Discussions covered real-world use cases, challenges in data quality and integration, and the evolving relationship between internal tooling and external AI platforms. The roundtable reflected both enthusiasm and realism about AI’s role in drug discovery – underscoring that real progress depends on high-quality data, strong governance, and tools designed with scientific nuance in mind. Trust, transparency, and reproducibility emerged as core pillars for building AI systems that can support meaningful research outcomes.

If you’re in an R&D role, whether in computational biology, informatics, or scientific strategy and looking to scale literature workflows in an AI-enabled world, keep reading for practical insights, cautionary flags, and ideas for future-proofing your approach.

Evolving roles and tooling strategies

Participants emphasized the diversity of AI users across biopharma, distinguishing between computational biologists and bioinformaticians in terms of focus and tooling. While foundational tools like Copilot have proven useful, there’s a growing shift toward developing custom AI models for complex tasks such as protein structure prediction (e.g., ESM, AlphaFold).

AI adoption is unfolding both organically and strategically. Some teams are investing in internal infrastructure like company-wide chatbots and data-linking frameworks while navigating regulatory constraints around external tool usage. Many organizations have strict policies governing how proprietary data can be handled with AI, emphasizing the importance of controlled environments.

Several participants noted they work upstream from the literature, focusing more on protein design and sequencing. For these participants, AI is applied earlier in the R&D pipeline before findings appear in publications.

Data: Abundance meets ambiguity

Attendees predominantly use public databases such as GeneBank and GISAID rather than relying on the literature. Yet issues persist: data quality, inconsistent ontologies, and a lack of structured metadata often require retraining public models with proprietary data. While vendors provide scholarly content through large knowledge models, trust in those outputs remains mixed. Raw, structured datasets (e.g., RNA-seq) are strongly preferred over derivative insights.

One participant described building an internal knowledge graph to examine drug–drug interactions, highlighting the challenges of aligning internal schemas and ontologies while ensuring data quality. Another shared how they incorporate open-source resources like Kimball and GBQBio into small molecule model development, with a focus on rigorous data annotation.

Several participants raised concerns about false positives in AI-driven search tools. One described experimenting with ChatGPT in research mode and the Rinsit platform, both of which struggled with precision. Another emphasized the need to surface metadata that identifies whether a publication is backed by accessible data, helping them avoid studies that offer visualizations without underlying datasets.

A recurring theme was the frustration with the academic community’s reluctance to share raw data, despite expectations to do so. As one participant noted:

“This is a competitive area—even in academia. No one wants to publish and then get scooped. It’s their bread and butter. The system is broken—that’s why we don’t have access to the raw data.”

When datasets aren’t linked in publications, some participants noted they often reach out to authors directly, though response rates are inconsistent. This highlights a broader unmet need: pharma companies are actively seeking high-quality datasets to supplement their models, especially beyond what’s available in subject-specific repositories.

Literature and the need for feedback loops

Literature monitoring tools struggle with both accuracy and accessibility. Participants cited difficulties in filtering false positives and retrieving extractable raw data. While tools like ReadCube SLR allow for iterative, user-driven refinement, most platforms still lack persistent learning capabilities.

The absence of complete datasets in publications, often withheld due to competitive concerns, remains a significant obstacle. Attendees also raised concerns about AI-generated content contaminating future training data and discussed the legal complexities of using copyrighted materials.

As one participant noted:

“AI is generating so much content that it feeds back into itself. New AI systems are training on older AI outputs. You get less and less real content and more and more regurgitated material.”

Knowledge graphs and the future of integration

Knowledge graphs were broadly recognized as essential for integrating and structuring disparate data sources. Although some attendees speculated that LLMs may eventually infer such relationships directly, the consensus was that knowledge graphs remain critical today. Companies like metaphacts are already applying ontologies to semantically index datasets, enabling more accurate, hallucination-free chatbot responses and deeper research analysis.

What’s next: Trust, metrics, and metadata

Looking forward, participants advocated for AI outputs to include trust metrics, akin to statistical confidence scores, to assess reliability. Tools that index and surface supplementary materials were seen as essential for discovering usable data.

One participant explained:

“It would be valuable to have a confidence metric alongside rich metadata. If I’m exploring a hypothesis, I want to know not only what supports it, but also the types of data, for example, genetic, transcriptomic, proteomic, that are available. A tool that answers this kind of question and breaks down the response by data type would be incredibly useful. It should also indicate if supplementary data exists, what kind it is, and whether it’s been evaluated.”

Another emphasized:

“A trustworthiness metric would be highly useful. Papers often present conflicting or tentative claims, and it’s not always clear whether those are supported by data or based on assumptions. Ideally, we’d have tools that can assess not only the trustworthiness of a paper, but the reliability of individual statements.”

There was also recognition of the rich, though unvalidated, potential in preprints, particularly content from bioRxiv, which can offer valuable data not yet subjected to peer review.

Conclusion

The roundtable reflected both enthusiasm and realism about AI’s role in drug discovery. Real progress depends on high-quality data, strong governance, and tools designed with scientific nuance in mind. Trust, transparency, and reproducibility emerged as core pillars for building AI systems that can support meaningful research outcomes.

Digital Science: Enabling trustworthy, scalable AI in drug discovery

At Digital Science, our portfolio directly addresses the key challenges highlighted in this discussion.

• ReadCube SLR offers auditable, feedback-driven literature review workflows that allow researchers to iteratively refine systematic searches.
  
• Dimensions & metaphacts offers the Dimensions Knowledge Graph, a comprehensive, interlinked knowledge graph connecting internal data with public datasets (spanning publications, grants, clinical trials, etc.) and ontologies—ideal for powering structured, trustworthy AI models that support projects across the pharma value chain.
  
• Altmetric identifies early signals of research attention and emerging trends, which can enhance model relevance and guide research prioritization.

For organizations pursuing centralized AI strategies, our products offer interoperable APIs and metadata-rich environments that integrate seamlessly with custom internal frameworks or LLM-driven systems. By embedding transparency, reproducibility, and structured insight into every tool, Digital Science helps computational biology teams build AI solutions they can trust.

The post AI in drug discovery: Key insights from a computational biology roundtable appeared first on Digital Science.

Monday 15 April 2024

Digital Science solutions metaphacts and Dimensions are excited to announce the highly anticipated launch of the Dimensions Knowledge Graph, a large ready-made knowledge graph powering AI solutions in the pharmaceutical and life sciences industries. 

The Dimensions Knowledge Graph, powered by metaphactory, is an all-in-one knowledge graph solution, ready-made for easy integration with customers’ data infrastructure and existing internal knowledge graphs. At its core is an explicitly defined and flexible semantic model that can be easily extended to include internal data (which can range from domain expert knowledge or data from internal documents) and harness the synergy of global research and internal knowledge to drive business decisions. Through a unified semantic layer, it connects data across all relevant sources and adds richness and context that simplifies and accelerates the generation of actionable insights grounded in evidence. 

Companies in the pharmaceutical and life sciences fields can now gain unparalleled access to an abundance of interlinked global research data covering around 350 million records, such as patents, clinical study reports and publications as well as public datasets and ontologies, that they can use in conjunction with the wealth of internal data they already possess to drive business decisions and power AI applications.

“A unique component of the Dimensions Knowledge Graph is the symbolic AI layer it provides, as it introduces an enhanced level of transparency and trustworthiness to AI applications (e.g., LLMs and gen-AI), which is particularly crucial in pharma due to the sensitive and high-impact nature of its use cases. Companies pursuing independent development of AI within their own systems can benefit from the symbolic AI layer provided by the Dimensions Knowledge Graph, which serves as an essential building block that can enhance the efficiency and reliability of their AI initiatives,” said Dr Peter Haase, Founder & Chief Scientific Officer, metaphacts.

The underlying knowledge graph can be combined with LLMs and gen-AI to scale and support business decisions with machine-generated insights, augment explicit knowledge with AI algorithms and much more. This powerful combination of knowledge graphs and LLMs can also drive internal end-user interfaces for knowledge discovery.

Behind the scenes – What is the Dimensions Knowledge Graph?

The model for the Dimensions Knowledge Graph was built using metaphactory’s semantic modeling interface, which is based on open standards and supports data reusability across all use cases and domains within an organization, as well as extensibility to enable the capturing of internal data. End users can access the integrated knowledge through use-case-specific views for intuitive search, exploration and visualization. Alternatively, insights can be delivered via API and injected into already-established Business Intelligence or data visualization interfaces.

The built-in AI Assists across the solution can deliver trust and explainability to AI-generated insights and help scale business decisions, allowing customers to move from a human-driven model to a human-in-the-loop model.

The Dimensions Knowledge Graph data is built upon one of the world’s largest linked research databases – Dimensions – and includes data covering millions of publications, patents, grants, clinical trials, policy documents and technical reports. The Dimensions database not only enables search on the metadata of publications (e.g., title, abstract, authors), but it also offers search and discovery over full text.

The Dimensions Knowledge Graph enhances Dimensions data by offering integration with public datasets and ontologies, including data, metadata and relations for genomics, proteomics, metabolomics, molecular interactions, biological processes, and pharmacology and includes metadata and semantic annotations derived from 350 million research outputs. Additionally, customers can easily combine this data with internal data or any existing knowledge graphs they wish to integrate. 

We believe the Dimensions Knowledge Graph is the largest interconnected set of semantically annotated knowledge, including data from:

• 143 million publications
• 160 million patents
• 30 million datasets
• 7 million grants
• 2 million policy documents

People & organizations

• 34 million researchers
• 129 thousand organizations

Semantic annotations

• 307 billion linked semantic annotations
• 35 million research integrity trust markers

Pharma ontologies/vocabularies

• 30 million concepts from 38 domain ontologies

With the Dimensions Knowledge Graph, internal data becomes contextualized and consumable, and enhanced with knowledge from public data and global research. Through the integration of the Dimensions database and open datasets, you can uncover insights that may have been previously inaccessible and could lead to remarkable solutions and discoveries, and support quick validation of hypotheses.  

Dimensions Knowledge Graph Accelerator Package

Users who want to explore the Dimensions Knowledge Graph can sign up for the metaphacts Accelerator Package and gain 3-month access to the Dimensions Knowledge Graph, individualized support, and training and team enablement. Organizations could deliver a first MVP leveraging global research knowledge and internal data in less than two weeks. 

Included in the Accelerator Package:

• Access to the Dimensions Knowledge Graph for three months
• Expert support during the project duration to help connect with internal data sources for use cases
• Team enablement and training as part of the metaphacts Academy

About Dimensions

Part of Digital Science, Dimensions is among the world’s largest linked research database and data infrastructure provider, re-imagining research discovery with access to grants, publications, clinical trials, patents and policy documents all in one place. www.dimensions.ai. Follow @DSDimensions on Twitter and LinkedIn.

About metaphacts

Part of Digital Science, metaphacts helps global enterprises transform data into consumable, contextual and actionable knowledge. Our low-code, FAIR Data platform metaphactory simplifies capturing and organizing domain expertise in explicit semantic models, extracting insights from data and building knowledge discovery interfaces.

www.metaphacts.com. Follow @metaphactsGmbH on 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 contacts

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

The post Metaphacts and Dimensions launch the Dimensions Knowledge Graph, powered by metaphactory appeared first on Digital Science.

At Digital Science, we recognize that the journey toward AI adoption is as unique as the organizations and individuals we support. From bench researchers to medical affairs professionals to research offices, our approach is grounded in collaboration and deep understanding.

Since 2013, we’ve been investing in advanced AI methodologies, expanding our technical and analytical capabilities, and assembling a global team of AI experts…

Our capabilities

For the last decade, we have focused around machine learning innovations with Dimensions.ai, investment in Writefull and development of different LLMs…

Dimensions in ChatGPT

Available via OpenAI’s GPT Store… grounded in scientific evidence from Digital Science’s Dimensions database.

• Answers to research queries with publication data, clinical trials, patents and grant information
• Set up in the client’s private environment and only available to client’s end users
• Notifications each time content generated is based on Dimensions data, with references and citation details
• Possible for clients to have custom features (following prior discussion with Dimensions).

…access to the solution is free to anyone with a Plus or Enterprise subscription to OpenAI’s GPT Store.

Next-generation search experience

Dimensions has introduced a new summarization feature… Short, concise summaries are now available…

Smarter searching in Dimensions

Other AI solutions will follow shortly from Digital Science… they have been developed with a grounding in reliability and responsibility…

Connecting your Data

The Dimensions Knowledge Graph, powered by metaphactory, aims at helping customers harness… AI-powered applications and business decisions.

AI-powered writing support

Writefull uses big data and Artificial Intelligence to boost academic writing…

Deeper understanding of scholarly papers

Available within ReadCube Enterprise Literature Management… providing real-time, in-depth analysis, summarization, and contextual understanding…

Our latest AI insights

The post How we’re powering research innovation with responsible AI appeared first on Digital Science.

For example, combining global public research with proprietary data would provide pharmaceutical companies with valuable knowledge that could help drive significant advancements in research and product development. Linking to public knowledge can enrich existing internal research with metadata and contextual meaning, and equip decision-makers with new insights, context and perspectives they might not have had access to originally, leading to more strategic and informed decisions. For instance, it could help fast-track target discovery and reduce research costs or streamline processes from R&D to clinical trials to market access.

Additionally, this integration of data through a semantic layer unlocks the potential to drive many AI solutions across the pharma value chain and embed a layer of trustworthiness and explainability in these applications. Having reliable and precise AI solutions is critical, especially in the pharma sector, which deals with sensitive and high-stakes use cases, such as using AI to discover hidden relations between drugs, genes, diseases, etc., across multiple datasets, clinical trials or publications, for example.

Using fragmented data could cause AI to miss essential connections, or at worse lead to inaccurate predictions regarding drug interactions or outcomes.

Several root causes contributed to this gap between the public and private data spheres, including an absence of suitable infrastructure and technology to connect disparate data and global public research, and a lack of tools to contextualize retrieved data and derive meaning from it.

Integrating public knowledge

Although a considerable amount of research is public and open, many companies don’t have the necessary software or technology (though these are now widely available) to access these vast datasets, while for other research they require a license for full access to the available literature.

Even when full access is permitted, there is still a time and labor expenditure that impedes the immediate use of this data. For example, for companies that have dedicated internal resources to deep dive into scientific literature reviews, these literature research reports can take weeks or even months to complete because a (human) worker will need to review hundreds or thousands of these sources manually. Additionally, new data is being published constantly, making it difficult to keep up with emerging research.

Many pharma, biotechnology or medical devices organizations will also outsource research work to Contract Research Organizations (CROs) that carry out services such as clinical trials management or biopharmaceutical development, with the aims of simplifying drug development and entry into the market. However, collaborating with CROs can require extensive back-and-forth, from sharing data and results to constant meetings and email communication. As a result, the fragmentation between the company and CRO can lead to notable delays, miscommunication, or at worse, culminate in incorrect decision-making.

Fragmentation within public research

A divide exists not only between private and public data, but this fragmentation also occurs within public research, thus exacerbating the issue. For example, several datasets can stem from the same research. To establish connections between these datasets, they need to be extensively reviewed, cross-referenced and analyzed. However, many of these public datasets do not integrate well with each other or other public sources, such as KEGG (Kyoto Encyclopedia of Genes and Genomes) and GWAS (Genome-Wide Association Studies) Catalog, for various reasons, including a lack of a standardized format or insufficient annotation, for example. Consequently, linking the metadata from these sources becomes challenging, making it hard to gain a clear understanding of the relations between them.

The quality of public research can also vary. There’s a considerable amount of manually curated data available that doesn’t provide evidence (i.e. data from clinical trials) or fails to cite the original report/document from which it is referencing, making it challenging to validate the accuracy, quality and conclusions drawn from the data.

Internal data silos

When leveraging corporate data, companies in the pharma and life sciences space have individuals, teams and departments all producing valuable data that could be used immediately or in the future, such as in the later stage of the pharma life cycle. For example, data from clinical trials for one drug could be extremely valuable in understanding the application of a target (e.g. protein) with another disease (e.g. a side effect in one trial could become a targeted disease in another), this data can also be used for drug safety review or commercial purposes later on. However, this data becomes difficult to share and repurpose later on if it lacks the original meaning and context in which it was created (which it often does). It might be presented in a spreadsheet without a clear legend or instructions on how to use or interpret the data. Also, data can get stuck within internal systems that require software and specialized technical expertise to retrieve or is buried within documents and personal emails. Retrieving data is also a time-staking and laborious task leaving little room for actual analysis and application of insights.

Knowledge drain

Once data is retrieved and supplemented with information and meaning, insights can be derived from the data, which is what we consider ‘knowledge’. Knowledge is the penultimate step in the decision-making process, right before a final decision can be made, making it an essential asset to any company.

Unfortunately, knowledge often becomes lost. This phenomenon, where knowledge becomes trapped or irretrievable, is something we call the Bermuda Triangle of Knowledge Drain. It refers to knowledge that gets swallowed into a vortex, swirling away into oblivion. When knowledge is left stuck in the minds of domain experts (who are unable to pass on their expertise due to a leave or departure from the company), when it’s confined to physical documents, slideshows and emails, or becomes isolated within siloed systems and software, it creates the perfect storm for a knowledge drain to occur.

The solution is a knowledge graph. Knowledge graphs can bring together both private data and public research knowledge while addressing the challenges found within these two domains. It seamlessly connects to external datasets, utilizes existing metadata and ontologies, and imbues internal data with contextual meaning. The semantic layer within the knowledge graph allows you to connect to public data sources and transforms data into consumable, shareable and actionable knowledge while adhering to FAIR data practices, ensuring reusability and interoperability of data. As mentioned above, it also adds a trust and explainability foundation for AI applications, ensuring accuracy and eliminating potential hallucinations. This added layer of trust helps companies maximize existing AI investments and generate trustworthy and explainable AI solutions such as AI-driven drug discovery.

To conclude, the urgency and significance of addressing this fragmentation are unmistakable. Despite the existing challenges, the integration of public and private data presents substantial benefits for companies in the pharma and life sciences industries, which can be achieved through the implementation of a knowledge graph. A knowledge graph, such as the soon-to-be-launched Dimensions Knowledge Graph, can aid in streamlining the trial and manufacturing process, fast-tracking drug discovery, speeding up drug safety review processes and ensuring reusability of knowledge.

Stay tuned to learn about the Dimensions Knowledge Graph, a ready-made knowledge graph providing access to one of the world’s largest interconnected sets of semantically annotated knowledge databases while powering smart and reliable AI solutions across the pharma value chain.

For more information about knowledge graphs, check out this blog post by metaphacts.

The post Fragmented knowledge in pharma: Bridging the divide between private and public data appeared first on Digital Science.

London, UK – Tuesday 3 October 2023

According to new research conducted by Digital Science there is a worrying divide between Global North and Global South countries on both the funding and collaboration in pharmaceutical research. However, the most recent data shows that this divide may be closing, benefiting Global South countries hit hardest by lack of access to essential medicines and vaccines.

The report – detailed in a new blog launching the ‘Fragmentation: A Divided Research World campaign as part of Digital Science’s TL;DR initiative – is an evaluation of scientific publications including contributions from the top ten pharmaceutical companies in the Global North and Global South, which have been indexed in Digital Science’s Dimensions database in the past five years (2018 to 2022). The study maps aspects of the landscape in this area exploring differences in: 

• pharmaceutical research practices from different perspectives including funding and collaboration
• pharmaceutical research and its association with the Sustainable Development Goals (SDGs)
• the impact of the cost of medicines developed by pharma 
• their accessibility in distinct geographic regions. 

The results show significant gaps between the two global areas, but also some ways where these gaps are now closing.

North and South

The background to the research is the stark truth that an estimated two billion people worldwide still lack access to essential medicines and vaccines. Enabling access, especially for those in most need in the Global South, is a key component in the UN’s SDGs Agenda.

Dr Briony Fane, Director of Research Analytics at Digital Science and lead author of the study, says: “While countries in the Global South have obtained benefits from pharmaceuticals originally developed for high income country markets, little research has been conducted on diseases that primarily affect these countries, such as malaria or tuberculosis.

She adds: “And it is not just in healthcare but also in education, government, not for profits, etc that science is funded for the development of new pharmaceutical products aimed at transforming lives.

But there is good news as the data and recent initiatives show the gap between North and South is closing: “The geography of the pharmaceutical industry’s participation in this area of research indicates perhaps the start of a growing commitment to its involvement in addressing the access to medicine in all areas in the world and evidence of collaboration across the Global North and Global South, however small, shows a level of responsibility being taken by the industry,” says Dr Fane.

Progress for all

In 2023, Digital Science is looking to extend its mission to support better, open, collaborative and inclusive research through a number of different initiatives. The #Fragmentation campaign will ask: is research fragmented, and if so, how? Fragmentation in research can undermine societal progress – progress which Digital Science is committed to driving forward in its support of the research ecosystem. 

The exploration of fragmented research begins with the focus on global divides such as the ‘tale of two pharmas’ detailed above, then moving onto the domain of ‘siloed knowledge’, where we will concentrate on areas of research where a lack of integration can result in research findings remaining isolated, limiting their broader applicability across the research ecosystem. Following these two themes will be investigations on policy-making, research funding and complexity. 

Dr Juergen Wastl, Director of Academic Relations & Consultancy and Digital Science, says: “Our campaign aims to highlight the structural features of fragmentation, by consolidating concepts and by demonstrating a number of analytical approaches through the use of Digital Science tools such as Dimensions.”

In June 2023 Digital Science launched its #MindtheTrustGap campaign which highlighted the different ways trust in research was being threatened and how this erosion could be mitigated across issues such as a lack of competencies in generative AI tools, the impact of AI on predatory publishing problem and the importance of data availability statements. The campaign has helped increase the profiles of research and publication integrity, aided by new product releases in this area such as Dimensions Research Integrity. 

Find out more about the Digital Science Fragmentation campaign

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 Twitter/X or on LinkedIn.

Media contact

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

The post New research shows global divide in pharmaceutical research is significant – but closing appeared first on Digital Science.

Wednesday 7 June 2023 – London, UK and Halle, Germany

Digital Science is positioning itself to play an even greater role in the pharmaceutical industry’s all-important drug discovery, by helping industry sift through a sea of information and focus on the research that matters.

To achieve this, Digital Science – a technology company serving stakeholders right across the research ecosystem – has fully acquired OntoChem GmbH, a company highly specialized in AI-based solutions for finding and extracting key information from internal and external data and text, especially published research.

OntoChem has become the newest member of the Digital Science family following an almost two-year partnership between the two companies. OntoChem will continue to work as part of Digital Science’s portfolio product Dimensions – the world’s largest linked research database and data infrastructure provider – which will bring strategic value to the pharmaceutical industry as well as to the wider health and medical sector.

Based in Halle, Germany, OntoChem has 18 years’ experience creating innovative technologies for industry. OntoChem’s text mining, natural language processing and semantic data extraction technologies enable companies to obtain information from both unstructured and structured data, turning it into new knowledge for research and discovery, as well as for strategic decision-making. These tools are utilized by pharmaceutical, material science and technology-driven businesses.

OntoChem currently indexes more than 600 million public documents with 30 million semantic ontology concepts and 150 million synonyms, in fields such as compounds, proteins, diseases, drugs, materials, methods, devices, species, and many more. In compounds alone, OntoChem accesses data from research into 2 billion compounds, which are critical to drug discovery.

Lutz Weber, CEO OntoChem, said: “More and more, pharmaceutical companies are rapidly advancing their research with the use of AI, machine learning, and other technologies, to accelerate their discoveries, and to translate those discoveries into real outcomes.

“One of the biggest issues for pharmaceutical companies is the ‘data dilemma’ – there is so much information to sift through that it can be hard to know where to look or how to focus. Even in one field, such as cancer or diabetes, there is a sea of new knowledge being generated each day in very specific areas of research. This is where our work can help to provide that focus, assisting companies with their discovery and decision-making.

“At OntoChem, we are delighted to become part of Digital Science – this will greatly support our long-term vision to extract the world-wide and comprehensive semantic knowledge from any scientific and related documents or databases, both from enterprise as well as public sources.”

Christian Herzog, Chief Product Officer for Digital Science and co-Founder of Dimensions, said: “OntoChem is a strong strategic fit for Digital Science in combination with our flagship product, Dimensions. The pharmaceutical industry will directly benefit from this acquisition, and through their discoveries our work will benefit the health of individuals and communities right around the world.

“OntoChem’s highly specialized search and extraction tools combined with Dimensions’ access to data from hundreds of millions of research papers, linked grants, patents and clinical trials, and access to data via Google BigQuery, creates a powerful analytical environment. We are already working with life sciences companies, including those in drug research and development, and we expect this to continue and grow in the future.

“In addition to industry, research institutions, governments and funding bodies will greatly benefit from these unique insights suited to discovery and decision-making.”

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 Altmetric, Dimensions, Figshare, ReadCube, Symplectic, IFI CLAIMS Patent Services, Overleaf, Writefull, OntoChem, Scismic and metaphacts. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter or on LinkedIn.

About Dimensions

Part of Digital Science, Dimensions is the largest linked research database and data infrastructure provider, re-imagining research discovery with access to grants, publications, clinical trials, patents and policy documents all in one place. www.dimensions.ai

About OntoChem GmbH

OntoChem offers high-performance and highly customizable text analysis and data mining products that can be tailored to meet the specific needs of every client. OntoChem’s main products are: SciWalker – a semantic search and analysis solution to support life and material science communities; OntoChem Processor – a semantic processor tool where clients can enter text from any source; OntoChem Ontologies – a range of ontologies for specific medical and biochemical applications.

OntoChem’s information discovery tools are used by small and large life and material science companies to find information by automatically indexing and analyzing internal as well as external data collections. This data provides the raw material for AI and machine learning methods for predictive drug discovery or materials design. OntoChem is proud to work with some of the most influential companies in the fields of pharma, chemistry, specialty chemistry, material science, publishing and IT. Visit www.ontochem.com.

Media contact

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

The post Digital Science boosts pharma industry support following OntoChem acquisition appeared first on Digital Science.

Tuesday 16 May 2023

Altmetric is teaming up with ScienceOpen to provide rich alternative metrics for REPO4EU’s Open Science publishing portal, Drug Repurposing Central.

Altmetric Badges on all preprints and articles will track and aggregate online attention, complementing traditional indicators of research quality and impact, in line with the EU Open Science Policy.

REPO4EU stands for Precision drug REPurpOsing For EUrope and the world. The aim of the project is to ultimately host and grow an EU industry-level online platform for drug repurposing with a global reach to move the industry from imprecise drug therapy to precision medicine. The platform will provide expertise throughout the whole value chain in drug repurposing: from freedom-to-operate analysis to intellectual property protection and business development, health technology assessment, ethics, and data governance considerations.

As a member of the REPO4EU consortium, ScienceOpen will provide the technology and run the Drug Repurposing Central publishing portal.

Stephanie Dawson, ScienceOpen CEO, says: “Open research communication must be fast and agile. Citations are one powerful metric for assessing research impact but there are many more channels and social media networks available for communication that can play an important role. We believe strongly that a platform must provide a rich context for each publication including alternative metrics.”

Altmetric, part of Digital Science, is the industry leader in tracking, measuring, and visualizing online attention around research publication. Featuring a variety of colors representing the different sources tracked, Altmetric Badges provide a unique and instantly recognizable at-a- glance summary of the attention for an individual publication. Every Altmetric Badge includes an Altmetric Attention Score, a weighted count designed to help demonstrate the level of influence of a published work, and links through to an Altmetric details page, which provides a collated record of all the original mentions of the publication.

Catherine Williams, Managing Director, Data & Analytics at Digital Science, says: “We’re excited to partner with ScienceOpen and REPO4EU on this worthy initiative, which supports open science and the translation of that science to public benefit through healthcare. Altmetric is pleased to be playing its role by demonstrating the reach, visibility, and wider influence of research to the world.”

This new implementation will help advance Open Science and promote a more comprehensive understanding of research quality and impact in drug repurposing scholarly networks, and beyond. By supporting and promoting new metrics, REPO4EU, ScienceOpen and Altmetric together play a crucial role in advancing the European Union’s Open Science Policy goals and showcasing best-practices to respond to rapidly changing demands of the digital publishing environment.

About ScienceOpen

ScienceOpen is a free professional networking platform specializing in research discovery and impact. From promotional collections to full publishing capabilities, ScienceOpen provides a wide range of services to academic publishers, researchers, and users in an interactive discovery platform. ScienceOpen was founded in 2013 to propel academic communication towards the open access model.

About Altmetric

Altmetric is a leading provider of research metrics, helping everyone involved in research gauge the impact of their work. We serve diverse markets including universities, institutions, government, publishers, corporations, and those who fund research. Our powerful technology searches thousands of online sources, revealing where research is being shared and discussed. Teams can use customizable interactive dashboards to interrogate the data themselves, or get expert insights from Altmetric’s consultants. Altmetric is part of the Digital Science group, dedicated to making the research experience simpler and more productive by applying pioneering technology solutions. Find out more at altmetric.com and follow @altmetric on Twitter.

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 Altmetric, Dimensions, Figshare, ReadCube, Symplectic, IFI CLAIMS Patent Services, Overleaf, Writefull, and metaphacts. We believe that together, we can help researchers make a difference. Visit digital-science.com and follow @digitalsci on Twitter or on LinkedIn.

About REPO4EU

REPO4EU is building a unique platform for drug repurposing, pooling stakeholders and expertise globally to create a fully-fledged, made in Europe infrastructure for drug repurposing. This ambitious initiative brings together 28 partners from 10 countries worldwide.

Media contacts

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

Stephanie Dawson, CEO, ScienceOpen: Stephanie.Dawson@ScienceOpen.com

REPO4EU: info@repo4.eu

The post Altmetric and ScienceOpen cooperate on REPO4EU Open Science publishing portal appeared first on Digital Science.

Dimensions L&C analyzes more than 120 million scientific publications, millions of patents, grants and clinical trial documents. It is both larger than other databases, and unlike traditional manually curated tools, applies up-to-the-minute semantic text analysis tools and ontologies, providing powerful up-to-date discovery functionality previously unavailable at such scale. 

In the past, literature search tools required the user to know exactly what they wanted to find, narrowing down the relevant results and confirming or invalidating pre-defined hypotheses. Thanks to insights systematically captured in ontologies and computational power, researchers can get answers to complex and diverse queries directly from the source content. 

Users can search for small molecules, chemical reactions and gene sequences, validate biomarkers, understand disease mechanisms and identify drug targets. They can also quickly discover relevant chemical information in broader life sciences and chemistry research areas working with a chemistry structure editor and a biosequence search for nucleotides and proteins.

Digital Science recently announced a collaboration with OntoChem (www.ontochem.com). Dimensions L&C uses the ontologies from OntoChem, which include around 40 million concepts and 100 million synonyms from more than 35 knowledge domains such as compounds, proteins, diseases, drugs, materials, methods, devices or species, enabling a high quality, context sensitive knowledge discovery tool.

Christian Herzog, CEO Dimensions, said: “There is an ever growing publication haystack and researchers need to find the needles of information quickly and efficiently. In the past you needed to know what to ask the search engine in order to find it, but with Dimensions L&C, we are now providing next generation discoverability for life sciences and chemistry researchers: an ontology driven retrieval engine which identifies relationships and links in more than 120m publications – allowing the researcher to move on from search to AI-supported discovery.”

Dimensions is used by scientists, academic institutions, funders and industry. Its database offers the most comprehensive collection of linked data in a single platform; from grants, publications, datasets and clinical trials to patents and policy documents. Because Dimensions maps the entire research lifecycle, research can be followed from funding through output to impact. It has transformed the way research is discovered, accessed and evaluated. 

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 such as Altmetric, Dimensions, Figshare, ReadCube Papers, Symplectic, IFI CLAIMS, Overleaf, Ripeta, Scismic and Writefull. Digital Science’s Consultancy group works with organisations around the world to create new insights based on data to support decision makers. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.

Dimensions is a modern, innovative, linked-research-knowledge system that re-imagines discovery and access to research. Developed by Digital Science in collaboration with over 100 research organizations around the world, Dimensions brings together grants, publications, citations, alternative metrics, clinical trials, patents and datasets to deliver a platform that enables users to find and access the most relevant information faster, analyze the academic and broader outcomes of research, and gather insights to inform future strategy. Visit Dimensions’ website at https://dimensions.ai and find us on Twitter @DSDimensions.

Media contact

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

The post Digital Science launches Dimensions Life Sciences & Chemistry appeared first on Digital Science.

London, 15th July 2021 – Digital Science, a technology company serving stakeholders across the research ecosystem, is pleased to announce a new partnership with OntoChem GmbH. The partnership allows OntoChem and Digital Science to join forces for mutual clients, particularly in the Life Sciences industry, through OntoChem’s powerful semantic indexing capabilities.

OntoChem has more than 14 years experience creating innovative technologies. The German-based life sciences company develops cognitive computing solutions, indexing intranet and internet data and applying semantic search solutions for pharmaceutical, material science and technology-driven businesses.

Lutz Weber, CEO OntoChem, said: “At OntoChem, our mission is to provide added value to our customers by helping them to navigate today’s complex information world. With OntoChem now being part of the Digital Science family, we are excited to see the prospect of our advanced semantic search functionality being used more widely to help clients better discover relevant documents and gain deeper insights that were not possible to imagine before.”

Christian Herzog, Chief Portfolio Officer for Digital Science, said: “All Digital Science products aim to support researchers at universities or in the private sector to accomplish the results they have set out to achieve faster and with better results. With OntoChem joining Digital Science, we are now able to provide our shared clients, especially in the Life Sciences domain, with a more powerful analytical capability – based on the semantic indexing functionalities and extensive ontologies which OntoChem brings to the Digital Science portfolio. We are very excited to work with Lutz Weber and his outstanding team and welcome them to the DS family.”

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 such as Altmetric, Dimensions, Figshare, ReadCube Papers, Symplectic, IFI CLAIMS, Overleaf, Ripeta, Scismic and Writefull. Digital Science’s Consultancy group works with organisations around the world to create new insights based on data to support decision makers. We believe that together, we can help researchers make a difference. Visit www.digital-science.com and follow @digitalsci on Twitter.

About OntoChem GmbH:

OntoChem offers high performance and highly customizable text analysis and data mining products that can be tailored to meet the specific needs of every client. OntoChem’s information discovery tools are used by small and large life and material science companies to find information by automatically indexing and analyzing internal as well as external data collections. This data provides the raw material for AI and machine learning methods for predictive drug discovery or materials design. We are very proud to work with some of the most influential companies in the fields of pharma, chemistry, speciality chemistry, material science, publishing and IT. Additional information is available at www.ontochem.com.

Media contact

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

The post Digital Science partners with OntoChem appeared first on Digital Science.

This novel approach could help identify biomarkers and biomarker panels, which may be otherwise missed through traditional search methods, for expert review and exploration in a clinical setting.

The post Navigating networks of oncology biomarkers mined from the scientific literature appeared first on Digital Science.

IDENTIFYING GLOBAL EXPERTISE IN CAR-T

In this snapshot you will find global trends in Car-T research investment, research and innovation across a 10-year time span. We also use our NLP search technology to surface experts at the topic intersection of Car-T and solid tumor research using our unique database of awarded grants some running up to 2023 for our top expert Carl June – so what is funded now could indicate what becomes effective research in the near future.

Download now

The post Identifying global expertise in CAR-T appeared first on Digital Science.

Featured Resource: New agile methods to extract deeper knowledge

Find out how we provide an open and iterative approach by connecting our tools with those of the research community. We develop special functions within a matter of days allowing you to implement a search strategy, connect with a diverse range of data sources and so much more.

Watch the recording

Life Science Vendor Resources

Providing valuable data on funding flows for market model development

Find out how Illumina uses Dimensions to understand trends in academic funding, KOL identification and to find leads for sales.

Download the case study

“It is really beneficial to be able to use the same keywords across the 3 different content types – grants, pubs and clinical trials – in one platform.”

Aruna Rajan

Staff Market Research Analyst, Illumina

Life Science Vendor Resources

Getting started with Dimensions L&C

In this video learn how to run semantic search in Dimensions L&C to get a list of precise and comprehensive results. We will also explore the ontological tree within Dimensions L&C, which contains over 40 million concepts related to life sciences and chemistry, and finally we will see a quick overview of the analytical and search tools Dimensions L&C has to offer.

Watch the recording

Generate insights quicker using Co-occurrence Analysis

In this video learn how to run a Co-occurrence Analysis in Dimensions L&C, a powerful tool which enables you to generate insights from millions of scientific documents within seconds. We will explore general and specific use cases for this function to enable you to get the most out of Dimensions L&C.

Watch the recording

Request a demo

Understand disease mechanisms

In this video learn how to use “co-occurrence analysis” in Dimensions L&C to get a better understanding of disease mechanisms. We will generate insights from hundreds of millions of scientific documents by correlating a disease with semantic concepts – such as signalling pathways, genes & proteins and pathophysiological processes – to quickly generate hypotheses on disease mechanisms, which is an important first step in drug target identification and the drug development process in general.

Watch the recording

Request a demo

Understand drug candidate mechanisms of action

In this video learn how to use “chemistry search” to discover chemical compounds similar to your drug candidate or containing a specific chemical substructure of your interest. We will run a comprehensive search to identify documents, such as patents, publications, grants and clinical trials, that mention relevant compounds and apply a powerful semantic concepts extraction analysis to generate insights on the compounds’ mechanisms of action and therapeutic applications.

Watch the recording

Request a demo

Clarify the mechanism of action

To select the best therapeutic application for repositioning a known drug or avoid undesirable adverse events for novel compounds, it is critical to have a clear understanding how the drug or compound works.  

To uncover these insights, you need to find and analyze all information available on a drug/compound or those similar to it. The powerful semantic search of Dimensions L&C allows you to search through 40 million concepts, including new compounds, approved drugs, genes, and proteins alongside chemistry search and co-occurrence analysis.  This enables you to identify more relevant documents and gain deeper insights about drug and compound connections to proteins, disease, signalling pathways, pathophysiology, and toxicities.

Request a demo

Analyze disease mechanisms

Understanding target and disease biology is crucial for success when bringing a new drug to market. However, the data supporting this understanding is spread across hundreds of thousands and millions of scientific documents, including publications, patents, grants, and clinical trials.  Dimensions L&C provides a powerful semantic search with up to 40 million concepts that incorporate genes,  proteins and diseases, derived from hundreds of millions of scientific documents, together with co-occurrence analysis.

This enables you to quickly discover more relevant documents and gain deeper insights regarding disease mechanisms and the connections between diseases, proteins, signalling pathways, and biological processes in a real-time.

Request a demo

Identify drug targets

Identification of a “right” drug target is a critical step in drug development that helps to avoid later stage failures of drug candidates in clinical trials. To make an evidence-based decision on drug targets, a compilation and analysis of multiple scientific documents are required.

Dimensions L&C provides a powerful semantic search of up to 40 million concepts, including genes & proteins, diseases and corporate information, together with co-occurrence analysis. This enables you to rapidly discover more relevant documents and gain deeper insights as to potential drug targets and their connections to diseases, drugs, biological and pathological processes, and commercial potential in a real time.

Request a demo

Driving Innovation & Collaboration Across Boehringer Ingelheim

In this case study, Dr. Karlheinz Spenny and his team at the SIC share what triggered their internal innovation campaign to move from their legacy literature management solution to the Enterprise edition of ReadCube and their experiences along the way.

What’s inside:

• Factors driving the switch from BI’s legacy literature management/citation tool
• Why BI chose Papers Enterprise
• Preparing for & managing change through transition
• The rollout & reactions
• Standout features
• Reflections post-deployment

Download the case study

“What we have achieved within just 7 months after the roll-out of ReadCube can only be considered a jump to the next level of literature management for our research scientists and has positioned BI as front-runners within our industry.”

Dr. Karlheinz Spenny

Boehringer Ingelheim

Navigating Networks Of Oncology Biomarkers

Using large-scale analytics of published literature, biomarkers across six cancer types were successfully characterized in terms of their emergence in the published literature and the context in which they are described.

This novel approach could help identify biomarkers and biomarker panels, which may be otherwise missed through traditional search methods, for expert review and exploration in a clinical setting.

Download the poster

Access our interactive tool

Navigating Networks Of Oncology Biomarkers

Research is continually evolving and dynamics within and between fields change constantly. Landscape assessments ensure you understand the lay of the land and are prepared to adapt to change. Our analysts use Dimensions to help you:

• Identify major global research areas.
• Understand how scientific fields are changing. 
• Assess emerging trends within a specific field of study. 
• Recognize the experts in these fields.

Request a demo

Enhancing your Gap Analysis with Machine Enhanced Literature Retrieval

Dimensions data can be used to enhance your literature gap analysis. Dimensions is particularly suitable for performing literature gap analyses on a large scale, combining standard abstract searches with high precision full-text queries and subsequent aggregation. This allows creating heat maps on numbers of publications for combinations of terms, e.g., drugs versus adverse events, with the option to directly view the set of publications for a particular combination. Such an approach takes minutes to run and can be repeated to track changes over time.

Watch the recording

Real word databases identification 

While real-world databases are of increasing interest to researchers, there is no comprehensive dataset listing all real-world databases. We have created an algorithm which finds publications presenting real-world databases:

• Several search methods were applied to select articles which present research using real world databases
• Publications were identified which were frequently cited by the above articles, to arrive at the initial publications announcing real world databases
• With this approach the number of known real world databases could be doubled

Request a demo

How to Run Your Biotech R&D Virtually

Hire more people, take your groundbreaking data and hit milestones in just 3 steps! We provide a suite of research solutions to hire, store and share data across your companies to make your research efficient and cost-effective. Anyone working in a lab will benefit from this session.

Watch the recording

Pinpointing Global Expertise In Non-Alcoholic Fatty Liver Disease

Disease types associated with NAFLD present a tremendous research and development challenge for life sciences organizations and also a great opportunity for them to create value and innovation. Find out how Digital Science offers the data capabilities to help you uncover insights and expertise in disease-related topics and key indication areas.

Download now

Identifying Global Expertise in CAR-T-time data

Digital Science offers data capabilities and the connected data to help life science organizations understand the Car-T research and development landscape. Find out how Digital Science offers the data capabilities to help you uncover insights and expertise in disease-related topics and key indication areas.

Download now

Medical Affairs Resources

Measuring the value of medical affairs

As a medical affairs professional or publications planner in the pharmaceutical industry, we understand that you need to demonstrate the impact of the work you do. Download our whitepaper today to find out how altmetrics can help you.

Download now

Get better insights for medical affairs

Spend less time searching for the right information and more time acting on it. Dimensions is a comprehensive discovery and analytics platform with millions of data points, and an ideal information source for medical affairs professionals. 

Read more

Finding the right experts just got easier

Since finding KOLs is a frequently performed and important activity, the Dimensions team created a new tool that makes it even easier. It features a simple and intuitive user interface that accesses all of the data Dimensions has to offer.

Read more

Request sample report

Resources for Industry

Featured Resource: New agile methods to extract deeper knowledge

Find out how we provide an open and iterative approach by connecting our tools with those of the research community. We develop special functions within a matter of days allowing you to implement a search strategy, connect with a diverse range of data sources and so much more.

Watch the recording

Life Science Vendor Resources

Providing valuable data on funding flows for market model development

Find out how Illumina uses Dimensions to understand trends in academic funding, KOL identification and to find leads for sales.

Download the case study

“ It is really beneficial to be able to use the same keywords across the 3 different content types – grants, pubs and clinical trials – in one platform.”

Aruna Rajan, Staff Market Research Analyst, Illumina

Pharma and Biotech Resources

Getting started with Dimensions L&C

In this video learn how to run semantic search in Dimensions L&C to get a list of precise and comprehensive results. We will also explore the ontological tree within Dimensions L&C, which contains over 40 million concepts related to life sciences and chemistry, and finally we will see a quick overview of the analytical and search tools Dimensions L&C has to offer.

Watch the recording

Generate insights quicker using Co-occurrence Analysis

In this video learn how to run a Co-occurrence Analysis in Dimensions L&C, a powerful tool which enables you to generate insights from millions of scientific documents within seconds. We will explore general and specific use cases for this function to enable you to get the most out of Dimensions L&C.

Watch the recording

Request a demo

Understand disease mechanisms

In this video learn how to use “co-occurrence analysis” in Dimensions L&C to get a better understanding of disease mechanisms. We will generate insights from hundreds of millions of scientific documents by correlating a disease with semantic concepts – such as signalling pathways, genes & proteins and pathophysiological processes – to quickly generate hypotheses on disease mechanisms, which is an important first step in drug target identification and the drug development process in general.

Watch the recording

Request a demo

Understand drug candidate mechanisms of action

In this video learn how to use “chemistry search” to discover chemical compounds similar to your drug candidate or containing a specific chemical substructure of your interest. We will run a comprehensive search to identify documents, such as patents, publications, grants and clinical trials, that mention relevant compounds and apply a powerful semantic concepts extraction analysis to generate insights on the compounds’ mechanisms of action and therapeutic applications.

Watch the recording

Request a demo

Clarify the mechanism of action

To select the best therapeutic application for repositioning a known drug or avoid undesirable adverse events for novel compounds, it is critical to have a clear understanding how the drug or compound works.  

To uncover these insights, you need to find and analyze all information available on a drug/compound or those similar to it. The powerful semantic search of Dimensions L&C allows you to search through 40 million concepts, including new compounds, approved drugs, genes, and proteins alongside chemistry search and co-occurrence analysis.  This enables you to identify more relevant documents and gain deeper insights about drug and compound connections to proteins, disease, signalling pathways, pathophysiology, and toxicities.

Request a demo

Analyze disease mechanisms

Understanding target and disease biology is crucial for success when bringing a new drug to market. However, the data supporting this understanding is spread across hundreds of thousands and millions of scientific documents, including publications, patents, grants, and clinical trials.  Dimensions L&C provides a powerful semantic search with up to 40 million concepts that incorporate genes,  proteins and diseases, derived from hundreds of millions of scientific documents, together with co-occurrence analysis.

This enables you to quickly discover more relevant documents and gain deeper insights regarding disease mechanisms and the connections between diseases, proteins, signalling pathways, and biological processes in a real-time.

Request a demo

Identify drug targets

Identification of a “right” drug target is a critical step in drug development that helps to avoid later stage failures of drug candidates in clinical trials. To make an evidence-based decision on drug targets, a compilation and analysis of multiple scientific documents are required.

Dimensions L&C provides a powerful semantic search of up to 40 million concepts, including genes & proteins, diseases and corporate information, together with co-occurrence analysis. This enables you to rapidly discover more relevant documents and gain deeper insights as to potential drug targets and their connections to diseases, drugs, biological and pathological processes, and commercial potential in a real time.

Request a demo

Driving Innovation & Collaboration Across Boehringer Ingelheim

In this case study, Dr. Karlheinz Spenny and his team at the SIC share what triggered their internal innovation campaign to move from their legacy literature management solution to the Enterprise edition of ReadCube and their experiences along the way.

What’s inside:

• Factors driving the switch from BI’s legacy literature management/citation tool
• Why BI chose Papers Enterprise
• Preparing for & managing change through transition
• The rollout & reactions
• Standout features
• Reflections post-deployment

Download the case study

“What we have achieved within just 7 months after the roll-out of ReadCube can only be considered a jump to the next level of literature management for our research scientists and has positioned BI as front-runners within our industry.”

Dr. Karlheinz Spenny, Boehringer Ingelheim

Navigating Networks Of Oncology Biomarkers 

Using large-scale analytics of published literature, biomarkers across six cancer types were successfully characterized in terms of their emergence in the published literature and the context in which they are described.

This novel approach could help identify biomarkers and biomarker panels, which may be otherwise missed through traditional search methods, for expert review and exploration in a clinical setting.

Download the poster

Access our interactive tool

Landscape analysis 

Research is continually evolving and dynamics within and between fields change constantly. Landscape assessments ensure you understand the lay of the land and are prepared to adapt to change. Our analysts use Dimensions to help you:

• Identify major global research areas.
• Understand how scientific fields are changing. 
• Assess emerging trends within a specific field of study. 
• Recognize the experts in these fields.

Request a demo

Enhancing your Gap Analysis with Machine Enhanced Literature Retrieval

Dimensions data can be used to enhance your literature gap analysis. Dimensions is particularly suitable for performing literature gap analyses on a large scale, combining standard abstract searches with high precision full-text queries and subsequent aggregation. This allows creating heat maps on numbers of publications for combinations of terms, e.g., drugs versus adverse events, with the option to directly view the set of publications for a particular combination. Such an approach takes minutes to run and can be repeated to track changes over time.

Watch the recording

Real word databases identification 

While real-world databases are of increasing interest to researchers, there is no comprehensive dataset listing all real-world databases. We have created an algorithm which finds publications presenting real-world databases:

• Several search methods were applied to select articles which present research using real world databases
• Publications were identified which were frequently cited by the above articles, to arrive at the initial publications announcing real world databases
• With this approach the number of known real world databases could be doubled

Request a demo

How to Run Your Biotech R&D Virtually

Hire more people, take your groundbreaking data and hit milestones in just 3 steps! We provide a suite of research solutions to hire, store and share data across your companies to make your research efficient and cost-effective. Anyone working in a lab will benefit from this session.

Watch the recording

Pinpointing Global Expertise In Non-Alcoholic Fatty Liver Disease

Disease types associated with NAFLD present a tremendous research and development challenge for life sciences organizations and also a great opportunity for them to create value and innovation. Find out how Digital Science offers the data capabilities to help you uncover insights and expertise in disease-related topics and key indication areas.

Download now

Identifying Global Expertise in CAR-T-time data

Digital Science offers data capabilities and the connected data to help life science organizations understand the Car-T research and development landscape. Find out how Digital Science offers the data capabilities to help you uncover insights and expertise in disease-related topics and key indication areas.

Download now

Medical Affairs Resources

Measuring the value of medical affairs

As a medical affairs professional or publications planner in the pharmaceutical industry, we understand that you need to demonstrate the impact of the work you do. Download our whitepaper today to find out how altmetrics can help you.

Download now

Get better insights for medical affairs

Spend less time searching for the right information and more time acting on it. Dimensions is a comprehensive discovery and analytics platform with millions of data points, and an ideal information source for medical affairs professionals. 

Read more

Finding the right experts just got easier

Since finding KOLs is a frequently performed and important activity, the Dimensions team created a new tool that makes it even easier. It features a simple and intuitive user interface that accesses all of the data Dimensions has to offer. 

Read more

Request sample report

The post Resources for industry appeared first on Digital Science.