Figshare, a leading provider of institutional repository infrastructure that supports open research, is pleased to announce that the University of Technology Sydney (UTS) has chosen Figshare to support them in sharing, showcasing and managing their research reports and non-traditional research outputs.

UTS – Australia’s leading technology university – will use its Figshare repository and its integration with the Australian Research Data Commons Datacite DOI minting service to drive the discoverability and increase the impact of their research reports and non-traditional research outputs, which especially assists its applied researchers and creative practitioners.

UTS will benefit from Figshare’s support for over 1200 different file types and unique in-browser preview capabilities, which will enable them to showcase their non-traditional research outputs whilst maintaining best practices for FAIR and open research sharing. 

UTS already utilizes Altmetric, Dimensions and Symplectic Elements across their research ecosystem, making Figshare the fourth Digital Science portfolio to partner with the university.

Professor Kate McGrath, UTS Deputy Vice-Chancellor and Vice-President (Research), said: “Digital Science has historically been a great partner for UTS. With the inclusion of Figshare in their portfolio, we immediately have a way to bring to the forefront the body of work that our researchers deliver that has previously been challenging to identify, showcase and gain insights from. To be able to more fully explore the breadth of our research activities and make it more accessible is an exciting prospect.”

Figshare Founder and Digital Science’s VP Open Research, Mark Hahnel, said: “We’re thrilled to partner with the University of Technology Sydney on a Figshare repository for their non-traditional research outputs. It’s exciting to see another leading Australian institution join the Figshare community and commit resources to the sharing, showcasing and management of NTROs, an area of growing importance in open research.”

About the University of Technology Sydney

The University of Technology Sydney (UTS) is a public research university located in Sydney, New South Wales, Australia. The university was founded in its current form in 1988, though its origins as a technical institution can be traced back to the 1870s. UTS is a founding member of the Australian Technology Network (ATN), and is a member of Universities Australia (UA) and the Worldwide Universities Network (WUN).

The university is organised into 9 faculties and schools, which together administers 130 undergraduate courses and 210 postgraduate courses. In 2022, the university enrolled 44,615 students, including 32,825 undergraduate students and more than 2200 higher degree research students. The university is home to over 45 research centres and institutes, that regularly collaborate along with industry and government partners.

About Figshare

Figshare is a repository solution for institutions. Its infrastructure and global community provide institutions with a platform for their researchers to share and preserve their research outputs – including large datasets – in a findable, accessible, interoperable, and reusable (FAIR) way. Complete with altmetrics and citation data, researchers get credit for all their outputs. Figshare is part of Digital Science. Visit www.figshare.com  and follow @figshare on X or LinkedIn.

About Digital Science

Digital Science is an AI-focused technology company providing innovative solutions to complex challenges faced by researchers, universities, funders, industry and publishers. We work in partnership to advance global research for the benefit of society. Through our brands – Altmetric, Dimensions, Figshare, ReadCube, Symplectic, IFI CLAIMS Patent Services, Overleaf, Writefull, OntoChem, Scismic and metaphacts – we believe when we solve problems together, we drive progress for all. Visit www.digital-science.com and follow @digitalsci on X or on LinkedIn.

Media contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com

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This is a natural development based on Digital Science’s previous support and investment in the US-based start-up, which aims to make research more reproducible by identifying and highlighting elements of scholarly manuscripts that either appear to be difficult to reproduce or where additional provenance would increase the trust that can be placed in the paper. 

Ripeta’s software uses sophisticated machine-learning and natural language processing algorithms to evaluate research manuscripts for evidence of reproducibility. In our data-rich research environment, excellence of communication of research ideas is increasingly tied to transparent presentation. This includes appropriate referencing of techniques, methods and underlying data.  Better research communication is essential for research to have its maximum impact. Ripeta aims to build trust in the research, its associated data, and the authors.

A previous Digital Science Catalyst grant winner, ripeta focuses on assessing the robustness of the reporting of the scientific method rather than determining the novelty or citability of a piece of research. The company’s long-term goal includes the development of a suite of tools across the broader spectrum of research to help researchers and peer reviewers ensure that research is communicated in the most transparent and reproducible way.  

Digital Science has a long track record of nurturing tools that help researchers carry out more open and reproducible research through investment and development of products such as Figshare.  Bringing ripeta into the family extends Digital Science’s ability to support researchers as data becomes even more critical in research.

The company has worked on a number of pilot projects with high-profile organisations including Springer Nature, the International Association of Scientific, Technical and Medical Publishers (STM) and Research Square. In September 2020, ripeta announced a partnership with Wellcome to assess dataset availability in funded research. 

Daniel Hook, CEO of Digital Science, said: “New technologies and greater global participation in research are driving larger research volumes.  Research outputs are also becoming more diverse in type.  These factors all lead to an widening gap between the level of provenance that we are used to seeing around a piece of research and the level now needed to retain the level of trust that we once took for granted.  Thus, it is exciting to be able to support and invest in a company like ripeta, that has a real vision for how to address these challenges in a systematic way.”

Ripeta CEO, Leslie McIntosh, said: “We are very pleased to advance research quality by joining the dynamic Digital Science family. We believe ripeta will greatly benefit from the scholarly research ecosystem within Digital Science, which will allow us to scale more significantly.”

Notes to Editors

Digital Science is a technology company working to make research more efficient. We invest in, nurture and support innovative businesses and technologies that make all parts of the research process more open and effective. Our portfolio includes admired brands including Altmetric, Anywhere Access, Dimensions, Figshare, ReadCube, Symplectic, IFI Claims, GRID, Overleaf, CC Technology, Writefull, Gigantum and ripeta. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.

Ripeta detects and predicts reproducibility in the scientific research industry through software and analytics development; improving evidence-based science and fiscal efficiency of research investments. It is effectively a ‘credit report’ for scientific publications. Ripeta provides a suite of tools and services to rapidly screen and assesses manuscripts for the proper reporting of scientific method components. These tools leverage sophisticated machine-learning and natural language processing algorithms to extract key reproducibility elements from research articles. Visit www.ripeta.com and follow @ripetaReview.

Media contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com

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The post Our founder stories appeared first on Digital Science.

Joris has over 20 years of experience driving change in the publishing industry through new technologies and business models. His former roles include Director of Publishing Innovation at Elsevier and Director of Special Projects at Digital Science, a role in which he authored the Blockchain for Research report. He co-founded Peerwith in 2015, and currently serves as Research Data Director at STM, where he drives the adoption of sharing, linking and citing data in research publications.

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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LONDON, UK: Noon, 12 March 2020. Research industry technology company Digital Science has today revealed the latest winners of its prestigious Catalyst Grant award: SciSwipe and SciFlow have each been awarded a grant. 

An international initiative to develop innovative projects and technologies The Catalyst Grant offers an award of up to £25,000 or $30,000 for concepts with the potential to transform scientific and academic research. Digital Science is well known for its engagement with the research community, and the grant supports ideas at an early stage of development, without the need for a complete business or development plan.

SciSwipe – Switzerland-based

SciSwipe is a data pipeline platform which breaks down complex, large-scale datasets into simple images the public can view and label. Labeled reference sets are a prerequisite for supervised machine learning.

Co-founded by Mariëlle van Kooten and Anton Pols, who met at Delft University of Technology (TU Delft) in the Netherlands in 2014, the app helps scientists take academic and industry data and convert it into images that can then be labelled via a crowdsourced mobile-based platform.

“Crowdsourced exploration of complex, large-scale data holds great potential – a trained computer can mine nearly infinite amounts of data for clues to improve human health,” says co-founder Van Kooten.

“Each user can help train a computer by swiping and in effect labeling a small number of such images on a smartphone, dividing labour and increasing reliability,” adds Pols.

SciSwipe converts user actions into sets of labelled data which can be used to train a machine learning algorithm. The trained algorithm can infer new data, harnessing the power of machine learning to predict, prevent or cure disease.

Van Kooten has gained an MSc in Life Science and Technology from Leiden University and is a PhD student in Systems Biology at ETH Zurich. Pols is a data scientist at Roche in Basel, where he creates data-driven solutions for clinical trial and commercial operations. He holds a BSc in Electrical Engineering and an MSc in Applied Physics from TU Delft. 

SciFlow – Germany 

SciFlow is a collaborative authoring tool which simplifies writing and publishing workflows for researchers and their institutions. The tool aims to cut down communication time and take away the frustrations of re-formatting multiple manuscript versions and word files. Founded by Dr. Carsten Borchert and Frederik Eichler, who have known each other for more than a decade, the idea stemmed from Eichler’s experience as a student. His frustrations with formatting Word documents brought him to the idea for an easy-to-use text editing tool, which was powerful like LaTeX but easier to use than MS Word. Borchert, meanwhile, was hooked by the idea after experiencing similar frustrations writing and publishing research as a PhD student. 

Borchert, who has a PhD in marketing and worked as an account manager at Oracle for five years, is responsible for sales and marketing, as CEO. Eichler manages SciFlow’s product development and previously worked at Capgemini for five years as a software engineer and project manager. They also have an advisory group of academics and industry experts. 

“Research institutions and libraries follow our vision to re-establish the library as the best place to produce and access knowledge for researchers and millions of knowledge workers worldwide,” says Eichler.

SciFlow’s tool offers instant collaboration in one place, automated formatting and reference management works with drag and drop. The tool currently has over 1,000 active monthly users and four research institutions including Max Planck Society. 

“The publishing workflow can be a very complicated process to navigate, especially for new researchers or those moving into new fields through collaborations,” said Dr John Hammersley, Co-founder and CEO, Overleaf.

“It’s exciting to see innovative new ways of approaching this problem, and we look forward to sharing our experiences of working with publishers and institutions to help and support SciFlow as they grow. Congratulations to Carsten and Frederik on what they’ve achieved so far!” 

Press enquiries
Contact the Digital Science press team: newsroom@digital-science.com

Digital Science is an AI-focused technology company providing innovative solutions to complex challenges faced by researchers, universities, funders, industry and publishers. We work in partnership to advance global research for the benefit of society. Through our brands – Altmetric, Dimensions, Figshare, IFI CLAIMS Patent Services, metaphacts, OntoChem, Overleaf, ReadCube, Symplectic, and Writefull – we believe when we solve problems together, we drive progress for all. Visit digital-science.com and follow Digital Science on Bluesky, on X or on LinkedIn.

The post From machine learning to improving publishing workflows – 2020 Catalyst Grant Winners appeared first on Digital Science.

Scismic builds technology platforms focused on enhancing career outcomes in the life science workforce. Scismic Job Seeker is their diversity-promoting, automated recruiting platform for the biopharmaceutical industry. The platform matches scientists to jobs based on expertise and removes sources of bias with its gender and race-blind matching algorithms, helping increase diversity in scientific hiring.

Scismic was founded by three Boston-based scientists looking to address the $1B annual loss from inefficiencies in scientific recruiting in the US, with plans to further expand into the $8.5B STEM recruiting industry. Their motivations came from observing many talented colleagues, initially enthused with scientific drive, grow disengaged and unproductive in their jobs, and feeling stuck in their careers.

“Our goal is to help all scientists, no matter their background, find workplaces that empower them to propel ground-breaking science,” said co-founder Elizabeth Wu. “One major barrier to scientific innovation is workforce development. We wanted to build a way for fellow researchers to find workplaces in academia and industry that would empower them to do their best science, and drive more research into the market.”

“We went through career changes and personally experienced the challenges of transitioning from academia to industry,” said co-founder Danika Khong. They decided to build a platform, with support from entrepreneur Danny Gnaniah and his team that could address career challenges and recruiting inefficiencies.

The co-founders highlight that current recruiting processes take over 2.5 months to fill vacancies and often results in poor fitting roles, lowering productivity and delaying scientific innovation. With Scismic Job Seeker, the recruiting process is shortened to one month or less. Current recruiting processes include sources of biases in candidate evaluation, like candidate name, which have been shown to exclude underrepresented scientists. Scismic Job Seeker removes these biases and solely matches candidates based on their skills. 

“Scismic is very excited to be part of Digital Science’s portfolio of companies and contribute to the scientific ecosystem in ways that will foster impactful, ethical, and groundbreaking science. Digital Science’s passion and commitment for making an impact in the greater scientific world deeply connects with us and our community of users.”

The beta platform was awarded Digital Science’s Catalyst Grant last April and has so far attracted attention from biotech companies and pharma, as well as close to 2,300 scientists so far.

Steve Scott, Director of Portfolio Development at Digital Science said: “Compared to existing job boards, Scismic’s automated talent-matching offers a unique way to help recruiters and researchers find each other in a highly competitive job market. Improving on a slow and expensive hiring process alone would have made their service attractive, but offering built-in ways to create fairer hiring processes really does set them apart. It was clear from our first engagement through the Catalyst Grant that this was a sharp, conscientious founding team and we look forward to helping them build on their initial success.”

Notes to editors:

Digital Science is a technology company working to make research more efficient. We invest in, nurture and support innovative businesses and technologies that make all parts of the research process more open and effective. Our portfolio includes admired brands including Altmetric, CC Grant Tracker, Dimensions, Figshare, Gigantum, ReadCube, Symplectic, IFI Claims, GRID, Overleaf, Ripeta and Writefull. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.

Media contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com

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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.”

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LONDON, UK: Noon, 25 September 2019. Research industry technology company Digital Science has today revealed the latest winners of its prestigious Catalyst Grant award: BPT Analytics, Intoolab and MLprior have been awarded a grant.

An international initiative to develop innovative projects and technologies The Catalyst Grant offers an award of up to £25,000 or $30,000 for concepts with the potential to transform scientific and academic research. Digital Science is well known for its engagement with the research community, and the grant supports ideas at an early stage of development, without the need for a complete business or development plan.

BPT Analytics – Ukraine

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.

“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,” says co-founder Dr. Andrii Buvailo.

The early prototype of the analytics platform and database has already had good traction and Buvailo alongside fellow co-founder Dr. Oleg Kucheryavyi are looking to expand covering all major areas of the pharmaceutical industry. Buvailo believes BPT’s key strength is in its ‘extraordinary level of detail’ – as the tool clusters data about companies by numerous domain-specific parameters.

“BPT Analytics aims to eliminate as much guesswork from the practice of pharmaceutical industry strategists, business developers, and decision-makers, as is possible,” says Buvailo. “By providing them with visualized access to systematic and constantly curated data about the most innovative industry players, trends, and opportunities.”

They are looking into incorporating machine learning modules in the near future to help improve data mining and recommendation systems.

Intoolab – Greece / UK

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.

“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,” says CEO Nikos Tzagkarakis. He believes most of the solutions try to focus on specific segments of the field. “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 team behind Intoolab, based in London and Athens, believe Tzager has a unique approach which is ‘deeply needed in the field of artificial intelligence’. They intend to broaden Tzager’s knowledge and deploy more powerful servers using the winning grant funds.

“The grant will enable us to develop our deep learning methods faster and also connects us with the valuable network of Digital Science,” says Tzagkarakis. “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

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.

The co-founders had previously created a system which predicted share price on changes in the news, but have shifted focus to scientific articles. They’ve constructed an algorithm for personal paper recommendations based on user behaviour and are now working on creating a unique algorithm for scientific paper analysis and prediction of acceptance at conferences.

“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.”

 Volkhonskiy says paper rejection rates at conferences are high and often researcher feedback can take months. He believes their tool will help increase the probability of success by improving the article text using an AI-based model to search for poor sentence construction, article flow and more general grammatical issues.

 “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.”

About Digital Science
Digital Science is a technology company working to make research more efficient. We invest in, nurture and support innovative businesses and technologies that make all parts of the research process more open and effective. Our portfolio includes admired brands including Altmetric, Anywhere Access, Dimensions, Figshare, ReadCube, Symplectic, IFI Claims, GRID, Overleaf, Labguru, BioRAFT, PeerWith, TetraScience and Transcriptic. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.

Media contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com

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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.

London – April 30 2019 Digital Science, a technology company serving stakeholders across the research ecosystem, welcomes Artificial Intelligence (AI) based language platform, Writefull, to the Digital Science family of companies.

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.

Winner of a 2016 Digital Science Catalyst Grant, Writefull has been supported in their development from an early stage idea to a high-growth potential company, and is the first significant investment in AI by Digital Science. Writefull’s Deep Learning models have learned from billions of sentences taken from the scientific literature, making it very different from the standard rules-based grammar applications.

“I first met Juan and Alberto, the Writefull founders, in 2016, and their potential to make a huge impact in the research space was immediately clear,” said Steve Scott, Director of Portfolio Development, Digital Science. “Writefull will help authors to improve and express their ideas clearly before they submit articles for publication, especially those who have English as their second language. And publishers can use it to help relieve the administrative burden on editors, to maintain house style and to help with quality control.”

“Support from Digital Science has been invaluable,” said Juan Castro, CEO, Writefull. “From winning the Catalyst Grant, to the advice we were given through the programme, to receiving this significant investment, Digital Science has enabled us to quickly develop the idea towards becoming a service that researchers and publishers can use. We’re excited to be joining the portfolio.”

To find out more about Writefull, visit https://writefull.com/ or https://www.digital-science.com/products/writefull/ 

Notes to editors

About Digital Science
Digital Science is a technology company working to make research more efficient. We invest in, nurture and support innovative businesses and technologies that make all parts of the research process more open and effective. Our portfolio includes admired brands including Altmetric, Anywhere Access, Dimensions, Figshare, ReadCube, Symplectic, IFI Claims, GRID, Overleaf, Labguru, BioRAFT, TetraScience, Transcriptic, CC Technology, Gigantum and Ripeta. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.

About Writefull

Writefull is a startup that creates tools to help researchers improve their writing in English. The first version of the Writefull product allowed researchers to discover patterns in academic language, such as frequent word combinations and synonyms in context. The new version utilises Natural Language Processing and Deep Learning algorithms that will give researchers feedback on their full texts. Visit https://writefull.com/ and follow @writefullapp on Twitter.

Media contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com

The post AI-Based Language Platform Writefull Joins Digital Science Portfolio 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.

LONDON, UK: Noon (12) BST, 2 April 2019. Research industry technology company Digital Science has today revealed the latest winners of its prestigious Catalyst Grant award: Scismic and Rationally have each been awarded a grant.

An international initiative to develop innovative projects and technologies The Catalyst Grant offers an award of up to £25,000 or $30,000 for concepts with the potential to transform scientific and academic research. Digital Science is well known for its engagement with the research community, and the grant supports ideas at an early stage of development, without the need for a complete business or development plan.

Scismic – USA, Boston

Scismic Job Seeker is an online, diversity-promoting, automated recruiting platform for the biopharmaceutical industry. The platform matches scientists to jobs based on expertise and removes sources of bias, with its gender and race-blind matching algorithms, helping increase diversity in scientific hiring.

“Our goal is to help all scientists, no matter their background, find workplaces that empower them to propel ground-breaking science,” said co-founder Elizabeth Wu. “One major barrier to scientific innovation is workforce development.”

Scismic was founded by three Boston-based scientists looking to address the $1B annual loss from inefficiencies in scientific recruiting in the US, with plans to further expand into the $8.5B STEM recruiting industry. Their motivations came from observing many talented colleagues, initially enthused with scientific drive, grow disengaged and unproductive in their jobs, feeling stuck in their careers.

“Eventually, the three of us went through career changes and personally experienced the challenges of transitioning from academia to industry,” said co-founder Danika Khong. They decided to build a platform, with support from entrepreneur Danny Gnaniah, that could address career challenges and recruiting inefficiencies.

“We wanted to build a way for fellow researchers to find workplaces in academia and industry that would empower them to do their best science, and drive more research into the market,” adds Wu.

The co-founders highlight that current recruiting processes take over 2.5 months to fill vacancies and often results in poor fitting roles, lowering productivity and delaying scientific innovation. In addition, recruiting processes include sources of biases in candidate evaluation, like candidate name, which have been shown to exclude underrepresented scientists.

“Our market research and previous studies show that scientists from underrepresented groups face greater barriers in finding jobs, even though diversity has been shown to result in greater productivity and innovation,” said Khong.

“We will leverage our platform to address the lack of racial diversity in STEM. There are currently no widely accessible and scalable services that address diversity at the recruiting stage for scientists.”

The beta platform has so far attracted attention from biotech companies and pharma, as well as over 1,200 scientists so far, mostly in the Boston area. They hope to expand this year across the US and are also looking to enhance the platform with additional diversity and inclusion features.

Rationally – USA, Colorado

Rationally is an online platform that guides good research design according to published standards (e.g. CONSORT, PCORI) and reduces predictable sources of irreplicability. Founded by Kristin Lindquist, who has worked at technology startups and in product design for over 10 years, the platform aims to guide researchers in their efforts to design more replicable, feasible and less biased experiments.

Rationally initially started out as passion project with a very different premise — a crowd sourced site answering questions with scientific evidence. However, while interacting with researchers and being exposed to the problems of the replication crisis, Lindquist changed direction to focus on researcher tools.

Unreproducible science, Lindquist says, contributes to the estimated $200bn in wasted biomedical investments each year. The founder argues the “publish or perish” incentivises researchers to find the interesting / anomalous over the uninteresting / replicable.

“Compounding the problem, good research design is hard, peer review is untimely, and meticulous science isn’t readily distinguished from the sloppy. An epidemic of poor study design and R&D waste results. How do we get the 8 million researchers in the world to know about and adhere to better practices?”

The replication crisis is particularly impactful in fields such as biomedical, psychology and sociology. 89 percent of the foundational oncology studies evaluated by Amgen failed replication and an analysis by Bayer researchers revealed a mere 20-25% replication success rate of the biomedical research investigated. A Nature survey asking 1,500 scientists what factors would boost reproducibility showed a better understanding of statistics, better mentoring and supervision and more robust design were key priority areas.

“What makes for good research design isn’t so much a problem of insights as it is of dissemination”, said the Colorado-based founder. “The scientific community produces excellent guidance on research reproducibility, yet the replication crisis is still going strong.”

“We’ve tried to reduce an ambiguous and complex process into a guided, step-by-step experience,” says Lindquist. “It helps researchers think about how their study will be perceived in the meta-analysis process or by research reliability experts.”

The platform helps researchers to understand feasibility, rigour and generalisability trade-offs to make optimal design decisions. Currently in alpha, Rationally also helps connect researchers with biostatistics experts (at their institution or remote) for design review and help on areas of complexity. It can also generate checklists, methodology outlines or execution flow charts from research design specifications.

Steve Scott, Director of Portfolio Development at Digital Science said: “Twice a year we open the Catalyst Grant to applications from all over the world – and each year the number of entries continues to grow.

“The people best positioned to define areas for innovation are researchers themselves – but it’s incredibly hard for those with an idea to secure early-stage funding – finding investors who understand the research market is a challenge, meaning many potentially successful ideas remain just that, ‘ideas’. That’s exactly why we created the Catalyst Grant – our financial support, alongside our advice makes a real difference.”

For press enquiries, please contact

Alex Jackson

Head of Press, Digital Science

a.jackson@digital-science.com

About Digital Science

Digital Science is a technology company working to make research more efficient. We invest in, nurture and support innovative businesses and technologies that make all parts of the research process more open and effective. Our portfolio includes admired brands including Altmetric, Anywhere Access, Dimensions, Figshare, ReadCube, Symplectic, IFI Claims, GRID, Overleaf, Labguru, BioRAFT, TetraScience, Transcriptic and CC Technology. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.

Media contact

David Ellis, Press, PR & Social Manager, Digital Science: Mobile +61 447 783 023, d.ellis@digital-science.com

The post From improving diversity in the STEM workforce to helping the replication crisis – 2019 Catalyst Grant winners appeared first on Digital Science.