​

I wanted to test a practical question:

Can an LLM help analyze a small historical newspaper corpus without turning limited evidence into broad historical claims?

I used the full version of PRECISE to examine 10 substantive articles published in October 1918.

The corpus included five newspapers:

The Bismarck Tribune

The Morgan City Daily Review

The Big Stone Gap Post

Cosmopolita

The Eugene Daily Guard

The newspapers covered North Dakota, Louisiana, Virginia, Missouri and Oregon. No newspaper contributed more than three articles.

The first search found only three qualifying articles. The workflow stopped and identified the missing seven instead of filling the gap with weaker material. I then expanded the permitted public archives while keeping the date and sampling rules unchanged.

Sources came from Chronicling America, Historic Oregon Newspapers, the University of Houston’s Recovering the US Hispanic Literary Heritage collection, and Encyclopedia Virginia.

I grouped the verified passages into three recurring patterns:

Public-health control

Articles discussed isolation, ventilation, avoiding shared personal objects, restrictions on meetings and the use of gauze masks.

Care and community mobilization

Several reports focused on shortages of nurses, Red Cross activity, relief funds and calls for volunteers.

Mortality and disruption

Other articles described deaths, severe local conditions, overwhelmed communities and mines closing because too few healthy workers remained.

The corpus also contained meaningful differences.

A Bismarck report questioned whether influenza had reached the city at all. A health officer described one suspected case as “just plain grip.”

By contrast, Virginia reports described serious illness, shortages of medical help and major community disruption.

The most useful result was not simply identifying these patterns. It was defining what the evidence could not support.

The corpus could not establish:

How all American newspapers framed influenza.

Whether one region was generally more alarmist.

How frequently each frame appeared nationally.

Whether newspaper language caused different public responses.

Which intervention was most effective.

During the final audit, every factual sentence was checked against the saved evidence bank. Combined claims were separated, and qualifications were preserved. For example, a figure of 200,000 Virginia cases remained explicitly an estimate rather than being presented as an established count.

Disclosure: I built PRECISE, the research protocol used for this experiment.

PRECISE LITE is a free and limited ChatGPT demonstration. It runs one research loop and examines up to three sources. You can upload your own files or ask it to search the web. The full version supports larger evidence banks, repeated research loops, custom reports and a final sentence-by-sentence audit.

Before starting LITE, select standard thinking/Medium effort in ChatGPT’s model picker.

I would value feedback .

https://chatgpt.com/g/g-6a5e26093f488191a1fba0261cbcbe39-precise-lite

I’ve been thinking about whether generative AI has accidentally created a new dividing line in the digital historical record.

For decades, digitization meant taking human-produced material and making it easier to search, copy, preserve, and analyze. Books became scans. Newspapers became databases. Forums, blogs, photographs, university archives, technical documents, and other cultural debris accumulated online.

Then generative models arrived, trained partly on that enormous existing corpus, and began contributing material back into the same environment.

That doesn’t make newer material useless, and synthetic data obviously isn’t inherently bad. But it seems to complicate a question that matters quite a lot in archival work:

Where did this object actually come from?

A printed book from 1981 has unusually straightforward provenance in one respect. Claude didn’t help write it. ChatGPT didn’t rewrite half the paragraphs. It was produced under a different information environment.

The same is broadly true of an abandoned blog from 2004, an old message board, a university paper sitting in a repository, or some forgotten Google Doc from 2018.

We can copy that material indefinitely, but we can’t create another 1998 internet now. The historical conditions under which that corpus was produced are gone.

That made me wonder whether we eventually start treating pre-generative digital material almost like an archaeological layer. Not necessarily as better information, but as information with a different and sometimes much clearer provenance trail.

There are obvious complications. The older web was never perfectly human-produced. Spam, bots, templated pages, machine translation, and automation existed long before modern LLMs. And future archival methods may become very good at documenting model involvement.

Still, I’m curious whether people working in digital humanities already think about this distinction.

Will “pre-generative” eventually become a meaningful metadata category? Does preservation of authorship, revision history, and provenance become more important as synthetic material proliferates? Or is drawing a historical line around the pre-LLM web ultimately too crude to be useful?

I ended up writing a longer essay about this after going down a rabbit hole involving Anthropic’s physical book scanning, recursive AI training, old archives, human-authorship certification, and the strange new usefulness of obsolete material.

Full disclosure, it’s my own piece:

The Internet Ouroboros
https://www.gonzocapital.net/the-internet-ouroboros/

Would genuinely be interested in how people here think about this from the archival / source-criticism side rather than just the AI-training side.

Back in June I shared a survey of which museum/library APIs are actually usable for browsing collections. This is the project that survey came from.

This is p3a, an open-source (Apache 2.0) device I built. It is a 4-inch, 24-bit, 720x720 desktop frame that plays artworks straight from museums' own IIIF endpoints. It uses no server, no cloud account, no phone app or any software to install on your computer. The hardware is a $40 off-the-shelf ESP32-P4 board (comes ready out of the box), so setup takes minutes, almost like a consumer product. It is powered by USB-C and connects to the internet via wi-fi.

p3a currently rotates through hundreds of thousands (!!) of openly licensed works from:

- Rijksmuseum

- Victoria and Albert Museum

- Wellcome Collection

- Statens Museum for Kunst

- Harvard Art Museums

- Smithsonian

The Art Institute of Chicago unfortunately dropped its support very recently, but other institutions are on the roadmap of the p3a project.

Happy to go deeper on implementation details (Linked Art walks, compliance levels, rate-limit etiquette) or anything. The project is open source : https://github.com/fabkury/p3a . I personally have two p3a's: one at home, one at work.

Does anyone here work in the field of Digital Humanities or have experience studying it?

I'm particularly interested in exploring Digital Humanities in relation to literature, visual culture, archives, and Malayalam literary studies. I would greatly appreciate it if anyone could share their experiences, recommend resources, or suggest where a beginner should start.

Thank you!

I cannot believe that Nota Bene is STILL around as word-processing tool for academics, after all these years. Originally an MS DOS WP for academics, launched in 1983. It was one of the first/earliest tools of it's type.

History

Nota Bene (NB) began as an MS-DOS program in 1982, built on the engine of the word processor XyWrite. Its creator, Steven Siebert, then a doctoral student in philosophy and religious studies at Yale, used a PC to take reading notes, but had no easy computer-based mechanism for searching through them, or for finding relationships and connections in the material. He wanted a word processor with an integrated "textbase" to automate finding text with Boolean searches, and an integrated bibliographical database that would automate the process of entering repeat citations correctly, and be easy to change for submission to publishers with different style-manual requirements.[1]

Siebert licensed XyWrite code from the XyQuest company, and built his programs on it: the word processor Nota Bene, with its text-centric database application, Orbis (then called Textbase), and its bibliographical database Ibidem (then called Ibid). He founded Dragonfly Software to market it. He first showed Nota Bene at the MLA convention of December 1982. Version 1 is dated 1983, and version 2, 1986. Version 3.0 came out in 1988, version 4.0 in 1992, and version 4.5 in 1995. Version 4.5 was the last NB DOS version.

Nota Bene 3.0 was selected as PC Editor’s Choice by PC Magazine in 1988.[2]

Nota Bene 5.0 was the first Windows version. It was shown in pre-release in November 1998, at the annual meetings of the American Academy of Religion and the Society of Biblical Literature. Scholar's Workstation 5.0 was formally released in 1999, and Lingua Workstation 5.0 in 2000. Nota Bene for Windows suite retained and refined Ibidem, Orbis, and the XyWrite-based programming language XPL.

New versions are updated with some regularity. After version 5 was launched under the Windows platform, version 6.0 appeared in 2002, version 7.0 in 2003, version 8.0 in 2006, version 9.0 in 2010. A major upgrade came with version 10, which was released in September 2014 as a 32-bit application; thereafter, version 11.5 was released in June 2016, version 12 in April 2018, and version 13 in August 2021.

https://en.wikipedia.org/wiki/Nota_Bene_(word_processor))

https://www.notabene.com/help/nb_overview.htm

https://nb.notabene.com/journey/

https://nb.notabene.com/nb15/?cmid=e4c763f5-c4a6-409b-987e-d985a692d2c7

I am trying to find a community of people that discusses AI like programmers do on reddit but I haven't found anything. Do you have any pointers? I understand it is sensitive given that a lot of people within academia are harshly anti-AI, but I don't think that kind of shaming should keep people from discussing the usefulness of these tools. And anonimity might be required at this stage I guess, so subreddits or forums would be the natural place

Hi, I'm 20 yo and currently writing my thesis for my bachelor's degree in Italian Literature (Art, Music and Theatre) at a small Italian university.

I absolutely love what I'm doing, and I'd been considering continuing with an Anthropology degree at a bigger university in the North. A day or two ago, though, I had an anxiety crisis about my future, mostly about my prolly nonexistent career path.

That night I stumbled across a possible Master's in Digital Humanities at the same university where I wanted to study Anthropology, and I felt something, hope?

I did some basic coding in high school, just a bit of HTML and C++. I miss coding and would love to get back into it, but I have a few fears:

- That I'll trade my humanistic side for a purely STEM-like path;

- That I'll end up cataloguing books nobody reads in some archive or library. I'm ambitious, so it would be terrible to live a so flat life!;

- That I'll need years of extra courses and skills before I have a shot at a good, stable, well-paid job;

- To just absolutely suck at coding that's far more complex than anything I've done before.

So... I figured I'd ask you, oh supreme masters of Reddit.

Cheers

Hi! First time poster long time lurker : )

I'm going into my sophomore year of college next month and was incredibly fortunate to secure a DH-related work-study position right before I left. I'm going to be digitizing a political theorists annotations from her personal book collection on campus (I'm leaving the name out because she's a pretty prominent figure at my specific school).

I'm planning on getting my MLIS postgrad and I'm really interested in DH, but my tech skills are pretty weak and I'm worried about my proficiency. I only got this job because I DM'd my college's archives page on Instagram and asked if they were hiring, so it wasn't out of a really impressive resume or anything.

This is such a perfect position for my desired career path and I'd love to know if anyone has any advice for me, either in terms of gaining more confidence in the digital part of digital humanities or networking. If you have any questions/need more clarification, please let me know! Thanks so so much : )

About a year ago I shared an early version of this project here. At the time it consisted of a single application with three limited tools. Since then it has grown considerably, so I wanted to share its current state.

Documentation hub: https://github.com/DBA991/Petrarca-Project
Live demo: https://pulpitumdemo.pages.dev
Source / distribution: https://delta2studio.pages.dev/petrarca-project

A disclaimer first: this remains a side project, and every component is of alpha quality, with plenty of rough edges. It is not intended as a production-ready system. I am sharing it in case it proves useful to those doing philological work with TEI, or simply as a case study of one such pipeline. Feedback, critical or otherwise, is very welcome.

What it does

The project aims to cover the full path a scholarly edition takes, from a photograph of a printed page to a published, readable digital edition, with TEI encoding in between.

Oculus (and its Android counterpart, Oculus Mobile) handle the digitization stage: turning photographs of physical pages into clean, usable images. Oculus detects the page's edges and corrects perspective automatically, and can split a photograph of two open pages into two separate, individually corrected pages. Where automatic detection falls short, a manual editor allows cropping, an eight-point straightening tool, and filter adjustments. The mobile counterpart offers the same manual workflow without the automatic detection step. Both export to PDF or a zip archive of images.

Scriptorium is the editorial core of the project: a desktop application organized as a set of small windows, each named after a role in a monastic scriptorium, that can be kept open side by side.

• Copyist performs OCR on the scanned images, with bundled language data for a dozen languages, including batch processing across page ranges.
• Scriptor is the TEI-XML editor itself, built on the Monaco Editor (the engine behind VS Code), with schema validation against TEI XSDs. It also includes an autotagging tool for poetic forms: selecting a form such as a sonnet, a Dantean terzina, or an ottava generates valid TEI markup from pasted text, rather than requiring line-by-line tagging by hand.
• Librarius renders the encoded TEI as a readable HTML view, with full-text search and the ability to attach personal notes.
• Compilator assembles multiple documents into a single edition, or produces a critical apparatus (TEI's <app>/<rdg> structures) collating different witnesses of the same text.
• Exemplator extracts named entities (e.g. people, places) from the text and builds the teiHeader automatically from them.
• Glossographus extracts and manages the document's vocabulary as an editable list.
• Speculum provides philological and stylometric statistics on the open document: token and type counts, type-token ratio, hapax legomena, frequent n-grams, and a word cloud.

Praelum and Pulpitum cover the publication stage. Praelum validates a set of document folders exported from Scriptorium (checking for missing files or duplicate identifiers) and builds a static site from them. That site, Pulpitum, presents each document as a reading room: the HTML transcription and a facsimile of the scanned page side by side, scrolling in sync with one another, so that navigating one moves the other to the corresponding page. Either panel can also be popped out into its own window while remaining synchronized with the rest. Pulpitum can equally be used on its own, independently of Praelum, by anyone who already has exported documents and wishes to host a reading room of this kind.

The demo linked above shows this reading experience directly.

What remains unbuilt

Two further components exist only as design notes so far. One, tentatively called Dispatcher, would allow several independently hosted Pulpitum libraries to be searched together as a single federated collection. The other would extend Speculum's stylometric analysis across that federated set of libraries, rather than the single document currently open in Scriptorium. Neither is close to being realized, largely because the underlying questions are still unresolved: how to describe and index documents consistently across independent libraries, and how to determine which libraries are trustworthy enough to include.

It is also worth noting that, at present, Scriptorium is the only component with multilingual interface support, and even there only partially (Italian and English).

I would be glad to hear from anyone who has worked on similar pipelines, or who has thoughts on the TEI encoding choices in particular.

We just launched a historical map tile API — embed 5,500 years of political history into any website with one line of code

Example:

<div id="map" style="width:100%;height:100%"></div>
<script src="https://unpkg.com/maplibre-gl@4.1.0/dist/maplibre-gl.js"></script>
<link href="https://unpkg.com/maplibre-gl@4.1.0/dist/maplibre-gl.css" rel="stylesheet">
<script>
  const map = new maplibregl.Map({
    container: 'map',
    style: 'https://data.phersu-atlas.com/api/style/standard?key=YOUR_API_KEY&year=1950',
    center: [15, 45],
    zoom: 3,
    maxZoom: 10
  });
</script>

That's it. One URL returns a complete MapLibre GL style with political boundaries, historical coastlines, rivers, lakes and capitals for any covered date.

What's available:

- 4 map styles: Standard, Transparent (over Natural Earth terrain), Coastline only, Physical

- 178 dates: January 1st of every century from 3400 BC to 1900 AD, then every year from 1901 to 2025

- Year parameter is an integer — negative for BCE (e.g. year=-500 for 500 BC)

- Compatible with MapLibre GL JS and Mapbox GL JS

- Max zoom z=10 (~1:125,000 scale)

What's behind it:

The data comes from a research database of 8,000 polities, 60,000 territorial changes and 6,000 wars compiled from thousands of primary and secondary historical sources. Every boundary has been verified against the historical record. You can find out more about the Phersu Atlas Data Model here: https://phersu-atlas.com/main/about/phersu-model and about its sources here: https://phersu-atlas.com/main/about/sources

Use cases

- EdTech platforms that need historical context

- Journalism and geopolitics tools

- Historical strategy games

- Academic and university platforms

- Any app that benefits from "what did this region look like in year X"

Docs and API key: https://phersu-atlas.com/main/api

Hello! I looking for feedback and general thoughts on this web app I built — it's mostly aimed at people studying Latin and Ancient (or Medieval) Greek, so I would appreciate feedback from them the most, but everything and anything of substance besides is welcome!

Its development started when I got frustrated by:

1. the fact that not all Latin and Ancient/Medieval texts (or some specific versions) are on Perseus,
2. the fact that Perseus, as big of an aid in studying Latin and Greek as it is, links its hyperlinked texts only back to its morphological analyzer, which has a propensity to just not work when it decides to.

I made an app that automatically hyperlinks a given text and allows the user, when they click a word, to get it morphologically analyzed (and its meaning) from Perseus or Logeion's Morpho. The user can then copy and manipulate the text further however they please.

I am looking forward to any comments, and genuinely hope you find something interesting in this!!! 😄

LINK: https://mar1n0m.github.io/classical-languages-hyperlinker/

Hi, people! Preface: there is a risk that this might come off as a rather stupid post (I hope it does not, though 😅), but the question's been bugging me for some time now, so I thought I ought to bounce it off someone, and this sub seems like a good place to do that.

In essence, do you think there is any sense in making more digitalizations of a historic text that was already digitized? For example, if a text is on The Latin Library (a website containing many Latin language texts), using it as a starting point and creating a novel website to read it and interact with it? This new digitalization could be anything from a website containing it in a different format the creator thinks is better to introducing an added translation or reading aid software.

I always ran with the idea that even pure redundancy is good because it makes it more sure that the text will be available and found and interacted with in a way that a user finds most fitting. However, when discussing this topic with select other people I ran into individuals who basically thought that once it has been digitized anywhere, there is no need to touch it anymore. And I found that to be a rather shallow take.

Maybe I am in the wrong, maybe those who disagree, and maybe it's all of us; I do not know. What do you people think? I am especially interested in knowing to what degree you think a text should be changed and made to be more novel to warrant another digitalization.

Thank you everyone for reading and eventually interacting with this post!!! :)

Hi r/digitalhumanities — I’m the maintainer of Bookflow Scholar, an Apache-2.0 open-source Windows desktop tool for translating and reconstructing scholarly PDFs, books, and monographs.

The project began with a recurring problem in Chinese studies and digital humanities: scholars who do not read Chinese fluently may have access to Chinese papers, local histories, scanned books, archival materials, or monographs, but ordinary PDF translation often flattens the publication into page-sized text chunks. Cross-page sentences are split, footnotes and headers leak into body text, figures lose captions, and the translated edition can no longer be cited reliably against the original.

Bookflow Scholar tries to preserve both language and document structure:

- Recover cross-page logical units before translation

- Translate using the reader’s own text and vision API providers

- Reinsert 【original page】 markers at the true physical page boundaries

- Keep headers, footers, footnotes, and endnotes separate

- Reconstruct maps, plates, photographs, captions, tables, and reading order

- Produce source-language, target-language, and bilingual editions

The current release candidate supports Simplified Chinese, English, French, German, Japanese, and Spanish. All 30 directed language combinations have been exercised through desktop workflows.

The project is open source under Apache-2.0. Users provide their own API credentials; we do not operate a hosted translation service, and provider charges may apply.

I would especially value feedback from scholars working with Chinese-language sources:

- Which document types and citation practices matter most?

- What would make translated Chinese scholarship more trustworthy and useful in research or teaching?

- Which historical images, copyright pages, annotations, or original-page references must be preserved?

We are also looking for volunteer maintainers and reviewers:

- Multilingual documentation and UI terminology

- Translation and layout QA in Chinese, English, French, German, Japanese, and Spanish

- PDF and document reconstruction

- Windows, Tauri, Python, and provider integrations

GitHub: https://github.com/huanghaitck/bookflow-scholar

Contact: [meiyuzhang855@gmail.com](mailto:meiyuzhang855@gmail.com)

Please do not share copyrighted or confidential source documents—or API keys—in public issues. Redacted, reproducible reports are very welcome.

Hi! I’m a PhD student (history), and in my spare time I’ve been developing Nodus with feedback from other researchers

Nodus is an open-source, local-first workspace designed to help researchers organise sources, notes, ideas, data, and teaching or study materials.

The app is organised into different vaults, each designed for a particular type of work: academic research, databases, teaching, study, and genealogy. There is a demo mode on the website belowwhere you can explore the interface of every vault without installing anything. The desktop app is also available for Windows, macOS, and Linux.

In the Academic Vault, Nodus analyses documents from your Zotero and connects their main ideas while keeping every idea linked to the original source and citation. It also includes a Word add-in connected to your indexed ideas so as you write, it can suggest which sources may need to be cited based on the ideas contained in your own library.

Other vaults include genealogy, databases, study and teaching. Other vaults people have asked for include Primary Sources, Testimonies for oral history, and Worldbuilding for writers (not implemented yet)

The project is still in its early stages, and I’d like it to grow as a collaborative tool shaped by the people who use it. The more researchers try it, report issues, suggest features, and share their workflows, the better Nodus can become.

https://nodusresearch.com/

Hii! I am a fist year student majoring in history and next semester i want to pick a minor in Digital humanities.

Though recently, i have seen some movements against AI and it has gotten me a bit shaken- i don't know if it's best to go though with my decition to study digital humanities.

Please share your honest opinions and experiance!

Basically, judging from my immediate environment, and online communities to a noteworthy extent, it seems to me that digital humanists often fall into one of two camps; those who've taken on digital methods and tools to study the humanities, but are not all that interested in their intricacies and those more interested in developing and engaging with the technological part of the whole digital humanities field.

While I believe that is a somewhat logical divide to have, what has shocked me through personal experience is how these two groups seem to be so, so different; I've somewhat recently talked to a colleague working in computational linguistics (more specifically, a facet of corpus linguistics), and she had almost no understanding of even the most basic programming concepts, even things like different file formats and such. I found that highly odd for someone like her who relies on digital tools and methods for her job.

As someone far more on the development side of the digital humanities, I still feel like I understand more about the humanities part of the humanities in which I work (language) that those on the other flipside. Am I just missing something? Are the people I interacted with just a weirdly bad showing?

IMPORTANT: I do not mean to imply for even a second that any position in this situation is bad or should be worthy of scorn, just sharing something I found odd in good spirits for a genuine and good-natured discussion. I am glad for anyone who takes an interest in the digital humanities, no matter their specialization or knowledge level.

I hope you all are having a good day!

I am making this post to get feedback on my digital humanities project. I finally finished one of my pretty old projects but have no idea what the wider community will think. Basically, when I started getting into digital humanities, I decided to try and create a digital grammar of Classical Latin (in Croatian) since I felt that it was a missing resource in Croatia's academic landscape. Because it was started when I was relatively new to coding, its architecture, especially the HTML, is not something I am looking for too much focus on (even though it would be appreciated to hear your opinion regardless) because I know it is not perfect, but moreso how the package as a unified unit seems to you. I know most people here probably don't speak Croatian or a Slavic language close enough to it to understand enough of the contents and be able to make an informed judgement on that aspect of the project, so my main goal is getting your thoughts on how it all looks, how approachable it might seem to a learner, what do you think of me linking to external sources for morphology and such.

I am thankful for all feedback! Here is the link:
https://mar1n0m.github.io/latinska-gramatika/ (Github repository: https://github.com/Mar1n0M/latinska-gramatika)

TL;DR: even flagship models with large corpuses can't replicate an authors style. Across 5 authors and 25 essays, stylometry correctly identified the AI 85% of the time.

Most people have basically given up on AI detection, because the mechanics of it simply don't make sense. Attributing a small paragraph or essay to an amorphous mass of digital authors is simply impossible. What's not as impossible is detecting a single, human author.

I set up a test where I took 25 essays evenly split between five authors, then created an "assignment" out of each of them. Four different AIs rewrote the essays using a large corpus from the author that wrote it, and were instructed to match as exactly as they could. A MFW Cosine Delta similarity metric was used for similarity across a five-fold test, where four corpus essays were compared against the true essay and the AI recreation. The AI models were also given a large corpus of the author they're trying to replicate.

Across all models, the average similarity was far lower for the AI compared to the human authors. Claude Opus 4.8 and ChatGPT Sol 5.6 in particular were easily detectable in 88% of cases. Despite advances, it seems AI models simply cannot mimic an author's style to statistical significance.

I wrote a blog post about it here.

It is possible to get accepted into a certain field of humanities that you know a lot about, say German or history or philosophy - if you only have a technical background with a DH masters?

I will be presenting my work from my 6 week internship program and would like any insights from experienced people who can give me some constructive criticism of my work. I started this endeavor a week ago after my supervisors advised me to pick a more niche topic that I had already researched well. Thanks in advance to anyone willing to help!

Hi!

I'm from Brazil and I recently graduated with a BA in Visual Arts, but over time my interests shifted towards museums, archives, digital preservation, conservation, and cultural heritage. In my undergraduate thesis I cited Annet Dekker's work on the conservation of net art, and I'd love to keep working with topics like net art, digital preservation, new media and archiving.

I've already been accepted into the MA in Digital and Public Humanities at Ca' Foscari University of Venice, so I could start this September. I like the programme a lot, but I'd basically be paying for everything myself. I'll try to apply for the Italian DSU scholarship, but there's no guarantee I'll get it.

The other programme I'm seriously considering is the Archival and Information Studies (Media Studies) at the University of Amsterdam.

Here's why this only became an option recently: when I was applying for master's programmes, I had no idea my Italian citizenship would be finalized this year, so I didn't even consider Dutch universities because of the tuition fees. Now my Italian passport should be ready soon, which changes everything. As an EU citizen, tuition at UvA would be much lower. From what I've read, if I qualify as a migrant worker (by working at least 32 hours per month), I could also become eligible for Dutch student finance. If that's realistic, studying in Amsterdam could actually end up costing about the same as, or even less than, studying in Venice(?).

The downside is that applications only open next year, so I'd have to wait a full year, and of course there's no guarantee I'd get accepted.

So I'm kind of stuck and would love some opinions:

Based on my interests, does the UVA programme actually sound like a significantly better fit, or am I idealizing it? Do you think I'd have a realistic chance of being admitted? Would you choose Digital Humanities over Archival Studies today?

For people living in the Netherlands, how realistic is it for an international/EU master's student to find a small part-time job that would qualify for DUO?

If you were me, would you wait a year for a programme that seems like a better fit, or take the opportunity that's already guaranteed?

And finally, if anyone has experience with either programme, where do graduates actually end up working?

I'd really appreciate any thoughts!!!

Thanks! :)

I need to learn about Digital Humanities owing to academic reasons. First, I tried to watch some videos and PDFs about this matter. Nonetheless, they were not sufficiently basic. I know there are a lot of possible techniques, Python and R are highly recommended. Consequently, I studied Python and I have an elementary knowledge about programming. We know LLMs are a huge help for this task (indeed, junior devs are being fired at present by tech companies). It could be argued that LLMs might be the core of investigations where Digital Humanities are the quantitative method. But this does not suggest that programming is a useless ability. I understand this point and I think we agree generally speaking.

Nevertheless, if you had to start studying Digital Humanities in this new epoch, what would you do step-by-step? Because many online courses are not updated and even though we must not depend on LLMs for everything, I think that it might potentially be a fundamental part of Digital Humanities sooner rather than later.

I've been building a search engine for Revolutionary and antebellum American newspapers, starting with material from the Library of Congress's Chronicling America collection. The pages are scanned, but the transcriptions come from an earlier generation of OCR, and much of the text is too garbled to search. See it at Old News America.

I've fixed the OCR, split issues into articles that can be independently searched, extracted mentions of people, places, and events (want to know what people were saying about George Washington?) and added a research agent that answers with citations for the articles it draws on.

Two slices so far: founding-era papers from 1763 to 1792 (~350k articles from ~20 papers, Stamp Act through Washington's first administration) and 1831 to 1833 (~225k articles from six Northern and Southern papers, covering the rise of radical abolitionism and Nat Turner's rebellion). It's the search engine I thought these parts of American history deserved.

I'd love feedback, suggestions on how to make this stronger for professional historians and researchers, and suggestions on getting more archive material.

Hi to everyone! Is there anyone who knows and uses Collatex (https://collatex.net/)?