Extras and specialties

here comes an incomplete list of interesting aspects of Data Foundry that make working with it easier or the platform more useful.

Project website

Use Data Foundry to host a website for a project that you have created (without actual coding) by means of the Existing dataset.

  • Sign up or login into Data Foundry
  • Create a new project or use an existing project and add an "Existing Dataset". What is important is to name the dataset "www". This makes sure that you can access this dataset as a web page.
  • In this dataset, you can add files, for example, HTML files or images that you want to display on your site. You need an "index.html" or "index.md" file to start with, but all files are accessible via the page.
  • After going back to the project overview page, you will see a "project website" link at the top. Make sure that the project is "public" and that the dataset is still active. Now you are ready to go. We do not support folders yet, so your website needs to have a flat structure with all files at the same level.

Public parameters

Public parameters are extra properties of participants, devices and wearables. These parameters can contain information about a participant, device or wearable that will exported. When working with multiple participants, wearables and devices, these parameters can be helpful in organizing your resources.

Let's go through a quick example with different participants in a study:

  • Sign up or login into Data Foundry
  • Create a new project or use an existing project and add a dataset and participants.
  • When you have add a participant, you can define 3 parameters, these will be pp1, pp2 and pp3. Public parameters are part of participant-related datasets, so don't enter personally-identifiable information here. These parameters contain extra, valuable information. These parameters always contain the same type of information and won’t change their value (unless you change them).

In your study, you have a total of 18 participants that are split up in different ways. First, there is a control group (9 participants) and an experimental group (9 participants). Then, both groups are split by age into three sub groups: age_group_1, age_group_2 and age_group_3. We could continue here, but with 18 participants this will suffice. We can just use the third public parameter for the gender. Now, given the different segment we can assign public parameters to every participant depending on their age and whether they belong into control or experimental group. This could look like the following for just four participants:

Participant Alice

  • pp1: experiment_group
  • pp2: age_group_1
  • pp3: female

Participant Bob

  • pp1: control_group
  • pp2: age_group_1
  • pp3: male

Participant Alex

  • pp1: experiment_group
  • pp2: age_group_3
  • pp3: not given

Participant Vera

  • pp1: control_group
  • pp2: age_group_2
  • pp3: female

After you have collected data from your device on Data Foundry, you can download your dataset including these public parameters which now will be visible in the CSV file. With these parameters you can segment the dataset by age group or timeslot without revealing any personal information about the participants.

You might have noticed that what we call "public parameters" are basically independent variables of a study. Good! Extra points to you. We still like to call them public parameters, because "independent variable" carries more and different meaning than what we have built into Data Foundry.

We gave an example about participants above, but the same is possible for devices and wearables. So you could use public parameters to annotate the country and city location of devices, or age range and training level for wearables--all without releasing personal information into the dataset.

Getting a project review

We have an experimental feature about getting a project review (from ID staff). This could be a preparation for submitting the project for official ERB approval. For example.

How to get it? When your project meta-data is over 50% complete, meaning that you have provided meta-data for over 50% of aspects of your project and all its datasets, then you can get a review of your project. On your project page, there is already a button on the right side "review request". If you hover your mouse over this button, you will see the current percentage in the pop-up. You can increase this number (in order to reach not 50%, but hopefully 100%) by adding information to your project (click on the edit button in your project) and also to all your datasets (click on edit in every dataset). After you reached 50% (definitely go higher!), the "review request" button will become active.

The review will be sent to one reviewer registered in Data Foundry. During this review all aspects of your project will be checked, including the project itself, the datasets the data collection setup, and plans for publishing and archiving the project. The more information you have provided, the more detailed the reviewer of your project can see and work with. On each of the above described aspects the reviewer has the possibility to provide comments.

Ultimately the reviewer will approve or not approve the project, based upon the things described above. The feedback will also include more general remarks on the project. When the project is approved or not approved, the researcher will receive a notification about this.

Note that if you make changes to your project after an approved review, you will need to go through the review process again. This is important for scientific integrity.