Swinburne University of Technology

Amir Aryani

> Research Graph is a heterogeneous network that connects research
> outputs, research activities and researchers.

<figure>
<img
src="https://cdn-images-1.medium.com/max/917/1*6SmzMUfWzbDuXSCWs0kgXA@2x.jpeg" />
<figcaption>Image from
https://blog.apo.org.au/2018/02/27/researchgraph-pilot-project</figcaption>
</figure>

## Author 

* Amir Aryani (ORCID: [0000-0002-4259-9774](https://orcid.org/0000-0002-4259-9774))

# Introduction

In this article we look at Research Graph as an information model , and
an approach to connect and capture the connections between research
outputs, researchers and research activities. We explore the metadata
model, and we discuss how to capture this graph in a Neo4j Graph
Database.

While scientific papers and research publications are key components of
the research ecosystem, understanding the research and innovation
ecosystem involves more than just studying research papers. Researchers
need to be aware of currently active projects in their field to avoid
duplication and to identify collaboration opportunities. Also, they need
to learn from completed projects and their outcomes, engage with experts
in their fields, and continuously seek potential funding and
collaborative opportunities.

Scientific papers and research publications are key components of the
research ecosystem. For a young PhD student, the journey begins by
reading academic papers to learn about their field, while established
researchers use the latest publications to keep up with developments,
discoveries, and innovations in their area of expertise. However, a
thorough understanding of the research and innovation ecosystem involves
more than just studying research papers. Researchers need to be aware of
currently active projects in their field to avoid duplication and to
identify collaboration opportunities. They should also learn from
completed projects and their outcomes, engage with experts in their
fields, and continuously seek potential funding and collaborative
opportunities. Arguably, understanding this interconnected ecosystem is
crucial for a successful academic career. The challenge for early career
researchers lies in navigating this ecosystem in a way that provides
insights into the best opportunities. Similarly, for governments,
policymakers, and administrations, comprehending the dynamic components
of innovation, staying abreast of the state of the art, and identifying
research capabilities and expertise represent significant challenges.

Research Graph is composed of five primary node types:

- **Researcher**: An individual who conducts scientific or scholarly
  study, often within a specific field of knowledge. Their role involves
  creating new knowledge through research, which often includes running
  experiments, contributing to studies, and writing articles, books,
  or reports.
- **Publications**: Academic publications include journal articles,
  books, patents, conference abstracts, and theses, serving as the
  primary medium for disseminating research findings. Academic
  publications are often subject to a peer review process to ensure
  quality and validity. In the domain of humanities and social sciences
  (HASS), publications can form media pieces, performance arts, and
  other non traditional research outputs. Publications are essential for
  sharing new knowledge and advancing scholarly discourse.
- **Research Data**: Data that is used or generated in the course of
  academic studies. They form the empirical basis for research in many
  disciplines, providing the raw information needed for analysis,
  conclusions, and validation of research hypotheses.
- **Grants**: Award and grants are financial support provided by funding
  bodies (like government agencies, foundations, or institutions) to
  researchers or research projects. They are pivotal for enabling
  research by providing necessary resources, and they often influence
  the direction and scope of research activities. It is common for an
  awarded grant to support multiple projects or sub projects to an
  hierarchical. structure of research activities.
- **Organisations**: This encompasses institutions like universities,
  research institutes, and academic societies. Organizations play a
  crucial role in the research ecosystem by providing infrastructure,
  resources, and a collaborative environment for conducting research.
  They are also involved in disseminating research findings and may
  participate in the management of research projects and funding.

<figure>
<img
src="https://cdn-images-1.medium.com/max/960/1*1i25PuJG9X8r4n6tQyrK_g@2x.jpeg" />
<figcaption>Image from
https://blog.apo.org.au/2018/02/27/researchgraph-pilot-project, RDA
Plenary 11, when Research Graph announced a partnership with Australia
Policy Observatory</figcaption>
</figure>

### History

Research Graph formed as a project during the [Research Data Alliance
Working Group on Data Registry
Interoperability](https://www.rd-alliance.org/groups/data-description-registry-interoperability.html).
The initial version of this project was providing the graph schema to a
software platform called [Research Data Switchboard
(RD-Switchboard)](https://github.com/rd-switchboard). This was an
initial attempt by Australian National Data Service (Australia), Dryad
(United States), GESIS (Germany) and other partners to create a platform
for creating links between research data collections across global data
repositories. The graph schema later evolved to Research Graph as a meta
model, and later formed the bases for a series of open source connected
projects. Research Graph Foundation, a not for profit foundation, formed
in Australia in 2018 to coordinate these projects. You can learn more
about the foundation at
[www.researchgraph.org](http://www.researchgraph.org).

### Metadata

Research Graph nodes connected by relationships such as authorship links
between researchers and publications, employment links between
researchers and organisations, and funder links between organisations
and grants.

> In the context of this article, we refer to the node properties and
> structure of the relationships as **metadata**.

<figure>
<img
src="https://cdn-images-1.medium.com/max/658/1*w57fFcZs41aAn-GmL5kfRQ@2x.jpeg" />
<figcaption>Research Graph Schema,
https://researchgraph.org/schema</figcaption>
</figure>

The detail list of properties for nodes and relationships are presented
at [www.researchgraph.org/schema](http://www.researchgraph.org/schema).
However, it is important to highlight five main properties that applies
to all nodes:

- **source**: A string value that shows the domain name of the source
  repository such as crossref.org, or orcid.org.
- **local_id**: A string value that contains the local primary
  identifier of this node at the \*source\*.
- **key**: A string value with the function as a uniquely identifying
  the node across the global network of scholarly works. To achieve
  this, we combine the source [second-level
  domain](https://en.m.wikipedia.org/wiki/Second-level_domain)(e.g.
  crossref) and the local_id.
- **type**: A string that represent the type of the node including
  "researcher","organisation", "publication","dataset","grant".
- **last_updated**: Date and time of the last update for the metadata of
  this node in this research graph.

Beyond this properties, all five node types have properties that further
expand the information about each node. Some properties are optional and
some mandatory. For example, **researcher** nodes has a mandatory
property called "full_name" that would contain the complete name of the
researcher. However, it has several optional nodes such as "first_name",
"last_name", "orcid", "scopus_author_id".

### Persistent Identifiers (PIDs)

A persistent identifier is a long-lasting reference to a digital
resource. ORCID is an example of a persistent identifier for a person.
Learn more about [persistent
identifiers](http://www.ands.org.au/guides/persistent-identifiers-awareness).
DOIs (digital object identifiers) are persistent identifiers for things
or entities such as journal articles and
datasets. [Crossref](https://www.crossref.org) and [DataCite](https://www.datacite.org)
are the main organisations assigning DOIs in the academic ecosystem. We
recommend learning about [the work of DataCite and
Crossref](https://project-thor.readme.io/docs/who-are-datacite-and-crossref)\],
[the organization identifier
project](https://orcid.org/blog/2016/10/31/organization-identifier-project-way-forward), [Project
THOR (Technical and Human infrastructure for Open
Research](https://doi.org/10.5438/6423)),
and [Metadata2020](http://www.metadata2020.org/) (the scholarly
community's group effort to advocate for richer shared metadata).

PIDs are essential components for connecting scholarly networks and
forming a research graph. Most research graph nodes have PIDs as their
main identifier. For metadata from ORCID, such as such as the node that
represents https://orcid.org/0000-0002-4259-9774, the node properties
include

- **source**: "orcid.org"
- **local_id**: "0000--0002--4259--9774"
- **Key**:"orcid/0000--0002--4259--9774"
- **orcid**:"0000--0002--4259--9774"
- **first_name**:"Amir"
- **last_name**: "Aryani"
- **full_name**: "Amir Aryani"
- **scopus_author_id**: "35068996400"

Notice how "key" is formed as the results of concatenating the second
level domain name from source ("orcid") to the "local_id" property. In
this case, we use the orcid value as the "local_id" of the node. In
addition, there is a seperate property called "orcid". While this seems
redundant, it provides consistency for nodes from other sources that
might include ORCID identifier for researchers such as open access
repositories.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*z7bfYB1kjaCJtz6lPtFxOw.png" />
<figcaption>Neo4j Browser, Screenshot from Australian National PID
Graph, <a
href="https://researchgraph.org/national-graph/">https://researchgraph.org/national-graph/</a></figcaption>
</figure>

### Why Neo4j?

For large research graph data, we need a database to capture and analyse
the graph content. At the time of writing this article, there are
several graph databases such as [ArangoDB](https://arangodb.com/),
[Amazon Neptune](https://aws.amazon.com/neptune), and [Azure Cosmos
DB](https://azure.microsoft.com/en-au/products/cosmos-db). However,
Neo4j seems to be one of the most versatile options as it satisfy the
following requirements:

- Neo4j open source version enables free use and customisation for
  research.
- Neo4j has a large and active community with a lot of education
  materials and active user groups.
- Easy to use Neo4j integration with Python, Java and other programming
  languages makes it very easy to start working with this graph
  database.

The most prominent advantage of Neo4j is the programming environment and
user interface. It is highly accessible to novice programmers due to
excellent integration with Java, Python and other programming languages.
In addition, the graph explorer is easily usable by business analysts.
The query language to interact with a Neo4j database is called Cypher.
You can fin a comprehensive list of Cypher commands and useful tutorials
at

- Getting Started with Cypher:
  <https://neo4j.com/docs/getting-started/appendix/tutorials/guide-cypher-basics/>
- Cypher Cheat Sheet:
  <https://neo4j.com/docs/cypher-cheat-sheet/5/auradb-enterprise>
- Cypher Basics:
  <https://vladbatushkov.medium.com/learn-neo4j-cypher-basics-in-30-minutes-94d68a52544>

### Neo4j Labels

A Neo4j graph is constituent of nodes and edges. Both nodes and edges
have properties and labels. Properties are essential for information
retrieval as it enables us to find query database for particular nodes
and relationships. Labels are key components of the graph that provides
a level of classification. It enables grouping nodes and relationships
and connect them the same way we connect rows in a SQL database in a
form a table, or collect JSON objects in a MongoDB database in a
collection. The main advantage of these labels is indexing. We can index
properties agains labels. Indexes in Neo4j are pairs of \<property,
index\>, and that is essential for large graphs. Grouping nodes by
labels and creating index at this level enables smaller, and
faster indexes.

We have two major labels in a Research Graph Neo4j database:

- Source: these are labels that reference to the source Property such as
  "orcid", "corssref", and "datacite".
- Type: these labels refer to five main node types including
  "publication", "dataset", "researcher", "organisation", and "grant".

Note: Labels and properties are always lowercase, this enables us to
have consistent cypher queries.

### Summary

Research Graph is a heterogeneous network that connects research
outputs, research activities and researchers. This network provides
insights into the digital footprint of research, and we can leverage
Research Graph to analyse science and innovation networks such as
citation networks, collaboration networks, and research communities. In
addition, the Research Graph networks are the pathways to trace and
analyse the impact of research funding and research projects. In this
article, we explored the foundation of the Research Graph meta model,
recommended the minimum metadata and we introduced Neo4j as the best
approach to capture and link Research Graph nodes.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=0e1ab5df4497){width="1"
height="1"}


Author  Amir Aryani (ORCID: 0000-0002-4259-9774) Introduction   In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.

Research Graph 101

Cameron Neylon

The holy grail of research assessment is a means of automatically
tracking the way research changes the way practitioners act in the real
world. How does new research influence policy? Where has research been
applied by start-ups? And have new findings changed the way medical
practitioners treat patients? Tracking this kind of research impact
isÂ *hard*Â for a variety of reasons: practitioners don't (generally)
write new research papers citing the work they've used; even if they did
their work is often several steps removed from the original research
making the links harder to identify; and finally researchers themselves
are often too removed from the application of the research to be aware
of it. Where studies of downstream impact have been done they are
generally carefully selected case studies, generating a narrative
description. These case studies can be incredibly expensive, and by
their nature are unlikely to uncover unexpected applications of
research.

In recent talks I have used a specific example of a research article
reaching a practitioner community. This is a paper that I discovered
will search through the output of the [University of Cape
Town](http://www.uct.ac.za/) on [Euan
Adie](https://twitter.com/#!/Stew)'s
[Altmetric.com](http://altmetric.com) service. The
[paper](http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0024256)
deals with domestic violence, HIV status and rape. These are critical
social issues and new insights have a real potential to improve people's
lives, particularly in the area of the study. The paper was tweeted by a
number of accounts but in particularly by
[@Shukumisa](https://twitter.com/#!/shukumisa) and
[@SonkeTogether](https://twitter.com/#!/sonketogether) two support and
adovcacy organisations in South Africa. Shukumisa in particular tweeted
in response to another account "@lizieloots a really important study, we
have linked to it on our site". This is a single example but it
illustrates how it is possible to at least identify where research is
being discussed within practitioner and community spaces.

But can we go further? More recently I've shown some other examples of
heavily tweeted papers that relate to work funded by cancer charities.
In one of those talks I made the throw away comment "You've always
struggled to see whether practitioners actually *use*Â your
research...and there are a lot of nurses on Twitter". I hadn't really
followed that up until yesterday when [I asked on twitter about research
into the use of social media by
nurses](https://twitter.com/CameronNeylon/status/211749215322378240) and
was rapidly put in touch with a
[range](https://twitter.com/BrianSMcGowan/status/211753612664250369)
[of](https://twitter.com/amcunningham/status/211777853493219329)
[experts](https://twitter.com/amcunningham/status/212129273316773888) on
the subject (remind me, how *did* we ask speculative research questions
before Twitter?)Â . So the question I'm interested in probing is whether
the application of research by nurses is something that can be tracked
using links shared on Twitter as a proxy?

The is interesting from a range of perspectives. To what extent do
practicing nurses who use social media share links to web content that
informs their professional practice. How does this mirror the parallel
link sharing activity by academic researchers? Are nurses referring to
primary research content, or is this information mediated through other
sources? Do such other sources link back to the primary research? Can
those links be traced automatically? Â And a host of other questions
around how professional practice is changing with the greater
availability of these primary and secondary resources.

My hypothesis is as follows: Links shared by nurse practitioners and
their online community are a viable proxy of (some portion of) the
impact that research has in clinical practice. The extent to which links
are shared by nurses on Twitter, perhaps combined with sentiment
analysis, Â could serve as a measure of the impact of research targeted
at the professional practice of nurses.

Thoughts? Criticisms?


The holy grail of research assessment is a means of automatically tracking the way research changes the way practitioners act in the real world. How does new research influence policy? Where has research been applied by start-ups? And have new findings changed the way medical practitioners treat patients?

Rogue Scholar Posts

Research Graph 101

Tracking research into practice: Are nurses on twitter a good case study?