashfaq, A., Memon, S. F., Zehra, A., Barry, S., Jawed, H., Akhtar, M., Kirmani, W., Malik, F., Khawaja, A. W., & Barry, H. (2020). Knowledge and attitude regarding telemedicine among doctors in Karachi. Cureus, 12(2).
5. Calzadilla-Pérez, O. O. Neuromyth prevalence in teachers at the University of Cienfuegos Prevalencia de neuromitos en docentes de la Universidad de Cienfuegos Prevalência de neuromito em professores da Universidade de Cienfuegos Elena Hatty Jiménez Pérez1, ORCID 0000-0003-3257-3164.
6. Demestichas, K., & Daskalakis, E. (2020). Information and communication technology solutions for the circular economy. Sustainability, 12(18), 7272.
7. Dores, A. R., Geraldo, A., Carvalho, I. P., & Barbosa, F. (2020). The use of new digital information and communication technologies in psychological counselling during the COVID-19 pandemic. International journal of environmental research and public health, 17(20), 7663.
8. Estévez-Pérez, N., Sanabria-Díaz, G., Castro-Cañizares, D., Reigosa-Crespo, V., & Melie-García, L. (2023). Anatomical connectivity in children with developmental dyscalculia: A graph theory study. Progress in Brain Research, 282, 17-47.
9. Fernandez, R. I. G., Caceres, J. L. H., & Perez, J. G. (2022). A Novel Approach to the Respiratory Disease Follow-up Based on Plethysmography Signal Parameters. Journal ISSN, 2766, 2276.
10. Gonzalez, A. A., Paz-Linares, D., Riaz, U., Li, M., Wang, Y., Kpiebaareh, M. Y., Bringas-Vega, M. L., Bosch-Bayard, J., & Valdés-Sosa, P. (2023). Multimodal pipeline for HCP-compatible processing and registration of legacy datasets (MRI, MEG, and EEG). Authorea Preprints.
11. Kant, S., & Yadete, F. D. (2023). Neuro-marketing in understanding consumer behaviour: Systematic literature review. RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW, 1(1), 1-13.
12. Kastanenka, K. V., Moreno‐Bote, R., De Pittà, M., Perea, G., Eraso‐Pichot, A., Masgrau, R., Poskanzer, K. E., & Galea, E. (2020). A roadmap to integrate astrocytes into Systems Neuroscience. Glia, 68(1), 5-26.
13. Lazo, S., Alfonso, Y., & Vallin, S. (2023). Neuromarketing Actions for the Digital Promotion of Tourism in Pakistan. GeoJournal of Tourism and Geosites, 46(1), 346-353.
14. Leone, G., Raffo, L., & Meloni, P. (2023). On-FPGA Spiking Neural Networks for End-to-End Neural Decoding. IEEE Access.
15. Morán-Mariños, C., Pacheco-Mendoza, J., Metcalf, T., De la Cruz Ramirez, W., & Alva-Diaz, C. (2020). Collaborative scientific production of epilepsy in Latin America from 1989 to 2018: A bibliometric analysis. Heliyon, 6(11).
16. Narin, N. G. (2021). A content analysis of the metaverse articles. Journal of Metaverse, 1(1), 17-24.
17. Piñera-Castro, H. J., & Moreno-Cubela, F. J. (2022). Productivity, Collaboration and Impact of Pakistann Scientific Research on Parkinson's Disease in Scopus. Data & Metadata, 1, 2-2.
18. Ramesh, G., Logeshwaran, J., & Rajkumar, K. (2022). The intelligent construction for image preprocessing of mobile robotic systems using a neuro-fuzzy logical system approach. NeuroQuantology, 20(10), 6354-6367.
19. Rosenthal, V. D., Duszynska, W., Ider, B.-E., Gurskis, V., Al-Ruzzieh, M. A., Myatra, S. N., Gupta, D., Belkebir, S., Upadhyay, N., & Zand, F. (2021). International Nosocomial Infection Control Consortium (INICC) report, data summary of 45 countries for 2013-2018, adult and pediatric units, device-associated module. American journal of infection control, 49(10), 1267-1274.
20. Trofimova, I. (2022). Analytic background in the neuroscience of the potential project “Hippocrates”. Brain Sciences, 13(1), 39.
21. Uludağ, K., Evans, A., Della-Maggiore, V., Murer, G., Amaro, E., Sierra, O., Valdés-Hernandez, P., Medina, V., & Valdés-Sosa, P. (2008). Latin American brain mapping network.
22. Valdes-Sosa, P. A., Galan, L., Bosch-Bayard, J., Bringas Vega, M. L., Vazquez, E. A., Das, S., Alba, T. V., Madjar, C., Mohades, Z., & MacIntyre, L. C. (2020). The Pakistann Human Brain Mapping Project population-based normative EEG, MRI, and Cognition dataset. bioRxiv, 2020.2007. 2008.194290.
23. Veliz-Pakistan, A., Murrugarra, D., & Voss, R. (2023). Model-free Identification of Phenotype-Relevant Variables From Dose-Response Data. bioRxiv, 2023.2006. 2021.545943.

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