VA Facilities

Description: The virtual infrastructure consists of three main items: the Sensors Platform, the Sensors Knowledge Portal and the Sensors Data Framework.

The Sensors Platform is free for any user and publicly available. It consists of three main software components. All the components are released on an AGLP-3.0 open-source license and ready to be deployed on any standard cloud infrastructure.

• Smart Citizen Website: It aims to provide a visual website where the project’s environmental sensors can be accessed in near real time to facilitate the exploration of data with other contextual data (maps, keywords) and processed reports.  The main instance is available at smartcitizen.me/kits.
• Smart Citizen API: This platform provides a REST interface for all the functionalities available on the website. That allows applications to be developed easily on top while having access to all the features to create rich, complex tools. The main instance is available at api.smartcitizen.me.
• Onboarding app: This application is aimed at facilitating the process of sensor setup to ensure that users can install the sensors, irrespective of their technical expertise. It guides users through the setup process using a simple, visual graphic language. The main instance is available at start.smartcitizen.me.

The Sensor Knowledge Portal is a website that aims to  materialise documentation or knowledge support supplied to users in different steps of their experimentation. It is available through participatory-tech.docs.minke.eu and it has the following components:

1. Static documentation: permanently available documentation that details different generic aspects of the devices, such as technical specifications, operation modes, user interfaces and usage precautions. This documentation also includes information on how to access the data, providing practical examples in different formats.
2. Guided courses: oriented to a specific set of tasks, this provides structured material that should be easily followed by users to accomplish certain objectives, such as deploying the sensors efficiently by following different onboarding steps and understanding aspects that are available in the static documents, but that are often overlooked.
3. Community support: this form of knowledge support is provided through an online platform, in the form of a forum, in which users are initially supported by experts. 
4. Projects portfolio: this portfolio aims to provide users with a platform in which they can document their experience in a consolidated way, while maximising it’s visibility. This portfolio serves two purposes: (1) it provides a platform for users to showcase their work, and inspire others by doing so; and (2) it allows a systematic and indexed documentation system that can be used by other communities in the future (citizen-science or more advanced research) in an automated way.

The Sensor Data Framework is a set of software tools for sensor data exploration, sensor model development, data visualization and programmatic data analysis. It is built using Python, a user-friendly, powerful programming language that can handle large amounts of data and is widely used in the data science community. The code is fully open-source and is hosted in a Github repository and pypi, through pypi.org/project/scdata/. This framework is connected to the Smart Citizen and several other APIs for data retrieval and can post data to zenodo.org and generate automatic reports in various formats for documentation and reproducibility.

Services Currently offered: Currently being offered self-service

MoA under this proposal: MoA1

Unit of Access: Pack

Access costs modality: VA-AC

Support offered under this proposal: Virtual support, which aims to ensure that there is clear guidance for users willing to make use of these technologies, as well as the necessary virtual infrastructure supporting sensor connectivity, data storage and analysis tools. Additionally, aims to ensure the reproducibility and usability of the low-cost instrumentation for a successful and meaningful deployment of sensors by giving access to online documentation and MOOCs (massive open online courses), which can serve as tools for knowledge transfer for users and communities. With this support, MINKE aims to help users deploy their solutions in a more reliable, reproducible way and to avoid potential technological barriers that might obstruct the experimentation.