{"doi":"10.12688/f1000research.178101.1","title":"Q-SPARC: An Interactive Chatbot for Exploring SPARC SCKAN Connectivity with Flatmap Visualization","abstract":"<ns5:p>Background The SPARC program (SPARC Portal, RRID:SCR_017041; https://sparc.science) aggregates anatomy and connectivity knowledge across species. The SCKAN database (RRID:SCR_026088) provides structured connectivity relationships and an associated Natural Language Interface (NLI). However, the NLI currently supports only single-turn querying, lacks conversational memory, and does not integrate Flatmap visualization. Methods We developed Q-SPARC—a Python-based conversational system that integrates local or cloud-hosted LLMs (default: Qwen2.5-72B with optional GPT-4 support) with semantic retrieval, reranking, and Flatmap visualization. Results Users can submit queries such as “What are the input sources of the heart?” and receive a narrative summary, structured tables, and Flatmap anatomical diagrams. The system supports multi-turn conversational memory, allowing follow-up refinement and context- dependent queries. Conclusions Q-SPARC extends the SPARC ecosystem by enabling conversational exploration of SCKAN connectivity, integrating visualization, and improving usability and FAIRness.</ns5:p>","journal":"F1000Research","year":2026,"id":651388,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":10643,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1405120,"name":"Dan Zhang","orcid":"0000-0002-7443-1634","position":1,"is_corresponding":false},{"id":1698808,"name":"Matthew French","orcid":null,"position":2,"is_corresponding":false},{"id":1698809,"name":"Fangqiang Xu","orcid":null,"position":3,"is_corresponding":false},{"id":904749,"name":"Yun Gu","orcid":"0000-0003-0310-1790","position":4,"is_corresponding":false},{"id":1698807,"name":"Huayan Zeng","orcid":"0009-0008-2289-083X","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Q-SPARC: An Interactive Chatbot for Exploring SPARC SCKAN Connectivity with Flatmap Visualization","abstract":"<ns5:p>Background The SPARC program (SPARC Portal, RRID:SCR_017041; https://sparc.science) aggregates anatomy and connectivity knowledge across species. The SCKAN database (RRID:SCR_026088) provides structured connectivity relationships and an associated Natural Language Interface (NLI). However, the NLI currently supports only single-turn querying, lacks conversational memory, and does not integrate Flatmap visualization. Methods We developed Q-SPARC—a Python-based conversational system that integrates local or cloud-hosted LLMs (default: Qwen2.5-72B with optional GPT-4 support) with semantic retrieval, reranking, and Flatmap visualization. Results Users can submit queries such as “What are the input sources of the heart?” and receive a narrative summary, structured tables, and Flatmap anatomical diagrams. The system supports multi-turn conversational memory, allowing follow-up refinement and context- dependent queries. Conclusions Q-SPARC extends the SPARC ecosystem by enabling conversational exploration of SCKAN connectivity, integrating visualization, and improving usability and FAIRness.</ns5:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W7165158666","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.69418859,"influential_citations":0,"citation_trend":[],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://f1000research.com/articles/15-977/pdf","host_type":"journal"},{"url":"https://f1000research.com/articles/15-977/pdf","host_type":"publisher"},{"url":"https://f1000research.com/articles/15-977/v1/xml","host_type":"publisher"},{"url":"https://f1000research.com/articles/15-977/v1/pdf","host_type":"publisher"},{"url":"https://f1000research.com/articles/15-977/v1/iparadigms","host_type":"publisher"},{"url":"https://doi.org/10.12688/f1000research.178101.1","host_type":"journal"}],"fields_of_study":["Biomedical Text Mining and Ontologies","Artificial Intelligence in Healthcare and Education","AI in cancer detection"],"mesh_terms":[],"keywords":["Chatbot","Visualization","Usability","Interface (matter)","User interface","Narrative"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T08:30:40.507827Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}