The Catalyst Grant was born of our desire to help early-stage software ideas come to fruition. We invest in the community, and we come from the community. Our team is made up of people with research backgrounds and decades of experience within research software. For us, the grant’s real value is in starting conversations, some leading to awards, but the majority to the sharing of advice, the making of introductions and the cultivation of ideas.

Research integrity experts call for new forensics discipline

A new field of forensics is being proposed by research integrity experts to recognize their investigations into unscrupulous research behavior and misuse of scholarship: Forensic Scientometrics.

Read Now

Academic Community – supporting research integrity

Research integrity will be a dominant theme in scholarly communications over the next decade. Challenges around Generative AI, paper mills, and fake science will only get more complex.

Learn More

TL;DR Shorts: Chris DiBona

Chris DiBona, covers many themes across the research community, including open research, trust and integrity, and revamping research culture to make it more fit-for-purpose.

Read Now

For any other inquiries, we’d love to hear from you

Steve Scott | Director of Portfolio Development

Get in touch

The post Catalyst Grant appeared first on Digital Science.

Our founders are challenging the status quo to solve research’s biggest problems. Learn more about our investments or dive into some of the stories behind the Digital Science portfolios.

The post Founder Stories appeared first on Digital Science.

Built by founders, for founders.

Every single aspect of Digital Science is focused on helping game-changing founders succeed, building businesses that rewire the world of research for the better, for as many people as possible. We want to reduce the time from new ideas to life-changing innovations. By bringing our combined experience together, we provide access to an unrivalled array of knowledge, contacts, skills and capital.

We look for companies where Digital Science’s added value leads to competitive advantage. Our portfolio has grown because of the benefits each company can bring to each other. By combining our routes to market, data and commercial experience with great products, we succeed.

We work with companies from pre-seed through growth to series A and beyond, with a view to investing in businesses that are aligned with our strategic mission – to advance research for the broader good of humanity. 

For earlier stage ideas, our Catalyst Grant programme has received a wide-ranging set of proposals – from lab-automation tools and artificial intelligence approaches to just about every part of the research cycle. Several Digital Science investments have come either directly or indirectly out of the Catalyst programme, including Overleaf, Ripeta, Scismic, TetraScience and Writefull.

Have a questions? Please read our FAQs

We support a diverse set of businesses, markets and leaders

Got a really early stage idea?

Catalyst Grant provides funding opportunities up to £25,000 for early-stage innovation. With Research Integrity a pressing problem for all in the research community, in 2024 we are looking to support innovative ideas to ensure integrity in research.

Discover more and how to enter here

Are you a later-stage company?

We support later-stage companies with growth capital and acquire profitable businesses with the potential to be leaders in their domestic or international market. We are experienced investors and we want to help not hinder. Our approach is flexible on the transaction size and the shareholding in the company.

Get in touch

We work with game-changing founders

Founder Stories

Our founders are challenging the status quo to solve research’s biggest problems, dive into the stories behind the portfolios.

Meet Our Digital Science Role Models

To celebrate Ada Lovelace Day 2020 on 13th October 2020, meet six of our many Digital Science ‘Women in STEM’ role models

#FoundersFriday with Mark Hahnel from Figshare

We’d love to hear from you

Steve Scott | Director of Portfolio Development

Contact Us

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Understanding the risks

According to Professor Thomas Malone, Director of the MIT Center for Collective Intelligence, AI should essentially be about connecting people and computers so that they collectively act more intelligently than any individual person, group or computer has ever done before. This connectivity is at the core of science and research. Science is a collective activity par excellence, connecting millions of minds in space as well as time. For hundreds of years, scientists have been collaborating and discussing their ideas and results in academic journals. Computers are increasingly important for researchers: in conducting experiments, collecting and analyzing data and, of course, in scholarly communication. Reflecting on this, it is perhaps surprising that AI does not play a bigger role in science today. Although computers are indispensable for modern scientists, the application of artificial intelligence lags behind other industries, such as social media and online search. Despite its huge potential, uptake of AI has been relatively slow. This is in part due to the nascent state of AI, but also to do with cultural and technological features of the scientific ecosystem. We must be aware of these in order to assess the risks associated with unreflectively applying artificial intelligence in science and research.

AI and NLP in healthcare

A logical source of data for intelligent machines is the corpus of scientific information that has been written down in millions of articles and books. This is the realm of Natural Language Processing (NLP). By processing and analyzing this information, computers could come to insights and conclusions that no human could ever reach individually. Relationships between fields of research could be identified, proposed theories collaborated on or rejected based on an analysis of a broad corpus of information, and new answers to problems given.

This is what IBM’s Watson has attempted in the field of healthcare. Initiated in 2011, it aims to build a question-and-answer machine based on data derived from a wealth of written sources, helping physicians in clinical decisions. IBM has initiated several efforts to develop AI-powered medical technology, but many have struggled, and some have even failed spectacularly. What this lack of success shows is that it is still very hard for AI to make sense of complex medical texts. This will therefore most certainly also apply to other types of scientific and academic information. So far, no NLP technology has been able to match human beings in comprehension and insight.

Barriers to information

Another reason for the slow uptake of NLP in science is that scientific literature is still hard to access. The dominant subscription and copyright models make it impossible to access the entire corpus of scientific information published in journals and books by machines. One of the positive side effects of the move towards Open Access would be the access to information by AI engines, although a large challenge still lies in the immaturity of NLP to deal with complex information.

More data give greater context

Despite the wealth of information captured in text, it is important to realize that the observational and experimental scientific data that stands at the basis of articles and books is potentially much more powerful for machines. In most branches of science the amount of information collected has increased with dazzling speed. Think about the vast amount of data collected in fields like astronomy, physics and biology. This data would allow AI engines to fundamentally do much more than what is done today. In fact, the success of born-digital companies like Amazon and Google have had in applying AI is to a large extent due to the fact that they have a vast amount of data at their disposal. AI engines could create hypotheses on the genetic origin of diseases, or the causes for global warming, test these hypotheses by means of plowing through the vast amount of data that is produced on a daily basis, and so to arrive at better and more detailed explanations of the world.

Shifting the culture around data sharing to create better AI

A challenge here is that sharing data is not yet part of the narrative-based scholarly culture. Traditionally, information is shared and credit earned in the form of published articles and books, not in the underlying observational and experimental data.

Important reasons for data not being made available is the fear of being scooped and the lack of incentives, as the latest State of Open Data report showed. Thankfully in recent years efforts have been made to stimulate or even mandate the sharing of research data. Although these offers are primarily driven by the need to make science more transparent and reproducible, enhancing the opportunity for AI engines to access this data is a promising and welcome side-effect.

Like the necessary advancement of NLP techniques, making research data structurally accessible and AI-ready will take years to come to fruition. In the meantime, AI is being applied in science and research in narrower domains, assisting scientists and publishers in specific steps in their workflows. AI can build better language editing tools, such as in the case of Writefull, who we will hear from in the next article in this series. Publishers can apply AI to perform technical checks, such as in Unsilo, scan submitted methods sections for assessing the reproducibility of research, the way Ripeta and SciScore do, and analyze citations, like Scite. Tools are being developed to scan images of submitted manuscripts to detect manipulation and duplication, and of course scientists benefit from generic AI applications such as search engines and speech and image recognition tools. Experiments have also been done with tools that help editors in making decisions to accept or reject papers. The chance of publishing a highly cited paper is predicted based on factors including the subject area, authorship and affiliation, and the use of language. This last application exposes an essential characteristic of machine learning that should make us cautious.

Breaking barriers, not reinforcing them

Roughly speaking, in machine learning, computers learn by means of identifying patterns in existing data. A program goes through vast numbers of texts to determine the predominant context in which words occur, and uses that knowledge to determine what words are likely to follow. In the case of the tools that support editors in their decision to accept or reject papers, it identifies factors that characterize successful papers, and makes predictions based on the occurrence of these factors in submitted papers. This logically implies that these patterns will be strengthened. If a word is frequently used in combination with another word, the engine subsequently suggesting this word to a user will lead to that word being used even more frequently. If an author was successful, or a particular theory or topic influential, AI will make these even more so. And if women or people from developing countries have historically published less than their male counterparts from Western countries, AI can keep them underperforming.
In other words, AI has the risk of consolidating the contemporary structures and paradigms. But as the philosopher of science Thomas Kuhn showed, real breakthroughs are characterized by replacing breaking patterns and replacing paradigms with new ones. Think of the heliocentric worldview of Kepler, Copernicus and Galileo, Darwin’s theory of natural selection, and Einstein’s theory of relativity. Real progress in science takes place by means of the novel, the unexpected, and sometimes even the unwelcome. Humans are conservative and biased enough. We have to make sure that machines don’t make us even more so.

DOI: https://doi.org/10.6084/m9.figshare.12092403.v1

SEE MORE POSTS IN THIS NLP SERIES

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BPT Analytics, Intoolab and MLprior, three projects aiming to disrupt the academic space, are the latest recipients of the Catalyst Grant award for innovative startups. The grant is our international initiative to develop innovative projects and technologies and we award up to £25,000 or $30,000 for concepts with the potential to transform scientific and academic research.

BPT Analytics is an online business intelligence tool for the pharmaceutical industry. The tool is built on top of an up-to-date database of life science companies, which tracks what they do and how they perform in the market. It follows the team’s already established and growing publishing platform BioPharmaTrend.com, which features articles from leading pharma professionals and business leaders.

Co-founder Dr. Andrii Buvailo commented:

“While there is a plethora of large-scale business intelligence platforms on the market, the majority of them are too general for such a domain-specific market as drug discovery, so they can’t grasp important nuances, critical for decision making. BPT Analytics aims to eliminate as much guesswork from the practice of pharmaceutical industry strategists, business developers, and decision-makers, as is possible. By providing them with visualized access to systematic and constantly curated data about the most innovative industry players, trends, and opportunities.”

Intoolab is an artificial intelligence platform built for pharmaceutical companies and researchers. Its main feature, Tzager, an AI scientific tool which scours through millions of research papers, helps find causal connections and join the dots between papers that would otherwise take significant time. The tool has been developed in collaboration with a number of universities worldwide and a pilot has been completed at Aarhus University in Denmark.

CEO Nikos Tzagkarakis commented:

“The biggest problem in drug discovery is that there are millions of research papers with different information, but there are also millions of potential combinations of concepts that could solve specific problems. We are trying to solve the problem at its core by not just connecting information, but also creating an intelligence that understands the mechanics of ‘why’ things happen. The grant will enable us to develop our deep learning methods faster and also connects us with the valuable network of Digital Science. We are confident Tzager will become increasingly intelligent and we’re excited for the first time it will figure out an original solution in medicine and drug discovery.”

MLprior is a tool which uses AI-based analysis to predict whether a scientific paper will be accepted at a conference. The co-founders behind the product, Denis Volkhonskiy and Vladislav Ishimtsev, have both been actively researching AI with a focus on creating new models and algorithms at Skolkovo Institute of Science and Technology for the past five years. They are joined by PhD students Nikita Klyuchnikov from Skolkovo Institute of Science and Technology and Pavel Shvechikov from Higher School of Economics, who make up the four-person team.

Denis Volkhonskiy commented:

“Our product simplifies and speeds up the process of writing scientific papers,” says Volkhonskiy. “We use artificial intelligence for analysing the text of the article and suggesting improvements. We hope to become a must-have service for each researcher. Researchers spend several months on polishing scientific papers from draft to publication, checking formulas and correcting mistakes – our tool will hopefully help save a lot of time.”

Steve Scott, Director of Portfolio Development at Digital Science said:

“Once again, we would like to thank the community of researchers and entrepreneurs for sharing their ideas and passion with us. The field for this round of the Catalyst Grant was brimming with great ideas and narrowing down the entries proved a real challenge. The three winners reflect our belief that AI and machine learning solutions will offer step-changes in the way we analyse and interact with data, whether that be for business intelligence, discovery or creation. We hope the grant, and our ongoing support, will help each of them achieve their next milestone.”

The post From Artificial Intelligence and AI Scientists to Pharmaceutical Analytics – 2019 Catalyst Grant Winners appeared first on Digital Science.

Digital Science portfolio company, ripeta, aims to make better science easier by identifying and highlighting the important parts of research that should be transparently presented in a manuscript and other materials. The tool detects and evaluates the key evidence for reproducibility in science through software and analytics development; improving evidence-based science and fiscal efficiency of research investments. These tools leverage sophisticated machine-learning and natural language processing algorithms to extract key reproducibility elements from research articles.

Leslie McIntosh, CEO of ripeta, said: “We’d first of all like to congratulate Scite on winning the ALPSP Innovation Award. We were truly honoured to be an award finalist. ALPSP has helped introduce us to a great community and have truly supported our work, giving us more visibility and raising awareness of what we do.”

Ripeta focuses on assessing the quality of the reporting and robustness of the scientific method rather than the quality of the science. The company’s long-term goal includes developing a suite of tools across the broader spectrum of sciences to understand and measure the key standards and limitations for scientific reproducibility across the research lifecycle and enable an automated approach to their assessment and dissemination.

Ripeta this week launched a report focusing on falsifiability and reproducibility in scientific research. The report addresses three areas including appropriate documentation and sharing of research data, clear analysis and processes, and the sharing of code. Making Science Better: Reproducibility, Falsifiability and the Scientific Method looks at the current state of reproducibility in 2019, as well as the importance of falsifiability in the research process.

McIntosh added: “While technological innovations have accelerated scientific discoveries, they have complicated scientific reporting. Science is hard and reproducibility is important, so we need to make better science easier.

“We are developing the tools to make research methods transparent, enabling the verifiability, falsifiability and reproducibility of research.”

The post Ripeta honoured as ALPSP Awards for Innovation in Publishing finalist appeared first on Digital Science.

Today, we are excited to announce our investment in Writefull – a deep learning language platform applied to discipline-specific scientific texts to help improve the clarity of written English. We believe Writefull will help authors to express their work more clearly before they submit articles for publication, especially those who have English as their second language (and quite a few of us native speakers too for that matter). In addition, publishers will have a service to help relieve the administrative burden on editors, maintain house style and to help with quality control.

“When we first met the founders they demonstrated Writefull highlighting a sentence that read “…the tall mountains and high trees”. Although grammatically correct, a native speaker (subconsciously) wouldn’t describe mountains as “tall” and trees as “high” but instead as “…high mountains and tall trees”… Writefull points users to change this. We were impressed.”

Writefull helps authors improve the clarity of their work. It suggests improvements to grammar and spelling and to academic language usage such as sentence structures in scientific writing, discipline-specific vocabulary and appropriate word choice. These suggestions are based on real-world, context-specific usage rather than on a fixed set of grammatical rules.

When we first met the founders of Writefull, Juan and Alberto, they demonstrated the AI highlighting a sentence that read “…the tall mountains and high trees”. Although grammatically correct – as a native speaker, something does not sound quite right about that sentence. The nuance of English is such that we (subconsciously) wouldn’t describe mountains as “tall” and trees as “high”. The recommendation picked up by Writefull’s Deep Learning and N-gram approach pointed the user to change this to “…high mountains and tall trees”. Language is full of such aspects of usage, and these don’t have fixed rules that can be hard-coded in advance.

We were impressed.

In making our investment in Writefull, we now have a solution that steps beyond rules-based approaches to cover style and usage, and applies that technology specifically to scientific writing, training the AI to understand the vocabulary and style within that domain.

The founders, with their machine learning and AI backgrounds, have a feature-packed product roadmap and we here at Digital Science look forward to working with them to help the rest of us express our ideas more clearly.

Read our full press release.

The post Digital Science Invests in Deep Learning Language Platform Writefull appeared first on Digital Science.

CCT provides a modern, configurable grants management workflow system to leading research funders, public bodies, and international development charities throughout the world, including the Wellcome Trust, Caribbean Development Bank, and the Howard Hughes Medical Institute in the US. CCT’s software CC Grant Tracker  is a best-in-class solution that manages the entire end-to-end lifecycle of a grant: From the launch of a call for proposals through technical and financial performance monitoring, to the monitoring and evaluation of outcomes, evaluation and impact.

Our colleagues on the UberResearch team have known and collaborated with the CCT team for more than 10 years, and joint clients have asked us about the possibility of closer links between our two organisations for a number of years. It made a lot of sense to bring CCT into the Digital Science family. It’s both a cultural fit, in terms of the way we both use a highly configurable product solution to deliver client requirements, as well as our shared missions in supporting the business process and decisions that help to ensure the right research receives funding. This partnership now offers huge potential for funders bringing the CCT and Digital Science offerings together.

“We’re very pleased and proud that the CCT team has agreed to join Digital Science and we look forward to working together to support funders in growing opportunities to transform the research ecosystem for the benefit of all.” – Daniel Hook, CEO Digital Science

The roles, activities and dependencies of the players in the research process are changing

In the past five years, we have seen different parts of the research process become more interlinked and interdependent. Two examples come to mind straight away:

• ORCID has become a central “glue”, not just being a registry of data about people but profoundly linking the data of funders, publishers and research organisations. ORCID continues to lay the groundwork for closer integration of all the different parts of the ecosystem and on resolving core challenges around the ambiguity of researchers and their outputs.
• Open Access is becoming the dominant channel to publish scholarly work that has received public funding. Over the last few years, funders have shifted their focus from simply funding research and collecting and analyzing the resulting outputs, to taking a more active role in shaping the research ecosystem. With ‘cOAlition S’ there is a clear move to make research more open and hence to speed up the rate and efficiency with which researchers can achieve results. Some funders are even becoming publishers, a trend that we may well see accelerating in years to come.

These are just two examples of how funders and others are changing how research is done. These changes are welcomed by many but lead to new requirements, the need for new data to inform decisions and, consequently, new features in existing software tools. And it requires an understanding of backgrounds and intended or unintended ripple effects in other parts of the system.

How our shared values can support this change

As we’ve said many times, Digital Science is not a single company but rather a portfolio of companies, brought together by a shared set of values. This unique approach means that  small and innovative companies can remain focused on a particular challenge that supports the increasingly intertwined needs of the stakeholder groups of the research ecosystem. CCT clearly shares the Digital Science core values and will continue to focus its efforts on improving the interactions between researchers, institutions, and research funders to deliver enhancements for all the participants in funding activities through their core application processing and grant management system CC Grant Tracker.

“After years of discussions what we could do together to provide better and more joint up tools to our shared clients we are now able to just do it within the Digital Science family! I’m really excited to work with the wonderful CCT team on bringing our expertise, skills and products together!” – Christian Herzog, co-founder Dimensions / ÜberResearch

Beyond their core focus CCT will also collaborate with other parts of Digital Science, pursuing integrations that support and improve processes that relate to funders and that have impacts beyond funders. These activities are guided by some important principles, which are part of the shared values between CC Technology and Digital Science:

• Support of an open and interoperable system approach, no ‘lock-in’. The CCT and other Digital Science products and services are offered together, but have, or will develop, powerful APIs to allow them to be integrated with any other system;
• CCT is joining Digital Science, but like all the other portfolio companies will retain its culture and focus, but will have extended opportunities to innovate supported by Digital Science. Our approach has been well received by portfolio companies joining Digital Science and by the community with which we work. In a fast-changing environment, having focus and deep understand the problems of the people we serve allows us to be innovative and accurate in working to support and solve issues iteratively.
• Collaboration is at the heart of our culture and we embrace this both internally and externally. Addressing challenges that cut across the different stages and stakeholders of the research ecosystem is difficult. But, by working across our different portfolio teams and using their insights and skills, we collaborate effectively with many stakeholders to optimise solutions to deliver for many more members of our ecosystem. Digital Science’s Dimensions is an excellent example of this approach.

Supporting research funders was always a priority for Digital Science

Since its inception in 2010, Digital Science has had the ambition to serve and support research funders, providing them with tools, data sets and services to deliver on their mission. The simple guiding logic for this was that improving the decisions that connect the right funding with the right researchers and research projects is a key way to ensure that researchers can do more. Different aspects of Digital Science’s portfolio focus on:

• breaking down the science policy objectives into funding programs and calls;
• identifying the best-proposed research activities involving the research community;
• allowing funders to analyse the funding decisions from other funders to make aligned or complementary funding decisions; and
• supporting research funders in the analysis of the results achieved in these research projects using new data sets and innovative alternatives.

In 2013 ÜberResearch started to build the first comprehensive grants database. It allows funders to better understand the current funding activities of their peer institutions and provides a glimpse into the future – since the funded projects show resource levels given to intended research activities in the coming years. This allows more insights into future developments, compared to publication databases which reflect only the research carried out years ago.

The launch of Dimensions in early 2018 further helped to democratise and transform scholarly search. The tool breaks down barriers to discovery and innovation by making more than 860 million academic citations freely available and delivering one-click access to over 9 million Open Access articles. Whereas previous tools and datasets have focused mostly on publications and citations, Dimensions takes a different approach: by integrating funded grants, publications and citations, Altmetric data, clinical trials and patents, a complete picture of the research landscape emerges.

More than 200 funder clients are using already the Dimensions database with more than 4 million grants covering more than $1.4 trillion in historic and future funding to support their own funding decisions and portfolio analysis efforts.

Exciting opportunities ahead

Bringing CC Technology’s excellent software together with Digital Science’s existing range of products, tools and services will create new and exciting opportunities to solve problems in more innovative ways. We look forward to joining the dots and making better tools for funders.

“We can see a great cultural fit between CCT and the Digital Science family and we look forward to working with other members of the family to enhance the CC Grant Tracker product and the service we provide to funder organisations.” – Dave Allan, Founder CC Technology (CCT)

The post Digital Science welcome Glasgow-based grants management system provider to DS family appeared first on Digital Science.

We are very excited to bring you a new interview for our #FoundersFriday blog series! Founders Friday is a forum in which we interview the founders of different businesses, asking them to share their advice for others and their perspective on the industry as a whole.

For this edition, we have interviewed Dr. Coleman Krawczyk (@ColemanKrawczyk). Coleman is an American astrophysicist at the University of Portsmouth and is the technical lead for The Tactile Universe. He is also a data scientist for the Zooniverse working on data aggregation for various citizen science projects.

What is the Tactile Universe?

The Tactile Universe is a public engagement project at the University of Portsmouth’s Institute of  Cosmology and Gravitation (ICG) that aims to bring the astronomy research that we do to the blind and visually impaired community. Right now the project’s focus is on galaxies, and what their shapes and colours can tell us about then.

Why is the Tactile Universe needed?

Astronomy is typically thought of as a very visual subject, and this is certainly reflected in the way that the subject is usually communicated to the public. Although, when it comes to the research side of things, this is not the case. Telescopes are controlled robotically, images are taken with digital cameras, and those images are converted into tables of numbers. With the Tactile Universe, we want to make it clear that astronomy can be accessible and that a vision impairment (VI) should not exclude somebody from studying the universe.

Where did the idea for the Tactile Universe come from?

The idea for the project came from my colleague Nic Bonne (@coffee_samurai) who is a blind astronomer himself. He remembered how difficult it was to get into astronomy as a kid, and wanted to make it easier for other children with VI to get into it.

How are the galaxy models made?

We started by using a 3D printer to make our models. We wrote a bit of software that takes any black and white image and creates a 3D model using that image as a height map. The white parts of the image are raised up, the black parts stay flat, and grey parts are scaled to all the heights in between.

We’ve just started playing around with other ways of making those models and we’re now using a wood milling machine that makes better quality versions. These high-quality masters are being used to make silicone moulds, and we will soon go on to make liquid resin casts. The casting process is significantly faster than 3D printing and is helping us to scale up our production.

How will the STFC grant further the Tactile Universe?

Back in April the Tactile Universe received a Nucleus Award from The Science and Technology Facilities Council. In part, this grant is being used to hire me as the technical lead for the project to oversee the production of the models and maintain our website. It is also being used to take our project national by running training sessions for our resources across the UK to make sure any outreach officers, science communicators, or teachers who want to use our kits have a chance to work with us and learn how to use them in a classroom. We are also making a number of kits that we can send out to schools that need them.

Additionally, we’re going to be using the funds to make sure all of our models are freely available and open source, and that all of the software that we use is available and documented. That way, anyone who has an idea of something similar to our project can use our software to build their own models and print them.

Find out more about the incredible work Tactile Universe is doing in the video below.

The post #FoundersFriday with Dr. Coleman Krawczyk from Tactile Universe appeared first on Digital Science.

Dr. Sacha Noukhovitch is a STEM education and student research expert and is also an Executive director of the STEM Fellowship, a Canadian non-profit organization that prepares the next generation for STEM with vital skills in data science and scholarly writing through peer mentorship and a practical learning experience. He is a practicing educator implementing data science education at Earl Haig Secondary School, Toronto. He started his career as an electrical engineer in the nuclear power industry and obtained his PhD in Management theory from Moscow State University.

Can you tell us about your career to date and how you founded the STEM fellowship and the STEM fellowship journal?

I started my career as an electrical engineer/researcher in the nuclear power industry and quickly realized that my academic interests lay in the field of human interaction with Information Technology – that lead me to do a PhD in Management Theory.

I was drawn to public education when I discovered that a new generation of students have a unique relationship with information and communication technology. Today’s students are natural data scientists as well as digital learners. Moreover, they have their own body of knowledge and expertise in dealing with information that they share and develop as a community.

I thought that like astrophysicists, psychologists or any other academic community, they would need a society that would unite the most motivated among them, helping to define and consolidate knowledge about student-driven education, collective expertise and other novel forms of knowledge accumulation that are unique to this generation of learners.

That was the beginning of the STEM Fellowship which started with three students recruited at the University of Toronto, University of Calgary and the University of British Columbia. Currently, we are present on 17 university campuses across the country with over 250 student executives, branch presidents and editors that lead various STEM Fellowship programs.

This new knowledge community needed a scholarly resource of its own and it was only logical to approach the biggest and most established national academic publisher for support. With the help and guidance from Canadian Science Publishing, we publish the Open Access STEM Fellowship Journal entirely dedicated to original high school and undergraduate student research.

Why work with young students? What are you trying to accomplish?

Today’s students are the driving force behind knowledge-based community sites and social networks like Quora, Reddit, Papers We Love, etc. These are new forms of science communication and learning delivered through collective and collaborative contributions of knowledge community members. Analysis of these innovative practices allowed me to develop a new digital learner-oriented pedagogical model that implements the following principles:

• Tapping into answers and accumulating information by association
• Crowdsourcing data interpretations and knowledge references
• Accelerated critical thinking and learning within peer-groups and knowledge-sharing social networks, which provides a unique opportunity of non-verbal intro and trans-generational exchange of information and ideas

I want to see public education institutions adopt the digital learner-oriented model to enable today’s students to achieve their full academic potential. I believe that the way education is delivered today no longer fits how students acquire knowledge, and by adapting to a digital learner-oriented model, we will see more innovation and excellence from the growing generation and as a result, our society will thrive.

Can you give examples of what students have gone onto do?

Over the past four years we have accumulated a wealth of student research papers and abstracts presenting a range of ideas covering urban issues, the future of science, and sustainable development – these came out of the Big Data inquiry and the experiential learning program. This program attracts the best and the brightest of high school students from across the Americas to be mentored by their university peers and top-notch data scientists.

I designed it based on the principles of the digital learner-oriented model and with the goal of giving students an opportunity to discover and demonstrate their analytical and scholarly communication talents.

A good example of a study would be one that was conducted by four girls from a public high school. They authored the research paper Diving into Debt: A Study on Factors Related to Debt Risk Score in Toronto – among other findings, they identified that “an age-adjusted rate of people who received breast cancer screening had a negative correlation with increased debt risk”. I find that fascinating from a public health perspective.

What problems still exist in the learning experience?

It is obvious that the current class and curriculum organized learning process does not leave much room for digital learners’ learning style and the new pedagogy I am working on.

Successful transition to student-driven education requires significant institutional and organizational changes. It means moving away from the conventional lesson plan and course structure as well as modifications to the organizational aspects that would permit different relations between administrators, educators and students.

Where would you like to take this in the future?

In the case of the STEM Fellowship and STEM Fellowship Journal, I am the lucky founder who can be relaxed about the future lifespan of these organizations. These are student-driven organizations and they will grow with the digital learners.

My focus will continue to be on giving motivated, young people a voice and connecting them with influential members of the academic community. I would like the successes of students who are a part of STEM Fellowship to be shared and publicized so that they serve as an example of the power of a digital learner-oriented education.

Follow @STEM_Fellowship to hear about their latest news and more. 

The post #FoundersFriday with Sacha Noukhovitch from the STEM Fellowship appeared first on Digital Science.