Similar tools: potential collaborations/integrations

[npscience]

Here's a place to assemble links and notes about similar tools.

What do we provide extra to these?
Should we incorporate or build on them?
Should we reach out to collaborate with them?

[npscience]

It looks like SpringerNature are making their own version of a scholarly metadata database: http://www.springernature.com/gp/researchers/scigraph?countryChanged=true

RMap
creates map of interconnected scholarly artefacts
http://rmap-project.info/rmap/?page_id=98#presentations-papers

VIVO
open source code for co-author networks and common related terms
https://www.youtube.com/watch?v=DqdwgLKmr00
info at http://vivoweb.org/

Thresher
early prototype script for pulling useful research artefacts out of the SHARE database https://docs.google.com/presentation/d/14Gea_QQnksfX9NtUerw8s4Du08DjhLnS9xYSP-agwso/edit#slide=id.g1f231781e9_0_127

Microsoft Researcher Desktop
Idea canned, worth asking Kenji about this

[npscience]

VIVO
open source code for co-author networks and common related terms

We should give this a closer look and see if it's useful

More on co-author networks:

(1) A workflow for creating co-author networks using .bibtex, Sci2, and Gephy: https://abhishekkathuria.wordpress.com/2014/07/01/visualizing-your-co-author-network/

(2) An R script for creating co-author network using PubMed and R (seems fairly manual):
https://github.com/mjmaenner/coAuthor/blob/master/postdoc_coauthor_graph.R

(3) An interesting research paper about the maths of co-author networks (PNAS, 2016):
http://www.pnas.org/content/101/suppl_1/5200.full
article{Newman06042004,
author = {Newman, M. E. J.},
title = {Coauthorship networks and patterns of scientific collaboration},
volume = {101},
number = {suppl 1},
pages = {5200-5205},
year = {2004},
doi = {10.1073/pnas.0307545100},
abstract ={By using data from three bibliographic databases in biology, physics, and mathematics, respectively, networks are constructed in which the nodes are scientists, and two scientists are connected if they have coauthored a paper. We use these networks to answer a broad variety of questions about collaboration patterns, such as the numbers of papers authors write, how many people they write them with, what the typical distance between scientists is through the network, and how patterns of collaboration vary between subjects and over time. We also summarize a number of recent results by other authors on coauthorship patterns.},
URL = {http://www.pnas.org/content/101/suppl_1/5200.abstract},
eprint = {http://www.pnas.org/content/101/suppl_1/5200.full.pdf},
journal = {Proceedings of the National Academy of Sciences}
}

(4) And a paywalled article with a method for constructing co-author networks
(anyone got access? I've requested via the OA button):
Jie Liu, Yunpeng Li, Zichan Ruan, Guangyuan Fu, Xiaowu Chen, Rehan Sadiq, Yong Deng, A new method to construct co-author networks, Physica A: Statistical Mechanics and its Applications, Volume 419, 1 February 2015, Pages 29-39, ISSN 0378-4371, http://dx.doi.org/10.1016/j.physa.2014.10.006.
(http://www.sciencedirect.com/science/article/pii/S0378437114008449)
Abstract: Abstract
In this paper, we propose a new method to evaluate the importance of nodes in a given network. The proposed method is based on the PageRank algorithm. However, we have made necessary improvements to combine the importance of the node itself and that of its community status. First, we propose an improved method to better evaluate the real impact of a paper. The proposed method calibrates the real influence of a paper over time. Then we propose a scheme of evaluating the contribution of each author in a paper. We later develop a new method to combine the information of the author itself and the structure of the co-author network. We use the number of co-authorship to calculate the effective distance between two authors, and evaluate the strength of their influence to each other with the law of gravity. The strength of influence is used to build a new network of authors, which is a comprehensive topological representation of both the quality of the node and its role in network. Finally, we apply our method to the Erdos co-author community and AMiner Citation Network to identify the most influential authors.
Keywords: Co-author network; PageRank; Effective distance; Erdos number

[awakenting]

Hey,
nice project idea !
It seems to me that Open Knowledge Maps would also be interesting for you. They're also here on github: https://github.com/OpenKnowledgeMaps.