[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-detail-174591-en":53,"doc-seo-174591-105":76},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":5,"data":54},{"doc_id":55,"user_id":56,"nickname":57,"user_avatar":58,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":60,"doc_content":61,"file_id":62,"file_url":63,"file_type":64,"file_size":65,"view_count":66,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":67,"language":68,"language_code":69,"site_id":70,"html_lang":69,"table_of_contents":71,"faqs":72,"seo_title":73,"seo_description":60,"update_tm":74,"read_time":75},174591,1099525198933,"Terk","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Characterizing the Vector Data Ecosystem","A growing body of information on vector-borne diseases supports anticipating risk, optimizing surveillance, and understanding core vector-biology for control and mitigation. The data landscape spans many database efforts, data storage, and serving approaches, producing distributed resources across formats and types, from collection records to molecular characterization and geospatial traits. New initiatives emerge across disciplines while others fade due to funding shifts or access gaps. The work presents a snapshot of current vector data efforts, evaluates accessibility, and assesses interoperability through an illustration tracing a specimen through the data ecosystem toward prospective database description of it and related specimen data.","Characterizing the Vector Data Ecosystem\nCatherine Lippi1,2, Samuel Rund3, & Sadie Ryan1,2*\n1 Quantitative Disease Ecology and Conservation (QDEC) Lab Group, Department of Geography, University of Florida, Gainesville, FL 32611\n2 Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610\n3 Center for Research Computing & Eck Institute for Global Health\nUniversity of Notre Dame, Notre Dame, IN 46556\n* Correspondence should be addressed to  \u0013 HYPERLINK \"mailto:sjryan@ufl.edu\" \\h \u0014sjryan@ufl.edu\u0015\nShort Abstract: A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct efforts to control and mitigation. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, mean that it is distributed across many formats, and data types. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is ‘out there’, and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data).\n\u000f\nAbstract\nA growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct efforts to control and mitigation. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, mean that it is distributed across many formats, and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data - the Vector Data Ecosystem - can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is ‘out there’, and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data).\nKeywords: vector-borne diseases; databases; metadata; interoperability; ecoinformatics; data accessibility\n\u000f\nIntroduction\nVector-borne diseases pose a major threat to public health and agricultural systems globally \u0013 HYPERLINK \"https://paperpile.com/c/C0bvxk/12Gs+MXZs+xmtG\" \\h \u00141–3\u0015. These systems are typically complex and span multiple spatial and temporal scales. Consequently, there is often a considerable burden of data needed to conduct meaningful research in this area \u0013 HYPERLINK \"https://paperpile.com/c/C0bvxk/XJsk\" \\h \u00144\u0015. Repositories that aggregate disease vector data from multiple sources (e.g., museums, individual research projects, or publ","cbCaiiP66vhkkFxF","https://ap.wps.com/l/cbCaiiP66vhkkFxF","doc",1729416,3,20,"English","en",105,"# Introduction\n## Data ecosystem overview\n## Access, privacy, and ownership constraints\n# Abstract\n## Scope and scale of vector data\n## FAIR and interoperability focus","[{\"question\":\"What problem does the document address about vector data ecosystems?\",\"answer\":\"It highlights that vector data efforts are distributed across many formats and approaches, making the overall ecosystem unclear and sometimes hindering data flows due to obsolescence and access gaps.\"},{\"question\":\"Why is characterizing the current vector data efforts useful?\",\"answer\":\"Characterizing what exists and how different data aggregation and collection efforts interact helps evaluate accessibility and interoperability, supporting FAIR data goals.\"},{\"question\":\"What factors can limit access to vector database records?\",\"answer\":\"Access can be restricted by privacy concerns related to identifiability of human or agricultural data, as well as by data ownership terms, contributor agreements, and intellectual property stipulations.\"}]","Characterizing the Vector Data Ecosystem | DOC",1788314860,7,{"code":4,"msg":77,"data":78},"ok",{"site_id":70,"language":69,"slug":79,"title":59,"keywords":80,"description":60,"schema_data":81,"social_meta":137,"head_meta":139,"extra_data":141,"updated_unix":142},"characterizing-the-vector-data-ecosystem","",{"@graph":82,"@context":136},[83,98,119],{"@type":84,"itemListElement":85},"BreadcrumbList",[86,90,93,95],{"item":87,"name":88,"@type":89,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":91,"name":10,"@type":89,"position":92},"https://docshare.wps.com/template/",2,{"item":94,"name":51,"@type":89,"position":66},"https://docshare.wps.com/template/general/",{"item":96,"name":59,"@type":89,"position":97},"https://docshare.wps.com/template/characterizing-the-vector-data-ecosystem/174591/",4,{"url":96,"name":59,"@type":99,"image":100,"author":105,"headline":59,"publisher":107,"fileFormat":110,"inLanguage":69,"description":60,"dateModified":111,"datePublished":112,"encodingFormat":110,"isAccessibleForFree":113,"interactionStatistic":114},"DigitalDocument",{"url":101,"@type":102,"width":103,"height":104},"https://docshare.wps.com/thumbnails/characterizing-the-vector-data-ecosystem/174591.png","ImageObject",442,249,{"name":57,"@type":106},"Person",{"url":87,"name":108,"@type":109},"DocShare","Organization","application/octet-stream","2026-10-03","2026-09-02",true,{"@type":115,"interactionType":116,"userInteractionCount":118},"InteractionCounter",{"@type":117},"ViewAction",6,{"@type":120,"mainEntity":121},"FAQPage",[122,128,132],{"name":123,"@type":124,"acceptedAnswer":125},"What problem does the document address about vector data ecosystems?","Question",{"text":126,"@type":127},"It highlights that vector data efforts are distributed across many formats and approaches, making the overall ecosystem unclear and sometimes hindering data flows due to obsolescence and access gaps.","Answer",{"name":129,"@type":124,"acceptedAnswer":130},"Why is characterizing the current vector data efforts useful?",{"text":131,"@type":127},"Characterizing what exists and how different data aggregation and collection efforts interact helps evaluate accessibility and interoperability, supporting FAIR data goals.",{"name":133,"@type":124,"acceptedAnswer":134},"What factors can limit access to vector database records?",{"text":135,"@type":127},"Access can be restricted by privacy concerns related to identifiability of human or agricultural data, as well as by data ownership terms, contributor agreements, and intellectual property stipulations.","https://schema.org",{"og:url":96,"og:type":138,"og:title":59,"og:site_name":108,"og:description":60},"article",{"robots":140,"canonical":96},"index,follow",{"doc_id":55,"site_id":70},1790023565]