WIDE – Open Source Science (2018 projects archive)
This repository is an archive of projects created for the Open Data Oracle coding track in our WIDE – Open Source Science event in 2018. Please see below for more information on the submitted projects and respective teams.
Learn more about the event at: http://thinkcompany.fi/wide/
Open Data Oracle projects & teams
Extension Boyz (challenge winner)
Team members: Akangbe Samuel, Steve Nebo, Yazan Al Halabi
Project link: https://github.com/YazanAlhalabi/Hackathon_chrome-extension
Annif has a good text indexing algorithm, which when combined with the Finna API, we can create something that improves the usability and accessibility of both platforms. This browser extension allows you to select any text or topic of interest and in return shows you the relevant books on the topic you’re researching.
¾BOA (honorable mention)
Team members: Aleksander Alafuzoff, Henri Kotkanen, Jari Torniainen
Project link: https://github.com/brains-on-art/widechallenge
Using the Finna API, the relative popularities of terms used to describe images are visualized within the ontology tree of YSO. This makes it easier to understand the ontology tree and allows the temporal evolution of the conceptual space of Finnish imagery to be made visible, giving a glimpse into the Finnish mindscape.
FennicaTrends (honorable mention)
Team members: Henri Ylikotila, Jasu Viding, Taru Airola
Project link: https://github.com/turger/serious-spin
An interactive collection of subjects from Fennica-LD visualizing trends in non-fiction literature in Finland from the 1980s to today, showing a cluster of words with calculated frequencies of each individual subject from the YSO ontology.
Quantum Lizard Brains (honorable mention)
Team members: Hannu Kämäräinen, Harri Hirvonsalo, Mikael Mieskolainen, Ville Pyykkönen
Project link: https://github.com/quantum-lizard-brains/scrapeomatic
An algorithm for extracting scientific information by compressing textual input (or speech input) into keywords using Annif, which is then fed as a search through open science archives and databases, facilitating the basic mechanism of doing science via algorithmic search.
Team members: An Cao, Chau Tran, Ilse Tse
Project link: https://github.com/kcjpop/wide
A web app that helps students look up topics they’re interested in with visualizations of publications categorized by organizations and related keywords, creating an attractive representation of possible options for future Bachelors, Masters, and PhDs in terms of publication proactivity.
Arts ´n Coding
Team members: Ava Heinonen, Minna Turunen, Pavla Oubret, Rachel Fay-Leino
Project link: https://github.com/AvaHeino/FinnaTreasures
There’s an overwhelming amount of information to be found in Finna and we came up with a treasure tracker to make that data more accessible. It’s a button that randomly grabs from the collections, allowing you to stumble on something interesting or find inspiration.
Team members: Riku Walve, Saska Karsi, Tafseer Ahmed
Project link: https://github.com/tafseerahmed/WIDEhackathon
We can return location data for a query term from the Finna API to determine where pictures have been taken related to that specific keyword. Using this timeline it’s also possible to see where and when a keyword was prevalent, leading us to pursue this further with an artistic style transfer using a neural network.
We Do What We Must
Team members: Janne Lavila, Jarkko Savela, Mathias Pellas, Ni Chen
Project link: https://github.com/J-Savela/WIDE
Our goal coming into the hackathon was to make open science more available and visualizations are a good way to condense data into an understandable form. We use the Finna API and show graphs that indicate connections between keywords and the number of publications that match specific keywords, as well as the trend of the data.
Wiki Loves Crowdsourcing
Team members: Katri Niinikangas, Kimmo Virtanen, Susanna Ånas
Project link: https://github.com/Ajapaik/wide-hackathon
We are testing a workflow idea at the hackathon where enriched metadata and rephotographs from crowdsourcing platform Ajapaik are first curated and then published over OAI-PMH. Published rephotos and a then-and-now pair are then layered on top of Finna with enriched metadata.