In June, we announced the official launch of Archives Research Compute Hub (ARCH) our platform for supporting computational research with digital collections. The Archiving & Data Services group at IA has long provided computational research services via collaborations, dataset services, product features, and other partnerships and software development. In 2020, in partnership with our close collaborators at the Archives Unleashed project, and with funding from the Mellon Foundation, we pursued cooperative technical and community work to make text and data mining services available to any institution building, or researcher using, archival web collections. This led to the release of ARCH, with more than 35 libraries and 60 researchers and curators participating in beta testing and early product pilots. Additional work supported expanding the community of scholars doing computational research using contemporary web collections by providing technical and research support to multi-institutional research teams.
We are pleased to announce that ARCH recently received funding from the Institute of Museum and Library Services (IMLS), via their National Leadership Grants program, supporting ARCH expansion. The project, “Expanding ARCH: Equitable Access to Text and Data Mining Services,” entails two broad areas of work. First, the project will create user-informed workflows and conduct software development that enables a diverse set of partner libraries, archives, and museums to add digital collections of any format (e.g., image collections, text collections) to ARCH for users to study via computational analysis. Working with these partners will help ensure that ARCH can support the needs of organizations of any size that aim to make their digital collections available in new ways. Second, the project will work with librarians and scholars to expand the number and types of data analysis jobs and resulting datasets and data visualizations that can be created using ARCH, including allowing users to build custom research collections that are aggregated from the digital collections of multiple institutions. Expanding the ability for scholars to create aggregated collections and run new data analysis jobs, potentially including artificial intelligence tools, will enable ARCH to significantly increase the type, diversity, scope, and scale of research it supports.
Collaborators on the Expanding ARCH project include a set of institutional partners that will be closely involved in guiding functional requirements, testing designs, and using the newly-built features intended to augment researcher support. Primary institutional partners include University of Denver, University of North Carolina at Chapel Hill, Williams College Museum of Art, and Indianapolis Museum of Art, with additional institutional partners joining in the project’s second year.
Thousands of libraries, archives, museums, and memory organizations work with Internet Archive to build and make openly accessible digitized and born-digital collections. Making these collections available to as many users in as many ways as possible is critical to providing access to knowledge. We are thankful to IMLS for providing the financial support that allows us to expand the ARCH platform to empower new and emerging types of access and research.
We are excited to announce the public availability of ARCH (Archives Research Compute Hub), a new research and education service that helps users easily build, access, and analyze digital collections computationally at scale. ARCH represents a combination of the Internet Archive’s experience supporting computational research for more than a decade by providing large-scale data to researchers and dataset-oriented service integrations like ARS (Archive-it Research Services) and a collaboration with the Archives Unleashed project of the University of Waterloo and York University. Development of ARCH was generously supported by the Mellon Foundation.
ARCH Dashboard
What does ARCH do?
ARCH helps users easily conduct and support computational research with digital collections at scale – e.g., text and data mining, data science, digital scholarship, machine learning, and more. Users can build custom research collections relevant to a wide range of subjects, generate and access research-ready datasets from collections, and analyze those datasets. In line with best practices in reproducibility, ARCH supports open publication and preservation of user-generated datasets. ARCH is currently optimized for working with tens of thousands of web archive collections, covering a broad range of subjects, events, and timeframes, and the platform is actively expanding to include digitized text and image collections. ARCH also works with various portions of the overall Wayback Machine global web archive totaling 50+ PB going back to 1996, representing an extensive archive of contemporary history and communication.
ARCH, In-Browser Visualization
Who is ARCH for?
ARCH is for any user that seeks an accessible approach to working with digital collections computationally at scale. Possible users include but are not limited to researchers exploring disciplinary questions, educators seeking to foster computational methods in the classroom, journalists tracking changes in web-based communication over time, to librarians and archivists seeking to support the development of computational literacies across disciplines. Recent research efforts making use of ARCH include but are not limited to analysis of COVID-19 crisis communications, health misinformation, Latin American women’s rights movements, and post-conflict societies during reconciliation.
ARCH, Generate Datasets
What are core ARCH features?
Build: Leverage ARCH capabilities to build custom research collections that are well scoped for specific research and education purposes.
Access: Generate more than a dozen different research-ready datasets (e.g., full text, images, pdfs, graph data, and more) from digital collections with the click of a button. Download generated datasets directly in-browser or via API.
Analyze: Easily work with research-ready datasets in interactive computational environments and applications like Jupyter Notebooks, Google CoLab, Gephi, and Voyant and produce in-browser visualizations.
Publish and Preserve: Openly publish datasets in line with best practices in reproducible research. All published datasets will be preserved in perpetuity.
Support: Make use of synchronous and asynchronous technical support, online trainings, and extensive help center documentation.
How can I learn more about ARCH?
To learn more about ARCH please reach out via the following form.
Art historians, critics, curators, humanities scholars and many others rely on the records of artists, galleries, museums, and arts organizations to conduct historical research and to understand and contextualize contemporary artistic practice. Yet, much of the art-related materials that were once published in print form are now available primarily or solely on the web and are thus ephemeral by nature. In response to this challenge, more than 40 art libraries spent the last 3 years developing a collective approach to preservation of web-based art materials at scale.
Supported by the Institute of Museum and Library Services and the National Endowment for the Humanities, The Collaborative ART Archive (CARTA) community has successfully aligned effort across libraries large and small, from Manoa, Hawaii to Toronto, Ontario and back resulting in preservation of and access to 800 web-based art resources, organized into 8 collections (art criticism, art fairs and events, art galleries, art history and scholarship, artists websites, arts education, arts organizations, auction houses), totalling nearly 9 TBs of data with continued growth. All collections are preserved in perpetuity by the Internet Archive.
Today, CARTA is excited to launch the CARTA portal – providing unified access to CARTA collections.
🎨 CARTA portal 🎨
The CARTA portal includes web archive collections developed jointly by CARTA members, as well as preexisting art-related collections from CARTA institutions, and non-CARTA member collections. CARTA portal development builds on the Internet Archive’s experience creating the COVID-19 Web Archive and Community Webs portal.
CARTA collections are searchable by contributing organization, collection, site, and page text. Advanced search supports more granular exploration by host, results per host, file types, and beginning and end dates.
🔭 CARTA search 🔭
In addition to the CARTA portal, CARTA has worked to promote research use of collections through a series of day long computational research workshops – Working to Advance Library Support for Web Archive Research – backed by ARCH (Archives Research Compute Hub). A call for applications for the next workshop, held concurrent to the annual Society of American Archivists meeting, is now open.
Moving forward CARTA aims to grow and diversify its membership in order to increase collective ability to preserve web-based art materials. If your art library would like to join CARTA please express interest here..
Guest Post by Daniel Van Strien, Machine Learning Librarian, Hugging Face
Machine learning has many potential applications for working with GLAM (galleries, libraries, archives, museums) collections, though it is not always clear how to get started. This post outlines some of the possible ways in which open source machine learning tools from the Hugging Face ecosystem can be used to explore web archive collections made available via the Internet Archive’s ARCH (Archives Research Compute Hub). ARCH aims to make computational work with web archives more accessible by streamlining web archive data access, visualization, analysis, and sharing. Hugging Face is focused on the democratization of good machine learning. A key component of this is not only making models available but also doing extensive work around the ethical use of machine learning.
Below, I work with the Collaborative Art Archive (CARTA) collection focused on artist websites. This post is accompanied by an ARCH Image Dataset Explorer Demo. The goal of this post is to show how using a specific set of open source machine learning models can help you explore a large dataset through image search, image classification, and model training.
Later this year, Internet Archive and Hugging Face will organize a hands-on hackathon focused on using open source machine learning tools with web archives. Please let us know if you are interested in participating by filling out this form.
Choosing machine learning models
The Hugging Face Hub is a central repository which provides access to open source machine learning models, datasets and demos. Currently, the Hugging Face Hub has over 150,000 openly available machine learning models covering a broad range of machine learning tasks.
Rather than relying on a single model that may not be comprehensive enough, we’ll select a series of models that suit our particular needs.
A screenshot of the Hugging Face Hub task navigator presenting a way of filtering machine learning models hosted on the hub by the tasks they intend to solve. Example tasks are Image Classification, Token Classification and Image-to-Text.
Working with image data
ARCH currently provides access to 16 different “research ready” datasets generated from web archive collections. These include but are not limited to datasets containing all extracted text from the web pages in a collection, link graphs (showing how websites link to other websites), and named entities (for example, mentions of people and places). One of the datasets is made available as a CSV file, containing information about the images from webpages in the collection, including when the image was collected, when the live image was last modified, a URL for the image, and a filename.
Screenshot of the ARCH interface showing a preview for a dataset. This preview includes a download link and an “Open in Colab” button.
One of the challenges we face with a collection like this is being able to work at a larger scale to understand what is contained within it – looking through 1000s of images is going to be challenging. We address that challenge by making use of tools that help us better understand a collection at scale.
Building a user interface
Gradio is an open source library supported by Hugging Face that helps create user interfaces that allow other people to interact with various aspects of a machine learning system, including the datasets and models. I used Gradio in combination with Spacesto make an application publicly available within minutes, without having to set up and manage a server or hosting. See the docs for more information on using Spaces. Below, I show examples of using Gradio as an interface for applying machine learning tools to ARCH generated data.
Exploring images
I use the Gradio tab for random images to begin assessing images in the dataset. Looking at a randomized grid of images gives a better idea of what kind of images are in the dataset. This begins to give us a sense of what is represented in the collection (e.g., art, objects, people, etc.).
Screenshot of the random image gallery showing a grid of images from the dataset.
Introducing image search models
Looking at snapshots of the collection gives us a starting point for exploring what kinds of images are included in the collection. We can augment our approach by implementing image search.
There are various approaches we could take which would allow us to search our images. If we have the text surrounding an image, we could use this as a proxy for what the image might contain. For example, we might assume that if the text next to an image contains the words “a picture of my dog snowy”, then the image contains a picture of a dog. This approach has limitations – text might be missing, unrelated or only capture a small part of what is in an image. The text “a picture of my dog snowy” doesn’t tell us what kind of dog the image contains or if other things are included in that photo.
Making use of an embedding model offers another path forward. Embeddings essentially take an input i.e. text or image, and return a bunch of numbers. For example, the text prompt: ‘an image of a dog’, would be passed through an embedding model, which ‘translates’ text into a matrix of numbers (essentially a grid of numbers). What is special about these numbers is that they should capture some semantic information about the input; the embedding for a picture of a dog should somehow capture the fact that there is a dog in the image. Since these embeddings consist of numbers, we can also compare one embedding to another to see how close they are to each other. We expect the embeddings for similar images to be closer to each other and the embeddings for images which are less similar to each other to be farther away. Without getting too much into the weeds of how this works, it’s worth mentioning that these embeddings don’t just represent one aspect of an image, i.e. the main object it contains but also other components, such as its aesthetic style. You can find a longer explanation of how this works in this post.
Finding a suitable image search model on the Hugging Face Hub
To create an image search system for the dataset, we need a model to create embeddings. Fortunately, the Hugging Face Hub makes it easy to find models for this.
The Hub has various models that support building an image search system.
Hugging Face Hub showing a list of hosted models.
All models will have various benefits and tradeoffs. For example, some models will be much larger. This can make a model more accurate but also make it harder to run on standard computer hardware.
Hugging Face Hub provides an ‘inference widget’, which allows interactive exploration of a model to see what sort of output it provides. This can be very useful for quickly understanding whether a model will be helpful or not.
A screenshot of a model widget showing a picture of a dog and a cat playing the guitar. The widget assigns the label `”playing music`” the highest confidence.
For our use case, we need a model which allows us to embed both our input text, for example, “an image of a dog,” and compare that to embeddings for all the images in our dataset to see which are the closest matches. We use a variant of the CLIP model hosted on Hugging Face Hub: clip-ViT-B-16. This allows us to turn both our text and images into embeddings and return the images which most closely match our text prompt.
Aa screenshot of the search tab showing a search for “landscape photograph” in a text box and a grid of images resulting from the search. This includes two images containing trees and images containing the sky and clouds.
While the search implementation isn’t perfect, it does give us an additional entry point into an extensive collection of data which is difficult to explore manually. It is possible to extend this interface to accommodate an image similarity feature. This could be useful for identifying a particular artist’s work in a broader collection.
Image classification
While image search helps us find images, it doesn’t help us as much if we want to describe all the images in our collection. For this, we’ll need a slightly different type of machine learning task – image classification. An image classification model will put our images into categories drawn from a list of possible labels.
We can find image classification models on the Hugging Face Hub. The “Image Classification Model Tester” tab in the demo Gradio application allows us to test most of the 3,000+ image classification models hosted on the Hub against our dataset.
This can give us a sense of a few different things:
How well do the labels for a model match our data?A model for classifying dog breeds probably won’t help us much!
It gives us a quick way of inspecting possible errors a model might make with our data.
It prompts us to think about what categories might make sense for our images.
A screenshot of the image classification tab in the Gradio app which shows a bar chart with the most frequently predicted labels for images assigned by a computer vision model.
We may find a model that already does a good job working with our dataset – if we don’t, we may have to look at training a model.
Training your own computer vision model
The final tab of our Gradio demo allows you to export the image dataset in a format that can be loaded by Label Studio, an open-source tool for annotating data in preparation for machine learning tasks. In Label Studio, we can define labels we would like to apply to our dataset. For example, we might decide we’re interested in pulling out particular types of images from this collection. We can use Label Studio to create an annotated version of our dataset with these labels. This requires us to assign labels to images in our dataset with the correct labels. Although this process can take some time, it can be a useful way of further exploring a dataset and making sure your labels make sense.
With a labeled dataset, we need some way of training a model. For this, we can use AutoTrain. This tool allows you to train machine learning models without writing any code. Using this approach supports creation of a model trained on our dataset which uses the labels we are interested in. It’s beyond the scope of this post to cover all AutoTrain features, but this post provides a useful overview of how it works.
Next Steps
As mentioned in the introduction, you can explore the ARCH Image Dataset Explorer Demo yourself. If you know a bit of Python, you could also duplicate the Space and adapt or change the current functionality it supports for exploring the dataset.
Internet Archive and Hugging Face plan to organize a hands-on hackathon later this year focused on using open source machine learning tools from the Hugging Face ecosystem to work with web archives. The event will include building interfaces for web archive datasets, collaborative annotation, and training machine learning models. Please let us know if you are interested in participating by filling out this form.
This Spring, the Internet Archive hosted two in-person workshops aimed at helping to advance library support for web archive research: Digital Scholarship & the Web and Art Resources on the Web. These one-day events were held at the Association of College & Research Libraries (ACRL) conference in Pittsburgh and the Art Libraries Society of North America (ARLIS) conference in Mexico City. The workshops brought together librarians, archivists, program officers, graduate students, and disciplinary researchers for full days of learning, discussion, and hands-on experience with web archive creation and computational analysis. The workshops were developed in collaborationwith the New York Art Resources Consortium (NYARC) – and are part of an ongoing series of workshops hosted by the Internet Archive through Summer 2023.
Internet Archive Deputy Director of Archiving & Data Services Thomas Padilla discussing the potential of web archives as primary sources for computational research at Art Resources on the Web in Mexico City.
Designed in direct response to library community interest in supporting additional uses of web archive collections, the workshops had the following objectives: introduce participants to web archives as primary sources in context of computational research questions, develop familiarity with research use cases that make use of web archives; and provide an opportunity to acquire hands-on experience creating web archive collections and computationally analyzing them usingARCH (Archives Research Compute Hub) – a new service set to publicly launch June 2023.
Internet Archive Community Programs Manager Lori Donovan walking workshop participants through a demonstration of Palladio using a dataset generated with ARCH at Digital Scholarship & the Web In Pittsburgh, PA.
In support of those objectives, Internet Archive staff walked participants through web archiving workflows, introduced a diverse set of web archiving tools and technologies, and offered hands-on experience building web archives. Participants were then introduced to Archives Research Compute Hub (ARCH). ARCH supports computational research with web archive collections at scale – e.g., text and data mining, data science, digital scholarship, machine learning, and more. ARCH does this by streamlining generation and access to more than a dozen research ready web archive datasets, in-browser visualization, dataset analysis, and open dataset publication. Participants further explored data generated with ARCH in Palladio, Voyant, and RAWGraphs.
Network visualization of the Occupy Web Archive collection, created using Palladio based on a Domain Graph Dataset generated by ARCH.
Gallery visualization of the CARTA Art Galleries collection, created using Palladio based on an Image Graph Dataset generated by ARCH.
At the close of the workshops, participants were eager to discuss web archive research ethics, research use cases, and a diverse set of approaches to scaling library support for researchers interested in working with web archive collections – truly vibrant discussions – and perhaps the beginnings of a community of interest! We plan to host future workshops focused on computational research with web archives – please keep an eye on our Event Calendar.
Tell us about your research & how you use the Internet Archive to further it! We are gathering testimonials about how our library & collections are used in different research projects & settings.
From using our books to check citations to doing large-scale data analysis using our web archives, we want to hear from you!
On November 14, 2022, the Internet Archive hosted Humanities and the Web: Introduction to Web Archive Data Analysis, a one-day introductory workshop for humanities scholars and cultural heritage professionals. The group included disciplinary scholars and information professionals with research interests ranging from Chinese feminist movements, to Indigenous language revitalization, to the effects of digital platforms on discourses of sexuality and more. The workshop was held at the Central Branch of the Los Angeles Public Library and coincided with the National Humanities Conference.
Attendees and Facilitators at Humanities and the Web: Introduction to Web Archive Data Analysis, November 14, 2022, Los Angeles Public Library
The goals of the workshop were to introduce web archives as primary sources and to provide a sampling of tools and methodologies that could support computational analysis of web archive collections. Internet Archive staff shared web archive research use cases and provided participants with hands-on experience building web archives and analyzing web archive collections as data.
Senior Program Manager, Lori Donovan, guiding attendees in using Voyant to analyze text datasets extracted from an Archive-It collection using ARCH.
The workshop’s central feature was an introduction to ARCH (Archives Research Compute Hub). ARCH transforms web archives into datasets tuned for computational research, allowing researchers to, for example, extract all text, spreadsheets, PDFs, images, audio, named entities and more from collections. During the workshop, participants worked directly with text, network, and image file datasets generated from web archive collections. With access to datasets derived from these collections, the group explored a range of analyses using Palladio, RAWGraphs, and Voyant.
Visualization of the image files contained in the Chicago Architecture Biennial collection, created using Palladio based on an Image File dataset extracted from the collection using ARCH.
The high level of interest and participation in this event is indicative of the appetite within the Humanities for workshops on computational research. Participants described how the workshop gave them concrete language to express the challenges of working with large-scale data, while also expressing how the event offered strategies they could apply to their own research or could use to support their research communities. For those who were not able to make it to Humanities and the Web, we will be hosting a series of virtual and in-person workshops in 2023. Keep your eye on this space for upcoming announcements.
In the six weeks since announcing that Internet Archive has begun gathering content for the Digital Library of Amateur Radio and Communications (DLARC), the project has quickly grown to more than 25,000 items, including ham radio newsletters, podcasts, videos, books, and catalogs. The project seeks additional contributions of material for the free online library.
You are welcome to explore the content currently in the library and watch the primary collection as it grows at https://archive.org/details/dlarc.
The new material includes historical and modern newsletters from diverse amateur radio groups including the National Radio Club (of Aurora, CO); the Telford & District Amateur Radio Society, based in the United Kingdom; the Malta Amateur Radio League; and the South African Radio League. The Tri-State Amateur Radio Society contributed more than 200 items of historical correspondence, newspaper clippings, ham festival flyers, and newsletters. Other publications include Selvamar Noticias, a multilingual digital ham radio magazine; and Florida Skip, an amateur radio newspaper published from 1957 through 1994.The library also includes the complete run of 73 Magazine — more than 500 issues — which are freely and openly available.
More than 300 radio related books are available in DLARC via controlled digital lending. These materials may be checked out by anyone with a free Internet Archive account for a period of one hour to two weeks. Radio and communications books donated to Internet Archive are scanned and added to the DLARC lending library.
Amateur radio podcasts and video channels are also among the first batch of material in the DLARC collection. These include Ham Nation, Foundations of Amateur Radio, the ICQ Amateur/Ham Radio Podcast, with many more to come. Providing a mirror and archive for “born digital” content such as video and podcasts is one of the core goals of DLARC.
Additions to DLARC also include presentations recorded at radio communications conferences, including GRCon, the GNU Radio Conference; and the QSO Today Virtual Ham Expo. A growing reference library of past radio product catalogs includes catalogs from Ham Radio Outlet and C. Crane.
DLARC is growing to be a massive online library of materials and collections related to amateur radio and early digital communications. It is funded by a significant grant from Amateur Radio Digital Communications (ARDC) to create a digital library that documents, preserves, and provides open access to the history of this community.
Anyone with material to contribute to the DLARC library, questions about the project, or interest in similar digital library building projects for other professional communities, please contact:
Kay Savetz, K6KJN Program Manager, Special Collections kay@archive.org Mastodon: dlarc@mastodon.radio
Internet Archive has begun gathering content for the Digital Library of Amateur Radio and Communications (DLARC), which will be a massive online library of materials and collections related to amateur radio and early digital communications. The DLARC is funded by a significant grant from the Amateur Radio Digital Communications (ARDC), a private foundation, to create a digital library that documents, preserves, and provides open access to the history of this community.
The library will be a free online resource that combines archived digitized print materials, born-digital content, websites, oral histories, personal collections, and other related records and publications. The goals of the DLARC are to document the history of amateur radio and to provide freely available educational resources for researchers, students, and the general public. This innovative project includes:
A program to digitize print materials, such as newsletters, journals, books, pamphlets, physical ephemera, and other records from both institutions, groups, and individuals.
A digital archiving program to archive, curate, and provide access to “born-digital” materials, such as digital photos, websites, videos, and podcasts.
A personal archiving campaign to ensure the preservation and future access of both print and digital archives of notable individuals and stakeholders in the amateur radio community.
Conducting oral history interviews with key members of the community.
Preservation of all physical and print collections donated to the Internet Archive.
The DLARC project is looking for partners and contributors with troves of ham radio, amateur radio, and early digital communications related books, magazines, documents, catalogs, manuals, videos, software, personal archives, and other historical records collections, no matter how big or small. In addition to physical material to digitize, we are looking for podcasts, newsletters, video channels, and other digital content that can enrich the DLARC collections. Internet Archive will work directly with groups, publishers, clubs, individuals, and others to ensure the archiving and perpetual access of contributed collections, their physical preservation, their digitization, and their online availability and promotion for use in research, education, and historical documentation. All collections in this digital library will be universally accessible to any user and there will be a customized access and discovery portal with special features for research and educational uses.
We are extremely grateful to ARDC for funding this project and are very excited to work with this community to explore a multi-format digital library that documents and ensures access to the history of a specific, noteworthy community. Anyone with material to contribute to the DLARC library, questions about the project, or interest in similar digital library building projects for other professional communities, please contact:
Kay Savetz, K6KJN Program Manager, Special Collections kay@archive.org Twitter: @KaySavetz
“Technology embodies a set of values,” opened Internet Archive’s Director of Partnerships, Wendy Hanamura, in Ethics of the Decentralized Web & Uses in Humanitarian Work, the final session with METRO Library Council and Library Futures. “What values drive our current web?”
Watch Video:
While most of the audience responded by discussing web monetization or opined about lack of privacy, many still believe in the power of the internet for better sharing. As we build a new web, most would like for it to be driven by a different set of values, particularly community, collaboration, freedom, sovereignty, democracy, and trust.
Beginning with Mai Ishikawa Sutton’s work on the five principles of the DWeb and ending with a demo of the Mapeo project, this session brought in designers, coders, policy professors, and ethicists building a new “web for the people” that would embody the above values, and much more.
In 2016 at a campout in California, Sutton and a community of technology enthusiasts came together to rethink the values embedded in the technology of the web we use and the web we could build. While technology was a major factor in the resulting work, ethical considerations and standing for better technology were just as crucial. They created a document that reflected the interests and values of their community with five principles:
Technology for Human Agency
Distributed Benefits
Mutual Respect
Humanity
Ecological Awareness
The group hopes to revisit some of these principles this summer at DWeb Camp 2022 to better define the “web that we want.” In the Q&A with Hanamura, Sutton clarified the ways in which the DWeb addresses crucial aspects of power, control, and capital. Rather than staying static or solely basing itself in technological innovation, the DWeb community is a way to ensure that benefits “flow back into the community.”
Author and Professor Nathan Schneider followed Sutton to discuss how human rights can be encoded into the blockchain. Schneider’s presentation, “Policy Proposals for Less Dystopian Crypto Protocols” began with a recognition of the issues within blockchain, stating that he wishes to explore how crypto can be “up there with libraries” in terms of building “true civic institutions.” Faced with the dystopia of the current web and recognizing that it could perpetuate the same harms, crypto could present a new form of economic democracy and pluriverses for all. For Schneider, “if code is law,” there are a number of policy proposals that can support a better crypto future. These include building sufficiently decentralized systems, transparent governance, labor over capital, taxation for public goods, reparations, provable zero-carbon, and human rights fail-safes. For his community, this is not about “catching up” to institutions as we know them, but instead doing the work to build a more humane world.
Following Schneider, Luandro Vieira of Digital Democracy demoed his project, Mapeo, a decentralized app built with and for communities. Mapeo is a mobile application that provides free and accessible geospatial technology that is translatable, designed for community, private, and available offline. Originally built for earth defenders, or marginalized people at the front lines of defending their land around the world, Mapeo is highly customizable and used mainly by indigenous people in 16 countries. It is used to map and monitor threats from invasions, mining, logging, and oil activities. Mapeo’s power was demonstrated through the #WaoraniResistance, which protected 1/2 million acres in the Amazon and jeopardized a 7 million acre oil auction.
Through the lens of these three activists and experts, the promise of the DWeb was clear. A new, highly democratic web is possible, but it will take all of us to build it.
