Here we show you a few nice use-cases for Data Foundry. This is work in progress as you can imagine, we discover always new ways to use the infrastructure and we will add them here.
All use-cases start with the basics (registering, creating a project) and then go into specifics.
This use-case shows how to log exchanged databetween two or more prototypes in a system. The prototypes need to communicate their data via OOCSI on a shared channel. Data Foundry will pull data from this channel and store the items in an IoT dataset for further analysis. [more]
This use-case shows how to use the Data Foundry Telegram integration to easily collect data from participant or to exchange messages with them. The benefit of using Telegram instead of email is that you can reach them on their mobile device which might be useful for collecting in-the-moment data.
This use-case shows how to store objects in Data Foundry as a way to exchange data between different prototypes that are not online or connected at the same time. Data Foundry acts as a persistent storage place for their data. [more]
The use-case about movement patterns combines the collection of movement data obtained from GPS trackers (in the GPX) format. Similar to the Experience Sampling case, these data will be uploaded and then imported by the Data Foundry. In addition, this use-case shows how to add different external data to another dataset in the same project. All data in this project can then be analysed together and exported for further analysis in a third-party tool. [more]
Object ethnography is about the use of designed artefacts as co-ethnographic devices in-situ. This use-case shows how to combine the privacy-aware collection of media (photos, images) with sensor data. We show in this use-case how to use the data-tool to visualise both images and sensor data together and to annotate the images from this visualisation. [more]
The remote study use-case is about testing digital prototypes of a design using both OOCSI and Data Foundry to track information from participants. This use-case is about tracking how individual particpants of the remote study interact with the prototype. [more]
This use-case is split into two separate sections, contextual and informed (tbd.) in line with the theory behind Data-enabled Design. In the contextual step, we see how we can collect contextual data from both devices and participants living in the context. The informed step shows how we can design an intervention for the context and notify participants and researchers over Telegram.
The Experience Sampling show how we can use a third-party mobile app (PIEL survey) to collect experience samples and upload them to Data Foundry. After the upload, Data Foundry imports the experience sampling data and stores it as a timeseries dataset that can be exported similar all other datasets. [more]