List of recommendations for usage tracking and analysis for institutions with research networking systems such as VIVO, Profiles or SciVal Experts. Version 1 of these recommendations was authored by a subgroup of the CTSA Research Networking Affinity Group (Wash U, Elsevier, UCSF).
The document discusses the Web Impact Factor (WIF), which aims to quantify the impact and ranking of websites, domains, and subdomains based on link analysis, analogous to the Journal Impact Factor for academic journals. It reviews how the WIF was developed and studies its advantages and disadvantages. While the WIF may be useful for intra-country comparisons, the document concludes its application beyond this has little value due to differences between web and journal content and quality controls.
BrandSpotter is an advanced social media monitoring tool that can:
- Track over 60 million social media mentions across various sources like blogs, forums and reviews.
- Automatically assign sentiment to mentions using linguistic models.
- Provide insights through metrics like mention dynamics, influential users, and word clouds.
- Detect brand threats early through features like repetitive mention tracking and reputation indexes.
- Export customizable reports to evaluate marketing campaigns and customer sentiment over time.
This document summarizes three journal articles on the relationship between website usability (WU) and search engine optimization (SEO) elements. The first article found that a website optimized for usability (EW) outperformed a control website (CW) in conversions, despite the EW receiving less traffic. The second article found that a website optimized for SEO elements (EW2) outperformed the CW in search engine rankings over time, though the EW had better conversions. Based on these results, the third article constructed a model to fuse WU attributes and SEO elements to maximize both visitors' experiences and search engine performance. The model aims to resolve contradictions between WU and SEO to benefit small-to-medium
Are Ads on Google search engine results pages labeled clearly enough?Dirk Lewandowski
In an online experiment using a representative sample of the German online population (n=1,000), we compare users’ selection behavior on two versions of the same Google search engine results page (SERP), one showing advertisements and organic results, the other showing organic results only. Selection behavior is analyzed in relation to users’ knowledge on Google’s business model, on SERP design, and on these users’ actual performance in marking advertisements on SERPs correctly. We find that users who were not able to mark ads correctly selected ads significantly more often. This leads to the conclusion that ads need to be labeled more clearly, and that there is a need for more information literacy in search engine users.
OpenSocial in Practice - presented at VIVO14Brian Turner
This document discusses how OpenSocial can benefit clinical and translational science institutions like UCSF. OpenSocial allows for easy customization of profiles through embedded gadgets without altering core code. It provides benefits to institutions, researchers, and users. For institutions, it facilitates displaying institution-specific information and engaging content from sites like YouTube and Twitter. Researchers gain a more complete portfolio and younger researchers can showcase non-publication achievements. For users, customization makes profiles more visually appealing and provides a more holistic view of researchers through additional content and connection options.
The document discusses the Web Impact Factor (WIF), which aims to quantify the impact and ranking of websites, domains, and subdomains based on link analysis, analogous to the Journal Impact Factor for academic journals. It reviews how the WIF was developed and studies its advantages and disadvantages. While the WIF may be useful for intra-country comparisons, the document concludes its application beyond this has little value due to differences between web and journal content and quality controls.
BrandSpotter is an advanced social media monitoring tool that can:
- Track over 60 million social media mentions across various sources like blogs, forums and reviews.
- Automatically assign sentiment to mentions using linguistic models.
- Provide insights through metrics like mention dynamics, influential users, and word clouds.
- Detect brand threats early through features like repetitive mention tracking and reputation indexes.
- Export customizable reports to evaluate marketing campaigns and customer sentiment over time.
This document summarizes three journal articles on the relationship between website usability (WU) and search engine optimization (SEO) elements. The first article found that a website optimized for usability (EW) outperformed a control website (CW) in conversions, despite the EW receiving less traffic. The second article found that a website optimized for SEO elements (EW2) outperformed the CW in search engine rankings over time, though the EW had better conversions. Based on these results, the third article constructed a model to fuse WU attributes and SEO elements to maximize both visitors' experiences and search engine performance. The model aims to resolve contradictions between WU and SEO to benefit small-to-medium
Are Ads on Google search engine results pages labeled clearly enough?Dirk Lewandowski
In an online experiment using a representative sample of the German online population (n=1,000), we compare users’ selection behavior on two versions of the same Google search engine results page (SERP), one showing advertisements and organic results, the other showing organic results only. Selection behavior is analyzed in relation to users’ knowledge on Google’s business model, on SERP design, and on these users’ actual performance in marking advertisements on SERPs correctly. We find that users who were not able to mark ads correctly selected ads significantly more often. This leads to the conclusion that ads need to be labeled more clearly, and that there is a need for more information literacy in search engine users.
OpenSocial in Practice - presented at VIVO14Brian Turner
This document discusses how OpenSocial can benefit clinical and translational science institutions like UCSF. OpenSocial allows for easy customization of profiles through embedded gadgets without altering core code. It provides benefits to institutions, researchers, and users. For institutions, it facilitates displaying institution-specific information and engaging content from sites like YouTube and Twitter. Researchers gain a more complete portfolio and younger researchers can showcase non-publication achievements. For users, customization makes profiles more visually appealing and provides a more holistic view of researchers through additional content and connection options.
One example of a feature built as an OpenSocial gadgetlesliey
This document describes how researchers can display their conference posters and presentations on their profile page using OpenSocial gadgets. It provides an example of a researcher named Mini who has used this feature to showcase her presentations directly on her profile. OpenSocial gadgets allow users to add customizable content and applications to their profile.
Plumage: Open Source Web App Leveraging eagle-i to Make Core Resources Discov...anirvanchatterjee
Plumage is an open source front-end software developed by the University of California, San Francisco's Clinical and Translational Science Institute to make core research resources discoverable through search engines like Google. It leverages the eagle-i platform's ontology and data to create an intuitive, searchable interface. Since launching the Plumage-powered UCSF Cores Search site in July 2012, total page views have increased 103% and visits from search engines have risen 498%, showing the software substantially improves discoverability of research resources.
This document summarizes an experiment conducted by the Clinical and Translational Science Institute (CTSI) at the University of California, San Francisco to deliver trending publications to researchers via email. The CTSI pulled publication interests from researchers' profile pages and matched recent PubMed publications to their interests. The top 5 publications based on AltMetric scores were included in individualized emails sent to 50 researchers on a monthly basis. Based on the low 4% response rate to a follow-up survey, the CTSI concluded it was a qualified failure but learned targeted, higher-value information like funding opportunities may be better received, and presenting results directly in researcher profiles rather than via email could gain more traction. Next steps include adding a gadget to
This document describes how researchers at UCSF used crowdsourced 5-second testing, A/B testing, and web analytics to assess and improve the usability of their research networking homepage over time. They identified substantial barriers to usage of the original site and made three targeted design improvements between January and June 2011. Through short tests with untrained strangers and real user data from site interactions, they successfully solved measurable usability issues and improved how the site purpose was communicated to unfamiliar users.
The document discusses user interface enhancements made to profiles on a research networking site. Usability testing found the homepage failed to communicate its purpose and had a high bounce rate. Crowdsourced 5-second tests with screenshots were done with strangers to understand how users perceive pages. Tests of profile pages with different banner variations found adding an image decreased the bounce rate by over 15% and increased conversions. Heat maps showed which page elements received clicks.
UCSF Profiles is a research networking tool used by over 60,000 people per month at UCSF to find experts, collaborators, mentors, and peers based on their expertise. It contains profiles for over 6,200 UCSF faculty, fellows, postdocs, research academics, and staff, populated with information from HR systems, publications databases, and NIH grants. Profile owners can customize their profiles by adding photos, narratives, awards, and other information. The system was originally developed by Harvard Catalyst as open source software and has been extended by UCSF.
Reduce the pain of updating faculty web pages lesliey
presentation created by Leslie Yuan, Eric Meeks, and Ed Chen. Presented at 2012 UCCSC (UC Developers conference). Describes use of UCSF Profiles APIs by various departments to automatically mine data and decrease administrative burden.
Growth Hacking 101 for Research Networking (for VIVO Implementation & Dev call)anirvanchatterjee
This document outlines steps for increasing traffic to a research networking site called UCSF Profiles using search engine optimization techniques. It begins by establishing baselines using web analytics to understand traffic patterns. It recommends focusing efforts on researcher profile pages rather than the home page. Next it provides actions to ensure search engines can access pages through the robots.txt file and sitemaps. Further steps include optimizing page titles, descriptions and URLs to attract searchers. The document concludes by recommending ways to build inbound links from other university websites and through APIs to establish reputation.
Deconstructing a Dashboard: Inside the UCSF Profiles Team’s Monthly Key Metricsanirvanchatterjee
The document summarizes the key metrics that the UCSF Profiles team tracks in a monthly dashboard to monitor usage and performance of the UCSF Profiles research networking platform. It describes the types of metrics tracked, such as website traffic sources, user engagement, customization rates, and site performance. The dashboard is shared with stakeholders each month along with commentary on trends, issues, and goals. The metrics provide insights into how users interact with the site and how to better enable research networking through the platform.
VIVO2015 - Leveraging Personalized Google Analytics for Greater RNS EngagementBrian Turner
Description of a new feature in the ORNG suite that shows a researcher her/his profile page views and information about them in a user-friendly dashboard.
This document discusses secrets to having a successful UCSF Profiles page based on analytics of over 1.2 million visits in the last year. The top factors that predict a popular profile are: 1) being popular, newsworthy, and well-funded; 2) being an active online communicator through social media and blogging; 3) including links to other professional websites; 4) including a bio and photo; and 5) adding slides and videos. Simply including awards, keywords, or education does not strongly correlate with more profile visits.
10 simple ways UCSF Profiles has been used to win funding, find collaborators...lesliey
UCSF Profiles has been used in 10 ways to help researchers, clinicians, and the university:
1) It connects students and trainees with potential faculty mentors based on shared interests.
2) It saves staff and faculty time by allowing campus websites to automatically update information from researcher profiles.
3) Administrators use profile data to generate reports that recognize researchers' achievements and new publications in top journals.
UCSF Profiles is a campus resource that enables collaboration by identifying expertise. It provides public data on researchers that is syndicated across many UCSF websites and used for targeted emails. As an open source platform, it allows many to contribute additional applications. The site sees high traffic, with over 2,000 daily visits mostly from search engines like Google.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
The document discusses collecting data on a mobile app over two time periods to analyze the impact of new features on key metrics like new users, sessions, and session duration. Data was collected using Google Analytics on dimensions like user type, sessions, and devices. The hypothesis is that the app would see a sudden influx of new users and increased usage with the addition of a new useful feature. Statistics were analyzed and visualized to evaluate if the hypotheses were true and identify reasons for the outcomes. A non-visualized data set with metrics for app versions over the periods is also presented.
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET Journal
This document presents a proposed system architecture for implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews and sentiment analysis. The proposed architecture involves collecting customer reviews from social media, preprocessing and analyzing the data using natural language processing techniques like stemming, and storing the results in a database. Sentiment analysis is performed to categorize reviews by aspects and sentiment. The analyzed data is then presented to users through an interface to help the online grocery shopping platform better understand customer needs and improve products/services based on feedback.
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET Journal
The document discusses implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews. It proposes collecting customer reviews from social media and other sources, refining the data, analyzing it using natural language processing and machine learning techniques, and storing the results in a database. This would allow the platform to better understand customer sentiment and needs to improve products, services and the customer experience.
Intranet focus search strategy a z - from Findability Day 2014Findwise
This document provides an A-Z checklist of 40 topics that should be included in an organization's enterprise search strategy. It discusses each topic in 1-2 paragraphs, describing what the section should address. Key topics include analytics, architecture, best bets, budget, business cases, cloud search, communications, connectors, content quality, governance and more. The goal is to help organizations develop a comprehensive search strategy that reflects their information management needs and supports effective enterprise search.
IRJET- Rating based Recommedation System for Web ServiceIRJET Journal
This document summarizes several research papers on recommendation systems for web services. It discusses using rating data from users to recommend web services based on quality of service. Approaches include collaborative filtering to find similar users and services. Dynamic features are designed to describe user preferences and recommendations are made by weighting these features. The document also discusses using content-based filtering and social network data to provide recommendations. Improving recommendation diversity and combining collaborative and content-based filtering is addressed. Experimental results on real world datasets show hybrid approaches can improve performance on metrics like diversity, relevance and quality of service.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
One example of a feature built as an OpenSocial gadgetlesliey
This document describes how researchers can display their conference posters and presentations on their profile page using OpenSocial gadgets. It provides an example of a researcher named Mini who has used this feature to showcase her presentations directly on her profile. OpenSocial gadgets allow users to add customizable content and applications to their profile.
Plumage: Open Source Web App Leveraging eagle-i to Make Core Resources Discov...anirvanchatterjee
Plumage is an open source front-end software developed by the University of California, San Francisco's Clinical and Translational Science Institute to make core research resources discoverable through search engines like Google. It leverages the eagle-i platform's ontology and data to create an intuitive, searchable interface. Since launching the Plumage-powered UCSF Cores Search site in July 2012, total page views have increased 103% and visits from search engines have risen 498%, showing the software substantially improves discoverability of research resources.
This document summarizes an experiment conducted by the Clinical and Translational Science Institute (CTSI) at the University of California, San Francisco to deliver trending publications to researchers via email. The CTSI pulled publication interests from researchers' profile pages and matched recent PubMed publications to their interests. The top 5 publications based on AltMetric scores were included in individualized emails sent to 50 researchers on a monthly basis. Based on the low 4% response rate to a follow-up survey, the CTSI concluded it was a qualified failure but learned targeted, higher-value information like funding opportunities may be better received, and presenting results directly in researcher profiles rather than via email could gain more traction. Next steps include adding a gadget to
This document describes how researchers at UCSF used crowdsourced 5-second testing, A/B testing, and web analytics to assess and improve the usability of their research networking homepage over time. They identified substantial barriers to usage of the original site and made three targeted design improvements between January and June 2011. Through short tests with untrained strangers and real user data from site interactions, they successfully solved measurable usability issues and improved how the site purpose was communicated to unfamiliar users.
The document discusses user interface enhancements made to profiles on a research networking site. Usability testing found the homepage failed to communicate its purpose and had a high bounce rate. Crowdsourced 5-second tests with screenshots were done with strangers to understand how users perceive pages. Tests of profile pages with different banner variations found adding an image decreased the bounce rate by over 15% and increased conversions. Heat maps showed which page elements received clicks.
UCSF Profiles is a research networking tool used by over 60,000 people per month at UCSF to find experts, collaborators, mentors, and peers based on their expertise. It contains profiles for over 6,200 UCSF faculty, fellows, postdocs, research academics, and staff, populated with information from HR systems, publications databases, and NIH grants. Profile owners can customize their profiles by adding photos, narratives, awards, and other information. The system was originally developed by Harvard Catalyst as open source software and has been extended by UCSF.
Reduce the pain of updating faculty web pages lesliey
presentation created by Leslie Yuan, Eric Meeks, and Ed Chen. Presented at 2012 UCCSC (UC Developers conference). Describes use of UCSF Profiles APIs by various departments to automatically mine data and decrease administrative burden.
Growth Hacking 101 for Research Networking (for VIVO Implementation & Dev call)anirvanchatterjee
This document outlines steps for increasing traffic to a research networking site called UCSF Profiles using search engine optimization techniques. It begins by establishing baselines using web analytics to understand traffic patterns. It recommends focusing efforts on researcher profile pages rather than the home page. Next it provides actions to ensure search engines can access pages through the robots.txt file and sitemaps. Further steps include optimizing page titles, descriptions and URLs to attract searchers. The document concludes by recommending ways to build inbound links from other university websites and through APIs to establish reputation.
Deconstructing a Dashboard: Inside the UCSF Profiles Team’s Monthly Key Metricsanirvanchatterjee
The document summarizes the key metrics that the UCSF Profiles team tracks in a monthly dashboard to monitor usage and performance of the UCSF Profiles research networking platform. It describes the types of metrics tracked, such as website traffic sources, user engagement, customization rates, and site performance. The dashboard is shared with stakeholders each month along with commentary on trends, issues, and goals. The metrics provide insights into how users interact with the site and how to better enable research networking through the platform.
VIVO2015 - Leveraging Personalized Google Analytics for Greater RNS EngagementBrian Turner
Description of a new feature in the ORNG suite that shows a researcher her/his profile page views and information about them in a user-friendly dashboard.
This document discusses secrets to having a successful UCSF Profiles page based on analytics of over 1.2 million visits in the last year. The top factors that predict a popular profile are: 1) being popular, newsworthy, and well-funded; 2) being an active online communicator through social media and blogging; 3) including links to other professional websites; 4) including a bio and photo; and 5) adding slides and videos. Simply including awards, keywords, or education does not strongly correlate with more profile visits.
10 simple ways UCSF Profiles has been used to win funding, find collaborators...lesliey
UCSF Profiles has been used in 10 ways to help researchers, clinicians, and the university:
1) It connects students and trainees with potential faculty mentors based on shared interests.
2) It saves staff and faculty time by allowing campus websites to automatically update information from researcher profiles.
3) Administrators use profile data to generate reports that recognize researchers' achievements and new publications in top journals.
UCSF Profiles is a campus resource that enables collaboration by identifying expertise. It provides public data on researchers that is syndicated across many UCSF websites and used for targeted emails. As an open source platform, it allows many to contribute additional applications. The site sees high traffic, with over 2,000 daily visits mostly from search engines like Google.
The Internet, which brought the most innovative
improvement on information society, web recommendation
systems based on web usage mining try to mine user’s behavior
patters from web access logs, and recommend pages or
suggestions to the user by matching the user’s browsing behavior
with the mined historical behavior patterns. In this paper we
propose a recommendation framework that considers different
application status and various contexts of each user. We
successfully implemented the proposed framework and show how
this system can improve the overall quality of web
recommendations.
I
The document discusses collecting data on a mobile app over two time periods to analyze the impact of new features on key metrics like new users, sessions, and session duration. Data was collected using Google Analytics on dimensions like user type, sessions, and devices. The hypothesis is that the app would see a sudden influx of new users and increased usage with the addition of a new useful feature. Statistics were analyzed and visualized to evaluate if the hypotheses were true and identify reasons for the outcomes. A non-visualized data set with metrics for app versions over the periods is also presented.
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET Journal
This document presents a proposed system architecture for implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews and sentiment analysis. The proposed architecture involves collecting customer reviews from social media, preprocessing and analyzing the data using natural language processing techniques like stemming, and storing the results in a database. Sentiment analysis is performed to categorize reviews by aspects and sentiment. The analyzed data is then presented to users through an interface to help the online grocery shopping platform better understand customer needs and improve products/services based on feedback.
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET Journal
The document discusses implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews. It proposes collecting customer reviews from social media and other sources, refining the data, analyzing it using natural language processing and machine learning techniques, and storing the results in a database. This would allow the platform to better understand customer sentiment and needs to improve products, services and the customer experience.
Intranet focus search strategy a z - from Findability Day 2014Findwise
This document provides an A-Z checklist of 40 topics that should be included in an organization's enterprise search strategy. It discusses each topic in 1-2 paragraphs, describing what the section should address. Key topics include analytics, architecture, best bets, budget, business cases, cloud search, communications, connectors, content quality, governance and more. The goal is to help organizations develop a comprehensive search strategy that reflects their information management needs and supports effective enterprise search.
IRJET- Rating based Recommedation System for Web ServiceIRJET Journal
This document summarizes several research papers on recommendation systems for web services. It discusses using rating data from users to recommend web services based on quality of service. Approaches include collaborative filtering to find similar users and services. Dynamic features are designed to describe user preferences and recommendations are made by weighting these features. The document also discusses using content-based filtering and social network data to provide recommendations. Improving recommendation diversity and combining collaborative and content-based filtering is addressed. Experimental results on real world datasets show hybrid approaches can improve performance on metrics like diversity, relevance and quality of service.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
A Systematic Literature Survey On Recommendation SystemGina Rizzo
This document provides a literature review of recommendation systems. It discusses different recommendation models including collaborative filtering, content-based filtering, and hybrid approaches. Collaborative filtering techniques make recommendations based on the ratings and preferences of similar users, while content-based filtering relies on the characteristics of the items. The document also outlines key application areas of recommendation systems like movies, products, jobs, and friends. Overall, the review examines research trends in recommendation techniques and their use across different service industries to improve user experience and business outcomes.
Query Recommendation by using Collaborative Filtering ApproachIRJET Journal
This document proposes a system called QDMiner to mine query facets from the top search results for a query. It uses collaborative filtering techniques to recommend the top-k results that are most relevant to a user's interests.
QDMiner first retrieves the top search results from a search engine. It then mines frequent lists from the HTML tags and free text within the results to identify query facets. It groups common lists and ranks the facets and items based on their appearances. QDMiner represents the search results in two models: the Unique Website Model and Context Similarity Model, to order the query facets.
To recommend results, QDMiner uses collaborative filtering techniques including item-based and user-based
An introduction to active|watch where insight comes as standard. Exploit social media, blogs, forums and websites. Rapidly create competitive intelligence and steal a winning advantage. safeguard your reputation and valuable assets. look ahead and get out in from of the competition.
IRJET- Virtual Business Analyst using a Progressive Web ApplicationIRJET Journal
The document proposes a virtual business analyst platform that uses progressive web applications, SEO, and SEM to help small and medium enterprises improve their brands and connect with investors and customers. The platform would provide customized designs for company applications and help improve customer relationship management. It analyzes how innovation adoption of social media technology can help small businesses formulate digital marketing strategies to become more agile organizations. The methodology discusses using the platform to help clients target specific demographics to increase conversion rates and capital yields from paid digital advertising campaigns.
IJRET : International Journal of Research in Engineering and TechnologyImprov...eSAT Publishing House
This document summarizes techniques for improving web search results through web personalization. It discusses how web usage mining can be used to optimize information by monitoring user interaction histories and profiles. The proposed system aims to reduce manual user feedback by implicitly gathering preferences from behaviors like click-through rates and dwell times. It introduces an algorithm that calculates new ranking values for websites based on keyword matches and time spent on pages, and swaps ranks accordingly. This system provides personalized search results by continuously updating rankings based on implicit user interactions.
VIVO 2014: Google Analytics, Email Marketing & Vanity to Increase User Engage...Nooshin Latour
Poster presented at VIVO 2014 conference: Utilized UCSF Profiles web analytics data to deliver a customized “UCSF Profiles Annual Report” to individual researchers at UCSF, which listed their total annual unique pageviews, broken down by major relevant categories (e.g. pageviews from the UCSF campus, NIH, pharmaceutical companies, foundations, and other universities). Result: Increase in user engagement and edite Profiles pages. UCSF-CTSI Team: Nooshin Latour, MA, Sr. Communications & Marketing Manager and Anirvan Chatterjee, Director of Data Strategy
VIVO 2014: Google Analytics, Email Marketing & Vanity to Increase User Engage...CTSI at UCSF
Poster presented at VIVO 2014 conference: Utilized UCSF Profiles web analytics data to deliver a customized “UCSF Profiles Annual Report” to individual researchers at UCSF, which listed their total annual unique pageviews, broken down by major relevant categories (e.g. pageviews from the UCSF campus, NIH, pharmaceutical companies, foundations, and other universities). Result: Increase in user engagement and edite Profiles pages. UCSF-CTSI Team: Nooshin Latour, MA, Sr. Communications & Marketing Manager and Anirvan Chatterjee, Director of Data Strategy
This document provides details on a proposed thesis project to develop software that uses clustering algorithms and data visualization to analyze high-dimensional clickstream data. The software would group website pages into clusters based on similarities in user behavior and display the results in a graphical user interface. This approach aims to provide insight into naturally occurring patterns in the data that could help website managers better understand user segments and behavior.
This document proposes a thesis project to visualize high-dimensional clickstream data from a commercial website using clustering algorithms and an interactive Java interface. The project will group website pages into clusters based on user behavior metrics, represent the clusters in 2-3 dimensions, and allow users to explore the clusters. This may provide insight into user patterns that could help optimize the site.
Using Ontology to Capture Supply Chain Code HalosCognizant
Manufacturers need to create a lingua franca that extends throughout the supply chain ecosystem, in order to generate insights from the digital data encircling their employees, partners, processes and customers.
This document summarizes a research paper that proposes a bi-objective recommendation framework (BORF) for venue recommendation on mobile social networks. The BORF uses multiple objective optimization techniques to generate personalized recommendations. It addresses issues like cold start and data sparsity by preprocessing data using a Hub-Average inference model. Both scalar optimization using a Weighted Sum Approach and vector optimization using a NSGA-II evolutionary algorithm are implemented to provide optimal venue recommendations to users. Experimental results on a large real-world dataset confirm the accuracy of the proposed recommendation system.
Similar to Recommendations for usage tracking for research networking systems, v.1. July 2013 (20)
Designing Digital Products for Diverse Populationslesliey
The Diverse eCohort project at UCSF aims to transform the university into a model research institution that champions diversity, equity, and inclusion. The project seeks to 1) build infrastructure to support online research tailored to diverse communities, 2) demonstrate how to partner with communities to improve research outcomes and health equity, and 3) jumpstart innovation to include diverse communities in collaboration with UCSF clinical research projects.
1. The document discusses four implemented clinical decision support apps that deliver AI to clinicians in real time within their workflow in the electronic health record.
2. The apps varied in the complexity of the AI/computation used, amount of patient-specific data required, and how the decision support was delivered to clinicians.
3. Apps that maintained patient data and computation externally were able to support more complex AI, while still delivering results seamlessly within the EHR using standards like SMART on FHIR.
Research Networking SEO state of the union 2015lesliey
The document summarizes the results of an SEO analysis of 52 research networking sites. It finds that sites using their own institutional domain and custom or Profiles software scored highest in Google rankings. Additionally, sites with many diverse incoming links from other domains performed better. The analysis provides recommendations for improving SEO, including establishing benchmarks, optimizing for Google, and encouraging other sites to link back. Overall, the state of SEO across the sites showed mixed results.
UCSF is developing an OpenSocial framework to extend the functionality of its Profiles research networking tool without modifying the underlying code. This will allow UCSF to create reusable "gadgets" and foster sharing across institutions. Currently UCSF has implemented several gadgets for its Profiles including faculty mentoring, document sharing, and profile search capabilities. The goal is for OpenSocial to enable collaboration both within and across institutional social networks like Profiles, VIVO, and LinkedIn.
UCSF Profiles provides a searchable database of over 2,600 UCSF faculty profiles populated with publicly available data like publications from PubMed. It is part of a national effort to enable research networking across institutions. UCSF is leading a project for national research networking involving 15-20 other institutions using Profiles and other tools. A pilot launch is anticipated in January 2011 to showcase an aggregated federated search across participating institutions.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
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Recommendations for usage tracking for research networking systems, v.1. July 2013
1. 1
Recommendations for Research Networking Systems (RNS)
Usage Tracking – Version 1, July 2013
Foreword
A subgroup of the CTSA Research Networking Affinity Group (RNAG) has developed a set of recommendations for SGC3, focused on measuring
the usage of research networking systems (RNS).
Introduction
Biomedical institutions have widely deployed institutional expertise discovery and researcher networking systems (RNS), including VIVO, Profiles
RNS, and SciVal Experts. Institutions have invested in networking systems drawn by the promise of discovering complementary expertise,
enabling collaboration, facilitating funding opportunity matching, and other key needs for accelerating translational research.
Evidence is currently limited on the extent of usage of RNS tools and the extent to which they promote research-oriented networking. However,
it is possible to measure usage of these systems using web traffic analytics tools, via analysis of web logs. The most well-known of these tools is
Google Analytics, but a number of attractive alternatives are emerging.1
In addition, qualitative methods can be employed to collect feedback
with respect to value and utility, especially from the user perspective. The recommendations here reflect current measures being used by 8
CTSA consortium member institutions and one commercial entity:
• Harvard University
• Medical University of South Carolina
• Northwestern University
• University of Iowa
• University of Minnesota
• University of California, San Francisco*
• Vanderbilt University
• Washington University*
• Elsevier*
This set of recommendations is a start. This document is meant to be a living and changing set of recommendations that will inform institutions
and SGC3 of the value and power of research networking systems for accelerating translational research. For any specific institution, adopting a
subset of these measures can help to shape strategy, decision making, and practical tactics.
1
Some notable examples include Google Analytics (http://www.google.com/analytics/), Site AI (http://www.siteai.com/), Woopra (http://www.woopra.com/),
with NewRelic (http://newrelic.com ) being used for performance monitoring. See Appendix 1 for more on tools.
*Authored Version 1 of this document
2. 2
The structure of this document is as follows: The set of recommended RNS Usage Measures below a description of each measure and an
accompanying rationale and explanation as to why the measure should be tracked. Institutional examples and case studies then describe
successful strategies and tactics that have evolved from the analysis of the data, which range from informing the overall marketing strategy for
adoption, to targeting specific faculty meetings for presentations and training, to gaining stakeholder and financial support. For ease of
discussion and presentation, Google Analytics will be used as an example of an analytics tool to track tool usage.
Recommended RNS Usage Measures
For all measures:
Track trends
R1
Establish a baseline and
track over time
Track the same measures over time to illustrate patterns and responsiveness to actions (e.g., a new marketing
campaign).
Set expectations
R2
Set expectation for trend
over time
For trends, set expectations for how the measures
will trend.
At UCSF, while they hope to see the number of 2+
minute on-campus visits per day increase over time,
they expect this measure to plateau over time if the
majority of the core user community comes to make
appropriate use of the tool whenever needed.
Separate internal from external usage
R3
Distinguish between traffic
internal and external to the
institution
It easy to distinguish between institutional and
extra-institutional traffic with Google Analytics by
looking at the visitor’s service provider. By
creating advanced segments that show only traffic
from your institution’ service provider(s) vs. those
from all other service providers, you can filter
UCSF set up two advanced segments: users with service
provider “university of california san francisco,” and
those with any other service provider. They then look at
every statistic via both segments. This shows, for
example, that they get over ten times as many off-
campus visits as they do on-campus visits, and that on-
3. 3
every statistic through an internal vs. external
lens. (Be sure to that your team’s own accesses to
the site are being ignored by filtering out your IP
addresses as an account-level filter.)
campus visitors are more likely to arrive via campus
websites than external visitors.
The basics:
Implement Google Analytics
R4
Track basic web traffic
statistics via Google
Analytics
Google Analytics affords easy access to basic usage analytics s so you can determine:
1. Visits to your site
2. Page views
3. Unique visitors
4. % new vs. % returning visitors
5. Bounce rate on home and profile pages
6. # pages looked at per visit
7. Average visit duration
Understand search traffic
R5
Use search traffic statistics
to inform marketing and
outreach opportunities
Google Analytics shows not only the amount of
traffic from public search engines like Google and
Bing, but also the keywords that brought users to
your site. This can be used to understand search
behavior and intent. For example, if search traffic
is low, it might be an opportunity do basic search
engine optimization; or if users are coming to the
site after searching for people’s name on Google,
it may provide an opportunity to highlight their
networks on the landing page.
UCSF received only 2,500 visits a month from external
search engines during launch month. They subsequently
did search engine optimization work, and now receive
over 40,000 visits a month from external search engines
— 72% of all visits.
4. 4
Delve into referral traffic
R6
Use referral traffic statistics
to inform marketing and
outreach opportunities
Google Analytics also provides data on which
website refer the most traffic to your system. By
understanding these patterns, you can look at
institutional traffic and sites and it can help to
inform your outreach and marketing efforts. For
example, if a web resource popular in the local
research community does not refer much traffic it
could be an opportunity for outreach to get links
back to your system added at the appropriate
spots.
Comparisons across institutions can also help here. For
example, the 2nd highest referring site at UCSF is the
campus directory. UCSF partnered with the campus
group responsible for each person’s page on the main
online people directory to link back to the corresponding
UCSF Profiles page; the result was a substantial increase
in traffic to UCSF Profiles -- about 1,700 visits a month.
Understand what’s popular
R7
Understand traffic patterns
to different parts of the site
to understand preferential
usage, and prioritize
outreach and development
Google Analytics allows users to see what parts of
the site are getting the most traffic, and how users
behave on each page, e.g. by looking at bounce.
Understanding these measures is critical to
understanding usage of the site.
A month after launch, UCSF staff were surprised to
discover that only 9% of visits begin on the home page,
with most users arriving directly on profile pages. This
provided the impetus to spend substantially more time
improving data on profile pages, and deemphasize tweaks
to the home page.
Look at your geographic breakdown
R8
Examine regional and
global traffic patterns to
inform potential economic
development impact
Google Analytics’s audience location demographics will show your traffic from within your region, state,
nation, and the world. If you’re like many institutions, you might be surprised at how much of the traffic to
your RNS comes from outside of your institution, let alone your state.
In addition, look both at city, (for the institution’s city and the surrounding suburbs), as well as network
provider which is often be linked to the university, sometimes even down to school or college within the
university.
5. 5
Benchmarking:
Define meaningful engagement
R9A & R9B
Track the number of “X+”
minute visits to the RNS
from within the institution
(where “X+” represents the
length of time you deem
meaningful)
Track the number of visits
where users from within
the institution view at least
“Y” pages (where “Y”
represents the # pages you
deem meaningful)
For context, we are using the model of an RNS that
does not require log-in to search the network. As
such, discuss and define a surrogate for what
constitutes meaningful engagement with your
RNS. Once defined, these measures can be found
via Google Analytics; complex queries can be used
through advanced segments and/or custom
dashboards.
At UCSF, they pay particular attention to the average
number of visits from within the UCSF campus network
lasting 2 minutes or longer (Google Analytics defines a
visit as a period of continuous usage where the gap
between subsequent page views or interactions is under
half an hour). Because users do not need to log in to
search UCSF Profiles, they use these 2+ minute on-
campus visits as a proxy for deep active use of the
product by researchers, administrators, and other staff on
campus.
Benchmark the traffic
R10
Benchmark relative to
other campus web
properties, both on and off
campus
One way to infer relative usefulness of your RNS is
to track and benchmark the RNS traffic relative to
other campus properties such as the directory or
the main institutional web site.
At UCSF, the RNS gets as much as 60% of the visits of the
campus directory from all visitors, and 12% of the visits of
the campus directory from UCSF campus visitors. This
signals the relative importance of this system as a
resource.
6. 6
Segment populations
R11
Segment and compare
populations of profiled
users, and of visitors (when
possible)
Using the data in the RNS, segment populations of
engaged users to determine differing or similar
patterns of use. The differences can inform
outreach and training opportunities, and value can
be inferred. Examples of segments: faculty rank,
department, school, etc. Segments with lesser
adoption could be candidates for outreach and
training, whereas segments with greater adoption
are candidates for interviews on why and what is
going well. If the system is behind a login wall,
there may also be opportunities to segment end
users.
By looking at UCSF Profile adoption rates (where adoption
is defined as a profile that has some curation or
customization completed such as an uploaded photo),
they found that faculty at all levels – junior to senior –
have adopted their profiles in close to similar proportions.
This seems to indicate equal importance of the tool
across the segments, and helps as they plan outreach and
marketing for the system.
Analyze where people click
R12
See what your users are
clicking on and use the data
to improve your product
A heatmap is an easy way to understand what
users do on your site. It’s a visual representation
showing where users click and what they do. This
can help you determine what aspects of your RNS
are most popular & what is overlooked. Google
Analytics provides this data via in-page analytics
feature, or a separate heatmap tool may be used.
If certain classes of links on your site are being
systematically ignored, this data can help inform
user interface changes and/or marketing
messages.
UCSF has installed an inexpensive heat mapping tool and
used that along with Google Analytics in-page analytics to
prioritize improvements to their RNS home page.
7. 7
Qualitative measures:
R13
Measure traffic impact
resulting from various
communications (e.g., PR
and marketing campaigns)
and then judiciously use
the successful strategies in
the future
Analyze traffic to your RNS before and after
various marketing campaigns such as emails or
trainings.
Try different messages using A/B testing and
measure which ones are most successful.
Partner with other groups on campus so all of the
messages don’t come from one source. Measure
traffic impact from these various sources.
UCSF noticed a big spike in traffic after the Executive Vice
Chancellor & Provost (EVCP) sent out an email
announcing UCSF Profiles. This traffic spike was larger
than any other email campaign and speaks to the reach
and value of communications from UCSF’s EVCP.
Also, at UCSF, groups across campus from the Library to
University Relations have helped to spread the word
about UCSF Profiles. These groups are on board to help
promote UCSF Profiles because data mined from UCSF
Profiles have facilitated those groups’ projects.
Survey users on value & usage
R14
Survey users for direct
feedback; use the feedback
to inform strategy and gain
support for your system
To really find out how people use the system and
what value they garner from your RNS, you will
need to ask them. The feedback will identify gaps
and improvements, inform planning and hopefully
help gain support to sustain and enhance your
system.
A number of SciVal Experts’ sites track numbers of
inquiries and/or profile corrections as additional evidence
of how many people are really looking at the site and its
content.
REACH-NC (NC state SciVal Experts portal) created a
contact form that users fill out when trying to contact a
researcher and they do not display direct contact
information. The contact form goes to the campus liaison
who coordinates the contact. In addition, a copy of the
request goes to the REACH-NC Executive Director and
Program Coordinator so they can track request and
monitor connections. In addition, inquiries and feedback
from profiled researcher come to a group mail box that
the ELS Account Development Manager personally
manages, instead of directly going to the general ELS
helpdesk.
8. 8
Arizona State University had a ‘Submit Feedback’ button
on the Experts site that linked to an end-user survey.
(They felt that the information they were receiving was
biased toward the negative and also unhelpful, so they
discontinued use of the survey.) Note: this offers up a
lesson on being specific about what kind of feedback you
solicit, and not necessarily a warning against soliciting
feedback in this way
UCSF has implemented a pop up survey on UCSF Profiles
that while annoying to many, has resulted in the
collection of substantive feedback such as:
“Great resource for finding potential research
collaborators and for PhD dissertation
committees”
“I am hoping that it just helped me find a
mentor...”
“It has helped find new nursing research
problems”
Interventional networking:
Given the significant lag time between identifying a potential collaborator and the first outcome of a successful collaboration, such as a
published paper or even a funded grant award, it is difficult to directly link RNS usage to interventional networking. Additional studies need to
be done to link short-term RNS usage patterns to longer-term outcome measures such as:
• Number of new introductions/meetings/active collaborations
• Interdisciplinary nature of new introductions/meetings/collaborators
• Resultant joint grant applications and publications
Enable & track collaboration right from your RNS
9. 9
R15
Enable collaboration from
within your RNS
Identify potential collaborations and enable
communication between researchers directly from
your RNS.
The Michigan Corporate Relations Network (MCRN) portal
functions like a multi-university Experts site that allows
for searching across participating universities in Michigan
(U of MI, University of Michigan’s Dearborn Campus,
Michigan State University, Wayne State University at
launch, and Western Michigan University shortly
thereafter, and likely also Michigan Tech sometime after
that). Thinking of it like an ecommerce metaphor, the
site allows a businessperson interested in working with
state of Michigan researchers to add researchers of
interest to a list (‘shopping cart’) and the call to action
(‘checkout’) process routes the researchers of interest
along with a user-entered problem statement to the
appropriate university business relations offices. These
will be tracked centrally and by each office, hopefully with
some evaluation of quantity of contacts and ‘quality’ of
contacts (was it a legitimate research collaboration
interest or a solicitor trying to sell something? did it turn
into a collaboration?)
UCSF has an operational pilot that has integrated UCSF
Profiles with Salesforce Chatter (Chatter is UCSF’s
enterprise social networking tool). This allows one to
“follow” a person directly from UCSF Profiles and also
allows a person to select a few people of interest and
create a “Chatter Group” directly from UCSF Profiles.
10. 10
Appendix 1
Notes / community comments on analytic tools
From various sources, including: https://wiki.duraspace.org/display/VIVO/VIVO+Web+Analytics
Analytic Tool Comments
Google Analytics Used as default Profiles solution
Also used as default VIVO solution – see googleAnalytics.ftl FreeMarker template
Woopra "all-in-one web application performance tool"
Paul Albert at Weill notes: "I've personally used Woopra before. It seems to be better than Google Analytics about tracking outbound
links and for watching people click through your site in real time. (Paul Albert, WCMC)
New Relic: For performance monitoring, we use New Relic, which you can get a sense of here:
https://dl.dropboxusercontent.com/u/2014679/NewRelic.png and https://dl.dropboxusercontent.com/u/2014679/EmailAlert.png.
The best part about New Relic is that it will send a notice to other team members when you have tried to execute several malformed
SPARQL queries in a row." (Paul Albert, WCMC)
Site AI: "The problem with dashboards is they don’t directly provide insights or deliver knowledge about the data. Even worse, most
visualizations require the user go through the mental exercise of interpreting the results. Site Ai does the analysis for you and presents
the information in plain English."
example: http://www.siteai.com/share/2672/2013/5/22/fcsjiggewdpechaafwpfjedlkvjhvipu
(Mark Fallu, research-hub.griffith.edu.au at Griffith University)
Mixpanel: "Page view counts are popular because they are easy to report, but ultimately cannot tell you how engaged your visitors are. Mixpanel
lets you measure what customers do in your app by reporting actions, not page views." (Alex Viggio, University of Colorado Boulder)
Chartbeat See demo
CrazyEgg CrazyEgg provides data on what links users click on once they arrive on key pages or selected profiles. Click statistics are provided in
several formats: heatmap, scrollmap, overlay, and ‘confetti.’ This allows us to improve design of page elements strategically to
encourage interaction. (Leslie Yuan, UCSF)
Qualaroo User feedback is collected via a popup survey tool; detailed reports are available online. We ask “How has UCSF Profiles helped you?”
to solicit success stories and better understand use cases, but the survey functions as a customer service access point as well. Users
may enter an email address if they’d like a response. (Leslie Yuan, UCSF)