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pen objects every age has its orthodoxy and no orthodoxy is ever right menu and widgets search for recent posts what can you learn about 270 000 records in a week documenting ai created enhanced records in catalogues metadata displays ai and machine learning in libraries promising but not ready yet cultures of collective work and crowdsourcing notes from the museum data service launch recent comments mia on documenting ai created enhanced records in catalogues metadata displays mia on ai and machine learning in libraries promising but not ready yet tony hirst on ai and machine learning in libraries promising but not ready yet cultures of collective work and crowdsourcing open objects on national approaches to crowdsourcing citizen science cultures of collective work and crowdsourcing open objects on crowdsourcing the world s heritage crowdsourcing our cultural heritage curious about crowdsourcing in cultural heritage my introduction to my edited volume crowdsourcing our cultural heritage is free online for now as a preview pdf from the publisher of crowdsourcing our cultural heritage about this blog posts from a cultural heritage technologist on digital humanities heritage and history and user experience research and design a bit of wishful thinking about organisational change thrown in with a few questions and challenges to the cultural heritage sector on audience research museum interpretation interactives and collections online open objects archives 2026 jan feb mar apr may jun jul aug sep oct nov dec 2025 jan feb mar apr may jun jul aug sep oct nov dec 2024 jan feb mar apr may jun jul aug sep oct nov dec 2023 jan feb mar apr may jun jul aug sep oct nov dec 2022 jan feb mar apr may jun jul aug sep oct nov dec 2021 jan feb mar apr may jun jul aug sep oct nov dec 2020 jan feb mar apr may jun jul aug sep oct nov dec 2019 jan feb mar apr may jun jul aug sep oct nov dec 2018 jan feb mar apr may jun jul aug sep oct nov dec 2017 jan feb mar apr may jun jul aug sep oct nov dec 2016 jan feb mar apr may jun jul aug sep oct nov dec 2015 jan feb mar apr may jun jul aug sep oct nov dec 2014 jan feb mar apr may jun jul aug sep oct nov dec 2013 jan feb mar apr may jun jul aug sep oct nov dec 2012 jan feb mar apr may jun jul aug sep oct nov dec 2011 jan feb mar apr may jun jul aug sep oct nov dec 2010 jan feb mar apr may jun jul aug sep oct nov dec 2009 jan feb mar apr may jun jul aug sep oct nov dec 2008 jan feb mar apr may jun jul aug sep oct nov dec 2007 jan feb mar apr may jun jul aug sep oct nov dec 2006 jan feb mar apr may jun jul aug sep oct nov dec popular posts a new year s resolution for start ups prs and journalists writing about museums the rise of interpolated content max anderson indianapolis museum of art moving from virtual to visceral how did play shape the design and experience of creating serendip o matic new challenges in digital history sharing women s history on wikipedia my talk notes sharing is caring keynote enriching cultural heritage collections through a participatory commons subscribe via rss log in entries feed comments feed wordpress org tags accessibility apis archaeology audiences best practice collections conference conference papers conferences crowdsourcing cultural content cultural heritage sector development models digital heritage digital history digital humanities digital objects experimental glam hackday mash ups mcg metadata museums museums computer group museum technologists mw2008 open data organisational change organisational resistance outside museum walls participatory web programming research science museum semantic web social software tagging technology uncategorised usability user centred design user generated content web 2 0 web development what can you learn about 270 000 records in a week not the cooper hewitt a view from my accommodation way back in 2012 i had a week long residency at the cooper hewitt smithsonian design museum in new york it s interesting reading the post i wrote for the cooper hewitt lab blog again and thinking about which of the challenges i faced would be easier now and which are fundamental to working with museum data the post is also an accidental paean for tools i loved that that are now gone manyeyes fusion tables at least google refine openrefine lives on my experience wrestling with collections metadata at the cooper hewitt profoundly influenced how i taught data visualisation and digital cultural heritage for at least the next decade go back in time with this post from 2012 mia ridge explores the shape of cooper hewitt collections posted on 8 july 2026 categories fellowships and residencies museums museums libraries and archives technology leave a comment on what can you learn about 270 000 records in a week documenting ai created enhanced records in catalogues metadata displays i ve been thinking about principles for documenting where ai was used to create or enhance metadata records in library museum and archive catalogues collections management systems ideally one could also document the tool and version used which is always helpful context and might also be used to note e g where re doing automatic text transcription atr with newer tools might make a big difference edit 26 feb 2026 it s also important in situations where an organisation might feel the need to deploy good enough tools to meet legislative accessibility requirements or feel that any description even if potentially inaccurate is better than no description in that case they d probably want to record which fields in which records have good enough for the time information so it could be improved in future it s become timely in conversations with the oral history team and digital curator for atr at the bl i d posted about it in various places including the jiscmail ai in ch list ai in cultural heritage not to be confused with ai4lam it s hard to link to threads on jiscmail so as most of the posts are mine and the other is a helpful post from the always helpful stephen mcconnachie i ve copied them here to make them shareable mia ridge 2025 11 13 documenting automatic text transcription tools in catalogues metadata displays i have a question from our digital curator for automatic text recognition that we re hoping others working with digitised texts have some thoughts on do you know of any standards or processes for providing information about the use of ai ml tools to create transcriptions for metadata for collections management or public interfaces or given the wider focus of this group the use of any other ai ml tools to create or enhance metadata records i know that some libraries like the bnf s gallica share ocr error rates in public interfaces and others include information about the software software version date of processing in alto files but is everyone doing this in a slightly bespoke way or are any shared conventions or standards emerging i ve had related conversations with the british library s metadata standards team about recording the use of ai ml to enhance metadata in marc fields for printed heritage items but many of the items we re looking at might be newspapers and periodicals catalogued differently as well as sound av and manuscript archive files stephen mcconnachie 2025 11 13 re documenting automatic text transcription tools in catalogues metadata displays i m not aware of any emerging or established standards for documenting and contextualising text extraction via ocr or similar would certainly be keen to find out though so thanks very much for raising it one thing that i am aware of and aiming to implement in our speech to text subtitle output for our a v collection is the fadgi recommendations for vtt file creation https www digitizationguidelines gov guidelines fadgi_webvtt_embed_guidelines_v0 1_2024 04 15 pdf some of its modelling might be portable to text extraction metadata capture i guess mia ridge 2026 01 09 re documenting automatic text transcription tools in catalogues metadata displays on a slightly related note i realised that axiell have been generating disclaimers when writing back records updated with ai named entity recognition with wikidata as on https help collections axiell com en topics ai 20and 20collections htm a disclaimer is added to every keyword extracted in this way indicating that ai was used in the matching process the disclaimer identifies the origin of the keyword the field and character position it was extracted from its confidence score and the date and time of extraction the sample text says this was added using al based entity linking entity text between character 66 and 71 in the description field occurrence 1 entity label person confidence score 97 85 date time of extraction job creation 2025 02 12t08 46 25 entity uuid a63853c8 dca4 4dc9 9ec0 07eb36eb8843 mia ridge 2026 01 22 re documenting automatic text transcription tools in catalogues metadata displays adding to this thread in case people are interested owen king got in touch via the ai4lam slack join link to share an update from the ai4lam speech to text wg we have been trying to find a standard way to record metadata about the provenance especially the ai tooling used of our audio transcripts it seems to me that almost all of it applies to atr and htr transcripts as well this is our current draft is this of any help and while i m here is anyone working on the ethics of oral history recordings online and ai transcription i had an interesting conversation with a european university librarian who reported that their oral history recordings were behind a login but that browsers were now offering to transcribe the recordings raising new questions about data scraping etc posted on 9 february 2026 26 february 2026 categories digital humanities machine learning and ai museums libraries and archives technology tags ai atr machine learning transcription 1 comment on documenting ai created enhanced records in catalogues metadata displays ai and machine learning in libraries promising but not ready yet i ve turned a version of the talks i ve been doing on ai in libraries for the past few years into an article for a library magazine this is very much a pre print but it ll serve to capture a moment in time apparently it s an 11 minute read artificial intelligence has become one of the most discussed technologies of our time but what does it actually mean for libraries and other cultural heritage institutions in a decade working in digital scholarship at the british library i ve witnessed firsthand the potential for ai to transform access to our collections while also learning about its very real limitations understanding ai beyond the hype before exploring what ai can and can t do for libraries it s worth defining what we mean by ai the term encompasses several related technologies that have evolved over time several years ago we talked about digital research with big data then we were excited about machine learning and data science methods for developing software that was able to complete more complex tasks we experimented with assistive or discriminative machine learning tools that could transcribe handwritten text predict tags for images detect entities like people places and dates in text or correct your spelling these tools were useful but relatively narrow in scope now we have generative ai tools that can produce plausible new texts images and videos however it s crucial to understand their limitations they can t count or do mathematics reliably they don t truly understand words or concepts they can t grasp real world physics and they can t determine truth i often use an image i generated with an ai tool using the prompt rare books and special collections to illustrate this point the result looks impressive it has all the visual elements we associate with ancient books including leather binding and aged paper but when you examine it closely it s a physically impossible book the pages can t be opened the text can t be read it captures the superficial appearance of a rare book without any of the characteristics of a physical book this perfectly demonstrates both ai s impressive capabilities and its fundamental limitations figure 1 an image generated by ai with the prompt rare books and special collections or to put it more flippantly we call it machine learning when it works and ai when it doesn t practical applications in library collections despite these limitations ai and machine learning offer significant benefits for libraries one of the most powerful applications for libraries is automatic text transcription from images and audio a whole world of possibilities opens up once you have digital text librarian and developer matt miller demonstrated what is possible with an early version of gpt he took digitised images of a handwritten historical diary and used ai to automatically transcribe the text generate summaries of each entry extract dates and locations and identify people and places mentioned within this transforms previously inaccessible handwritten documents into searchable structured data which can be used in research or to find related items entity detection and linking identifying people places dates and concepts within text allows us to create rich connections across our collections object detection in images is another breakthrough ai can identify items in photographs and generate keywords labels and descriptions this technology is probably already working on your smartphone you can search your photos for concepts like dog or dinner or select copy and paste text from a photo for libraries this means our visual collections become much more discoverable perhaps most importantly for library users ai can cluster similar images and words enabling conceptual rather than keyword based searching instead of users needing to know how library catalogues work they can search for vibes or concepts making library collections far more accessible to diverse audiences we can also translate text and speech into other languages if you attended my talk at knihovny současnosti 2025 then you probably saw the live transcription and translation of my australian accented english into czech subtitles ai can also rewrite content for different audiences perhaps explaining complex research for tourists or children as an example the process of writing this article was augmented by ai machine learning tools i recorded my rehearsal and live delivery of my conference presentation then copied the text transcriptions my phone generated from those recordings into an ai tool claude ai i asked the tool to turn my spoken words into an article then edited the result into the article you re reading now it s all my own thoughts but with a level of polish that it would have taken me a lot longer to produce building institutional capacity the british library s experiments with machine learning and ai didn t happen overnight we ve invested in training and digital literacy around ai and machine learning for...