To get support for any of these client libraries, please consult each project’s README.
https://github.com/aeonSolutions/OpenScience-Dataverse-API-C-library is the official C/C++ library for Dataverse APIs.
https://github.com/IQSS/dataverse-client-java is the official Java library for Dataverse APIs.
It was created and is maintained by The Agile Monkeys.
https://github.com/gaelforget/Dataverse.jl is the official Julia package for Dataverse APIs. It can be found on JuliaHub (https://juliahub.com/ui/Packages/Dataverse/xWAqY/) and leverages pyDataverse to provide an interface to Dataverse’s data access API and native API. Dataverse.jl provides a few additional functionalities with documentation (https://gaelforget.github.io/Dataverse.jl/dev/) and a demo notebook (https://gaelforget.github.io/Dataverse.jl/dev/notebook.html).
It was created and is maintained by Gael Forget.
There is no official PHP library for Dataverse APIs (please get in touch if you’d like to create one!) but there is a SWORD library written in PHP listed under Client libraries in the SWORD API documentation.
There are two Python modules for interacting with Dataverse APIs.
pyDataverse primarily allows developers to manage Dataverse collections, datasets and datafiles. Its intention is to help with data migrations and DevOps activities such as testing and configuration management. The module is developed by Stefan Kasberger from AUSSDA - The Austrian Social Science Data Archive.
dataverse-client-python had its initial release in 2015. Robert Liebowitz created this library while at the Center for Open Science (COS) and the COS uses it to integrate the Open Science Framework (OSF) with Dataverse installations via an add-on which itself is open source and listed on the Apps page.
https://github.com/IQSS/dataverse-client-r is the official R package for Dataverse APIs. The latest release can be installed from CRAN. The R client can search and download datasets. It is useful when automatically (instead of manually) downloading data files as part of a script. For bulk edit and upload operations, we currently recommend pyDataverse.