Now that you’ve installed Dataverse, 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.
Contents:
A variety of integrations are oriented toward making it easier for your researchers to deposit data into your installation of Dataverse.
If your researchers have data on Dropbox, you can make it easier for them to get it into Dataverse 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 installation of Dataverse, 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 installation of Dataverse, 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 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 from Dataverse, 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 Dataverse via the command-line interface (CLI).
Renku dataset documentation: https://renku-python.readthedocs.io/en/latest/commands.html#module-renku.cli.dataset
Flagship deployment of the Renku platform: https://renkulab.io
Renku discourse: https://renku.discourse.group/
OpenScholar is oriented toward hosting websites for academic institutions and offers Dataverse Widgets that can be added to web pages. See also:
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.
TwoRavens is a web application for tabular data exploration and statistical analysis with Zelig.
For installation instructions, see the External Tools section.
WorldMap helps researchers visualize and explore geospatial data by creating maps.
For installation instructions, see Geoconnect and WorldMap.
The “Compute” button is still highly experimental and has special requirements such as use of a Swift object store, but it is documented under “Setting up Compute” in the Configuration section of the Installation Guide.
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 Dataverse 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 Dataverse dataset on https://mybinder.org
Institutions can self host BinderHub. Dataverse is one of the supported repository providers.
Researchers can import Dataverse datasets into their Renku projects via the
command-line interface (CLI) by using the Dataverse DOI. See the renku Dataset
documentation
for details. Currently Dataverse >=4.8.x is required for the import to work. If you need
support for an earlier version of Dataverse, please get in touch with the Renku team at
Discourse or GitHub.
Researchers can use a Google Sheets add-on to search for Dataverse CSV data and then import that data into a sheet. See Avgidea Data Search for details.
Integration with DataCite is built in to Dataverse. When datasets are published, metadata is sent to DataCite. You can futher increase the discoverability of your datasets by setting up additional integrations.
Dataverse 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 instances 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.
Dataverse can be configured to submit a copy of published Datasets, packaged as Research Data Alliance conformant zipped BagIt bags to the Chronopolis via DuraCloud
For details on how to configure this integration, look for “DuraCloud/Chronopolis” in the Configuration section of the Installation Guide.
The Dataverse roadmap is a good place to see integrations that the core Dataverse 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.
Many integrations take the form of “external tools”. See the External Tools section for details. External tool makers should check out the Building External Tools section of the API Guide.
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.