Applications
Figshare tools
Integrations
Lab notebooks

featured apps

a collection of apps developed to help with various types of interactions
and simplify workflows to make Figshare even easier to work with

Bitbucket

An integration with Bitbucket to harvest code into Figshare.

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Integrations

https://help.figshare.com/article/figshare-code-repository-setup-implementation

GitLab

An integration with GitLab to harvest code into Figshare.

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Integrations

https://help.figshare.com/article/figshare-code-repository-setup-implementation

GitHub Upload Action

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.

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Integrations

https://github.com/figshare/github-upload-action

API

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.

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Figshare tools

https://docs.figshare.com/

FTP uploader

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.

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Figshare tools

https://help.figshare.com/article/upload-large-datasets-and-bulk-upload-using-the-ftp-uploader-desktop-uploader-or-api

Symplectic Elements

You can now integrate your Current Research Information System (CRIS), powered by Symplectic Elements, with your research output repository, powered by Figshare.

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Integrations

https://www.symplectic.co.uk/solutions/theelementsplatform/

Binder

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.

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Integrations

https://mybinder.org/

Elsevier Pure

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.

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Integrations

https://www.elsevier.com/en-gb/solutions/pure

RSpace

Integrate your RSpace Electronic Lab Notebook with your Figshare account. Follow these steps to learn how to enable the integration.

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Integrations
Lab notebooks

https://www.researchspace.com/enterprise/help-and-support-resources-enterprise/figshare-integration/

Overleaf

Publish your Overleaf projects directly to Figshare. Click here for more information.

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Integrations

https://www.overleaf.com/blog/10-publish-to-figshare-with-overleaf-formerly-writelatex#.W5J8Fj1KjBI

Open Science Framework (OSF)

Connect Figshare to your OSF project. More information available here.

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Integrations

https://help.osf.io/hc/en-us/articles/360019929793-Connect-figshare-to-a-Project

ImpactStory

Link your Figshare and ImpactStory accounts. More information here.

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Integrations

http://blog.impactstory.org/link-your-figshare-and-impactstory-accounts/

GitHub

Import from GitHub from your list of public repositories. Documentation on how to connect Figshare with your GitHub account is available here.

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Integrations

https://github.com/

ORCiD

With this integration, push all of your public items from Figshare to ORCiD. Instructions for setting up this integration are available here.

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Integrations

https://knowledge.figshare.com/articles/item/how-to-sync-orcid-and-datacite-for-figshare

labfolder

Import your Figshare items into your labfolder account using this integration, documented here.

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Integrations
Lab notebooks

https://www.labfolder.com/apps-integration/

Uploader for large files and associated metadata

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.

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Applications

https://doi.org/10.25377/sussex.8850656

GitHub: OmniAuth Strategy

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.

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Applications

https://github.com/jdleesmiller/omniauth-figshare

GitHub: Figshare Harvester for OpenVIVO

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.

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Applications

https://github.com/OpenVIVO/figshare-rdf

GitHub: Figshare Python Recommender

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.

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Applications

https://github.com/gittyeric/figshare-recommender-etl

GitHub: Scala/Apache Ignite Recommendation Engine

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.

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Applications

https://github.com/gittyeric/freql-recommendation-engine

GitHub: Client for Figshare API

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.

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Applications

https://github.com/cognoma/figshare

GitHub: iRODS

Playground for iRODS integration.

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Applications

https://github.com/danielduduta/irods_figshare

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