Seamless research intelligence solution — integrating and elevating research data
Seamless research intelligence solution — integrating and elevating research data
B2B Data engine for scientific research & actionable insights
B2B Data engine for scientific research & actionable insights
Timeline
May 2023 - May 2024
Contribution
Conceptualization, end-to-end design & delivery of key features
Team
Sole designer, 2 PM, and 5+ engineers
Context
Context
Context
ResearchTrack is a data engine for the scientific research community. The platform leverages automated data aggregation to deliver actionable insights that enhance research outcomes.
ResearchTrack intends to offer a comprehensive 4-dimensional data analysis that evaluates academic impact, societal value, clinical performance, and commercial activities. This multi-faceted approach aims to benefit academic institutions, enterprises, and individuals by offering a holistic view of research performance.
ResearchTrack is a data engine for the scientific research community. The platform leverages automated data aggregation to deliver actionable insights that enhance research outcomes.
ResearchTrack intends to offer a comprehensive 4-dimensional data analysis that evaluates academic impact, societal value, clinical performance, and commercial activities. This multi-faceted approach aims to benefit academic institutions, enterprises, and individuals by offering a holistic view of research performance.
The problem
The problem
The problem
Human capital, or the collective skills, knowledge, and experience possessed by individuals, is a critical resource in any industry. In 2022, when we looked at the healthcare industry, we saw an overwhelming focus on technology.
While advancements in technology, equipment, and processes can be easily quantified and have tangible impacts on R&D(Research and Development), solely focusing on technology can overshadow the importance of the human element in the research process.
Human capital often remains intangible and more challenging to measure, leading to it being overlooked. Moreover, identifying emerging science experts also typically involves navigating and sifting outdated, low-quality, and unstructured data, a process that can be both time-consuming and tedious.
Human capital, or the collective skills, knowledge, and experience possessed by individuals, is a critical resource in any industry. In 2022, when we looked at the healthcare industry, we saw an overwhelming focus on technology.
While advancements in technology, equipment, and processes can be easily quantified and have tangible impacts on R&D(Research and Development), solely focusing on technology can overshadow the importance of the human element in the research process.
Human capital often remains intangible and more challenging to measure, leading to it being overlooked. Moreover, identifying emerging science experts also typically involves navigating and sifting outdated, low-quality, and unstructured data, a process that can be both time-consuming and tedious.
Product Goal
Product Goal
Human capital is indispensable in life science R&D. ResearchTrack provides objective evaluation of scientists. With the mission-critical insights offered by the platform, ResearchTrack help identify emerging scientists and promising research for any organization looking to innovate in the healthcare industry.
The value of technology amplifies when the right people are using it as human capital is the driving force behind them.
Human capital is indispensable in life science R&D. ResearchTrack provides objective evaluation of scientists. With the mission-critical insights offered by the platform, ResearchTrack help identify emerging scientists and promising research for any organization looking to innovate in the healthcare industry.
The value of technology amplifies when the right people are using it as human capital is the driving force behind them.
Problem Statement
Problem Statement
Problem Statement
How might we design a flexible technology that automates data aggregation, integration, and analytics, empowering research institutions & enterprises to gain a holistic evaluation of research performance and the identification of scientific experts?
How might we design a flexible technology that automates data aggregation, integration, and analytics, empowering research institutions & enterprises to gain a holistic evaluation of research performance and the identification of scientific experts?
How might we design a flexible technology that automates data aggregation, integration, and analytics, empowering research institutions & enterprises to gain a holistic evaluation of research performance and the identification of scientific experts?
Problems with existing solutions
Problems with existing solutions
Problems with existing solutions
Limited scope of analysis
Limited scope of analysis
Most tools tend to focus on a single dimension of research impact, lacking a comprehensive, multi-dimensional view.
Most tools tend to focus on a single dimension of research impact, lacking a comprehensive, multi-dimensional view.
Most tools tend to focus on a single dimension of research impact, lacking a comprehensive, multi-dimensional view.
User experience constraints
User experience constraints
Traditional academic and research databases can be cumbersome to navigate and lack intuitive interfaces. This can hinder the adoption rate and user satisfaction.
Traditional academic and research databases can be cumbersome to navigate and lack intuitive interfaces. This can hinder the adoption rate and user satisfaction.
Traditional academic and research databases can be cumbersome to navigate and lack intuitive interfaces. This can hinder the adoption rate and user satisfaction.
Lack of integration & customization
Lack of integration & customization
Many platforms operate in silos, lacking the ability to integrate with other systems or be tailored to specific user or institutional needs. This limits their flexibility and utility.
Many platforms operate in silos, lacking the ability to integrate with other systems or be tailored to specific user or institutional needs. This limits their flexibility and utility.
Many platforms operate in silos, lacking the ability to integrate with other systems or be tailored to specific user or institutional needs. This limits their flexibility and utility.
Inconsistency in data accuracy & timeliness
Inconsistency in data accuracy & timeliness
Some existing solutions might not offer real-time updates or might be prone to errors, affecting decision-making and insights.
Some existing solutions might not offer real-time updates or might be prone to errors, affecting decision-making and insights.
Some existing solutions might not offer real-time updates or might be prone to errors, affecting decision-making and insights.
Design Solution
Design Solution
Design Solution
The database automates data aggregation, integration, and analytics, which are at the heart of the product.
The database automates data aggregation, integration, and analytics, which are at the heart of the product.
Key Design Highlights
Key Design Highlights
Key Design Highlights
The project encompasses many sets of key design features. This case study showcases my focus areas during the summer of 2023, specifically on DATABASE.
The project encompasses many sets of key design features. This case study showcases my focus areas during the summer of 2023, specifically on DATABASE.
Automated data aggregation
ResearchTracks works on a database that aggregates data from 20+ sources and continually expanding datasets. This data undergoes unstructured data cleaning, ensuring it remains up-to-date.
Automated data aggregation
ResearchTracks works on a database that aggregates data from 20+ sources and continually expanding datasets. This data undergoes unstructured data cleaning, ensuring it remains up-to-date.
database
database
The database is divided into four main sections: researchers, publications, applications, and grants. Each section features automated data updates, smart data integration, filters and functionalities specifically tailored to its content.
The database is divided into four main sections: researchers, publications, applications, and grants. Each section features automated data updates, smart data integration, filters and functionalities specifically tailored to its content.
streamlining the validation process
streamlining the validation process
When integrating smart data, the back end guarantees seamless and private integration, along with smart relevancy identification, and it asks admins to verify the data's validity. Each information set has its own validation requirements, so the placement and design of the status indicators are tailored to those specifics. Consistent, clear, and easily recognizable language is used to streamline the validation process.
When integrating smart data, the back end guarantees seamless and private integration, along with smart relevancy identification, and it asks admins to verify the data's validity. Each information set has its own validation requirements, so the placement and design of the status indicators are tailored to those specifics. Consistent, clear, and easily recognizable language is used to streamline the validation process.
rich researcher profile
A rich researcher profile is central to the database, as publications, applications, and grants all link back to the researcher. This profile brings together information from all data sources, making it available to admins with a single click. It offers insights into one's research impact, further enabling admins to improve funding allocation and inform board decisions.
rich researcher profile
A rich researcher profile is central to the database, as publications, applications, and grants all link back to the researcher. This profile brings together information from all data sources, making it available to admins with a single click. It offers insights into one's research impact, further enabling admins to improve funding allocation and inform board decisions.
trajectory sketch
trajectory sketch
The unique timeline, specifically designed within the researcher's profile, highlights their past experiences and trajectories. It encompasses employment history, affiliations with professional societies, and participation in public advisory committees.
The unique timeline, specifically designed within the researcher's profile, highlights their past experiences and trajectories. It encompasses employment history, affiliations with professional societies, and participation in public advisory committees.
Enough case studies today? Jump ahead and see the pretty pictures!
Design Process
Design Process
Design Process
Market Analysis
Market Analysis
Market Analysis
How big is the market? Is this addressing a real customer need?
How big is the market? Is this addressing a real customer need?
Background data collection
Background data collection
Life Sciences R&D: The life sciences research and development sector is vast, with its market size likely reaching hundreds of billions of dollars globally. This encompasses pharmaceuticals, biotechnology, and medical research, among others. With the continuous push for new medical treatments, innovative drugs, and cutting-edge biotechnologies, this market is projected to grow consistently.
Research Data Platforms: Considering the number of academic institutions, research labs, pharmaceutical companies, biotech startups, and individual researchers globally, the potential audience for a comprehensive research data tool like ResearchTrack is significant.
There's a consistent underestimation and overlooking of human capital in life sciences R&D. While technology plays a vital role, the human element, including skills, knowledge, and experience, is paramount.
Conclusion: The need is for a tool that offers a comprehensive view of research impact, emphasizing the human capital component, and providing a platform for objective evaluation of scientists.
Life Sciences R&D: The life sciences research and development sector is vast, with its market size likely reaching hundreds of billions of dollars globally. This encompasses pharmaceuticals, biotechnology, and medical research, among others. With the continuous push for new medical treatments, innovative drugs, and cutting-edge biotechnologies, this market is projected to grow consistently.
Research Data Platforms: Considering the number of academic institutions, research labs, pharmaceutical companies, biotech startups, and individual researchers globally, the potential audience for a comprehensive research data tool like ResearchTrack is significant.
There's a consistent underestimation and overlooking of human capital in life sciences R&D. While technology plays a vital role, the human element, including skills, knowledge, and experience, is paramount.
Conclusion: The need is for a tool that offers a comprehensive view of research impact, emphasizing the human capital component, and providing a platform for objective evaluation of scientists.
Key opportunities
The largest opportunities digital and analytics could help unlock are derisking drug discorver, accelerating clinical trials, and reinventing engagement with healthcare practitioners.
New research on the digital maturity of pharma, relative to other industries, reveals clear opportunities to improve performance by better connecting digitally with patients and physicians.
Key opportunities
The largest opportunities digital and analytics could help unlock are derisking drug discorver, accelerating clinical trials, and reinventing engagement with healthcare practitioners.
New research on the digital maturity of pharma, relative to other industries, reveals clear opportunities to improve performance by better connecting digitally with patients and physicians.
Key opportunities
The largest opportunities digital and analytics could help unlock are derisking drug discorver, accelerating clinical trials, and reinventing engagement with healthcare practitioners.
New research on the digital maturity of pharma, relative to other industries, reveals clear opportunities to improve performance by better connecting digitally with patients and physicians.
Growth trends in the market
Growth trends in the market
The 15 largest pharma companies spent a record $138B on R&D in 2022.
Funding in emerging biopharma and biotech companies was up double-digits year over year in Q1 2023.
FDA approval is increasing to 13 new drug approvals in Q1 2023, compared to an average of 9 at this time over the past 5 years.
The 15 largest pharma companies spent a record $138B on R&D in 2022.
Funding in emerging biopharma and biotech companies was up double-digits year over year in Q1 2023.
FDA approval is increasing to 13 new drug approvals in Q1 2023, compared to an average of 9 at this time over the past 5 years.
Competitive Analysis
Competitive Analysis
I started the competitive analysis by looking at some legacy softwares, vertical and individual competitions.
I started the competitive analysis by looking at some legacy softwares, vertical and individual competitions.
I went on to analyze some key competitors in the following areas:
Traditional bibliometric databases which dominate the academic citation analysis realm.
(Scopus / Web of Science)
Altmetrics platforms measuring the online attention and engagement a scholarly article gets.
(Altmetric / Plum Analytics.)
I went on to analyze some key competitors in the following areas:
Traditional bibliometric databases which dominate the academic citation analysis realm.
(Scopus / Web of Science)
Altmetrics platforms measuring the online attention and engagement a scholarly article gets.
(Altmetric / Plum Analytics.)
Clinical trial databases and platforms that offer insights into ongoing or past clinical trials.
(Komodo Health / HiHealthcare / MarketWare / Trella Health)
Research management tools, which offer integrated research data management
(PURE)
Clinical trial databases and platforms that offer insights into ongoing or past clinical trials.
(Komodo Health / HiHealthcare / MarketWare / Trella Health)
Research management tools, which offer integrated research data management
(PURE)
What are some best selling products?
What are some best selling products?
Citation Analysis Tools:
Given the longstanding importance of citations in academic research, tools that provide citation counts and analysis, like Scopus and Web of Science, are likely top sellers.
Collaborative Research Platforms:
Tools that enable researchers to collaborate, share data, and manage their research projects, such as Labguru or Benchling.
Altmetric Analysis Tools:
With the rise of digital media, tools that measure the digital footprint of research articles are becoming increasingly popular.
The research above all informed the direction for ResearchTrack's dashboard, profile and database features.
Citation Analysis Tools:
Given the longstanding importance of citations in academic research, tools that provide citation counts and analysis, like Scopus and Web of Science, are likely top sellers.
Collaborative Research Platforms:
Tools that enable researchers to collaborate, share data, and manage their research projects, such as Labguru or Benchling.
Altmetric Analysis Tools:
With the rise of digital media, tools that measure the digital footprint of research articles are becoming increasingly popular.
The research above all informed the direction for ResearchTrack's dashboard, profile and database features.
User research
User research
User research
Objective:
1) What is the need?
2) What needs are unaddressed?
3) Can we develop a solution that addresses those problems?
Objective:
1) What is the need?
2) What needs are unaddressed?
3) Can we develop a solution that addresses those problems?
There are multiple types of customers/users/use cases as the potential users could span a broad spectrum, the following users pertain to the current (institution) version of the product:
There are multiple types of customers/users/use cases as the potential users could span a broad spectrum, the following users pertain to the current (institution) version of the product:
Government & Non-Governmental Organizations (NGOs):
These entities fund a large portion of research. A tool like ResearchTrack can help them assess the impact of their funded projects and decide on future funding allocations.
Academic Researchers:
They need to understand the impact of their research, find areas for improvement, identify collaborators, and showcase their work's significance for promotions or grants.
Academic Researchers:
They need to understand the impact of their research, find areas for improvement, identify collaborators, and showcase their work's significance for promotions or grants.
University Departments & Institutes:
Departments need to track the research output, impact, and contributions of their faculty to assess their overall performance, secure funding, and strategize their future research endeavors.
University Departments & Institutes:
Departments need to track the research output, impact, and contributions of their faculty to assess their overall performance, secure funding, and strategize their future research endeavors.
Use case examples
Use case examples
Use case examples
FAER (Foundation for Anesthesia Education and Research) and IARS (International Anesthesia Research Society) are pivotal entities in the realm of anesthesiology and related research.
FAER (Foundation for Anesthesia Education and Research) and IARS (International Anesthesia Research Society) are pivotal entities in the realm of anesthesiology and related research.
Research & Grant Funding (Especially for FAER)
FAER's primary focus is on advancing anesthesiology through research and education. They offer grants for innovative research. Using ResearchTrack, FAER can efficiently assess the impact and reach of the research they fund. It allows them to measure the effectiveness of their grants in terms of academic influence, societal value, clinical performance, and commercial activities.
Research & Grant Funding (Especially for FAER)
FAER's primary focus is on advancing anesthesiology through research and education. They offer grants for innovative research. Using ResearchTrack, FAER can efficiently assess the impact and reach of the research they fund. It allows them to measure the effectiveness of their grants in terms of academic influence, societal value, clinical performance, and commercial activities.
Comprehensive Data Analysis for Research (IARS & FAER)
Both organizations can use ResearchTrack to garner a holistic view of the research landscape in anesthesiology. This can inform their strategic decisions, policy-making, and even the themes for conferences or seminars.
Comprehensive Data Analysis for Research (IARS & FAER)
Both organizations can use ResearchTrack to garner a holistic view of the research landscape in anesthesiology. This can inform their strategic decisions, policy-making, and even the themes for conferences or seminars.
User Persona
User Persona
The following user personas represent broad categories: board members, administrative personnel, policy makers, viewers, and researchers within FAER and IARS. Please note that these personas are fictional and are not based on real individuals.
The following user personas represent broad categories: board members, administrative personnel, policy makers, viewers, and researchers within FAER and IARS. Please note that these personas are fictional and are not based on real individuals.
Translating findings into needs statements
Translating findings into needs statements
Translating findings into needs statements
Identifying the unaddressed and latent needs.
Identifying the unaddressed and latent needs.
Identifying the unaddressed and latent needs.
User Flow
User Flow
User Flow
This case study only pertains to the 'pink' sections of the user flow below.
This case study only pertains to the 'pink' sections of the user flow below.
Identifying the unaddressed and latent needs.
Design iterations
Design iterations
Starting from the wireframe, each iteration is an opportunity to refine and improve upon the initial design.
Starting from the wireframe, each iteration is an opportunity to refine and improve upon the initial design.
The main challenges and iterations were centered around the idea of 'information hierarchy' & 'consistency', as it is crucial for intuitive navigation and user experience.
The main challenges and iterations were centered around the idea of 'information hierarchy' & 'consistency', as it is crucial for intuitive navigation and user experience.
Information hierarchy
Information hierarchy
Information hierarchy
To avoid longer search times, missed sections, or misinterpreted functionalities from an unclear hierarchy, multiple iterations were undergone. These iterations involve reordering and regrouping items based on user needs and tasks, conducting usability testing with a diverse group of users to validate the effectiveness of the changes, then iterating further based on the user feedback and test results.
To avoid longer search times, missed sections, or misinterpreted functionalities from an unclear hierarchy, multiple iterations were undergone. These iterations involve reordering and regrouping items based on user needs and tasks, conducting usability testing with a diverse group of users to validate the effectiveness of the changes, then iterating further based on the user feedback and test results.
Consistency
Consistency
Consistency
Once a term is selected, ensure it's used consistently throughout the platform to avoid any confusion. Additionally, I need to check with the backend team to ensure that the term doesn't conflict with any data-handling names.
Once a term is selected, ensure it's used consistently throughout the platform to avoid any confusion. Additionally, I need to check with the backend team to ensure that the term doesn't conflict with any data-handling names.
Design Handoff
Design Handoff
I worked closely with the product teams to successfully deliver key designs in Q3 2023. Additionally, I set up meetings with front-end developers for design handoffs, ensuring that designs aligned with the viewport requirements.
We are currently conducting further user testing.
I worked closely with the product teams to successfully deliver key designs in Q3 2023. Additionally, I set up meetings with front-end developers for design handoffs, ensuring that designs aligned with the viewport requirements.
We are currently conducting further user testing.
Feedbacks
Feedbacks
Feedbacks
We gathered feedbacks from users by conducting feedback sessions and having them test out the experience. To our immense satisfaction, users could all effortlessly initiate and conclude their searches, seamlessly engage with the key features, and encountered minimal issues. They expressed satisfaction with the interface's simplicity and clean design, noting how it facilitated quick and straightforward actions and decisions.
We gathered feedbacks from users by conducting feedback sessions and having them test out the experience. To our immense satisfaction, users could all effortlessly initiate and conclude their searches, seamlessly engage with the key features, and encountered minimal issues. They expressed satisfaction with the interface's simplicity and clean design, noting how it facilitated quick and straightforward actions and decisions.
outcome
outcome
outcome
Final Experience
Final Experience
Final Experience
Database that aggregates data from 20+ sources and continually expanding datasets
Database that aggregates data from 20+ sources and continually expanding datasets
Database features automated data updates, smart data integration, filters and functionalities specifically tailored to its content.
Database features automated data updates, smart data integration, filters and functionalities specifically tailored to its content.
Rich researcher profile brings together information from all data sources
Rich researcher profile brings together information from all data sources
Unique timeline design highlights their past experiences and trajectories
Unique timeline design highlights their past experiences and trajectories
Learnings
Learnings
Learnings
As a software-driven company, the team has primarily adopted the spiral development process while occasionally running sprints (from the Scrum framework.) Given the nature of a data analytics startup, it is crucial that the software being developed is both accurate and flexible to accommodate the rapidly evolving data landscape. The software needs to be iterative, as feedback from the users and the data itself can lead to significant changes in the product's direction.
The spiral development process is efficient as we can present prototypes to potential clients early on, ensuring that the final product is more aligned with the market needs. However, the spiral development process, which emphasizes risk assessment and prototyping, can become challenging as the team might end up spending more time than anticipated in certain spirals.
With data analytics, requirements might not be fully clear until some initial data processing and analysis is done. Sometimes it’s also challenging to ensure that every iteration not only addresses functionality but also usability combining both a front-end and back-end perspective.
For future improvements, instead of occasionally using Scrum, we might consider a consistent integration of Scrum within the spiral model — each spiral is composed of a set number of sprints — this can help in breaking down the work into manageable chunks, ensuring regular feedback, and maintaining a consistent pace of development.
As a software-driven company, the team has primarily adopted the spiral development process while occasionally running sprints (from the Scrum framework.) Given the nature of a data analytics startup, it is crucial that the software being developed is both accurate and flexible to accommodate the rapidly evolving data landscape. The software needs to be iterative, as feedback from the users and the data itself can lead to significant changes in the product's direction.
The spiral development process is efficient as we can present prototypes to potential clients early on, ensuring that the final product is more aligned with the market needs. However, the spiral development process, which emphasizes risk assessment and prototyping, can become challenging as the team might end up spending more time than anticipated in certain spirals.
With data analytics, requirements might not be fully clear until some initial data processing and analysis is done. Sometimes it’s also challenging to ensure that every iteration not only addresses functionality but also usability combining both a front-end and back-end perspective.
For future improvements, instead of occasionally using Scrum, we might consider a consistent integration of Scrum within the spiral model — each spiral is composed of a set number of sprints — this can help in breaking down the work into manageable chunks, ensuring regular feedback, and maintaining a consistent pace of development.
