GREAT D3.2 MVP Analytics Tools Module
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| Format: | Recurso digital |
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2024
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| _version_ | 1866902055261569024 |
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| author | Egenfeldt Nielsen, Simon Schuur, Joost |
| author_facet | Egenfeldt Nielsen, Simon Schuur, Joost |
| contents | <p>This document describes the Minimum Viable Analytics (MVP) Module that combines data sources from the proprietary technology platforms DiBL and PlayMob. The Analytics Module is the realisation of what was specified in D3.1, where most of the features envisioned are available in the MVP. This can be summarised in the following:</p> <p>● Ensure collected data is organised in a sufficiently segmented way for later analysis.</p> <p>● Create tools to monitor recent data quality/volume to validate data collection.</p> <p>● Share access to the data in a suitable format for in depth analysis.</p> <p>● Implement reasonable solutions for both PlanetyPlay survey system data as well as SGI DiBL data The key areas that are on the roadmap are refinements to the survey system for improving data analysis and the data export flow for in-depth analysis and data sharing. As the MVP analytics module is further used for research on the first case studies, we expect that new feature requests will emerge that will enhance the Analytics Module at a later stage. The flexible and agile approach we envisioned in D2.1 and D3.1 has been mostly successful as we have continuously involved partners in user requirements and shared data samples. However, we are still waiting for the data collection from the first cycles to complete, which will allow GREAT partners to seriously work with the data towards answering research questions, and then get further feedback in regards to how effective the Analytics module is. Based on this input further refinement and adjustments will be made.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_13132064 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | GREAT D3.2 MVP Analytics Tools Module Egenfeldt Nielsen, Simon Schuur, Joost Minimum viable analytics Data collection Survey system Data sharing Case studies Agile methodology Stakeholder collaboration Iterative development Open access Data quality monitoring Research questions Platform extension Policy-makers Technical specification Data segmentation Flexibilty GREAT Project <p>This document describes the Minimum Viable Analytics (MVP) Module that combines data sources from the proprietary technology platforms DiBL and PlayMob. The Analytics Module is the realisation of what was specified in D3.1, where most of the features envisioned are available in the MVP. This can be summarised in the following:</p> <p>● Ensure collected data is organised in a sufficiently segmented way for later analysis.</p> <p>● Create tools to monitor recent data quality/volume to validate data collection.</p> <p>● Share access to the data in a suitable format for in depth analysis.</p> <p>● Implement reasonable solutions for both PlanetyPlay survey system data as well as SGI DiBL data The key areas that are on the roadmap are refinements to the survey system for improving data analysis and the data export flow for in-depth analysis and data sharing. As the MVP analytics module is further used for research on the first case studies, we expect that new feature requests will emerge that will enhance the Analytics Module at a later stage. The flexible and agile approach we envisioned in D2.1 and D3.1 has been mostly successful as we have continuously involved partners in user requirements and shared data samples. However, we are still waiting for the data collection from the first cycles to complete, which will allow GREAT partners to seriously work with the data towards answering research questions, and then get further feedback in regards to how effective the Analytics module is. Based on this input further refinement and adjustments will be made.</p> |
| title | GREAT D3.2 MVP Analytics Tools Module |
| topic | Minimum viable analytics Data collection Survey system Data sharing Case studies Agile methodology Stakeholder collaboration Iterative development Open access Data quality monitoring Research questions Platform extension Policy-makers Technical specification Data segmentation Flexibilty GREAT Project |
| url | https://doi.org/10.5281/zenodo.13132064 |