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  • James A. Smith
Evidence of insufficient quality of reporting in patent landscapes in the life sciences
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CC BY
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Despite the importance of patent landscape analyses in the commercialization process for life science and healthcare technologies, the quality of reporting for patent landscapes published in academic journals is inadequate. Patents in the life sciences are a critical metric of innovation and a cornerstone for the commercialization of new life-science- and healthcare-related technologies. Patent landscaping has emerged as a methodology for analyzing multiple patent documents to uncover technological trends, geographic distributions of patents, patenting trends and scope, highly cited patents and a number of other uses. Many such analyses are published in high-impact journals, potentially allowing them to gain high visibility among academic, industry and government stakeholders. Such analyses may be used to inform decision-making processes, such as prioritization of funding areas, identification of commercial competition (and therefore strategy development), or implementation of policy to encourage innovation or to ensure responsible licensing of technologies. Patent landscaping may also provide a means for answering fundamental questions regarding the benefits and drawbacks of patenting in the life sciences, a subject on which there remains considerable debate but limited empirical evidence.

Subject:
Engineering
Biology
Material Type:
Reading
Provider:
Nature Biotechnology
Author:
Andrew J. Carr
David A. Brindley
Hannah Thomas
James A. Smith
Zeeshaan Arshad
Date Added:
08/07/2020
Plotting and Programming in Python
Unrestricted Use
CC BY
Rating

This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Steer
Allen Lee
Andreas Hilboll
Ashley Champagne
Benjamin
Benjamin Roberts
CanWood
Carlos Henrique Brandt
Carlos M Ortiz Marrero
Cephalopd
Cian Wilson
Daniel W Kerchner
Dan Mønster
Daria Orlowska
Dave Lampert
David Matten
Erin Alison Becker
Florian Goth
Francisco J. Martínez
Greg Wilson
ian
Jacob Deppen
Jarno Rantaharju
Jeremy Zucker
Jonah Duckles
Kees den Heijer
Keith Gilbertson
Kyle E Niemeyer
Lex Nederbragt
Logan Cox
Louis Vernon
Lucy Dorothy Whalley
Madeleine Bonsma-Fisher
Mark Phillips
Mark Slater
Maxim Belkin
Michael Beyeler
Mike Henry
mzc9
Narayanan Raghupathy
Nigel Bosch
Olav Vahtras
Pablo Hernandez-Cerdan
Paul Anzel
Phil Tooley
Raniere Silva
Robert Woodward
Ryan Avery
Ryan Gregory James
Sarah M Brown
SBolo
Shyam Dwaraknath
Sourav Singh
Stéphane Guillou
Steven Koenig
Taylor Smith
Thor Wikfeldt
Timothy Warren
Tyler Martin
Vasu Venkateshwaran
Vikas Pejaver
Date Added:
08/07/2020