Elsevier September Data Showcase

Date: 

Wednesday, September 15, 2021, 12:00pm to 1:00pm

Location: 

Zoom

Registration (Harvard affiliation required)

This event will feature 3 speakers discussing various Elsevier data sets available to the Harvard community. To learn more about the Elsevier Master Data Use Agreement please visit the resource webpage.

Robbertjan Kalff, VP SciVal

Scival

SciVal is a web-based analytics solution with unparalleled power and flexibility that provides comprehensive access to the research performance of over 20,000 research institutions and their associated researchers. SciVal allows you to visualize your research performance, benchmark relative to peers, identify potential strategic partnerships, analyse new, emerging research trends, and create uniquely tailored reports.

In this demo we'll show you a variety of data you might be interested in:

  • Metrics data like Field-Weighted Citation Impact, Corporate-Academic collaboration, Output in Top Citation Percentiles or Annualized Awards Value
  • SciVal Topics of Prominence: a collection of documents with a common intellectual interest. These topics are clustered based upon direct citation analysis and can be large or small, new or old, growing or declining in momentum.
  • Funding landscapes where we have linked awarded grants to documents, researchers, organisations and SciVal Topics of Prominence (part of the knowledge graph of research analytics)

Kristy James, Data Scientist

ICSR Lab

Stephanie Faulkner, Dir Product Mgmt & Ops

Plum X Metrics

ICSR Lab is a cloud-based computational platform that enables researchers to analyze large, structured datasets, including the datasets that power Elsevier solutions such as Scopus and PlumX Metrics. It enables scholarly scientometric research into themes such as research impact, careers and practices, open science, inclusivity and sustainability, by coding in a notebook environment in a web browser, using Python/Pyspark, Scala or SQL to find quantitative insights from these datasets at scale. While there is a learning curve getting started with Pyspark and working with these large data volumes, this powerful platform is an opportunity to perform innovative bibliometrics research that makes use of more data than can be handled on one computer or accessed via API, or for working with new techniques such as machine learning.

This presentation will describe the datasets that are available through ICSR Lab, the features available on the platform, and the information we need to evaluate whether ICSR Lab is a good fit for your project.