Redivis Documentation
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  • Introduction
  • Redivis for open science
    • FAIR data practices
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    • Discover & access data
      • Discover datasets
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      • Create a study
    • Analyze data in a workflow
      • Reshape data in transforms
      • Work with data in notebooks
      • Running ML workloads
      • Example workflows
        • Analyzing large tabular data
        • Create an image classification model
        • Fine tuning a Large Language Model (LLM)
        • No-code visualization
        • Continuous enrollment
        • Select first/last encounter
    • Export & publish your work
      • Export to other environments
      • Build your own site with Observable
    • Create & manage datasets
      • Create and populate a dataset
      • Upload tabular data as tables
      • Upload unstructured data as files
      • Cleaning tabular data
    • Administer an organization
      • Configure access systems
      • Grant access to data
      • Generate a report
      • Example tasks
        • Emailing subsets of members
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    • Workflows
      • Workflow concepts
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      • Transforms
        • Transform concepts
        • Step: Aggregate
        • Step: Create variables
        • Step: Filter
        • Step: Join
        • Step: Limit
        • Step: Stack
        • Step: Order
        • Step: Pivot
        • Step: Rename
        • Step: Retype
        • Step: SQL query
        • Variable selection
        • Value lists
        • Optimization and errors
        • Variable creation methods
          • Common elements
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        • SAS notebooks
        • Using the Jupyter interface
      • Access and privacy
    • Data access
      • Access levels
      • Configuring access
      • Requesting access
      • Approving access
      • Usage rules
      • Data access in workflows
    • Organizations
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        • Account
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        • Export environments
        • Advanced: DOI configuration
        • Advanced: Stata & SAS setup
        • Advanced: Data storage locations
        • Advanced: Data egress configuration
    • Institutions
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  • Reproducibility
  • Availability
  • Longevity

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Redivis for open science

Last updated 6 months ago

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Redivis is a platform for data distribution and analysis that prioritizes and . We believe that supporting open science principles drives better research outcomes, collaboration, and scientific impact.

Redivis is creating tools not just for data distribution and analysis, but also to help drive forward standards for transparent research that is accessible to all.

Reproducibility

Every Redivis dataset has a that is automatically generated while the data owner uploads and edits data contents. This history is fully exposed to end users and the dataset can be accessed at any version. Citations are generated for every version of a dataset which include the ORCID iDs for all creators. Data owners can opt to create Digital Object Identifier (DOI) for all versions of their dataset, which is available at a persistent URL along with the required metadata describing the content at that point in time.

As users analyze data in a Redivis each step they take is recorded in a visual tree that can be navigated sequentially. Every step has a full history which can be viewed at any point in time.

Availability

Datasets in any format can be deposited into Redivis and made available immediately to the world via open access licenses at no cost to the end user.

All Redivis datasets and analysis workflows are available to anyone on the internet without a Redivis account unless the owner decides to add restrictions. In this case someone wishing to view the work would need to create a free Redivis account to request access from the owner.

Longevity

The stable technical infrastructure and dedicated Redivis team is funded by a coalition of academic institutions paying license fees for large scale data distribution. This diverse group of funders ensures the long-term availability of the datasets.

open access
FAIR data practices
versioned history
workflow