{"doi":"10.1093/pm/pnaf003","title":"Building community through data: the value of a researcher driven open science ecosystem","abstract":"Exponential scientific data growth presents challenges and opportunities for addressing complex public health issues like the opioid epidemic and chronic pain management. Despite the vast amount of research conducted globally, many datasets remain inaccessible or underutilized due to publication access policies and stringent data use agreements. The amount of data generated through research activities is enormous. Limited access to scientific datasets stifles discovery and delays the translation of proven scientific advances into real-world applications.1 To address these challenges, we argue for the critical importance of open science ecosystems, using the National Institutes of Health Helping to End Addiction Long-term® Initiative (NIH HEAL Initiative®) as a case study. We discuss how building community around data can accelerate scientific discovery by enabling dataset integration, increasing statistical power, and fostering interdisciplinary collaboration. Open science ecosystems represent a fundamental shift in how research is conducted, shared, and utilized. An open science ecosystem is a comprehensive research environment that combines technological infrastructure, standardized protocols, and collaborative networks to enable transparent sharing and integration of scientific data, methods, and findings. These interconnected networks integrate data collection, storage, processing, analysis, and use across organizations while adhering to FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. The ecosystem encompasses not just the technical components for data sharing, but also the human elements: Researchers, institutions, funding bodies, and community partners who work together under shared governance frameworks and data standards to accelerate scientific discovery. The NIH HEAL Initiative® demonstrates how such ecosystems can accelerate discovery in critical public health areas through transparent research methods, open access to data and findings, collaborative approaches to complex problems, and standardized data elements enabling cross-study analyses. Data sharing is crucial to HEAL's mission of addressing the interconnected public health crises of chronic pain and opioid use disorder (OUD). While appropriate medical use of opioids remains important for pain management, the rise in OUD presents distinct challenges requiring comprehensive research approaches. By distinguishing between therapeutic opioid use and OUD, researchers can better target interventions and support both pain management and addiction treatment needs. HEAL researcher and community partner data dashboards actively identify and monitor emerging threats in the drug supply, such as highly potent fentanyl and xylazine.2 This capability enables rapid resource deployment where needed, potentially saving lives. More broadly, combining data sets can increase sample size, provide more statistical power to make conclusions, help test hypotheses that a single data set alone would be insufficient to generate, and increase result generalizability. Data can be analyzed differently by multiple groups to answer numerous research questions and consolidate trust in the validity of reported results.3 Secondary analysis of shared data can also uncover patterns in disparate datasets, such as signatures of different chronic pain types,4 genetic factors that may underlie similar pain phenotypes (or symptoms), or qualities likely to make an individual respond to treatment.5 The impact of data inaccessibility on research progress is particularly evident in pain management studies. For example, when clinical trial data remains siloed, researchers cannot identify subtle patterns in treatment response across different patient populations. This limitation has historically hampered the development of personalized pain management approaches and delayed the identification of risk factors for OUD development. Through the HEAL Data Ecosystem, researchers ha","journal":"Pain Medicine","year":2025,"id":517743,"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":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8879,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":388541,"name":"Carla Bann","orcid":"0000-0001-9305-8305","position":1,"is_corresponding":false},{"id":866127,"name":"Emine O. Bayman","orcid":"0000-0003-2615-0915","position":2,"is_corresponding":false},{"id":355824,"name":"Maria T. Chao","orcid":"0000-0001-9846-7044","position":3,"is_corresponding":false},{"id":226955,"name":"Georgene W. Hergenroeder","orcid":"0000-0002-6170-2191","position":4,"is_corresponding":false},{"id":541052,"name":"Charles Knott","orcid":"0000-0002-3340-1850","position":5,"is_corresponding":false},{"id":456198,"name":"Martin A. Lindquist","orcid":"0000-0003-2289-0828","position":6,"is_corresponding":false},{"id":1052830,"name":"Z. David Luo","orcid":"0000-0001-6811-9380","position":7,"is_corresponding":false},{"id":39501,"name":"Rosemarie A. Martin","orcid":"0000-0002-0412-8074","position":8,"is_corresponding":false},{"id":19003,"name":"Maryann E. Martone","orcid":"0000-0002-8406-3871","position":9,"is_corresponding":false},{"id":1279210,"name":"John McCarthy","orcid":"0000-0003-0765-7884","position":10,"is_corresponding":false},{"id":376137,"name":"Micah McCumber","orcid":"0000-0003-2570-1180","position":11,"is_corresponding":false},{"id":623583,"name":"Sharon B. Meropol","orcid":"0000-0003-2025-1766","position":12,"is_corresponding":false},{"id":499718,"name":"Ty A. Ridenour","orcid":"0000-0002-9709-6808","position":13,"is_corresponding":false},{"id":676622,"name":"Lissette M. Saavedra","orcid":"0000-0001-8880-0624","position":14,"is_corresponding":false},{"id":96345,"name":"Abeed Sarker","orcid":"0000-0001-7358-544X","position":15,"is_corresponding":false},{"id":65482,"name":"Kevin J. Anstrom","orcid":"0000-0001-6452-2172","position":16,"is_corresponding":false},{"id":79256,"name":"Wesley K. Thompson","orcid":"0000-0002-1148-1976","position":17,"is_corresponding":false},{"id":506660,"name":"Meredith C B Adams","orcid":"0000-0002-3969-4279","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T02:49:04.756302Z","pmid":"39836639","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":[]}