Digitization - Basic questions
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Things to consider before, during and after a digitization project in an historical institution. Lecture by Daniel Jeller on the 13th September 2011 in Volterra.
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I say emulate
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Digitization
Digitization is the process of converting analog materials like written records, photographs, films and artifacts into a digital format. While digitized materials allow for greater accessibility through efficient search and preservation, some information is lost in translation to digital 1s and 0s. However, digitization transforms how we research, present and access historical materials. There are different methods of digitization including page imaging, markup with standards like TEI to make text machine-readable and searchable, and optical character recognition. Each method has advantages and disadvantages for representing the original content.
About Scanning and Metadata Standards - NEMO 2010
About Scanning and Metadata Standards - NEMO 2010University of Connecticut Libraries Map and Geographic Information Center - MAGIC
Presented at the New England Map Organization conference on June 3, 2010 at the University of Southern Maine in Portland, Maine.Architecting a datalake
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Intro to Big Data
Introduction to Big Data and NoSQL.
This presentation was given to the Master DBA course at John Bryce Education in Israel.
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Brev loc cloud data storage, backup and recovery pres
This document discusses data backup and recovery solutions, specifically Brevard Local Cloud. It notes that data loss can be catastrophic for businesses and outlines potential causes of data loss like hardware and human errors. Brevard Local Cloud is presented as a local backup solution where a user's data is backed up every 15 minutes to storage in Brevard County. The service provides up to 4 devices with 100GB storage, remote access, version restoration, and assistance for $35 per month.
Managing active data: storage, access, academic dropbox services
Researchers need to store and access active data for their work. They often use local storage devices and email to manage their data, but these methods lack resilience. Institutions provide network storage, but their systems are rarely used by researchers for active data access and sharing. An ideal solution would transparently synchronize local and network storage, providing both the convenience of local access and the resilience of network attached storage. However, there are challenges to managing active research data at large scales, including file sizes, storage locations, costs, ease of use, network transfers, versions, and access rights that institutions aim to address.
2010 AIRI Petabyte Challenge - View From The Trenches
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IT security for all. Bootcamp slides
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Technical track-afterimaging Progress Database
Technical track-afterimaging Progress Database
Contact us anytime: Think Next - QAD Partner in Vietnam
- Website: http://www.thinknext.vn
- Email: contact [at] thinknext.vn
A Case Study of NoSQL Adoption: What Drove Wordnik Non-Relational?
Wordnik migrated from a MySQL relational database to the non-relational MongoDB database for 5 key reasons: speed, stability, scaling, simplicity, and fitting their object model better. They tested MongoDB extensively, iteratively improving their data mapping and access patterns. The migration was done without downtime by switching between the databases. While inserts were much faster in MongoDB, updates could be slow due to disk I/O. Wordnik addressed this through optimizations like pre-fetching on updates and moving to local storage. Overall, MongoDB was a better fit for Wordnik's large and evolving datasets.
Digital Preservation Discussion Group
The document summarizes key discussion points from Axiell East Coast Roadshow 2018's Digital Preservation Discussion Group. The group discussed: 1) the types of digital materials cultural heritage organizations manage like images, documents, and born-digital files; 2) who has organizational digital preservation policies in place; and 3) what digital preservation means and current challenges organizations face in preserving digital collections over time given risks like file formats becoming unreadable and materials becoming lost or unrecoverable without preservation planning. The group also explored options for on-site and cloud-based preservation solutions and factors to consider when developing a digital preservation program and policy.
Q2 Briefing Presentation
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1. ManagePoint 4.3.2 will include improved office integration, a new ManageAnywhere interface, updated image editing and viewing tools, and input queue functionality.
2. CaptureMail is an email archiving solution that will be available in June, with options for full archiving setup or importing existing emails.
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A little simple explanation abut Digital imaging
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Brev loc cloud data storage, backup and recovery pres
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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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- 2. What is digitization? • The translation of analogue data into digital data • The process of taking a digital image of something • The creation of a digital representation of something
- 3. Why digitize? ?
- 4. Phases 1) Initial reflections 2) Preparation phase 3) Metadata creation 4) Actual digitization 5) Postproduction 6) Long-term storage
- 5. Initial reflections • What do I want to digitize? • Why do I want to do it? • Do I already have the necessary metadata? • What safety precautions are necessary? • Do I want to digitize myself or hire a company? • What will happen with the digital object, once produced?
- 6. Technical reflections • Image format (TIFF, JPEG, JPEG2000,…) • Image quality (Resolution, Image compression) • Equipment (Scanner vs. Digital Camera) • Storage space (webhosting?) • Long-term storage • Filename (Human vs. Computer readable)
- 7. Scanner vs. Digital Camera Scanner Digital Camera • Variable resolution • Constant resolution • Lower speed • High speed • Mainly for flat objects • Higher adjustability
- 8. Metadata for Digitization Why is this important? • Every digital object has to be identifiable • Most file systems only permit unique names • Changes to filenames are difficult to make after digitization
- 9. Preparation • Enough space • Constant indirect lighting, no direct sunlight • Tables for preparation • Enough power outlets
- 10. Digitization • Speed vs. Quality • Number of people depending on source material • Storage of sources during the night • Delivery of new sources • Quality control • Image postprocessing
- 11. Source material • Flat surface • Book • One sheet • Possibly very large • Possibly folded • Possibly difficult to • Possibly packaged open
- 12. File naming Human readability Computer readability “HHStA_AUR_16890201_r.jpg” „7c7a1400-dd12-11e0.jpg“ • Easy to read and share • Can be created quickly • Has to be created by hand before digitization • Easy to make mistakes • Has to be copied, not • Temptation to make entered by hand changes later on • Doesn’t mean anything • Can stay the same regardless of metadata
- 13. Postproduction • Image editing (cutting, compression,…) • Quality control (missing images) • Preparation for storage (file organization)