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The post How data science can drive competitive advantage in drug discovery research appeared first on Digital Science.

Developed in collaboration with over 100 leading research organisations around the world, Dimensions is a unique platform combining data about publications, data sets, grants, patents, clinical trials, and policy documents. Our database spans the broader global scientific landscape to enable individual researchers as well as research funders, research organizations, and publishers to discover, analyse, and understand multiple aspects of the research life cycle.

In this chapter, Juergen Wastl introduces the development and deployment of the Dimensions platform and describes the breadth of available functionality with a focus on bibliometric applications and question sets that can be applied to the academic and broader outcomes of research, and gather insights to inform future strategy.

Read the Chapter

The post A Bibliometrician Toolbox appeared first on Digital Science.

Research metrics, closely related to statistics and network analysis, benefit greatly from visualizations. This chapter, written by Helene Draux, introduces some guidelines for visualizations before describing the visualizations frequently used.

Helene introduces Chart Chooser and then considers the characteristics of the data. This includes either static or interactive graphs that are common in statistics, which she complements with advanced graphs more able to represent the links between scholarly objects. These flow diagrams are the opportunity to get an overview of the problem and dig deeper in it.

Read the Chapter

The post Visualization of Research Metrics appeared first on Digital Science.

External (left) and internal (right) collaboration patterns are presented here for Australasian Research Organisations (selected by top 20 ). Researchers are coloured by the field of research that they most commonly publish in, and sized by total number of journal articles that they have published (relative to the network). To create the networks, journal articles published between 2015 and 2018 were analysed.

If you want to find out more check out our interactive dashboard.

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The post What does New Zealand Research Look Like? appeared first on Digital Science.

Click logos to find out more about the data

The world’s largest linked research information dataset

• 117m publications from 86k journals, 49 preprint servers and over 1m books
• 6m grants worth more than 1.9 trillion USD from 600 funders worldwide
• 8m datasets from over 900 repositories worldwide
• 583K policy documents, 622k clinical trials, and 135m patents from 130 countries

A unique mixture of academic and mainstream sources help build a more complete picture of research impact

• Our system scans a manually curated list of over 9,000 academic and non-academic blogs and over 2,000 media outlets every day. We’ve captured over 10m mentions so far
• We also provide public policy sources, Wikipedia citations, and have captured over 134m social media mentions

The leading platform for patent data analytics

• Bibliographic data from DocDB, the EPO master documentation database covering data from over 90 countries
• Legal status from Inpadoc, a database produced by the EPO covering over 40 international patent authorities
• Legal status data from our Chinese data source
• Bibliographic and full-text data from national patent offices – see the full list here

Embedding Context into Your Decision-Making Processes

Brexit Could Trigger £1 Billion Cut in Annual Funding for UK Research Base

Increasing the Scope of Researcher Engagement Through Technology

On the Benefits of Institutional Identity – A Guest Post by Richard Padley

Collaborating and Co-Creating Your Insights

Leading patent data platform joins Digital Science

Digital Science is pleased to announce that it has fully acquired IFI CLAIMS, a leading international platform for patent and innovation data.

How data science can drive competitive advantage in drug discovery research

The disciplines of biology, chemistry, and medicine have anchored drug discovery research since its inception, data science is a recent development in comparison.

Evolution or Revolution

Beyond Benchmarking

Together we can help you overcome your challenges

The post Data That Makes a Difference appeared first on Digital Science.

Working with Dimensions data and some additional information from Nature, Dr Hélène Draux, a data scientist here at Digital Science, collaborated with Nature’s Richard Monastersky and Richard Van Noorden, to produce some infographics uncovering some of these trends, including:

• The yearly amount of content published in Nature peaked at around 1960
• A large amount of this is News, which has certainly dominated in the last twenty years
• The biological sciences remain the most dominant field of research, with biochemistry and cell biology as the biggest subfield
• The keyword analysis follows science’s big breakthroughs in understanding – with early entries focused on big natural phenomena and more recent ones reflecting advances in physics and molecular biology.
• Research is increasingly a collaborative endeavour, with a growing number of authors on an article
• The percentage of female authors has grown over time.
• The largest contributors by country are diversifying over time
• Collaborations are becoming more multinational

What the infographics don’t tell us is the data science story behind the trends. A quick chat with Hélène revealed a whole host of data curation steps that were taken to ensure that the stories were best representing research over time. For a start, the team had to identify what they were going to count as a piece of research output. A quick dive into Dimensions  shows that the most prolific Nature author is David Cyranoski, with 441 publications, largely down to the fact that he is an Asia-Pacific Correspondent for Nature, rather than a researcher publishing novel findings.

A review of the past 150 years allows us to hold a mirror up to the face of research, and see how it has changed. Knowledge of the history of research culture helps make sense of some of the trends we see. For example, Nature  grew more selective in later decades of the 20th century in part because of changes in editorial leadership. As Hélène puts it, they were “letters to the world”, a chance for scientists and natural philosophers to showcase their latest ideas freely.

The comprehensive nature of Dimensions metadata allows for many of these trends to be teased out of this vast pool of information. For example, article-level categorisation of papers allows us to see trends in publication volumes across different fields of research. Indeed, the detailed article metadata adds further context to trends seen over time, such as the prevalence of water in the titles and abstracts of the research articles published in the first few decades of Nature. Dimensions fields of research show that water was a fundamental focal point of study across many fields of natural and physical science. As our understanding of the behaviour of water developed, these fields became more specialised.

The areas of interest also track technological advances of research. While telescopes were used to look at research on a cosmological scale, as microscopes developed, the topics of interest shrunk down to focus on human-sized problems, and even further to look at the cells, proteins and cell contents that make us who we are, with the quantum realm becoming truly popular from the 2000s onwards.

While investigating the data behind these keywords, Hélène recalls a story whereby they were surprised that the use of the term “gene” had been used before it was invented – Nature may be at the forefront of research, this was still an impressive feat. It was thanks to the diversity in the knowledge of this team that this observation was quickly debunked pending closer inspection. The definition of gene that we used today was first used in the early 1960s, so why were there references to genes before? Was the word being used to describe something else? Was the city of Gene a hotbed for research breakthroughs? No; there was a much simpler explanation. In many journals, formatting requires longer words to be hyphenated and spread across two lines. The appearance of ‘gene’ picked up in the analysis was actually down to many a ‘generally’ that had been chopped into two to become ‘gene-’ and ‘rally’. This is one example of why having a range of people with different experiences working to solve a problem is important, as without this knowledge, making sense of the data would have taken even longer. This also reminds us to remain critical when we analyse data, and be mindful of the logical context within which data falls.

While the results of this retrospective look at Nature’s content shows us how research is changing over time, and often for the better, this research also gives a flavour of the level of analysis that is possible when high quality data is available. Through better metadata and more open research, we can get higher quality data, though this study also serves as a reminder to remain sceptical of all trends in data until they can be better understood in context, and also the benefits of having a diverse range of skills and experiences within a team to make the most sense of the trends being identified.

The post From ‘letters to the world’ to leading international journal: a journey into the evolution of scientific research appeared first on Digital Science.

Expectations around reproducible research are clear, particularly in the area of computational research. A research paper is more than an account of the research that was undertaken; it is a narrative that surrounds an orchestration of research assets from the raw data and code to the processed data and visualisations that result. A paper should invite a reader to trace the results back. How was this figure produced? What was the code that produced that particular result? The reader’s transition from narrative to exploring data or code should be as easy as turning the page.

Seen from the researcher’s perspective, the ideal computational paper arises organically from the research – the data that is created is the data that ends up in the paper. The code as it is written is the code that can be accessed in the paper. As analysis bubbles up from research into images for publication, those images keep their providence back to the data, and back to the code that produced them.

How close are we to this ideal today? Within the Digital Science family, methods for openly publishing data are ably supported by Figshare. Overleaf allows researchers to easily publish their research collaboratively using LaTeX.  As part of a poster presentation for the 2019 VIVO conference we took a broad research question that could be answered with Dimensions data, and undertook the research using workflows that knit these tools together. In doing so, our project, documented in our white paper, demonstrates an approach to undertaking reproducible computational science that operates on multiple levels. Specifically, it addresses:

• How can data assets be structured and organised throughout the life of a project inside Figshare (and not just at the end of a project)?
• What is a good approach to tying code, data, and papers together using identifiers?

In this paper we demonstrate that not only is our poster reproducible, but that the methods we have adopted are useful to others as well. We feel we learnt a lot throughout this project, and hope to continue to refine these approaches in our analysis projects moving forward.  From analysis through to publication, we would love to hear about some of the ways that you have used research productivity tools in similar ways. Get in touch!

PS – This is the first Digital Science Report to be made entirely in Overleaf…

Technical Report: https://doi.org/10.6084/m9.figshare.9741890

Poster: https://doi.org/10.6084/m9.figshare.9742055

Online Version: https://wdaull.ds-innovation-experiments.com/

The post This Poster is Reproducible appeared first on Digital Science.

The research world has moved faster than many would have suspected possible in response to the current pandemic. In five months, a volume of work has been generated that even the most intensive of emergent fields have taken years to create.

We investigate the research landscape trends and cultural changes in response to COVID-19. The report includes analysis of publication trends, geographic focal points of research, and collaboration patterns.

Download The Paper

The post This Poster is Reproducible appeared first on Digital Science.

STEM Fellowship’s High School Big Data Challenge sees the tech-native generation put their data analysis talents to the test and grow into digital citizens. The theme for this year was “Think Global – Act Local”, and addressed the issues facing sustainable development around the world.

The challenge involves teams of high school students tackling a question and writing a paper on their choice of topic for the year’s theme. The papers are then read and the top-scoring ones are presented at SAS headquarters for a chance to win prizes.

While all the papers submitted this year were interesting and insightful, awards were given to research that was of exceptional quality.

The late Right Honourable Arnold Chan was a huge part of the reach and impact of last year’s Big Data Challenge. To honour him and his contribution, RBC sponsored the Arnold Chan Student Innovation Award this year – and it went to TanenbaumCHAT’s team with Jason Arbour, Jordan Juravsky, Shahar Lazarev, and Josh Zwiebel.

TanenbaumCHAT created an investment plan for greener Canadian energy. They established a greenhouse gas emissions cap and then used neural networks to predict the cost of energy of Canadian wind, solar, and hydro, as well as that of coal and natural gas. Using neural networks, they also created a model of electricity demand in Canada. By analysing these models together, keeping in mind their emissions cap, they developed an ideal investment plan. With this plan, the cost-efficiency of renewable energy in Canada will outweigh that of fossil fuels by the time the emission cap is reached, allowing for an emission-free future.

The SAS Analytics Award went to the team with exceptional analysis – Webber Academy’s Aaron Abraham and Kevin Lin.

The duo aimed their study at factors relating to violent crime in the United States. Using five different machine learning models, they predicted which variables were most correlated to high crime rates and found that they could be traced back to limited resources, difficult living conditions, and psychological harm. Their models showed that family dynamics played a large role in determining crime rates. Understanding patterns in violent crime can help law enforcement allocate their resources more effectively and can give a boost to preventive efforts.

The Digital Science Award, from Altmetric, Overleaf, and Figshare, was awarded to the team from University of Toronto Schools – Katherine Gotovsky, Alain Lou, Arielle Shannon, Jing Yi Wang.

This team ran a broad investigation of data from weather stations and from NASA with the intention of identifying the optimal locations for solar farms in Ontario. Using weighted values for factors such as humidity, cloud coverage, and temperature, they were able to identify trends in photovoltaic efficiency and pinpoint locations that promised optimal locations for solar farms. Their research can be used in the development of renewable energy sources as well as the optimization of existing solar panel farms.

Last but certainly not least, Tony Xu and Shayan Khalili from Earl Haig Secondary School were invited to the SAS executive box for a TFC game and networking opportunity.

They ran a multi-faceted analysis of demographics’ effect on greenhouse gas emissions and used data from over two hundred countries and twenty years. They found that the urbanized, wealthy meat-lovers had the greatest carbon footprint. Their insightful conclusions can change the way we approach developing a solution to climate change and reveal what factors are most important in the quest for sustainability.

Find out more about the Big Data Challenge here.

Authors:

Michal Fishkin and Dr Sacha Noukhovitch  

The post STEM Fellowship Big Data Challenge Winners appeared first on Digital Science.

Digital Science and three of its portfolio companies Altmetric, Overleaf and Figshare were delighted to sponsor this year’s STEM Fellowship Big Data Challenge. Now in its fourth year, the STEM Fellowship Big Data Challenge is a competition that helps high school students get excited about data science and its potential to support inquiry-based learning and problem-solving.

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.

Tell us a little about the STEM Fellowship Group, how it originated and its mission?

The STEM Fellowship is an association of students from high to graduate school that share a common interest in big data inquiry and knowledge crowdsourcing. You’ll find participants and representatives of the STEM fellowship within students’ groups and clubs on 22 university campuses and in over 50 high schools in 10 provinces across Canada, as well as in schools and colleges in the US, Iran and Malawi. We organize a student-driven practical learning process to encourage critical thinking and the use of data science tools. We’re also focused on teaching students how to process open data in order to supplement their reasoning and assist them in the scientific endeavor.

How is this year’s Big Data Challenge different to last years, and how was the topic and subject area chosen?

Every year we create a major theme to inspire student-driven independent inquiry. Last year, we looked at the future of science and what topics and trends attracted the majority of research efforts. It was investigated by utilizing data provided by world leader in research impact measurement innovation, Altmetric.

This year, students challenged us with a question of digital citizenship that laid the foundations for the competition and defined its theme: Think Global – Act Local with Big Data.

For the 2018 Big Data Challenge, students used open climate change data from a range of sources. Students were also given information from a variety of media outlets commenting on the latest climate research – this helped support an informative and diverse learning ecosystem.

What do you hope the students will take away from a competition like this?

The competition helps students to develop their natural data analysis abilities and also acts as a platform for them to investigate complex socioeconomic and interdisciplinary problems. The students learned how to use open data and how to navigate through open science resources to develop their own ideas around climate change and sustainable development. Everyone gained a practical understanding of the scientific method and also got to experience real-time scholarly collaboration using the Overleaf platform.

What does it mean to have industry partners like Digital Science, Altmetric, Overleaf and Figshare?

Our partnership with these industry partners is critical for an authentic scholarly communication experience for the students. For all participants, Overleaf becomes their first and primary tool for scholarly writing. They are able to get a real experience in manuscript preparation and academic collaboration.

Altmetric and Figshare have provided a gateway into open science, changing the perspectives and opinions that students previously held regarding how to find and use sources of information and knowledge. Students are able to become contributors, collaborators and consumers of knowledge!

How do you see the event evolving over the years? There’s now more than one BDC event, is that correct?

2018 marked the fourth year of the Big Data Challenge for High School Students and the first national competition. It has already generated interest abroad and in the Canadian competition, we have teams from Princeton International School of Mathematics and Science (PRISMS) and Phillips Exeter Academy in USA.

Recently, we have teamed up with the New York Academy of Science to offer the Think Global – Act Local with Big Data competition in 51 countries through their international Junior Academy network.

The challenge was always more than a competition, but rather a new form of learning in computational inquiry. For that reason, it is highly sought-out amongst university students. Following last year’s Big Data Challenge pilot for biomedical students at the University of Toronto we have had requests to continue it at UofT and three other universities.

I foresee a bright future for the Big Data Challenge and I’m confident it will grow and continue to serve digital learners of all ages opportunities to learn new and valuable skills.

Adrian Stanley, Vice President of Global Business Development, Publishers, noted about the competition:

“It’s really excellent to see and support this vital grass roots level programme with all the valuable experiences its competitors are receiving. There is something very special in the raw insights students identify in the data and I’m amazed at the technical level of expertise, and lifelong learning that Dr. Noukhovitch and his team achieve here. The event is really going from strength to strength and Digital Science welcomes the opportunity to be involved and support the STEM Fellowship Big Data Challenge competition.”

Last year, Digital Science and a number of its portfolios supported the Big Data Challenge. Read about what the winning participants had to say about their experience partaking in the challenge. Find out more about the STEM Fellowship Big Data Challenge.

The post STEM Fellowship Big Data Challenge appeared first on Digital Science.