Access & sharing

Overview

Workflows are designed as a collaborative space, where many researchers can work together in real-time as they build out an analysis and explore data.

However, the underlying data in a workflow may be governed by strict access rules, and it is ultimately up to the dataset owner(s) to govern who can see their datasets and any derivatives.

As such, when conceptualizing workflow access, there are two points to consider:

1. The workflow's sharing configuration, which defines the set of collaborators that will be able to interact with the workflow.

2. The data access rules inherited by each node, representing the combination of all datasets whose data is present in that node.

Sharing a workflow

To update a workflow's sharing configuration, click the Share button in the left section of the workflow menu.

All workflows are owned by either a user or an organization, and can then be shared with other users and organizations. When a workflow is owner or shared with an organization, all administrators of that organization will have corresponding access.

When you use an organization's data, administrators of that organization will automatically be granted "view" access to your workflow, which will be reflected in the share modal.

You will not be able to remove such organizations until you have removed all their data from the workflow.

Workflows can also be associated with a study, which may be necessary if access to certain datasets in the workflow was granted to that study. In this case, you can specify a level of access to the workflow for other collaborators on the study.

Finally, you can choose to make a workflow public by granting access to Anyone on the internet.

When you share a workflow, you are not sharing access to any of the data in the workflow – this is governed by each node's underlying data access rules.

As a researcher, you can generally confidently share a workflow without worrying about violating and data usage agreements, since you aren't actually giving access to the underlying data.

The following access roles can be assigned to workflow collaborators:

• Owner: Each workflow has one owner. The owner operates as an editor, but has the additional ability to change the owner and delete the workflow. The workflow will also show up under the owner's namespace in the API.

• Edit: Workflow editors can create, modify, delete, and execute any node in the workflow, update workflow metadata, and perform other editing actions. They can also share the workflow with others (but can't change the owner).

• Comment: Workflow commenters have read-only access to the workflow, though they can also leave comments on individual nodes.

• View: Viewers have read-only access to the workflow, though they will be able to clone the workflow to have their own editable copy.

Workflow node access

All tables, transforms, and notebooks in your workflow are derived from one or more source datasets. When determining a user's access level to a particular node, Redivis computes the lowest access level across all source datasets. Your ability to interact with workflow nodes for different access levels is as follows:

• None: The node will be black, and you will not be able to see any information about it outside of its name and owner.

• Overview: The node will be black, and you will only be able to see its name and description.

• Metadata: The node will be checkered. If a table, you will be able to see variable names and univariate statistics, but you will not be able to look at cells or download the data. If a transform, you can view its configuration and code, but cannot run it.

• Data: The node will be grey, and you will be able to interact with it normally.

If your access level to a node is lower than "data", when you click on the node you will see a red button in the top right showing your current access level. Click on this button to see which datasets are contributing to your reduced access and to subsequently apply for access.

View an example workflow with limited data access. The workflow is public, which means anyone can open it, but what you can see in different nodes is dependent on your access to the source dataset(s) for that node.

Access to individual nodes in a workflow is governed by their source dataset(s)

Export rules

Similarly to node access, table export restrictions (if any) will represent the union of all export restrictions assigned to the datasets from which that table is derived.

In many cases, you will be able to request an export exception in order to download a particular table. If the table contains datasets with export restrictions from multiple organizations, you will have to request approval from each organization.

Study assignment

Workflows can optionally be assigned to a study, allowing you to organize your workflows around specific research aims. Additionally, by assigning your workflow to a study, you will have the option to automatically share that workflow with other collaborators in the study.

In some cases, your access to a dataset may be approved within the context of a particular study. If this is the case, your workflow must be assigned to that study in order for you to run transforms and notebooks within the workflow.

You can change the study a workflow is in by clicking on the workflow's name in the top black bar, then clicking on Study.