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Using it to study galaxy formation is difficult because spatially interconnected measurements are stored with coarse chunking, leading to slow downloading and local curation. Accessing, querying, visualizing, and running statistical analyses at fine granularity is impractical with FITS-only workflows. Marvin provides a Python package, API, and web application backed by a remote database, designed for robustness and extensibility, with core capabilities being abstracted for future science applications.",{"@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/marvin-a-toolkit-for-streamlined-access-and-visualization-of-the-sdss-iv-manga-data-set/155412/",{"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/marvin-a-toolkit-for-streamlined-access-and-visualization-of-the-sdss-iv-manga-data-set/155412.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-05","2026-08-28",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is the MaNGA data set challenging to use locally for fine-grained analysis?","Question",{"text":112,"@type":113},"MaNGA pipelines produce large files that preserve spatial correlations, but this results in coarsely chunked storage and a huge overall data volume, making downloading and local curation time-consuming. Fine-grained access, querying, visualization, and statistics become extremely difficult with only FITS files.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What components make up the Marvin toolkit?",{"text":117,"@type":113},"Marvin consists of a Python package, an Application Programming Interface (API), and a web application. It uses a remote database to support streamlined access and visualization.",{"name":119,"@type":110,"acceptedAnswer":120},"How does Marvin support extensibility and long-term maintenance?",{"text":121,"@type":113},"Its robust and sustainable design reduces maintenance burden while enabling user-contributed extensions, such as high-level analysis code. The project also abstracts Marvin’s core functionality into a separate product to enable similar systems for new science applications.","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},155412,1787911620,{"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":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","Draft version December 5, 2018  \nPreprint typeset using LATEX style AASTeX6 v. 1.0  \nMARVIN: A TOOLKIT FOR STREAMLINED ACCESS AND VISUALIZATION OF THE SDSS-IV MANGA  \nDATA SET  \nBrian Cherinka1 , Brett H. Andrews2 , Jos Snchez-Gallego3 , Joel Brownstein4 , Mara Argudo-Fernndez5,6 ,  \nMichael Blanton7 , Kevin Bundy8 , Amy Jones13 , Karen Masters10,11 , David R. Law1 , Kate Rowlands9 , Anne-Marie Weijmans12 , Kyle Westfall8 , Renbin Yan14  \n1 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  \n2 Department of Physics and Astronomy and PITT PACC, University of Pittsburgh, 3941 OHara Street, Pittsburgh, PA 15260, USA  \n3 Department of Astronomy, Box 351580, University of Washington, Seattle, WA 98195, USA  \n4 Department of Physics and Astronomy, University of Utah, 115 S 1400 E, Salt Lake City, UT 84112, USA  \n5 Centro de Astronom􀀓􀀐a (CITEVA), Universidad de Antofagasta, Avenida Angamos 601 Antofagasta, Chile  \n6 Chinese Academy of Sciences South America Center for Astronomy, China-Chile Joint Center for Astronomy, Camino El Observatorio, 1515, Las Condes, Santiago, Chile  \n7 Department of Physics, New York University, 726 Broadway, New York, NY 10003, USA  \n8 University of California Observatories, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA  \n9 Department of Physics and Astronomy, Johns Hopkins University, 3400 N. Charles St. , Baltimore, MD 21218, USA  \n10 Department of Physics and Astronomy, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA  \n11 Institute of Cosmology & Gravitation, University of Portsmouth, Dennis Sciama Building, Portsmouth, PO1 3FX, UK  \n12 School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews, KY16 9SS, UK  \n13 Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA  \n14 Department of Physics and Astronomy, University of Kentucky, 505 Rose St. , Lexington, KY 40506-0057, USA  \nABSTRACT  \nThe Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, one of three core programs of the fourth-generation Sloan Digital Sky Survey (SDSS-IV), is producing a massive, highdimensional integral 􀀌eld spectroscopic data set. However, leveraging the MaNGA data set to address key questions about galaxy formation presents serious data-related challenges due to the combination of its spatially inter-connected measurements and sheer volume. For each galaxy, the MaNGA pipelines produce relatively large data 􀀌les to preserve the spatial correlations of the spectra and measurements, but this comes at the expense of storing the data set in a coarsely-chunked manner. The coarse chunking and total volume of the data make it time-consuming to download and curate locally-stored data. Thus, accessing, querying, visually exploring, and performing statistical analyses across the whole data set at a 􀀌ne-grained scale is extremely challenging using just FITS 􀀌les. To overcome these challenges, we have developed Marvin: a toolkit consisting of a Python package, Application Programming Interface (API), and web application utilizing a remote database. Marvin's robust and sustainable design minimizes maintenance, while facilitating user-contributed extensions such as high level analysis code. Finally, we are in the process of abstracting out Marvin's core functionality into a separate product so that it can serve as a foundation for others to develop Marvin-like systems for new science applications.  \n1. INTRODUCTION  \nLarge astronomy collaborations with dedicated facilities pursuing multi-year surveys are producing massive data sets at furious rates. The data sets from the current generation of surveys, such as the Sloan Digital Sky Survey (hereafter SDSS; York et al. 2000; Strauss et al.  \n[bcherinka@stsci.edu](bcherinka@stsci.edu)  \n2002), require more disk space than is available on personal computers and some moderate-sized institutionlevel servers. However, the next generat","cbCaibWIq6Z6BWpf","https://ap.wps.com/l/cbCaibWIq6Z6BWpf","pdf",4279989,26,"English","# Abstract\n# Introduction","[{\"question\":\"Why is the MaNGA data set challenging to use locally for fine-grained analysis?\",\"answer\":\"MaNGA pipelines produce large files that preserve spatial correlations, but this results in coarsely chunked storage and a huge overall data volume, making downloading and local curation time-consuming. Fine-grained access, querying, visualization, and statistics become extremely difficult with only FITS files.\"},{\"question\":\"What components make up the Marvin toolkit?\",\"answer\":\"Marvin consists of a Python package, an Application Programming Interface (API), and a web application. It uses a remote database to support streamlined access and visualization.\"},{\"question\":\"How does Marvin support extensibility and long-term maintenance?\",\"answer\":\"Its robust and sustainable design reduces maintenance burden while enabling user-contributed extensions, such as high-level analysis code. The project also abstracts Marvin’s core functionality into a separate product to enable similar systems for new science applications.\"}]","MARVIN - A ToolKit for Streamlined Access and Visualization of the SDSS-IV MaNGA Data Set | PDF",66]