{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-asreview","slug":"asreview","name":"ASReview","type":"webapp","url":"https://asreview.nl/","page_url":"https://unfragile.ai/asreview","categories":["research-search"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-asreview__cap_0","uri":"capability://planning.reasoning.active.learning.driven.document.ranking.and.prioritization","name":"active-learning-driven document ranking and prioritization","description":"Implements an iterative human-in-the-loop active learning loop where the system presents documents to reviewers, collects relevance judgments, retrains ML models on labeled data, and re-ranks unlabeled documents by predicted relevance for the next screening cycle. The approach prioritizes documents most likely to be relevant based on accumulated human feedback, reducing the total number of documents a reviewer must manually assess.","intents":["I want to screen a large corpus of academic papers but only review the most relevant ones first","I need to reduce the time spent on systematic literature reviews by having AI predict which papers to prioritize","I want to iteratively improve screening accuracy as I label more documents"],"best_for":["researchers conducting systematic reviews with 1000+ documents","information specialists managing large-scale literature screening workflows","evidence-based medicine teams building clinical guidelines"],"limitations":["Requires human judgment for every screening decision — cannot operate fully autonomously","Model retraining latency unknown; unclear if system supports real-time document addition during active review cycles","Minimum labeled examples needed for effective model retraining not specified","No multi-language support documented; language of corpus unknown"],"requires":["Document corpus in supported format (format types unknown)","At least one human reviewer to provide initial relevance judgments","Self-hosted infrastructure or access to hosted deployment (deployment options unknown)"],"input_types":["document collection (format unspecified — likely PDF, plain text, or structured metadata)","human relevance judgments (binary: include/exclude)"],"output_types":["ranked document list sorted by predicted relevance score","inclusion/exclusion classification for each document","model confidence scores (format unknown)"],"categories":["planning-reasoning","active-learning","human-in-the-loop"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_1","uri":"capability://data.processing.analysis.multi.model.machine.learning.backend.with.pluggable.algorithm.support","name":"multi-model machine learning backend with pluggable algorithm support","description":"Supports multiple machine learning models for document relevance prediction with an extensible architecture allowing third parties to add custom models. The system abstracts model selection and retraining, though specific algorithms (Naive Bayes, SVM, neural networks, etc.) are not documented. Models are retrained on accumulated human judgments after each screening batch to adapt to reviewer preferences.","intents":["I want to compare different ML algorithms to see which performs best on my specific document corpus","I need to extend ASReview with custom models tailored to my domain","I want to understand which model is being used and how confident it is in its predictions"],"best_for":["data scientists optimizing screening workflows for specific domains","developers contributing custom ML modules to ASReview","researchers benchmarking active learning algorithms"],"limitations":["Specific model names and default configurations not documented","No information on hyperparameter tuning options or model selection criteria","Unclear whether models are retrained incrementally or from scratch after each batch","No performance benchmarks (accuracy, recall, precision) provided for different models","Model interpretability and explanation generation not mentioned"],"requires":["Python environment (version unknown)","Understanding of active learning and ML model training","Access to ASReview source code for custom model development"],"input_types":["document embeddings or raw text (embedding method unknown)","human relevance labels (binary or multi-class unknown)"],"output_types":["relevance probability scores per document","model performance metrics (format unknown)","uncertainty estimates for active learning sample selection"],"categories":["data-processing-analysis","machine-learning","model-selection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_10","uri":"capability://automation.workflow.learning.materials.and.community.support.infrastructure","name":"learning materials and community support infrastructure","description":"Provides open learning materials, documentation, and community support channels including weekly Thursday stand-ups and user meetings. The project is coordinated at Utrecht University with active community engagement. Learning resources enable researchers and developers to understand systematic review methodology, active learning concepts, and ASReview usage without formal training.","intents":["I want to learn how to use ASReview and understand active learning for systematic reviews","I need help troubleshooting issues or have questions about best practices","I want to connect with other researchers using ASReview and share experiences"],"best_for":["researchers new to systematic reviews and active learning","teams implementing ASReview for the first time","developers contributing to ASReview or building extensions"],"limitations":["Learning materials quality and comprehensiveness not documented","Time commitment for learning and onboarding unknown","Community support response times and availability unknown","Documentation may lag behind software releases","No formal training or certification programs mentioned"],"requires":["Access to learning materials (online, format unknown)","Time for self-directed learning (estimated hours unknown)","Optional: participation in weekly meetings (timezone and scheduling unknown)"],"input_types":["user questions and support requests","feedback on learning materials"],"output_types":["learning materials (documentation, tutorials, guides)","community discussion and peer support","meeting notes and recordings (if available)"],"categories":["automation-workflow","community-support","education"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_2","uri":"capability://planning.reasoning.simulation.and.benchmarking.mode.for.screening.workflow.optimization","name":"simulation and benchmarking mode for screening workflow optimization","description":"Allows researchers to simulate AI-aided reviewing by replaying historical screening decisions against different model configurations and active learning strategies. The simulation mode evaluates how different algorithms would have performed on past screening tasks, enabling comparison of model effectiveness without requiring new human labeling effort. Includes a Benchmark Platform for standardized performance comparison across configurations.","intents":["I want to test how different ML models would perform on my previous systematic review before committing to a new screening workflow","I need to compare active learning strategies to see which reduces reviewer workload most effectively","I want to publish benchmarks showing how ASReview compares to other screening tools on standard datasets"],"best_for":["researchers designing and optimizing screening workflows before deployment","data scientists evaluating active learning algorithm performance","institutions comparing ASReview to alternative screening tools"],"limitations":["Requires historical screening data with complete human judgments for all documents (cannot simulate with partial labels)","Benchmark Platform details not documented — unclear what datasets are available or how comparisons are structured","No information on which metrics are compared (accuracy, recall, precision, workload reduction, etc.)","Simulation results may not predict real-world performance due to differences in reviewer behavior and document characteristics"],"requires":["Complete screening dataset with all documents labeled (include/exclude)","Multiple model configurations to compare","Access to Benchmark Platform (availability and access requirements unknown)"],"input_types":["historical screening decisions (binary labels for all documents)","document corpus (format unknown)","model configuration specifications"],"output_types":["performance metrics comparing models (specific metrics unknown)","workload reduction estimates (percentage of documents that could be skipped)","ranking of models by effectiveness"],"categories":["planning-reasoning","data-processing-analysis","benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_3","uri":"capability://automation.workflow.crowdscreen.parallel.multi.reviewer.coordination.and.consensus","name":"crowdscreen parallel multi-reviewer coordination and consensus","description":"Distributes document screening across multiple expert reviewers in parallel, with AI proposing records to the crowd and coordinating their judgments. The system manages workflow distribution, collects independent relevance assessments from multiple reviewers, and aggregates their decisions. Enables large-scale screening by parallelizing reviewer effort across a team rather than requiring sequential single-reviewer assessment.","intents":["I need to screen a large document corpus quickly by distributing work across multiple reviewers","I want to measure inter-rater agreement and identify documents where reviewers disagree","I need to coordinate screening across a distributed team of experts"],"best_for":["large research teams conducting systematic reviews with 5+ reviewers","institutions managing parallel screening workflows across departments","guideline development organizations requiring consensus-based screening"],"limitations":["Conflict resolution algorithm not documented — unclear how disagreements between reviewers are handled","Consensus mechanism unknown (majority vote, Fleiss' kappa, other)","No information on inter-rater agreement metrics or how they influence model retraining","Scalability limits unknown — maximum number of concurrent reviewers not specified","No details on reviewer assignment strategy (random, expertise-based, load-balanced)"],"requires":["Multiple human reviewers with access to ASReview interface","Mechanism for managing reviewer accounts and permissions (unknown)","Coordination infrastructure for distributing documents and collecting judgments"],"input_types":["document corpus","reviewer pool (number and expertise levels unknown)","relevance judgments from multiple reviewers (format unknown)"],"output_types":["per-document consensus relevance classification","inter-rater agreement metrics (specific metrics unknown)","reviewer-specific performance statistics (unknown format)"],"categories":["automation-workflow","collaboration","consensus-building"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_4","uri":"capability://data.processing.analysis.document.corpus.ingestion.and.preprocessing.pipeline","name":"document corpus ingestion and preprocessing pipeline","description":"Accepts large-scale document collections and prepares them for screening through an ingestion pipeline. The system handles document parsing, metadata extraction, and preparation for ML model processing. Specific input formats, preprocessing steps, and vectorization methods are not documented, but the system claims to handle large-scale text screening without specified upper limits on corpus size.","intents":["I want to upload my collection of academic papers and prepare them for screening","I need to import documents from a bibliographic database (PubMed, Web of Science, etc.) into ASReview","I want to ensure my document corpus is properly formatted and deduplicated before screening begins"],"best_for":["researchers with document collections in standard formats (PDF, CSV, BibTeX, etc.)","information specialists managing literature imports from bibliographic databases","teams migrating screening workflows from other tools"],"limitations":["Supported input formats not documented (likely PDF, plain text, CSV, BibTeX, but unconfirmed)","Maximum corpus size and scalability limits unknown","Preprocessing steps and deduplication logic not specified","Metadata extraction capabilities unknown (title, abstract, authors, publication date, etc.)","No information on handling of non-English documents or special characters"],"requires":["Document collection in supported format (format types unknown)","Sufficient storage for corpus (requirements unknown)","Access to ASReview ingestion interface or API (API details unknown)"],"input_types":["PDF files (support level unknown)","plain text documents","CSV/Excel with document metadata","BibTeX or other bibliographic formats (support unknown)","direct imports from bibliographic databases (supported sources unknown)"],"output_types":["indexed document collection ready for screening","document metadata (title, abstract, authors, etc.)","deduplication report (format unknown)","import error log (format unknown)"],"categories":["data-processing-analysis","automation-workflow","document-ingestion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_5","uri":"capability://automation.workflow.interactive.document.screening.interface.with.relevance.judgment.collection","name":"interactive document screening interface with relevance judgment collection","description":"Provides a user interface for reviewers to assess document relevance one-at-a-time or in batches, collecting binary (include/exclude) or multi-class relevance judgments. The interface presents documents prioritized by the active learning model, allowing reviewers to make rapid relevance decisions. Human judgments are immediately fed back to the system for model retraining and re-ranking of remaining documents.","intents":["I want a simple, fast interface to review documents and mark them as relevant or irrelevant","I need to see why the system is prioritizing certain documents for my review","I want to review documents in batches and see how my decisions affect the model's future recommendations"],"best_for":["individual researchers conducting systematic reviews","information specialists screening large document collections","teams of reviewers working in parallel on the same corpus"],"limitations":["Interface design and usability features not documented","No information on keyboard shortcuts, batch operations, or accessibility features","Unclear whether reviewers can see document full-text, abstract-only, or both","No details on how documents are presented (one-at-a-time vs. paginated batches)","Model confidence scores and reasoning explanations not mentioned","Undo/revision capabilities unknown"],"requires":["Web browser or desktop application (deployment method unknown)","Access to ASReview instance (self-hosted or cloud-hosted)","Reviewer account with permissions (authentication method unknown)"],"input_types":["document metadata (title, abstract, authors, etc.)","document full-text (if available)","model-generated relevance scores and rankings"],"output_types":["binary relevance judgment (include/exclude) per document","timestamp of judgment","reviewer identifier","optional notes or comments (if supported)"],"categories":["automation-workflow","user-interface","data-collection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_6","uri":"capability://planning.reasoning.workload.reduction.estimation.and.progress.tracking","name":"workload reduction estimation and progress tracking","description":"Estimates and tracks the reduction in manual screening effort achieved through active learning prioritization. The system monitors how many documents reviewers can skip by relying on model predictions, typically claiming 95% workload reduction. Progress tracking shows reviewers how many documents remain to be screened and provides estimates of time to completion based on current screening velocity.","intents":["I want to know how much time the AI is saving me compared to reviewing all documents manually","I need to estimate how long my systematic review will take given current screening speed","I want to demonstrate to stakeholders the efficiency gains from using AI-assisted screening"],"best_for":["researchers justifying AI tool adoption to funding bodies or institutions","project managers tracking systematic review progress and timelines","teams evaluating whether to continue with AI-assisted screening"],"limitations":["Workload reduction metric not precisely defined — unclear whether it's based on documents skipped, time saved, or cost reduction","95% reduction claim is unverified and may vary significantly by corpus, model, and reviewer behavior","Progress estimates assume constant screening velocity, which may not hold as document difficulty changes","No information on how stopping criteria are determined (when to stop screening)","Confidence intervals or uncertainty bounds on estimates not mentioned"],"requires":["Active screening session with human judgments","Baseline for comparison (e.g., time to review all documents manually)","Sufficient screening progress to generate meaningful estimates (minimum documents unknown)"],"input_types":["human relevance judgments collected during screening","document corpus size","reviewer screening velocity (documents per hour)"],"output_types":["workload reduction percentage (e.g., 95%)","estimated time to completion","documents reviewed vs. remaining","progress visualization (charts, progress bars)"],"categories":["planning-reasoning","automation-workflow","progress-tracking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_7","uri":"capability://automation.workflow.screening.project.management.and.configuration","name":"screening project management and configuration","description":"Enables researchers to create and manage screening projects with configurable parameters such as model selection, active learning strategy, stopping criteria, and reviewer assignments. Projects encapsulate a complete screening workflow including document corpus, human judgments, trained models, and metadata. Supports project persistence, allowing screening to be paused and resumed across sessions.","intents":["I want to set up a new systematic review project with specific ML models and active learning strategies","I need to pause my screening workflow and resume it later without losing progress","I want to configure stopping criteria so the system knows when screening is complete"],"best_for":["researchers managing multiple concurrent systematic reviews","institutions standardizing screening workflows across projects","teams with long-running screening projects spanning weeks or months"],"limitations":["Configurable parameters not documented — unclear which settings are exposed to users","Stopping criteria options unknown (e.g., minimum documents reviewed, confidence threshold, time limit)","Project export/import capabilities unknown — unclear if projects can be shared or migrated","Version control for screening decisions and model iterations not mentioned","No information on project access control or sharing permissions"],"requires":["ASReview instance with project management interface","User account with project creation permissions","Understanding of active learning parameters and model selection"],"input_types":["project name and description","document corpus","model and algorithm selections","active learning strategy configuration","reviewer assignments"],"output_types":["project configuration (saved settings)","screening progress state (documents reviewed, model state)","project metadata (creation date, last modified, owner)"],"categories":["automation-workflow","project-management","configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_8","uri":"capability://tool.use.integration.open.source.extensibility.and.third.party.module.integration","name":"open-source extensibility and third-party module integration","description":"Provides an extensible architecture allowing developers to add custom modules for ML models, data processors, and screening workflows without modifying core code. The system is open-source with a public GitHub repository, enabling community contributions and custom deployments. Third parties can integrate custom algorithms, preprocessing pipelines, and domain-specific screening logic through a documented module interface.","intents":["I want to add a custom ML model trained on my domain-specific data to ASReview","I need to integrate ASReview into my existing research infrastructure and data pipelines","I want to contribute improvements to ASReview and have them available to the community"],"best_for":["developers building domain-specific screening tools on top of ASReview","institutions with custom ML models or preprocessing requirements","open-source contributors improving ASReview core functionality"],"limitations":["Module interface and extension points not documented in source material","No information on API stability or backward compatibility guarantees","Custom module testing and validation procedures unknown","Integration with external ML frameworks (TensorFlow, PyTorch, scikit-learn) not specified","Documentation quality and completeness for developers unknown"],"requires":["Python development environment (version unknown)","Understanding of ASReview architecture and module interface","Access to ASReview source code (GitHub repository)","Familiarity with active learning and ML model development"],"input_types":["custom Python module code","model weights or trained artifacts","configuration files for module registration"],"output_types":["integrated custom module available in ASReview","module metadata and documentation","test results and validation reports"],"categories":["tool-use-integration","automation-workflow","developer-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-asreview__cap_9","uri":"capability://automation.workflow.self.hosted.deployment.and.infrastructure.independence","name":"self-hosted deployment and infrastructure independence","description":"Supports self-hosted deployment on local infrastructure, providing full control over data, models, and screening workflows without reliance on cloud services or vendor infrastructure. The open-source codebase can be deployed on institutional servers, ensuring data privacy and compliance with research governance requirements. Deployment method (Docker, direct installation, etc.) and infrastructure requirements are not documented.","intents":["I need to deploy ASReview on our institutional servers to maintain data privacy and compliance","I want to avoid vendor lock-in and maintain full control over our screening infrastructure","I need to integrate ASReview with our existing institutional IT infrastructure and security policies"],"best_for":["institutions with strict data privacy or compliance requirements (HIPAA, GDPR, etc.)","research organizations wanting to avoid cloud vendor lock-in","teams with existing on-premises infrastructure and IT support"],"limitations":["Deployment instructions and infrastructure requirements not documented","No information on containerization (Docker, Kubernetes support)","Database backend and persistence layer not specified","System requirements (CPU, memory, storage) unknown","Backup, disaster recovery, and high-availability configurations not mentioned","IT support and maintenance burden unknown"],"requires":["Server infrastructure (specifications unknown)","Operating system support (Linux, Windows, macOS unknown)","Database system (type and version unknown)","IT personnel for deployment and maintenance","Network connectivity for multi-user access (if applicable)"],"input_types":["ASReview source code or release package","deployment configuration (database credentials, etc.)","document corpus and screening data"],"output_types":["running ASReview instance accessible to reviewers","persistent storage for screening projects and models","deployment logs and monitoring data"],"categories":["automation-workflow","infrastructure","deployment"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Document corpus in supported format (format types unknown)","At least one human reviewer to provide initial relevance judgments","Self-hosted infrastructure or access to hosted deployment (deployment options unknown)","Python environment (version unknown)","Understanding of active learning and ML model training","Access to ASReview source code for custom model development","Access to learning materials (online, format unknown)","Time for self-directed learning (estimated hours unknown)","Optional: participation in weekly meetings (timezone and scheduling unknown)","Complete screening dataset with all documents labeled (include/exclude)"],"failure_modes":["Requires human judgment for every screening decision — cannot operate fully autonomously","Model retraining latency unknown; unclear if system supports real-time document addition during active review cycles","Minimum labeled examples needed for effective model retraining not specified","No multi-language support documented; language of corpus unknown","Specific model names and default configurations not documented","No information on hyperparameter tuning options or model selection criteria","Unclear whether models are retrained incrementally or from scratch after each batch","No performance benchmarks (accuracy, recall, precision) provided for different models","Model interpretability and explanation generation not mentioned","Learning materials quality and comprehensiveness not documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=asreview","compare_url":"https://unfragile.ai/compare?artifact=asreview"}},"signature":"dma9I6Qb6cDBYbHWSUpNYTG8bF9educFkgJw1Suvl1lGKIOBbMR7jrjJ2MIu9kimvgNS45Z1l0wxM8rVNGUmDg==","signedAt":"2026-06-20T08:23:51.324Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/asreview","artifact":"https://unfragile.ai/asreview","verify":"https://unfragile.ai/api/v1/verify?slug=asreview","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}