C omputa tiona l T ools a nd S tra te g ie s in E nha nc ing A c c e - PowerPoint PPT Presentation

C omputa tiona l T ools a nd S tra te g ie s in E nha nc ing A c c e ss to C ultura l B ig D a ta C olle c tions Richard MARCIANO & Ryan COX marciano@umd.edu & ryan.cox@maryland.gov Director & Research Archivist Digital Curation Innovation Center (DCIC) & Maryland State Archives (MSA) http://DCIC.umd.edu & http://slavery.msa.maryland.gov Tuesday, April 30, 2019 Columbus Metropolitan Library, Main Library, Columbus, Ohio

Wha t a re Computa tiona l Stra te g ie s? Based on the concepts of Computational Archival Science (CAS) , both case studies use visualization and analytical tools to connect and display data in new ways. The goal is to create transparency in cultural Big Data. Case studies involve: • ! automating the detection of personally identifiable information (PII) in Japanese- American World War II Incarceration Camps • ! penetrating the complex pre-Civil War slave system in Maryland

What is CAS? Explore computational treatments of archival and cultural content GOAL: PORTAL: http://dcicblog.umd.edu/cas/ GOOGLE GROUP: computational-archival-science@googlegroups.com A tra nsdisc iplina ry fie ld c o nc e rne d with the a pplic a tio n o f: ! ! c o mputa tio na l me tho ds a nd re so urc e s to la rg e -sc a le re c o rds / a rc hive s: ! ! pro c e ssing , a na lysis, sto ra g e , lo ng -te rm pre se rva tio n, a nd a c c e ss, ! ! with the a im o f impro ving e ffic ie nc y, pro duc tivity a nd pre c isio n ! ! in suppo rt o f a ppra isa l, a rra ng e me nt a nd de sc riptio n, pre se rva tio n a nd a c c e ss de c isio ns, a nd e ng a g ing a nd unde rta king re se a rc h with a rc hiva l ma te ria ls. Foundational Book Chapter: May. 2018 Book: “Advances in Librarianship – Re-Envisioning the MLIS: Perspectives on the Future of Library and Information Science Education”. Book Chapter: “Archival Records and Training in the Age of Big Data”

The Emergence of Computational XXX’s ! ! XXX=So c ia l Sc ie nc e ! ! “I nve stig a ting so c ia l a nd b e ha vio ra l re la tio nships a nd inte ra c tio ns thro ug h: so c ia l simula tio n, mo de ling , ne two rk a na lysis, a nd me dia a na lysis”, Wikipe dia ! ! XXX=Biology ! ! “The science of using biological data to develop algorithms or models to better understand biological systems”, Wikipedia ! ! XXX=Journalism ! ! “Finding and telling news stories, WITH, BY, or ABOUT algorithms”, Nick Diakopoulos ! ! XXX=Archival Science ? ! ! The Focus of this seminar "

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An e xa mple : Ma pping Ine qua lity – a foc us on Big Da ta [Ra c ia l Zoning ] UMD Student Team: Mary Kendig Myeong Lee Sydney Vaile Maddie Allen Martin Almirón Jhon De La Cruz Shaina Destine Erin Durham Darlene Reyes Benjamin Sagay Richard Bool

Historic a l Conte xt ! ! Ho me Owne rs L o a n Co rpo ra tio n 1930’ s - 1940’ s ! ! Ra te d ne ig hb o rho o ds b y ra c ia l ma ke up ! ! Are a s witho ut lo a ns fe ll a pa rt ! ! 1950’ s Urb a n Re ne wa l ta rg e te d a re a s fo r c le a ra nc e ! ! Re sult: Ma ss displa c e me nt ! ! RG195: F e de ra l Ho me L o a n Ba nk Bo a rd, HOL C,1933 - 1951 ! ! Co nta ins Ma ps, Ne ig hb o rho o d Surve ys, L o a n I nfo rma tio n ! ! NYT ime s, Aug . 24, 2017: “Se lf-fulfilling pro phe c ie s: Ho w re dlining ’ s ra c ist e ffe c ts la ste d fo r de c a de s”

Ma pping Ine qua lity Do c ume nts ! ! E a c h surve y c o rre spo nde d to c ity ma p ! ! Gre e n: White / We a lthy = Be st ! ! Blue : White / Wo rking = Still De sira b le ! ! Ye llo w: F o re ig n / I nc re a se in Po C = De c lining ! ! Re d: Bla c k a nd Hispa nic = Ha za rdo us Co lle c tio n Sta tistic s ! ! 150 Bo xe s ! ! Ove r 10,000 surve ys a lo ne ! ! 250 c itie s

Neighborhood description for: Boyle Heights in L.A.: * Area D-53 * “ Red ” area “ Boyle Heights remained one of the most heterogeneous neighborhoods in the city for decades and it was a center of Jewish, Mexican and Japanese immigrant life in the early 20 th century, and also hosted large Yugoslav and Russian populations. ” Wikipedia, 6/17/2011

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U. Richmond Virginia Tech Johns Hopkins U. Maryland Mapping Inequality http://mappinginequality.net

A. Historical Lab Notebooks Paper-based Lab Notebooks: • ! Used in science research • ! Represent a record of: - observations - experiments - ideas - notes - formulas - data Electronic Lab Notebooks: • ! patient medical records

Learning Goals • ! Archival Practices • ! Computational Thinking Practices • ! Ethics and Values Considerations

B. Computational Framework for Library & Archival Education https://dcicblog.umd.edu/ComputationalFrameworkForArchivalEducation/ April 4 / 5, 2019 Motiva tion for Introduc ing Computa tiona l T hinking into L ibra ry a nd Arc hiva l Studie s Curric ulum: ! ! A b a sic unde rsta nding o f the c ha ra c te ristic s o f dig ita l ma te ria ls is impo rta nt fo r future lib r. & a rc hivists. ! ! Arc hiva l c o lle c tio ns a re inc re a sing ly c o mpo se d o f dig ita l ma te ria ls. ! ! T he to o ls a nd pra c tic e s a sso c ia te d a rc hiva l a c tivitie s a re inc re a sing ly de pe nde nt o n c o mputing . ! ! T he wa y use rs inte ra c t with a rc hiva l c o lle c tio ns re fle c ts the inc re a sing ly c o mputa tio na lly-me dia te d na ture o f o ur wo rld. ! ! F o r to da y’ s le a rne rs to suc c e e d in future a rc hiva l ta sks, it is e sse ntia l tha t c o mputa tio na l thinking is inc lude d a s pa rt o f the ir tra ining . Approa c h: ! ! De ve lo p c o mputa tio na l thinking e nha nc e d le sso n pla ns fo r a rc hiva l to pic s tha t c o uld b e use d b y iSc ho o l fa c ulty to intro duc e c o mputa tio na l thinking into the ir c o urse s. ! ! Build a n inte rna tio na l ne two rke d c o mmunity o f iSc ho o l fa c ulty a nd L ib ra ry a nd Arc hive s pra c titio ne rs to e ng e nde r the se c a pa b ilitie s?