An academic system at heart, Jotup was originally built for better course revision, with every Notebook corresponding to a course.
Since, it has grown into a powerful, modular system capable of handling any kind of complex knowledge task, from course revision through to complex research and theses.
This is a summary of how Jotup can help:
|Academic Task||Jotup Feature||How Jotup Supports the Task||Efficiency Gains*|
|Managing research notes and articles||Hierarchical note management||Facilitates your research management with organized, easily accessible notes and files, enhancing the efficiency and usability of information.||High |
|Interpreting and gaining insights from research data||AI-powered tools||Expedite data interpretation and unearth crucial insights, supporting better understanding of research data.||High |
|Keeping updated about research trends and news||Information sources integration||Seamlessly access and keep abreast of pertinent research information, empowering you to stay updated with recent developments in your field.||High |
|Collaborating with other researchers||Real-time collaboration||Boosts teamwork and communication by maintaining a synchronized "collective knowledge base," leading to more effective collaboration.||High |
|Processing and understanding large amounts of research data||Import, extract, and summarize information||Streamline data management and understanding by automatically importing, extracting, and summarizing relevant research data.||High |
|Customizing your research experience to align with your specific needs||Customizable tools and sources||Customize your Jotup experience by incorporating the tools and information sources most pertinent to your research, optimizing efficiency.||Moderate-high |
* The adoption of digital tools can result in considerable time savings for academic users, although the specific amount can vary widely depending on several factors. As per McKinsey Global Institute, knowledge workers could save up to 20% of their time, which equates to about one day per week, by employing social technologies for improved collaboration and communication (Chui et al., 2012). This time-saving estimation could potentially be applied to features such as real-time collaboration and information sources integration, as well as the use of AI tools.
Furthermore, as per Forrester (2017), data management and summarization tools could significantly diminish the time spent on data processing, saving the knowledge worker at least several hours per week.
These are general figures, and actual time savings may vary based on specific circumstances, including the user's proficiency with the technology.
Chui, M., Manyika, J., Bughin, J., Dobbs, R., Roxburgh, C., Sarrazin, H., Sands, G., & Westergren, M. (2012) 'The social economy: Unlocking value and productivity through social technologies.' McKinsey Global Institute
Forrester (2017) 'The sorry state of digital transformation in 2018.'
 The high efficiency gain in managing research notes and articles was inferred from the study by Bergman et al., 2008, indicating that improved information management significantly reduces time spent on searching for information and increases productivity.
Reference: Bergman, O., Beyth-Marom, R., & Nachmias, R. (2008) 'The user-subjective approach to personal information management systems.' Journal of the American Society for Information Science and Technology, 59(2), 235-246.
 The high efficiency gain in interpreting and gaining insights from research data was inferred from the study by Davenport, 2018, indicating that AI-powered tools significantly enhance the capacity to interpret complex datasets.
Reference: Davenport, T. H., & Ronanki, R. (2018) 'Artificial Intelligence for the Real World.' Harvard Business Review. [Online] Available at: https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
 The high efficiency gain in keeping updated about research trends and news was inferred from the study by Rowlands et al., 2008, suggesting that integrated information sources can save time spent on accessing and navigating different sources.
Reference: Rowlands, I., Nicholas, D., Russell, B., Canty, N., & Watkinson, A. (2011) 'Social media use in the research workflow.' Learned Publishing, 24(3), 183-195. [Online] Available at: https://doi.org/10.1087/20110206 (Accessed: 20 July 2021).
 The significant efficiency gain in collaborating with other researchers was inferred from the study by Olson et al., 2008, suggesting that real-time collaboration tools can significantly enhance productivity in research groups.
Reference: Olson, G. M., & Olson, J. S. (2000) 'Distance matters.' Human–computer interaction, 15(2-3), 139-178. [Online] Available at: https://doi.org/10.1207/S15327051HCI1523_4 (Accessed: 20 July 2021).
 The high efficiency gain in processing and understanding large amounts of research data was inferred from the study by Kitchin, 2014, suggesting that automation of data processing can significantly reduce time spent on managing and interpreting data.
Reference: Kitchin, R. (2014) 'Big Data, new epistemologies and paradigm shifts.' Big Data & Society, 1(1), 2053951714528481. [Online] Available at: https://doi.org/10.1177/2053951714528481 (Accessed: 20 July 2021).
 The moderate-high efficiency gain in customizing own research experience to align with the researcher's specific needs was inferred from the study by Niu et al., 2010, suggesting that customization of digital tools can enhance the user's efficiency and satisfaction.
Reference: Niu, X., Hemminger, B. M., Lown, C., Adams, S., Brown, C., Level, A., McLure, M., Powers, A., Tennant, M. R., & Cataldo, T. (2010) 'National study of information seeking behavior of academic researchers in the United States.' Journal of the American Society for Information Science and Technology, 61(5), 869-890.