Mobis is a citizen science platform that enables app developers and researchers to incorporate air and water quality monitoring, as well as biodiversity research, into their own apps. The software includes example integrations with Canair.io, mini-secchi, iSPEX and Plant*Net, which are tools for measuring air and water quality and studying biodiversity, respectively. Mobis also includes features for user login and storage on the European Open Science Cloud. This makes it easy for researchers and app developers to collect and share data for their projects.
Here you will find software and documentation for our low cost sensors (iSPEX, kDUINO, Fresh Water Watch) The software is developed using the IONIC framework, Parse Server (Back End). For iSPEX(r) we offer a basic interface. The iSPEX (r) professional software is using native closed source software and developed outside the EU research project.
Mobis is an open source citizen science software framework that allows app developers and researchers to easily integrate air and water quality monitoring, as well as biodiversity research, into their own apps. The sample software includes integrations with Canair.io (for air quality), mini-secchi (for water quality), and Plant*Net (for biodiversity research), as well as user login and storage on the European Open Science Cloud.
This readme acts as a reference for the mobis software and / or documentation. Both software and documentation are still into development, so the contents of this file/wiki can and will change over time.
Scientists, (app) developers, project partners, people who are lookikng for low cost sensor integration solutions.
More in depth documentation is available on ZENODO: https://zenodo.org/record/7615472#.Y-JidC8w2gQ
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Node.js
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Ionic / capactior
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Git
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Xcode (for iOS)
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Android Studio (for Android)
A Parse server (for example the one provided by Pocket Science, DIY or Back4App) is required for the app to work. A Firebase account for login and push notifications is required.
Rename the provided .app. constant example to app.copnstnt and fill in the required fields.
- Clone the repository
- Install the dependencies npm install
- Add these entries to Android and ios
android.permission.INTERNET android.permission.ACCESS_COARSE_LOCATION android.permission.ACCESS_FINE_LOCATION android.permission.BLUETOOTH android.permission.BLUETOOTH_SCAN android.permission.BLUETOOTH_ADVERTISE android.permission.CAMERA android.permission.VIBRATE android.permission.WRITE_EXTERNAL_STORAGE
Add capabilities: Push notifications and Apple logon Needed permissions (add to info.plist): camera, bluetooth, GPS
Provided functionalities in R 1.0: User login (Apple/Google/Email) Language switcher Data sync/upload
WIP: Full offline support (Parse Server) Push notifications
We created some sensor plugins for example. Sensors can be native (camera, GPS) or external (iSPEX and Canair.io)
Canair.io (see website) is a bluetooth peripheral to get PM2.5 and CO2 (depending on model) out. Connection is made via bluetooth.
Mini Secchi is a water quality tool that measures the Secchi depth of a lake or river. This is a measure of the water transparency and is used to assess water quality. The Mini Secchi is a low-cost, open-source sensor that can be used to collect water quality data in lakes, rivers and streams.
iSPEX is a spectropolarimeter that measures the optical properties of water or air. It is a low-cost, open-source sensor that can be used to collect water quality data in lakes, rivers and streams.
Plant*Net is a platformfor plant identification that allows users to identify plants using a smartphone camera.
Mobile back end. Used for storage, metadata, push notifications, offline sync. Parse Server
You can run your own (Dockerized) Parse Server or use the Cos4Cloud instance. If you want to run your own instance, please check the Parse Server documentation. If you want to use the Cos4Cloud instance, please contact us via the Cos4Cloud Slack channel.
The Cos4Cloud instance of Parse Server supports the Sensor Things API 1.1. Please check the Sensor Things API 1.1 documentation for more information.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 863463