Scite AI maintains a massive database of over 1.2 billion citations and 185 million full-text articles that helps researchers confirm their work. The Brooklyn-based startup receives funding from the National Science Foundation and National Institute on Drug Abuse. Their Smart Citations feature helps researchers find and understand academic papers better.

Smart Citations excel at providing context around citations that indicates whether referenced papers support or contradict research claims. Researchers can also get direct answers from research articles by asking questions in plain language. This feature makes the research process much simpler.

This piece shows how researchers can use Scite AI to create stronger research arguments, confirm citations, and make better decisions about their research materials.

Getting Started with Scite AI

Scite AI users can start by visiting their website to create an account with an email address. Google or ORCID accounts work too, offering continuous access. Premium features become available after users verify their institutional affiliation through their organisation’s licence.

Setting up your Scite AI account

Users can choose between individual and team plans. The individual plan costs GBP 9.53 monthly and gives unlimited access to core features. Students who recommend Scite to their institutions and reach out to the sales team can get special discounts.

Organisations need their administrators to fill out a registration form and meet with the Scite team. The authentication setup offers these options:

• IP/VPN/EZProxy access for smooth report viewing
• Email Authentication/SSO that unlocks all features

Understanding the dashboard interface

The dashboard works as your main hub to access Scite’s features. You can build your collections in four ways:

• Importing Zotero libraries
• Importing Mendeley libraries
• Manual DOI entry
• CSV file upload with DOI lists

Your dashboard shows these key metrics:

• Publication metadata and descriptions
• Total supporting, mentioning, and contrasting citations
• Editorial notice counts
• 2-year, 5-year, and all-time Scite Index measurements

Free plan users get one custom dashboard with a 100-DOI limit. Premium subscribers can create as many dashboards as they want, each holding up to 1,000 DOIs. A bell icon lets users set up alerts about new citations to papers they track.

Free trial vs premium features comparison

Scite’s 7-day free trial lets you try standard features. You’ll need to enter payment details when signing up, but you can cancel anytime during the trial.

Premium features that need login include:

• Visualisations and custom dashboards
• Reference checking capabilities
• Journal analytics
• Saved searches
• Notification systems
• Personalised feed

Features you can use without logging in:

• Report viewing
• Simple search functionality

Citation statement search stands out as an advanced tool that links content to context by showing where specific keywords are cited. The reference check feature looks at citations within articles and spots retractions while measuring citation metrics.

Browser extensions and integrations with tools like Zotero are a great way to get more from Scite. These tools help users check information online and explore supporting and contrasting citations while reading research papers.

Citation Statements are the foundations of the platform’s strength. This database contains snippets from full-text articles that show how references work in context. Scite makes use of information through advanced natural language processing and machine learning to analyse and index citations in each article. This helps researchers better evaluate research credibility.

Smart Citations: The Core Feature

Smart Citations mark a most important step forward in citation analysis. They classify research papers based on how later studies interpret and build upon their findings. Deep learning models help these citations extract and analyse contextual information from millions of scientific articles.

How Smart Citations work

The system processes citations through four main stages: access, detection, extraction, and internal tracking. Smart Citations go beyond simple sentiment analysis to understand why authors cite specific papers. The classification looks at the rhetorical function in scientific debate. This ensures that supporting or contrasting designations come from scientific arguments.

The classification process analyses citation statements that include the sentence with the in-text citation and its surrounding context. These statements, sometimes called citances, give an explanation of why authors choose specific citations.

Citation types follow a clear pattern, with approximately:

• 92.6% mentioning citations
• 6.5% supporting citations
• 0.8% contrasting citations

Citation statements’ length changes based on where they appear in papers. Introduction sections have longer citations with an average of 500 characters per statement. Method sections feature shorter ones averaging 420 characters. This difference shows how citations serve different purposes throughout academic papers.

Supporting vs contrasting citations

Supporting and contrasting citations play different roles in academic discourse. Supporting citations confirm previous research findings. Contrasting citations challenge or question earlier conclusions. Both types are the foundations of scientific rigour.

Citation pattern analysis shows clear differences in a variety of academic disciplines. Human-centred sciences, including:

• Medicine
• Psychology
• Economics
• Political science

show higher rates of contrasting citations compared to physical sciences like mathematics, physics, geology, and engineering.

Citation statements show subtle structural differences between supporting and contrasting types. Supporting citations average 176 characters at the first standard deviation. Contrasting citations average 168 characters. These small differences suggest both types follow similar writing patterns.

Smart Citations offer more contextual details than traditional citation indices, such as:

• Citation statement location within the citing article
• Editorial information from trusted sources
• Corrections and retractions
• Citation type indicators

Researchers can review citation quality through multiple metrics. Users sort results by involvement levels, editorial concerns, citation counts, and support or contrast ratios. This helps identify key arguments within specific research topics and assess papers’ methodological strengths.

Citation context analysis becomes especially valuable to review highly-cited works. Smart Citations help researchers understand if high citation counts come from widespread acceptance of ideas or from scholarly disagreement and critique.

The platform stays accurate through continuous testing of its classification system. The deep learning model undergoes regular testing against an immutable holdout set to minimise bias. This careful approach helps researchers build stronger arguments based on reliable citation analysis.

Building Research Arguments

Building stronger research arguments needs a systematic approach to citation analysis. Scite’s extensive database of citation statements helps researchers assess evidence and build well-supported arguments.

Finding supporting evidence

Researchers can explore over 900 million citation statements through Scite’s citation statement search to uncover evidence and expert opinions on specific topics. The platform confirms supporting citations with high precision, which ensures citations marked as supporting truly back up the referenced work.

Supporting citations usually contain longer, more detailed statements that average 176 characters at the first standard deviation. Authors often use these citations in introduction sections to establish background and context for their research arguments.

The platform lets users:

• Philtre results by supporting citations
• Search within citation statements for specific keywords
• Track emerging citations in trending research areas

Identifying contrasting views

Contrasting citations are vital in scientific discourse, especially in human-centred sciences like medicine, psychology, and economics. These citations make up 0.8% of total citations and serve as significant indicators of scholarly debate and methodological concerns.

Deep learning models analyse citation context to identify contrasting views. These citations follow a more formulaic structure and average 168 characters at the first standard deviation.

Citation context analysis

Citation context analysis goes beyond simple classification. Each citation statement has:

• The sentence containing the citation
• Surrounding contextual sentences
• Section location within the citing paper
• Classification confidence scores

Citation statements typically span 472 characters, which gives enough context to understand the citing author’s intent. Introduction sections have longer citations that average 500 characters, while method sections feature shorter ones averaging 420 characters.

Argument strength assessment

Researchers need to look at both citation quality and quantity to assess argument strength. Scite helps evaluate publications through multiple metrics:

The platform analyses citation patterns in different paper sections. Introduction sections often provide theoretical foundations, while methods sections focus on technical aspects.

Researchers can understand citation networks to find primary authors and see how specific arguments evolve in their field. This citation chaining technique helps establish research claims’ credibility through forward and backward analysis.

The platform’s dashboard lets users monitor citation changes over time to:

• Track new supporting or contrasting evidence
• Identify emerging debates in their field
• See the overall reception of research claims

These complete analysis tools help researchers build stronger arguments by showing how the scientific community receives their sources. Users can avoid uncritical citations and arrange their arguments with expert interpretations by seeing citation context.

Research Validation Workflow

Proving research references right has become a vital part of academic publishing. Scite AI’s Reference Check feature automatically assesses manuscript citations. This gives users a reliable way to check if their citations are accurate and dependable.

Reference checking process

The automated reference checking system works in several steps:

• Detection and extraction of references from uploaded manuscripts
• Cross-referencing with Smart Citations database
• Identification of retracted papers and editorial notices
• Analysis of citation context and usage patterns

The platform spots how authors use references by showing the text around them. Authors and reviewers can verify if citations back up their claims effectively. The system analyses over 16 million scientific articles. Its database now contains about 600 million classified citation statements.

Reference validation speeds up peer review by a lot. When authors submit their manuscript, editors and reviewers get instant access to:

• Citation reliability metrics
• Editorial notices and retractions
• Supporting and contrasting evidence patterns
• Contextual analysis of reference usage

Authors and reviewers no longer need to check references manually – a task that used to take lots of time. They can quickly check how credible their cited works are through a complete analysis of citation patterns and editorial notices.

Citation quality metrics

The platform uses advanced metrics that go beyond counting citations. These metrics look at three main areas:

1. Citation Intent Classification: Citations are grouped by their rhetorical purpose. This shows whether they support or contrast with the argument. Researchers can see exactly how their references strengthen their points.
2. Editorial Notice Tracking: The platform keeps an eye on references that have:
   • Retractions
   • Expressions of concern
   • Corrections
   • Other editorial notices
3. Citation Context Analysis: Each citation goes through detailed analysis of its:
   • Location within the citing paper
   • Surrounding textual context
   • Citation statement classification
   • Model confidence scores

The system stays accurate by testing itself against unchangeable holdout sets. Users can flag any wrong classifications, which lets human experts step in to help the machine learning system.

The platform helps tackle citation bias. Researchers can learn about patterns across different scientific communities. This helps especially when looking at how non-English research papers get noticed in English-dominated journals.

Reference validation does more than just verify sources. It shows how citations connect and affect research. Researchers can use citation pattern analysis to:

• See how their field receives cited works
• Spot potential method problems
• Check how strong their supporting evidence is
• Watch citation trends change over time

This integrated approach to reference validation keeps scientific standards high while making research easier. Researchers can make better choices about their references and build stronger arguments with instant access to citation context and quality metrics.

Practical Citation Analysis

Scite AI’s practical citation analysis tools help researchers learn about and utilise citation data more effectively. The platform uses advanced natural language processing and machine learning techniques to create a robust system that helps users navigate through scientific literature.

Using citation statements

Citation statements are the foundation of Scite AI’s analysis capabilities. These statements, which researchers also call citances or in-text citations, show how the scientific community uses and discusses research. The platform has analysed nearly 900 million citation statements from 26 million full-text articles in various disciplines.

The platform goes beyond basic citation counts and lets users:

1. Get into the full context of citations
2. Learn the intent behind citations
3. Find supporting or contrasting evidence

This detail helps researchers understand how their work, or the work they study, fits into the broader scientific world.

Scite AI classifies citations into three main categories:

• Supporting citations
• Mentioning citations
• Contrasting citations

This system helps users quickly see the nature of citations and their relevance to specific research questions. The distribution of these citation types shows a clear pattern – about 92.6% are mentioning citations, 6.5% are supporting citations, and 0.8% are contrasting citations.

Context interpretation

A citation’s context plays a significant role in interpreting research impact accurately. Scite AI shows the sentence with the citation and the surrounding text to give a complete picture of the citing author’s intent.

The platform’s analysis shows distinct patterns in citation length and structure:

• Citations average 472 characters in length
• Introduction sections have longer citations at 500 characters
• Method sections use shorter citations at 420 characters

These differences in citation length and structure reveal how various sections of scientific papers use and connect with previous research.

Scite AI looks at multiple aspects when interpreting context:

• The paper section where the citation appears
• The citation’s rhetorical function
• The classification’s confidence level

This detailed approach helps researchers better understand how others cite and discuss their work in the scientific community.

Citation network visualisation

Scite AI’s network visualisation feature stands out as one of its strongest tools. Researchers can see the complex connections between scientific papers and track how ideas and evidence flow across studies and disciplines.

The visualisation offers these key benefits:

1. Identifying key influencers: Users can spot the most influential papers in any field quickly.
2. Tracing idea evolution: Researchers can see how concepts and findings develop over time and how newer studies build on or challenge earlier work.
3. Discovering unexpected connections: The visual networks can reveal surprising links between seemingly unrelated studies that might spark new research directions.
4. Assessing research impact: The structure and density of citation networks show the broader impact of specific papers or research areas.

The tool uses directional arrows to show citation relationships and includes a panel with citation snippets for each article. This mix of visual and text data creates a rich, interactive experience for exploring scientific literature.

The network visualisation helps researchers with:

• Literature reviews
• Finding gaps in current research
• Learning about scientific discoveries’ historical context
• Evaluating different research streams’ importance within a field

Researchers who “surf the citation web” can better understand their field and make smarter decisions about their research direction.

Scite AI also offers practical tools that boost the research process:

• Browser extensions: Users can access Scite AI features while reading research papers online.
• Reference management integrations: The platform merges with popular tools like Zotero to streamline citation management.
• Custom dashboards: Users can create personal dashboards to track specific papers or research areas, while premium subscribers can create unlimited dashboards for up to 1,000 DOIs each.

These tools, combined with Scite AI’s analysis features, give researchers a detailed suite to navigate scientific literature. The platform’s context-rich citation data and accessible visualisation tools help researchers build stronger arguments, spot key trends, and make better decisions about their research directions.

Advanced Search Techniques

Becoming skilled at advanced search techniques in Scite AI will help you realise the full potential of its database with over 100 million publications. The platform’s smart search features let you philtre and analyse scientific literature with precision.

Boolean search operators

The search box works with elasticsearch query string syntax, which gives you flexible Boolean operations to refine your searches. The essential operators include:

• + for AND operations
• | for OR operations
• – for negating searches
• ” for exact phrase matching
• () for establishing search precedence

These operators are a great way to get more from complex queries. To name just one example, you can combine multiple search terms to find papers about specific concepts or methods. The query syntax lets you use bracketed searches with AND, OR, and NOT operators, along with wildcard and other string operators.

Philtre optimisation

Scite AI’s philtre system has two main categories: citation range philtres and metadata philtres. Citation range philtres let you:

• Set minimum thresholds for supporting citations
• Establish contrasting citation requirements
• Philtre by citation type combinations

A smart strategy is to set supporting citations to zero while asking for at least one contrasting citation. This helps you find publications that face scientific challenges or methodological critiques.

The metadata philtres give you complete options to refine search results based on:

• Editorial notices
• Publication types
• Document categories
• Year ranges

The system has a 1,000 DOI limit, so researchers should:

1. Use year philtres to focus on recent literature
2. Turn on the ‘has cites’ philtre for citation-based updates
3. Adjust citation sliders to target specific citation patterns

The advanced search capabilities go beyond simple keyword searches. The semantic search feature understands what you’re asking for and finds relevant papers based on meaning rather than exact matches.

You’ll get the best results if you:

• Use exact title matches to find specific papers
• Use citation classification tallies to order results
• Apply editorial notice philtres for quality control
• Use paper type philtres to focus on relevant document categories

The search interface lets you order results based on Scite data, so you can prioritise papers with specific citation patterns or editorial features. This feature really shines when you need to identify seminal papers or track new research trends.

These advanced search techniques help you make the most of Scite AI’s extensive database to find relevant literature and build stronger research arguments. Boolean operators combined with smart filtering options give you precise, targeted results that streamline your research while maintaining academic quality.

Conclusion

Scite AI revolutionises academic research by changing how scholars verify and construct research arguments. The platform provides deep insights into citation contexts and research effects through its massive database of over 1.2 billion citations and 185 million full-text articles.

Smart Citations are the life-blood of this platform. They give a detailed classification of supporting, mentioning, and contrasting citations. Researchers can better understand how their work fits into broader scientific discussions with this classification system. The platform makes peer review smoother with its reference validation workflow. Its advanced search features also let users explore literature with precision.

Scite AI shows its real-world value through these achievements:

• Automated reference checking across millions of papers
• Context-rich citation analysis
• Detailed citation network visualisation
• Sophisticated Boolean search capabilities
• Integration with popular reference management tools

These features work together to improve research quality and efficiency. Scholars can build stronger arguments based on thorough citation analysis. The platform’s ongoing evaluation system will give a reliable classification that maintains high academic standards throughout research.

The future looks promising for Scite AI’s approach to citation analysis. It will revolutionise research verification and scholarly communication. With its expanding database and sophisticated tools, Scite AI has become essential for researchers who want to strengthen their academic arguments and make meaningful contributions to scientific discourse.

FAQs

1. How does Scite AI differ from traditional citation analysis tools? 

Scite AI goes beyond simple citation counts by providing context-rich Smart Citations. It classifies citations as supporting, mentioning, or contrasting, offering researchers deeper insights into how their work is being used and discussed within the scientific community.

2. Can Scite AI help in validating research references? 

Yes, Scite AI’s Reference Check feature automates the evaluation of manuscript citations. It cross-references uploaded manuscripts with its extensive database, identifying retracted papers, editorial notices, and analysing citation context to ensure citation reliability and accuracy.

3. What are the key features of Scite AI’s search functionality? 

Scite AI offers advanced search techniques including Boolean operators, semantic search, and comprehensive filtering options. Users can refine searches based on citation types, publication metadata, and editorial notices, enabling precise and targeted literature exploration.

4. How does Scite AI visualise citation networks? 

Scite AI provides powerful citation network visualisation tools that allow users to explore relationships between scientific papers. This feature helps in identifying key influencers, tracing idea evolution, discovering unexpected connections, and assessing research impact across different studies and disciplines.

5. Is Scite AI suitable for all academic disciplines? 

While Scite AI is broadly applicable across academic fields, its analysis reveals that citation patterns vary between disciplines. Human-centred sciences like medicine and psychology tend to have higher rates of contrasting citations compared to physical sciences. The platform’s extensive database covers a wide range of fields, making it a valuable tool for most researchers.