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Structured metadata were compiled to standardize files using shared conventions, enabling analysts to combine data for structural racism study. Dashboards summarized geographic levels, coverage years, and missingness, revealing challenges such as recency bias, temporal and geographic mismatch, and data missingness. Transformation pipelines and a public repository of metadata with acquisition and processing tools supported reliable data linking while addressing open science needs, including data licenses and custody practices.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/lessons-from-the-processing-and-sharing-of-public-data-sets-for-the-study-of-structural-racism/426916/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/lessons-from-the-processing-and-sharing-of-public-data-sets-for-the-study-of-structural-racism/426916.png","ImageObject",300,407,{"name":92,"@type":93},"RuangKosong","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data sets were collected for the structural racism study?","Question",{"text":112,"@type":113},"The study collected 50 publicly available data sets containing geographic measures spanning over 100 years, six geographic units, and nine domains of inquiry.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the researchers standardize and combine different data files?",{"text":117,"@type":113},"They compiled structured metadata for each data set to standardize files with shared conventions, allowing analysts to combine data files for structural racism analysis.",{"name":119,"@type":110,"acceptedAnswer":120},"What kinds of problems did the dashboard and metadata work reveal for researchers using public data?",{"text":121,"@type":113},"Researchers faced issues including recency bias, temporal and geographic mismatch of data sources, and data missingness.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},426916,1790820582,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},962090883219,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","434 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.1477  \nLESSONS FROM THE PROCESSING AND SHARING OF  \nPUBLIC DATA SETS FOR THE STUDY OF  \nSTRUCTURAL RACISM  \nErik Westlund1, Boeun Kim2, Sierra Grey-Coker3,  \nKaren Bandeen-Roche4, and Sarah Szanton3, 1. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States, 2. University of Iowa, Iowa City, Iowa, United States, 3. Johns Hopkins University, Baltimore, Maryland, United States, 4. Johns Hopkins Center on Aging and Health, Baltimore, Maryland, United States  \nAs part of a larger study of structural racism, we collected  \n50 publicly available data sets containing geographic measures. These data sets covered over 100 years of history, six geographic units, and nine domains of inquiry (civics, credit/ income/wealth, education, employment, environment, healthcare, media/marketing, neighborhoods, and policing) .  \nStructured metadata about each data set were compiled and used to standardize data files with shared conventions, allowing analysts to combine data files to study structural racism.  \nTo allow researchers to assess the potential value of these data in relation to their areas of inquiry, we created dashboards that summarize key measures in each data set, including the geographic level of measurement, the years covered by the data, and the extent of missingness. This process made clear several problems researchers seeking to use public data files to study structural racism will face, such as recency bias, temporal and geographic mismatch of data sources, and data missingness. We also developed data transformation pipelines to process each data file to follow shared conventions that allow reliable linking of data files across spatial units and time periods. To help outside researchers to use these data in their own projects, we created a public repository of structured metadata about the form and content of data files, accompanied by tools to allow users to procure data files, process them, and analyze them. This effort provided insight into the challenges faced by researchers trying to follow best practices with respect to open science, particularly around data licenses and data custody.","cbCaivlgeWYDYKnJ","https://ap.wps.com/l/cbCaivlgeWYDYKnJ","pdf",420705,"English","# Abstract\n## Data collection and scope\n## Metadata standardization and file linking\n## Dashboards for value assessment\n## Challenges and transformation pipelines\n## Public repository and open science considerations","[{\"question\":\"What data sets were collected for the structural racism study?\",\"answer\":\"The study collected 50 publicly available data sets containing geographic measures spanning over 100 years, six geographic units, and nine domains of inquiry.\"},{\"question\":\"How did the researchers standardize and combine different data files?\",\"answer\":\"They compiled structured metadata for each data set to standardize files with shared conventions, allowing analysts to combine data files for structural racism analysis.\"},{\"question\":\"What kinds of problems did the dashboard and metadata work reveal for researchers using public data?\",\"answer\":\"Researchers faced issues including recency bias, temporal and geographic mismatch of data sources, and data missingness.\"}]","LESSONS FROM THE PROCESSING AND SHARING OF PUBLIC DATA SETS FOR THE STUDY OF STRUCTURAL RACISM | PDF",1790642178]