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Using Matomo in EBSCO's Discovery Service
ARTICLES Analytics and Privacy Using Matomo in EBSCO’s Discovery Service Denise FitzGerald Quintel and Robert Wilson ABSTRACT When selecting a web analytics tool, academic libraries have traditionally turned to Google Analytics for data collection to gain insights into the usage of their web properties. As the valuable field of data analytics continues to grow, concerns about user privacy rise as well, especially when discussing a technology giant like Google. In this article, the authors explore the feasibility of using Matomo, a free and open-source software application, for web analytics in their library’s discovery layer. Matomo is a web analytics platform designed around user-privacy assurances. This article details the installation process, makes comparisons between Matomo and Google Analytics, and describes how an open-source analytics platform works within a library-specific application, EBSCO’s Discovery Service. INTRODUCTION In their 2016 article from The Serials Librarian, Adam Chandler and Melissa Wallace summarized concerns with Google Analytics (GA) by reinforcing how “reader privacy is one of the core tenets of librarianship.”1 For that reason alone, Chandler and Wallace worked to implement and test Piwik (now known as Matomo) on the library sites at Cornell University. Taking a cue from Chandler and Wallace, the authors of this paper sought out an analytics solution that was robust and private, that could easily work within their discovery interface, and provide the same data as their current analytics and discovery service implementation. This paper will expand on some of the concerns from the 2016 Wallace and Chandler article, make comparisons, and provide installation details for other libraries. -
Matomo Como Herramienta Para La Obtención De Estadísticas De Uso
STS, Simposio Argentino sobre Tecnología y Sociedad Matomo como herramienta para la obtención de Estadísticas de Uso de los Documentos dispuestos en el Repositorio Institucional de Acceso Abierto de la Facultad de Tecnología y Ciencias Aplicadas, UNCA Ivanna Maricruz Lazarte1, María Vanesa Doria2, Carola Victoria Flores2, María Carolina Haustein2 y Efraín Córdoba3 1 Departamento de Formación Básica, Facultad de Tecnología y Ciencias Aplicadas, UNCA 2 Departamento de Informática, Facultad de Tecnología y Ciencias Aplicadas, UNCA 3 Instituto de Informática, Facultad de Tecnología y Ciencias Aplicadas, UNCA {ilazarte, vanesadoria, carolaflores, caro-h, efrainc}@tecno.unca.edu.ar Resumen. Las estadísticas de uso permiten realizar un seguimiento de la producción científica depositada en los repositorios digitales de acceso abierto y estudiar las pautas de su crecimiento, ayudando de esta manera a diseñar estrategias futuras para alimentarlos con más contenido. Reflejan, además, la visibilidad, la difusión nacional e internacional y las tendencias de uso de los documentos dispuestos en abierto. También poseen una utilidad inmediata para autores, editores e instituciones para ayudar a justificar la importancia, relevancia, valor de su trabajo y complementar la información dada por las citas. Por lo expresado, resulta de gran importancia que los repositorios digitales cuenten con estadísticas de uso que permitan demostrar su utilidad, relevancia y, por consiguiente, sus beneficios. En este artículo se describe la implementación y el uso de Matomo en el Repositorio Institucional de Acceso Abierto de la Facultad de Tecnología y Ciencias Aplicadas de la Universidad Nacional de Catamarca de la República Argentina. Matomo es una herramienta de Analítica Web que puede utilizarse para la obtención de estadísticas de uso de repositorios institucionales, con el propósito de facilitar el análisis y comprensión de los datos almacenados, medir su popularidad, utilidad y, además, contribuir a la correcta toma de decisiones. -
Web Analytics
WEB ANALYTICS Weblogs APRIL 5, 2019 Asif Khan Table of Contents 1 What Is Web Analytics ........................................................................................................6 1.1 Why Web Analytics Are Important ...............................................................................6 1.2 How Web Analytics Work ............................................................................................6 1.3 Sample Web Analytics Data ..........................................................................................7 1.3.1 Audience Data .......................................................................................................7 1.3.2 Audience Behavior.................................................................................................7 1.3.3 Campaign Data ......................................................................................................7 2 Web Analytics and Social Media From UX (user experience) Professionals ........................8 2.1 Web Analytics: .............................................................................................................8 2.1.1 Who Comes to Your Web Site?..............................................................................8 2.1.2 What Is the User Doing on Your Web Site? ...........................................................9 2.1.3 When Is the User Doing It? ....................................................................................9 2.1.4 Where Is the User Doing It? ...................................................................................9 -
Open Source Intelligence Tools and Resources Handbook
OPEN SOURCE INTELLIGENCE TOOLS AND RESOURCES HANDBOOK 0 OPEN SOURCE INTELLIGENCE TOOLS AND RESOURCES HANDBOOK 2018 Aleksandra Bielska Natalie Anderson, Vytenis Benetis, Cristina Viehman 1 Foreword I am delighted to share the latest version of our OSINT Tools and Resources Handbook. This version is almost three times the size of the last public release in 2016. It reflects the changing intelligence needs of our clients in both the public and private sector, as well as the many areas we have been active in over the past two years. No list of OSINT tools is perfect, nor is it likely to be complete. Indeed, such is the pace of change that by the time you read this document some of our suggestions may have been surpassed or have ceased to exist. Regrettably, today's tool might also be tomorrow's vulnerability. To counter the first problem, we have included a list of toolkits provided by other OSINT practitioners working to improve the state-of-the-art. To manage the second, we recommend that all tools be tested in a secure computing environment whenever possible. Work on the next iteration of the Handbook has already begun. For now, I hope this version contributes to improving your efficiency and effectiveness as a researcher, analyst, investigator or general OSINT practitioner. Please feel free to share it with your colleagues. To encourage its broadest possible dissemination, we are publishing the Handbook under a Creative Commons CC BY License. I would like to end by thanking my colleagues at i-intelligence for their efforts in compiling the Handbook.