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Original post on hcommons.social

New #intro post! I'm a historian who who builds digital things to help people explore and use #GLAM collections (galleries, libraries, archives & museums). For some examples from the past 30 years see: https://wraggelabs.com

Most recently I was Creative Technologist-in-Residence at the State […]

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Use Datasette-Lite to share a CSV file as a searchable, online database - GLAM Workbench A collection of tools and examples to help you work with data from galleries, libraries, archives, and museums

Pushed a fix to my custom Datasette-Lite repo so that the full-text indexing handles multiple columns properly. If you want to use it to share your CSVs as searchable databases, you can construct the necessary url with this form in the #GLAMWorkbench: https://glam-workbench.net/glam-tools/datasette/

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The GLAM Workbench introduction to how notebooks work now runs in Jupyter Lite I’ve just updated my introduction to using Jupyter notebooks in the GLAM Workbench so that it runs in Jupyter Lite – that means no more waiting for cloud services to spin up, it all happens in your browser! All the Jupyter notebooks in GLAM Workbench can be run in the cloud using the free Binder service – either through the ARDC (requires authentication), or through the public, community-run service. While it’s usually just a matter of clicking a link, Binder can take a while to build the necessary computing environment, and sometimes it just fails. Jupyter Lite takes a different approach. Instead of building things in the cloud, it sets up everything it needs to run notebooks _within your own browser_. I’ve been experimenting with Jupyter Lite a bit over the past couple of years, waiting for the technology to reach the point where I could integrate it into the GLAM Workbench without greatly multiplying the maintenance burden. The obvious place to start was my introductory notebook, which demonstrates how Jupyter notebooks themselves work. Using live data from the National Museum of Australia API, it describes the basic structure of notebooks, and shows you how to edit and run code within them. I’ve now set things up so this notebook runs in Jupyter Lite. What does this mean? Previously, the link to the introductory notebook spun up a new Binder instance. Now, the link retrieves a static web page hosted on GitHub. As this page loads, it installs a Python kernel and everything else it needs to run the notebook within your browser. It’s a lot faster than waiting for Binder, and provides a smoother experience for new users. And because it’s just an ordinary web page, I can even embed a live, working version of the notebook within this blog post. Try it out! Jupyter Lite won’t currently work with every notebook in the GLAM Workbench. Some Python packages are difficult to install, and some data sources can’t be accessed due to CORS problems. But I’m planning to add Jupyter Lite options where I can.

Wrote up a short post about the new Jupyter Lite version of the #GLAMWorkbench's getting started notebook – includes a live version of the notebook embedded in the post! updates.timsherratt.org/2025/04/28/the-glam-work...

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Public Record Office Victoria - GLAM Workbench Tools and resources for working with the Public Record Office Victoria's collection API.

The #GLAMWorkbench has a brand new section for the Public Record Office Victoria! The first notebook shows you how to use PROV's collection API. You can run it live on Jupyter Lite as well as Binder. https://glam-workbench.net/prov/ #GLAM #archives #histodons #digitalHumanities

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Original post on hcommons.social

I've created a Jupyter Lite version of the #GLAMWorkbench's 'getting started' notebook. It shows you how Jupyter notebooks work, with live examples that download data from the NMA collection API. glam-workbench.net/getting-started-jlite/la... […]

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Screen capture of the heading section of a Jupyter notebook entitled: "Getting started with the PROV API". The table of contents includes:

    A simple API request
    Using an 'empty' query to get everything
    The different types of entities in API results
    Identifiers and links
    Search facets
    Controlling the way results are delivered
    Retrieving a random result
    Harvest a complete set of results
    Constructing queries
        Boolean operators
        Text searches
        Filter results by using fields
        Filter by date
        Find digitised records
        Find records about people
        Find an individual record
        Find related entities

Screen capture of the heading section of a Jupyter notebook entitled: "Getting started with the PROV API". The table of contents includes: A simple API request Using an 'empty' query to get everything The different types of entities in API results Identifiers and links Search facets Controlling the way results are delivered Retrieving a random result Harvest a complete set of results Constructing queries Boolean operators Text searches Filter results by using fields Filter by date Find digitised records Find records about people Find an individual record Find related entities

This has taken me far too long (tired, distracted brain), but I think it's just about ready for the #GLAMWorkbench...

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A screen capture from the 'Overview' section of the PROV Data Dashboard. It includes the following information:

"The PROV API currently returns 10,144,070 results.

The API includes information about a number of different entities described in the PROV Archival Control Model.

The entity and category fields tell you what sort of data you're dealing with. The charts below use facet counts from the API to find the number of results for each value in these fields."

The value counts in the 'Entity' and 'Category' fields are displayed below this, with a table and a bar chart side by side for each field. The text for the entity field reads:

"Entity: Top-level conceptual entities defined by the PROV's PROV Archival Control Model"

The value counts are:

Record 	6,376,415
Agent 	3,172
Function 	321

The text for the category field reads:

"Category: A more fine-grained grouping of the descriptive entities used in PROV data – the top-level Record entity is sub-divided into Series, Consignment, Item, and Image"

The value counts are:

Item 	6,335,803
Image 	3,613,751
relatedEntity 	150,411
Consignment 	23,647
Series 	16,965
Agency 	3,172
Function 	321

A screen capture from the 'Overview' section of the PROV Data Dashboard. It includes the following information: "The PROV API currently returns 10,144,070 results. The API includes information about a number of different entities described in the PROV Archival Control Model. The entity and category fields tell you what sort of data you're dealing with. The charts below use facet counts from the API to find the number of results for each value in these fields." The value counts in the 'Entity' and 'Category' fields are displayed below this, with a table and a bar chart side by side for each field. The text for the entity field reads: "Entity: Top-level conceptual entities defined by the PROV's PROV Archival Control Model" The value counts are: Record 6,376,415 Agent 3,172 Function 321 The text for the category field reads: "Category: A more fine-grained grouping of the descriptive entities used in PROV data – the top-level Record entity is sub-divided into Series, Consignment, Item, and Image" The value counts are: Item 6,335,803 Image 3,613,751 relatedEntity 150,411 Consignment 23,647 Series 16,965 Agency 3,172 Function 321

I'm still fiddling with the Public Record Office Victoria API, so to help me and others get their heads around the data the API provides, I created a dashboard that gives a basic overview. It should update every Sunday […]

[Original post on hcommons.social]

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GLAM Workbench citations - GLAM Workbench This page brings together references to the GLAM Workbench and its associated tools.

I've added another 15 entries to my list of publications that mention the #GLAMWorkbench or one of its related tools (such as the #Trove Newspaper Harvester). That makes an even 100 references from around the world. https://glam-workbench.net/citations/ #GLAM #collectionsAsData #researchImpact

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Screenshot of banner at the top of the GLAM Workbench homepage, the message reads: "The National Library of Australia has cancelled my Trove API key. This makes it difficult to maintain or develop the Trove sections of the GLAM Workbench. Trove API users should also be aware of changes to the terms of use which limit access to content of resources, including the full text of digitised newspaper articles."

Screenshot of banner at the top of the GLAM Workbench homepage, the message reads: "The National Library of Australia has cancelled my Trove API key. This makes it difficult to maintain or develop the Trove sections of the GLAM Workbench. Trove API users should also be aware of changes to the terms of use which limit access to content of resources, including the full text of digitised newspaper articles."

Added a new banner to the #GLAMWorkbench... 😞

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Original post on hcommons.social

I was asked recently for help searching a community newsletter that had been uploaded to #Trove through the National eDeposit service (NED). That led me down a bit of a rabbit hole... I've now added a notebook to the #GLAMWorkbench that walks through the process of extracting metadata and text […]

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I've also created a new #GLAMWorkbench section for #Trove NED stuff. It's pretty much done, but neither MyBinder or ARDC Binder are working at the moment, so I can't fully test things...

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Original post on hcommons.social

I'd sort of like to nominate the #GLAMWorkbench in the Eureka Prize's research software category. It wouldn't win against all the science-y, institutionally-supported things, and it's a lot of work to submit, so not sure it's worth the effort just to fly the flag for #GLAM, HASS, and the work […]

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Original post on hcommons.social

I think this is working now... a custom search interface to a periodical uploaded to #Trove through the National eDeposit scheme. In Trove it's a collection of PDFs that can only be searched individually using the PDF viewer. I've pulled out all the text, created a full-text index in a SQLite […]

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Tonight I'm having a chat about data labs & my work on the #GLAMWorkbench with folks from the impressive Impresso project: https://www.impresso-project.ch/

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Original post on hcommons.social

The work of #maintenance is never done – some details of last week's updates to the #Trove newspapers section of the #GLAMWorkbench: updates.timsherratt.org/2024/09/23/major-update-... The Trove newspapers section includes 23 Jupyter notebooks and 6 datasets. #GLAM #histodons […]

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Original post on hcommons.social

I thought I'd pull together a little list of all the datasets I've created over the years that capture data about the history of online GLAM collections (specifically Trove and RecordSearch). They're scattered a bit between the #GLAMWorkbench, GitHub, and Zenodo, so I thought it's be useful to […]

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#GLAMWorkbench update! Saving #Trove's digitised periodicals as PDFs: updates.timsherratt.org/2024/09/19/saving-troves... #GLAM #histodons #digitalHumanities

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Original post on hcommons.social

It took a week, but the #Trove newspapers section of the #GLAMWorkbench is now fully updated. That's 25 notebooks and 6 datasets. https://glam-workbench.net/trove-newspapers/

For those who are interested (probably nobody), as well as updating the code to work with v3 of the Trove API, I pulled […]

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Image created by snipping words out of digitised newspapers, text reads: "Why does Trove hate API users"

Image created by snipping words out of digitised newspapers, text reads: "Why does Trove hate API users"

Ok, so I've updated all the notebooks in the Trove newspapers section of the #GLAMWorkbench to make sure they work with v3 of the Trove API. They're not in the repository yet – still have to update all the metadata & generate RO-Crate files for nbs and data.

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Original post on hcommons.social

I can't remember if I ever did an #intro post, but it's probably out of date anyway. I'm a historian who has spent much of the last couple of decades working in & around the #GLAM sector, building #open tools & resources to help other historians do interesting things with digital collections. I […]

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Finding ways to ask historical questions of #webarchives with @wragge's #GLAMWorkBench @uc_cccr

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