a collection of apps developed to help with various types of interactions
and simplify workflows to make Figshare even easier to work with
An integration with Bitbucket to harvest code into Figshare.
An integration with GitLab to harvest code into Figshare.
To help automate the publication step in GitHub-based research workflows, we've released the Figshare upload GitHub Action. You can incorporate the upload Action in your GitHub workflows and push your files into Figshare directly from GitHub. For details regarding the Action setup visit the GitHub repo page. Check out a demo of how this works.
The Figshare API allows you to programmatically move content to and from Figshare. Documentation is available using Open API Swagger. For examples on what you can do with the API, check out this article on how to use our API.
If you are uploading large files or many files, the FTP uploader might be a better option than the broswer. This method allows you to easily and securely upload files in your account directly from your computer by using a secure FTP connection.
You can now integrate your Current Research Information System (CRIS), powered by Symplectic Elements, with your research output repository, powered by Figshare.
Enter a data or code Figshare DOI to launch a Figshare project as a Binder. This will launch an interactive environment based on the content and configuration files in the project. Documentation is available here and a publicly available BinderHub is available here.
A one-way push of data records from Figshare to an institutional instance of Elsevier Pure, a current research information system (CRIS). More information on this integration can be found here.
Integrate your RSpace Electronic Lab Notebook with your Figshare account. Follow these steps to learn how to enable the integration.
Publish your Overleaf projects directly to Figshare. Click here for more information.
Connect Figshare to your OSF project. More information available here.
Link your Figshare and ImpactStory accounts. More information here.
Import from GitHub from your list of public repositories. Documentation on how to connect Figshare with your GitHub account is available here.
With this integration, push all of your public items from Figshare to ORCiD. Instructions for setting up this integration are available here.
Import your Figshare items into your labfolder account using this integration, documented here.
This application enables the bulk upload of files and associated metadata kept in an excel spreadsheet to the Figshare repository. To use the application you will need to log in to Figshare and create a personal token in the Applications section. This will ensure the files and metadata are uploaded to your own account.
For more information, visit this page for GitHub references.
An OmniAuth Strategy for the Figshare API. You can use it to authenticate users against the Figshare API in your ruby on rails/sinatra/other rack-based web application.
The harvester supports harvesting by tag. Given a tag, the harvester gathers all the content from Figshare with the specified tag, producing RDF for each work. The Harvester uses openVIVO URI conventions for dates and people. Only identified works and identified people are included in the RDF.
In addition, given an ORCiD identifier, the harvester finds all content in Figshare for the author, producing RDF for each work. The Harvester uses OpenVIVO URI conventions for dates and people. Only identified works are included in the RDF.
Note: The Figshare Harvester for OpenVIVO was developed for a demonstration of OpenVIVO at Force2016 (http://force2016.org). It was then used for the 2016 VIVO Conference http://vivoconference.org/vivo2016.
A Python script that ingests Figshare's API data and transforms it into data suitable for loading into a recommendation engine. In our case, we save the recommendation data in Kafka but you can easily change this in ingest.py.
Pulls fields relevant for a recommendation engine from all research papers using the API, written in Python. Writes the events to a Kafka stream for further use by ML libs like FREQL.
A Scala/Apache Ignite collaborative-filtering based recommendation engine largely stolen from a Spark library but re-written to be a lot faster, flexible and scalable, perfect for Figshare data.
FREQL is a realtime, highly scalable recommendation engine with a SQL-like query language. It can be used both as a huge in-memory distributed graph database and as a "multimodal", "collaborative filtering" rec engine.
This is a simple client for the Figshare API in python. Currently the following actions are implemented: create_article, update_article, get_article_details, list_files, and get_file_details.