Now that you’ve installed a Dataverse installation, you might want to set up some integrations with other systems. Many of these integrations are open source and are cross listed in the Apps section of the API Guide.
A variety of integrations are oriented toward making it easier for your researchers to deposit data into your Dataverse installation.
Dataverse integration with GitHub is implemented via a Dataverse Uploader GitHub Action. It is a reusable, composite workflow for uploading a git repository or subdirectory into a dataset on a target Dataverse installation. The action is customizable, allowing users to choose to replace a dataset, add to the dataset, publish it or leave it as a draft version on Dataverse. The action provides some metadata to the dataset, such as the origin GitHub repository, and it preserves the directory tree structure.
For instructions on using Dataverse Uploader GitHub Action, visit https://github.com/marketplace/actions/dataverse-uploader-action
If your researchers have data on Dropbox, you can make it easier for them to get it into your Dataverse installation by setting the dataverse.dropbox.key JVM option described in the Configuration section of the Installation Guide.
The Center for Open Science’s Open Science Framework (OSF) is an open source software project that facilitates open collaboration in science research across the lifespan of a scientific project.
For instructions on depositing data from OSF to your Dataverse installation, your researchers can visit https://help.osf.io/hc/en-us/articles/360019737314-Connect-Dataverse-to-a-Project
RSpace is an affordable and secure enterprise grade electronic lab notebook (ELN) for researchers to capture and organize data.
For instructions on depositing data from RSpace to your Dataverse installation, your researchers can visit https://www.researchspace.com/help-and-support-resources/dataverse-integration/
Open Journal Systems (OJS) is a journal management and publishing system that has been developed by the Public Knowledge Project to expand and improve access to research.
The OJS Dataverse Project Plugin adds data sharing and preservation to the OJS publication process.
As of this writing only OJS 2.x is supported and instructions for getting started can be found at https://github.com/pkp/ojs/tree/ojs-stable-2_4_8/plugins/generic/dataverse
If you are interested in OJS 3.x supporting deposit to Dataverse installations, please leave a comment on https://github.com/pkp/pkp-lib/issues/1822
Renku is a platform that enables collaborative, reproducible and reusable (data)science. It allows researchers to automatically record the provenance of their research results and retain links to imported and exported data. Users can organize their data in “Datasets”, which can be exported to a Dataverse installation via the command-line interface (CLI).
Renku dataset documentation: https://renku-python.readthedocs.io/en/latest/reference/commands.html#module-renku.cli.dataset
Flagship deployment of the Renku platform: https://renkulab.io
Renku discourse: https://renku.discourse.group/
Amnesia is a flexible data anonymization tool that transforms relational and transactional databases to datasets where formal privacy guarantees hold. Amnesia transforms original data to provide k-anonymity and km-anonymity: the original data are transformed by generalizing (i.e., replacing one value with a more abstract one) or suppressing values to achieve the statistical properties required by the anonymization guarantees. Amnesia employs visualization tools and supportive mechanisms to allow non expert users to anonymize relational and object-relational data.
For instructions on depositing or loading data from Dataverse installations to Amnesia, visit https://amnesia.openaire.eu/about-documentation.html
Data Explorer is a GUI which lists the variables in a tabular data file allowing searching, charting and cross tabulation analysis.
For installation instructions, see the External Tools section.
Whole Tale enables researchers to analyze data using popular tools including Jupyter and RStudio with the ultimate goal of supporting publishing of reproducible research packages. Users can import data from a Dataverse installation via identifier (e.g., DOI, URI, etc) or through the External Tools integration. For installation instructions, see the External Tools section or the Integration section of the Whole Tale User Guide.
Researchers can launch Jupyter Notebooks, RStudio, and other computational environments by entering the DOI of a dataset in a Dataverse installation on https://mybinder.org
Institutions can self host BinderHub. The Dataverse Project is one of the supported repository providers.
Researchers can import datasets from a Dataverse installation into their Renku projects via the
command-line interface (CLI) by using the dataset’s DOI. See the renku Dataset
for details. Currently Dataverse Software
>=4.8.x is required for the import to work. If you need
support for an earlier version of the Dataverse Software, please get in touch with the Renku team at
Discourse or GitHub.
Integration with DataCite is built in to the Dataverse Software. When datasets are published, metadata is sent to DataCite. You can further increase the discoverability of your datasets by setting up additional integrations.
The Dataverse Software supports a protocol called OAI-PMH that facilitates harvesting datasets from one system into another. For details on harvesting, see the Managing Harvesting Server and Sets section.
Geodisy will take your Dataverse installation’s data, search for geospatial metadata and files, and copy them to a new system that allows for visual searching. Your original data and search methods are untouched; you have the benefit of both. For more information, please refer to Geodisy’s GitHub Repository.
Archivematica is an integrated suite of open-source tools for processing digital objects for long-term preservation, developed and maintained by Artefactual Systems Inc. Its configurable workflow is designed to produce system-independent, standards-based Archival Information Packages (AIPs) suitable for long-term storage and management.
Sponsored by the Ontario Council of University Libraries (OCUL), this technical integration enables users of Archivematica to select datasets from connected Dataverse installations and process them for long-term access and digital preservation. For more information and list of known issues, please refer to Artefactual’s release notes, integration documentation, and the project wiki.
For details on how to configure this integration, look for “DuraCloud/Chronopolis” in the Configuration section of the Installation Guide.
The Dataverse Project Roadmap is a good place to see integrations that the core Dataverse Project team is working on.
The Community Dev column of our project board is a good way to track integrations that are being worked on by the Dataverse Community but many are not listed and if you have an idea for an integration, please ask on the dataverse-community mailing list if someone is already working on it.
Please help us keep this page up to date making a pull request! To get started, see the Writing Documentation section of the Developer Guide.