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