If you're interested deploying a data portal for your organization, contact us to get started.
If you are already an administrator of an institution using Redivis, you can navigate to the Organizations tab of your institution administration panel and click the + New button.
With your organization, you will be able to:
Your organization's data portal is the home for its data and researchers. To be the most effective, you'll want to provide information about your organization, links to external resources, and the appropriate branding to establish confidence amongst your researchers.
To begin, log in to Redivis, and navigate to your organization's administrator panel from the organization home page:
The administrator panel is the center of all management of your organization. It's from here that we'll upload datasets, manage members and access, and monitor usage metrics. To start, let's update the organization's presence and branding by navigating to the Profile tab of the administrator panel.
Here, we can configure:
The organization's full name
A custom brand color
The organization's logo
The data portal's cover photo
An optional header image that will appear in the top left of users' windows when on your data portal
Header links to other resources, such as organization-specific documentation, events, and other resources.
Rich text and images that will display in the main content section of the data portal. A place to provide high level information about your organization and how researchers should user your data portal.
As you build out your data portal, you'll likely want help from others in this process. You can add any Redivis account as an administrator of your organization — to do so, navigate to the Settings tab on the administrator panel, and click Edit administrators.
Note that all administrators will have full access to the organization and its datasets, including the ability to modify access rules and approve access requests. These administrators should be fully trusted and maintain robust security practices with the institutional account they use to log in to your organization on Redivis.
Our organization is looking pretty spiffy, but it's not very useful without some datasets. By creating datasets, you will be able to securely distribute rich, versioned, and well-documented data to your researchers. Follow the creating a dataset guide to begin uploading data to your organization.
But first, take a moment to think through your broader data management strategy. Assess the data that you currently have — what format are the files in? Where are they located? How are they organized?
Next, consider what datasets you'll want to create. Datasets on Redivis are made up of one or more semantically related tables, and each table can contain data from one or more uploaded files. Moreover, all permissions happen at the dataset level — if you grant a user access to a dataset, they will have access to all of its tables.
Let's say we currently have a bunch of data files sitting on hard disks. Our data is medical information, and conceptually contains information about patients (demographics), their hospital admissions (date, diagnosis), and hospitals (name, size, location), spanning the years 2005 - 2014.
But the files themselves are broken out by year — maybe we have a
patient_2006.csv etc., as well as
hosp_20xx.csv. That's 30 files — but when researchers are working with these data, there are really just three conceptual schema: patients, hospitals, and admissions. This tells us that we want one table for each, and we can combine all 10 years for each table during the upload process.
Moreover, assuming we can link these tables together, they're all conceptually related. This means we'll want to create them all within one dataset, wherein we can further document their usage and relationships.
It is common for organizations to host data of various security profiles. Some data may be publicly available to anyone on the internet, some data may require individuals to assert their identity or fill out paperwork, and some data may only be available to a handful of individuals who are prevented from ever exporting or downloading the data.
Redivis makes it easy for organization administrators to define, manage, and audit access rules across these various use cases.
All interactions with data on Redivis require the user to have the appropriate access level to the data for a given action.
There are five data access levels on Redivis
Overview: the ability to see a dataset and its documentation.
Metadata: the ability to view variable names and univariate summary statistics.
Sample: the ability to view and query a dataset's 1% sample. This will only exist for datasets that have a sample configured.
Data: the ability to view and query a dataset's tables.
Edit: the ability to edit the dataset and release new versions.
For a detailed breakdown of the content available at each tier, see the access level documentation.
It's helpful to consider upfront what sorts of users will be able to access your data at each level, and think about standardized workflows for data across your organization.
In order to configure access to your dataset, click on the Configure access button from within the dataset editor.
Within the access configuration modal, you can choose which access levels of a dataset are public, and which levels require specific approval. You can also assign dataset editors, as well as any usage restrictions on the dataset.
Initially, it might seem easier to configure access on each dataset individually, and manage access for users on a case-by-case basis. However, as your organization grows it will become important to have standardized access paradigms that exist across datasets.
Permission groups make this easy — simply define an access configuration through a permission group, and you can then apply it to any dataset. In order to create a permission group, navigate to the Permission groups tab of the administrator panel.
Learn more about permission groups.
In many cases, you will want to codify a set of requirements that researchers must fulfill before they can gain access to your organization's data. Requirements on Redivis allow you to collect, approve, and automatically expire information (project descriptions, IRB approval, funding status, DUA signatures) that is necessary for data access.
Requirements are global to your organization — navigate to the Requirements tab of the administrator panel to create your first requirement.
Learn more about requirements.
As users apply to work with data, they will become members of your organization, and they will appear in the Members tab of the administrator panel. You can also invite anyone to become a member by adding them to the members list. Additionally, any studies that are working with your organization's data will appear in the Studies tab of the administrator panel.
As members and studies apply for data access, you may need to take action to approve (or reject) their access requests. This will show up as an alert on the administrator panel, guiding you to the relevant access request.
It's often a good idea to double check which researchers have access to your datasets.
In order to audit a particular member's access to your organization's datasets, double click on that member, and navigate to the Access overview tab. This will list all of your organization's datasets, and the member's corresponding access to each.
In order to audit all members that have access to a particular dataset, click the filter icon to the right of the member search bar. This will allow you to filter all members by their access to, and usage of, your organization's datasets.
Redivis provides tools to help you keep an eye on your organization and how its data is being utilized.
Analytics allows you to track views, stars, and usage of datasets on the Analytics tab of your administrator panel. You can view this information in a Chart view or view how this information has changed over time by clicking the Timeline tab. Use the dropdown menus on the top left and bottom to adjust your metrics.
Redivis keeps detailed logs of all administrative actions (creating / updating datasets, approving access, etc.) as well as all queries run against your organization's data. These logs are filterable on numerous metrics, and preserve information about the timestamp, user, ip address, and all appropriate metadata relevant to the action performed.
After you've created some datasets, your researchers will likely want to work with and manipulate your data in a project. If your data are restricted, they may find the applying for access guide helpful.
For detailed information about managing your organization, consult the organization administration documentation.