Global Scholar Awards

Enhancing Genetic Diversity Through Advanced Breeding Strategies

Animal genetics explores genetic variation, inheritance, and molecular mechanisms influencing animal health, productivity, and disease resistance. Advanced genomic technologies and selective breeding strategies support improved livestock performance, conservation, and sustainable animal production.

Global Scholar Awards 🌟 Visit Our Website 🌐: globalscholarawards.com Nominate Now👍: https://globalscholarawards.com/doctor-awards-nobel-prize-scientists-award-nomination/?ecategory=Awards&rcategory=Awardee Contact us ✉️: info@globalscholarawards.com Get Connected Here: ================= Twitter : x.com/ScienceInventi1 Youtube : youtube.com/@nesinconferenceandawards4869 Pinterest : in.pinterest.com/scienceinventions/ Instagram : instagram.com/global_scholar_123 Linkedin : linkedin.com/in/global-scholar-awards-09664427b Blog : newscienceinventions2020.blogspot.com @WorldResearchAwards @GlobalScholarAwards #worldresearchawards #researchawards #researchexcellence #globalrecognition #academicawards #globalresearchawards #shorts #researchers #labtechnicians #awards #professors #teachers #lecturers #animalgenetics

Dr. Jingsheng Tong | Engineering | Innovative Research Award

Dr. Jingsheng Tong, affiliated with China Municipal Engineering Northwest Design and Research Institute Co., Ltd., China, is recognized in engineering through the Innovative Research Award. His profile highlights scholarly publications, research impact, technical expertise, and contributions to engineering innovation. Global Scholar Awards 🌟 Visit Our Website 🌐: globalscholarawards.com Nominate Now👍: https://globalscholarawards.com/doctor-awards-nobel-prize-scientists-award-nomination/?ecategory=Awards&rcategory=Awardee Contact us ✉️: info@globalscholarawards.com Get Connected Here: ================= Twitter : x.com/ScienceInventi1 Youtube : youtube.com/@nesinconferenceandawards4869 Pinterest : in.pinterest.com/scienceinventions/ Instagram : instagram.com/global_scholar_123 Linkedin : linkedin.com/in/global-scholar-awards-09664427b Blog : newscienceinventions2020.blogspot.com @WorldResearchAwards @GlobalScholarAwards #worldresearchawards #researchawards #researchexcellence #globalrecognition #academicawards #globalresearchawards #shorts #researchers #labtechnicians #awards #professors #teachers #lecturers #engineering

Citations are only part of the picture. Life Sciences needs the whole story.

Citations are a lagging indicator, but conversations start on day 1. Only alternative metrics can tell Medical Affairs teams if research is landing in real-time.

In the age of AI, research intelligence has to begin with trusted data. This series, Inside Our Data, explores the foundational data infrastructure that makes trust possible.

Medical Affairs teams are increasingly under pressure to act on emerging evidence and consistently demonstrate value to stakeholders, but this is impossible with citations alone. Citations can take years to manifest and capture only one aspect of a publication’s impact: its resonance in the world of academia.

To capture a holistic view of a publication’s performance and find new opportunities in publication strategy, teams have to integrate alternative metrics, often called altmetrics, to understand where research is gaining influence today. Altmetrics encompasses broader, more horizontal signals that bubble up across analog and digital third spaces, such as policy documents, social media, broadcast media and other peer-to-peer communication forums. When it comes to capturing a robust picture of a publication’s influence in real time, there’s no substitute. Unlike citations, altmetrics can help teams find direction and set measurable goals from the beginning of the publication cycle.

That teams are expected to do a lot with a little isn’t new. What has changed in recent years is that budget holders increasingly understand citation-based metrics alone aren’t enough. Leadership teams have come to recognize that the conversation about research is now happening in more places, and on a faster timeline, and there’s no reason to think this trend will slow or reverse.

That means now is the time for teams to work out their altmetrics set-up so they’re best placed to capitalize on relevant community activity, maximize a publication’s impact, and demonstrate that value upwards.

Tracking real-world impact with altmetrics

In the last few decades, advances in technology have made it much easier to share, converse about, and learn from research emerging all over the world. As journals moved online, so too did Life Sciences communities; Key Opinion Leaders (KOLs) and Healthcare Practitioners (HCPs) alike found community and conversation that helped them learn about new research and potentially inform their therapeutic behavior.

At the same time, the definition of KOL got wider. Beyond the traditional clinician-researcher, a distinct category of communication specialists has developed in recent years. These might be clinicians who focus on translating and communicating research rather than producing it, for example. Patient advocates have taken on a similar role, particularly in oncology, where major congresses now build in dedicated spaces for patient participation. In rare disease areas, that role often falls to parents of patients, who can become de facto experts in fields where the total body of published research may only amount to a handful of papers a year.

Knowing who is saying what about research provides invaluable insights which are critical for maximizing both the academic and real-life impact of a publication, and citation data alone is incapable of capturing the volume or nature of that community and conversation. But identifying and tracking these different populations is an ongoing process, and it varies by therapeutic area—teams need to know both who they’re looking for and what they’re looking for. To date, this was a task too complex and time-consuming to perform reliably, and this meaningfully hindered how Medical Affairs teams were able to perform.

This is what led Digital Science to invest in Altmetric, a platform which uses academic, non-academic, governmental, and professional sources to build a more complete picture of research impact. Its technology tracks, collates, and disambiguates shares and mentions of articles, books, reports, grey literature, and more, making it possible to track specific KOLs and analyze sentiment within specific communities. Data on researchers—job function, professional expertise, social media activity, location, career history—is blended with social and attention data to build a fuller picture of a given KOL, including how and where they’re active outside formal publication. Altmetric continues to expand its scope, having recently added seminars and readership information to its datasets.

The result is a clearer picture of who is reading what research and what they think about it, so teams can tailor publication plans as new opportunities arise.

Alternate timelines: where citations and altmetrics diverge

We know that research can have an extremely long shelf life. In fact, the challenge faced by Medical Affairs teams over the last 20 years—tracking both citations and altmetrics—results at least in part because these two measurements work on vastly different timescales.

Citations can emerge years after publication, but they form a critical part of impact measurement and planning for later publications. Citations are the best way to demonstrate high-level academic impact and they grant legitimacy to the research and the teams who carry it out—whether they’re published a year on or more.

Contrast this with altmetrics, which can emerge from day 1 and can ebb and flow dramatically over the life of a publication—and where each such instance can provide teams the opportunity to capitalize if they can move fast enough. Social media conversation can begin on the day of publication; podcasts often emerge at least a year later, and policy and guidelines citations typically materialize two to three years after publication. Where conversation is ongoing, sentiment can shift through time, creating opportunities for teams to engage with communities and refine strategy. Reliably tracking these instances manually is essentially impossible, but this low-frequency activity is the best way to understand real world impact.

These two measurements capture both sides of the same coin, and teams need both to know exactly how research is performing. But checking for citations weekly might turn up no results for a year or longer. And if a team only checks altmetric monitoring once per week, or overlooks a rising KOL, they could miss a critical flash in the pan.

To solve this problem, Altmetric tracks thousands of online mentions plus clinical guidelines and policy citations across social media, mainstream media, policy, patents and more all in one platform, and Altmetric Explorer centralizes that data so teams can easily and consistently monitor both citation and altmetrics for signals that could help shape strategy. This way, teams can pivot as conversation naturally evolves through time—despite the different timelines such conversation can take to develop.

Measuring real-life impact and demonstrating value with Digital Science

Medical Affairs teams are under immense pressure to act quickly—and to be able to explain their decisions and learnings as they go. They need to understand the real-life impact research is having, and how it is best measured, to make informed decisions.

To make this possible, Altmetric provides:

280M+ Altmetric data points within the Dimensions ecosystem
real-time attention records for individual scholarly outputs
academic and mainstream source coverage
clear attention and engagement signals

Altmetric data is continuously refined and curated by Digital Science teams to ensure fit and relevance. Digital Science has existed for nearly 20 years, so its data aggregation and curation processes are time-tested, ensuring the Altmetric data teams have come to rely on is still market-leading in breadth and completeness.

This is how the Altmetric score has taken on industry-wide importance. This score has existed for over a decade, and the interoperability of the dataset has proven value across research areas, making the Altmetric score a reliable, trustworthy indicator of performance. This is useful not only for benchmarking across competitors or research areas, like teams can do with Digital Science’s new platform, Compass, but also to help with upward reporting.

By putting a number on research attention—a single, clear metric to track and compare—teams can easily demonstrate value to stakeholders. This is more specific than broad social listening—which is characterized by fuzz and non-specific noise—and instead captures real, relevant sentiment which ensures teams can actually compare apples to apples with clean, consistent data.

This is important because raw attention numbers can only go so far on their own: oncology and pandemic-era research naturally draw disproportionate attention compared with rare disease research, so that number alone doesn’t tell a team whether their result in another therapeutic area is actually good. Being able to compare a consistent measurement, the Altmetric score, against publications within the same therapeutic class is what makes the number meaningful.

In 2025, Digital Science built sentiment analysis into its ecosystem. Adding sentiment analysis on top provides an unprecedented qualitative layer to this measurement. Teams can measure real-world impact, and how it changes, through time, adjusting activities to better target certain KOLs or communities from day 1. This sentiment tracking is focused on scientific and clinical attitudes that form towards research, and is tied to the specific publication rather than overall brand or company sentiment. This distinguishes it from the broader social listening that pharma companies often run at the brand level.

See where research is gaining influence before citations catch up

Citations take years to materialize and only capture a slice of the impact research has. Stakeholders recognize there’s more to performance measurement than citations, but there hasn’t been a suitable proxy for a holistic view of impact until now.

Altmetrics is the other piece of the puzzle, reflecting the real-world, real-life impacts of a publication in the conversations of KOLs, HCPs and policymakers, and in the lives of patients. To maximize publication performance and demonstrate value to stakeholders, Medical Affairs teams need both.

This is what Digital Science’s data ecosystem is built for: connecting the academic record with real-world attention so that Medical Affairs teams get one continuous view of a publication’s impact. Altmetric captures this influence layer by tracking millions of data points to give teams a real-time view of the conversation surrounding given research, and it’s one part of a wider toolset that Digital Science provides to enrich the entire research lifecycle. This layer provides crucial context for the burgeoning use of agentic AI in Medical Affairs; it also allows teams to make informed use of emerging evidence and act quickly—all with the confidence they’ll be able to explain their decisions and prove their value upwards.

Digital Science provides one of the world’s broadest collections of connected research intelligence—combining Dimensions, Altmetric, and IFI CLAIMS to help enterprise organizations with strategic decision-making, innovation, and AI-enabled workflows with intelligence that can be explained, audited, and defended. Request a demo to learn more about how Digital Science could revolutionize your team’s publication planning.

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The post Citations are only part of the picture. Life Sciences needs the whole story. appeared first on Digital Science.

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Dr. Yuxiang Shang | Computer Science | Best Researcher Award

Dr. Yuxiang Shang’s research in Computer Science focuses on computational technologies, scholarly information systems, digital research infrastructure, and data-driven analysis. His academic profile reflects interests in emerging computing methodologies, research evaluation, bibliographic analysis, and technology-enabled approaches supporting knowledge discovery and innovation.

Mr. Erli Lin | Materials Science | Best Researcher Award

Mr. Erli Lin’s research in Materials Science focuses on surface engineering, electrochemical anodization, corrosion-resistant composite oxide coatings, magnesium alloys, and advanced energy-storage materials. His work includes protective coatings for magnesium alloys, nanostructured lithium-ion battery anodes, biodegradable implant materials, and multifunctional electrochemical architectures, reflecting interdisciplinary contributions to advanced materials development and sustainable energy technologies.

Global Scholar Awards – Celebrating Research Excellence

Global Scholar Awards celebrates outstanding researchers, innovators, scholars, and institutions worldwide, recognizing impactful contributions, groundbreaking discoveries, innovation, collaboration, and excellence across diverse scientific and academic disciplines.

Grounding AI Agents: literature vs. structured databases in the biopharma data stack

As biopharmaceutical R&D transitions toward autonomous AI agents, the industry is forced to re-examine the core substrate upon which these agents reason. A critical roundtable discussion hosted by Digital Science at the AI in Drug Discovery hubXchange 2026 addressed a foundational bottleneck: How do we balance unstructured literature with specialized structured databases to build an AI-ready data stack?

The consensus was clear: while LLMs excel at parsing the vast sea of academic literature, they frequently struggle with structured data, exposing a deep division between literature consumption and structured database integration. In this article, we recap key insights from the roundtable discussion, and propose what this means for biopharma companies moving forward.

Unstructured literature: the promise and pitfalls of unstructured text

For many drug discovery teams, the primary value of Large Language Models (LLMs) today lies in their ability to conquer the sheer volume of academic literature. LLMs have dramatically optimized literature reviews by embedding text and measuring similar prompts across databases, removing the manual burden of reading endless papers.

However, relying strictly on LLMs introduces several acute pain points:

The ingestion bottleneck: While LLMs are excellent at reading text, they struggle to extract data from tables, images, and graphs. Roundtable participants highlighted that automated, validated methods to draw conclusions from tables—for example, answering, “Here are the top things you need to know in this table”—remain a critical visual gap.
The challenge of novelty: Experiments like Datasetpapers.com demonstrate that LLMs struggle to discover ‘unknown unknowns.’ True discovery requires explaining why a paper is groundbreaking or identifying novelty—a cognitive task where generative AI still struggles.
The content quality spectrum: Literature quality varies wildly. Curated papers from established publishers represent a far more reliable substrate than unvetted public repositories.
The missing negative results: A systemic bias exists where researchers are disincentivized from publishing negative results. In the AI age, dumping and organizing negative data must be simplified and incentivized (perhaps via shifts in H-index or S-index metrics) because machine learning models require negative data as much as positive data to build accurate classifiers.

‘Other’ databases: the cost and fragility of structured data

When moving beyond text consumption into computational biology and machine learning, biopharma relies on ‘other’ databases, such as structured sequence, mutation, chemical and clinical databases (e.g., UK Biobank, Gene PC, ADDI and PPMI for biomarker discovery validation).

These databases present a completely different set of challenges:

A scarcity of domain databases: There is a severe lack of structured databases in highly specific areas, such as amino acid mutations. Traditional structural models versus sequence-based models remain a continuous pain point.
The curation and funding crisis: Unlike the commercial publisher ecosystem, specialized public databases suffer from a chronic lack of ongoing funding. Representing this data, maintaining standards, and keeping it up to date is highly expensive. While large pharmaceutical corporations can absorb these costs, smaller biotech startups are effectively locked out.
The funding shift: Government and national lab funding is increasingly shifting away from pharmaceutical R&D toward materials and closed-loop discovery, leaving national standards (such as those from NIST) updated frequently but lacking deep biological curation resources.
Fouled public repositories: Without consistent curation and funding, public repositories easily become fouled with incorrect or mislabeled data. Because machine learning models are incredibly picky with data quality, many biopharma organizations are moving toward training models strictly with internal, highly-vetted data or avoiding public repositories altogether.

Harmonization: bridging the divide

The ultimate breakdown in the biopharma data stack occurs when attempting to harmonize unstructured literature findings with structured external databases and internal R&D data.

To make external data truly AI-ready, members of the roundtable discussed addressing several key operational requirements for their specific use cases and data needs:

Defining clean data extraction standards: Clean data must go deeper than prose. In assay standards, data in the ‘methods’ section must be pristine and explicitly represent both positive and negative results.
The metadata connection: Databases that rely on vague terms like ‘sample’ are highly problematic. Every raw read must be rigorously connected to downstream metadata, including omics data, patient profiles and study protocols.
The missing donor ledger: A glaring gap in the current data stack is the lack of an interconnected, harmonized database of human blood and tissue donor identifiers.
Re-identification risks: As AI agents cross-reference and harmonize independent datasets (e.g., matching blood donor IDs across omics databases), the risk of patient re-identification escalates, introducing complex legal and compliance hurdles.

Governance: build vs. buy and the role of knowledge graphs

Because external data sources are frequently unstructured, inconsistent, or lack verified provenance, biopharma organizations generally refuse to use external data for GxP-level decision-making and reporting. Instead, its use is confined to secondary exploratory reporting.

This reality forces organizations to ask: Is it worth purchasing external data and investing massive effort to prove its lineage, or is it more efficient to build it internally?

To navigate this landscape, organizations are leveraging two core architectural strategies:

Knowledge graphs: Tools like metaphactory, a Digital Science solution, are critical to building semantic ontologies, mapping disparate terminologies, and pointing autonomous AI agents directly to trusted data sources. They handle licensing, rights management, and legal boundaries.
Provenance machines: Tools like metaphactory’s metis act as black-box provenance machines, ensuring that every claim, entity, and relationship extracted by an agent is traceable back to its origin.

Systemic collaboration is needed

Ultimately, biopharma cannot rely on literature or databases in isolation. The future of AI-driven drug discovery depends on a composable substrate where unstructured literature discoveries are programmatically harmonized with highly structured external and internal databases. Building and maintaining this substrate is not a task for any single organization—it requires deep, systemic collaboration between biopharma, biotech, academic publishers, and AI tool developers to ensure the data powering tomorrow’s agents is reliable, traceable, and GxP-compliant.

Building an AI-ready data stack is a team effort. Talk to Digital Science about how metaphactory and metis can help.

Speak with an expert

The post Grounding AI Agents: literature vs. structured databases in the biopharma data stack appeared first on Digital Science.

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Global Scholar Awards

Enhancing Genetic Diversity Through Advanced Breeding Strategies

Dr. Jingsheng Tong | Engineering | Innovative Research Award

Citations are only part of the picture. Life Sciences needs the whole story.

Tracking real-world impact with altmetrics

Alternate timelines: where citations and altmetrics diverge

Measuring real-life impact and demonstrating value with Digital Science

See where research is gaining influence before citations catch up

Dr. Yuxiang Shang | Computer Science | Best Researcher Award

Mr. Erli Lin | Materials Science | Best Researcher Award

Global Scholar Awards – Celebrating Research Excellence

Grounding AI Agents: literature vs. structured databases in the biopharma data stack

Unstructured literature: the promise and pitfalls of unstructured text

‘Other’ databases: the cost and fragility of structured data

Harmonization: bridging the divide

Governance: build vs. buy and the role of knowledge graphs

Systemic collaboration is needed

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Enhancing Genetic Diversity Through Advanced Breeding Strategies

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Tracking real-world impact with altmetrics

Alternate timelines: where citations and altmetrics diverge

Measuring real-life impact and demonstrating value with Digital Science

See where research is gaining influence before citations catch up

Sharing perspectives from the hubXchange 2026 Roundtable: “Assembling the data stack: what pharma needs from external knowledge in the age of AI Agents”

Unstructured literature: the promise and pitfalls of unstructured text

‘Other’ databases: the cost and fragility of structured data

Harmonization: bridging the divide

Governance: build vs. buy and the role of knowledge graphs

Systemic collaboration is needed

Featured Post

Popular