The project, led by Diag Davenport, an assistant professor of technology policy, governance and society at Berkeley who holds appointments in the Goldman School of Public Policy and the School of Information, has been described by its creator as a fundamentally new instrument for civic knowledge. “We’ve built a new kind of telescope — one pointed at governance itself,” Davenport said. “It could be a fundamental shift in the way people interact with the rules that govern their daily lives.” While online repositories have cataloged state and federal statutes for years, no comparable resource has ever existed specifically for local laws, from Alameda, California, to Zephyrhills, Florida. The database, published online in June, opens the door for others to generate custom databases and local chatbots capable of quickly comparing laws on topics ranging from home construction to dog leash requirements.

The origins of the project trace back roughly six years, when Davenport was studying computer algorithms, systemic bias and the criminal legal system. At some point, he wondered whether he could invert that research focus and instead study bias embedded in the actual text of the law itself. To do that rigorously, he needed to examine local laws across the country and use statistics to measure how they differed between places with high levels of historic discrimination and places without it. That ambition collided immediately with a practical obstacle: no database of all local laws existed, and the more he looked, the more he realized the material was scattered across a mishmash of proprietary company databases and convoluted local government websites.

The scale of the fragmentation alone was daunting. The United States contains more than 3,000 counties and another 6,000 or so local jurisdictions, each with the power to make its own laws and each storing those laws in its own way. “Of course, nothing’s ever as simple as you want it to be,” Davenport said. “Once you realize how fragmented it all is, it’s easy to understand why no one’s done the work.” The idea simmered for years, but it took on new life about a year ago, when recent technological advances finally allowed the team to stack one AI tool on top of another to do the heavy lifting that had previously been impossible.

The first stage was collection. Davenport and his colleague Denis Peskoff, a postdoctoral scholar at Berkeley, needed to gather municipal and county laws from thousands of government and third-party websites. They consulted with lawyers as they developed the collection process and deliberately designed it to meet the technical requirements imposed by the sites hosting the documents. They also worked with Joe Barrow, an AI researcher specializing in documents and datasets, and Christopher Vu, a Berkeley undergraduate studying computer and data science. That effort produced an archive of nearly 10,000 often unwieldy documents — roughly 7 million pages in total, many stored as blurry, poorly structured or otherwise inaccessible PDFs.

Turning that archive into usable data was the larger technical challenge, and it is where the project’s AI pipeline proved decisive. The team deployed a vision-language optical character recognition model called LightOnOCR to extract both text and structure from the documents. Processing millions of pages required a massive amount of computing power, but the result was a relatively standardized body of machine-readable text that is far easier, faster and less costly to search, analyze and feed into modern AI systems than the original PDFs ever were. From there, the researchers used models from OpenAI to tag and organize samples of the laws, distilling that labeling work into categories that could then be applied across the full corpus, transforming a chaotic pile of scanned documents into a structured, analyzable dataset.

The resulting database contains millions of unique ordinances covering every state in the union, and it enables something that was previously extremely difficult: searching across local laws at national scale to identify patterns in how communities regulate everything from housing and public space to business activity and everyday conduct. “I think it was actually impossible to do this work until six or 12 months ago,” Davenport said. “As far as I’m aware, we’ve done this at a larger scale than anyone else.” The project’s scientific credibility received a significant boost last week, when a paper describing the work was accepted to the Conference on Neural Information Processing Systems, one of the premier venues in machine learning and AI, where the team will present its findings in December.

The practical implications extend well beyond academia. Developers, for instance, have long faced soaring project costs driven partly by the difficulty of deciphering dense, inconsistent local regulations. Computer scientists and other technically minded users can now train chatbots on specific slices of the repository — one could, for example, be trained on Bay Area building codes and quickly report the different requirements for building an apartment complex in Berkeley compared with neighboring El Cerrito. That could allow developers to build more accurate cost estimates into project plans, or help a homeowner navigate the rulebook for constructing a backyard accessory dwelling unit. Davenport emphasized that any forthcoming tools built on the data must be free and publicly accessible, a condition he considers critical to the project’s mission.

Journalists, researchers and policymakers stand to benefit as well, gaining streamlined access to compare laws and saving countless hours that would otherwise be spent wrestling with hard-to-navigate documents. Users can compare regional differences in regulation, study how difficult laws are to understand in different areas, and even probe regional differences in enforcement. Some members of the public have already created searchable interfaces based on the team’s published work, an early sign of the ecosystem the researchers hope will grow around the dataset. In effect, the database converts the country’s fragmented legal patchwork into a natural laboratory for policy analysis.

“Effectively, what we have are thousands of experiments about how to regulate housing, sidewalks and helmets,” Davenport said. “The real promise here is making local government legible. Once people can actually see and compare the rules that shape everyday life, we can ask much better questions about which ones work, who they work for and what we might want to do differently.” For a nation whose daily governance has always been local, diffuse and largely opaque, the arrival of a single, searchable corpus of millions of ordinances may mark the moment when the rules that shape ordinary life finally come into focus.

Subject of Research: An AI-built open-access corpus of United States local ordinances for policy and legal research

Article Title: To decipher local laws, researchers used AI to build a database of millions of them

Article References: To decipher local laws, researchers used AI to build a database of millions of them. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: local laws, ordinances, artificial intelligence, machine learning, optical character recognition, open-access database, public policy, UC Berkeley, governance, natural language processing, municipal law, NeurIPS

The scientific problem GLAMR addresses is deceptively simple to state and remarkably hard to solve. Lake metabolism refers to the balance between the carbon a lake takes up through photosynthesis, performed by plants and algae, and the carbon it releases through respiration, the process by which living organisms break down food into energy and carbon dioxide. That balance, expressed as the difference between gross primary production and ecosystem respiration, determines whether a given lake functions as a net carbon sink or a net carbon source, how much energy flows into its food web, and whether its waters remain hospitable to the uses people depend on. As Cary aquatic ecologist Christopher Solomon, who leads the effort, explained, the processes being quantified play a huge role in determining the suitability of lakes for drinking water, recreation, and fisheries.

Why has this fundamental quantity gone unmeasured at scale? Part of the answer lies in the sheer heterogeneity of the world’s lakes. Ecosystems everywhere show distinctive seasonal rhythms of production and respiration. Temperate deciduous forests in the northeastern United States, for example, run high rates of photosynthesis through the summer, then see photosynthesis plummet and respiration dominate during the leafless, snow-covered winter months. Mediterranean forests behave differently, keeping photosynthesis above respiration throughout the year in a pattern that depends more on spring and autumn rains. These metabolic signatures are well characterized for most major ecosystem types. Lakes, ponds, and reservoirs, by contrast, have resisted the same systematic treatment, in part because their conditions can shift dramatically from day to day and from one basin to the next, and because the instruments needed to capture those shifts have only recently become widespread.

The consequence of that blind spot is more than an academic inconvenience. Without a global picture of lake metabolism, scientists cannot accurately quantify the role inland waters play in the global carbon cycle or in the Earth system more broadly, and they cannot predict how lakes will respond to global change. Lakes are known to process, transport, and store substantial amounts of carbon, and warming temperatures, altered precipitation, nutrient runoff, and shifting ice cover all have the potential to reconfigure the metabolic balance of these systems. Solomon noted that the lack of understanding limits the ability to quantify the role of lakes in the carbon cycle and to forecast their trajectories under environmental change, a limitation GLAMR is explicitly designed to remove.

Assembling the repository is an exercise in data harmonization as much as data collection. Some of the information GLAMR will incorporate already exists, but it is scattered across hundreds of published and unpublished sources, collected with different instruments, calibrated differently, and stored in incompatible formats. GLAMR will organize and harmonize these datasets, pulling together daily measurements of dissolved oxygen, water temperature, wind speed, and other variables that together allow researchers to compute metabolism at the scale of individual lakes and then compare those patterns across continents. Solomon identified the data infrastructure as the most challenging part of the project, describing the task of integrating hundreds of millions of data points published by hundreds of different researchers as a big and ambitious undertaking. He added that the team already has a strong start, thanks to earlier support from Cary Institute’s Science Innovation Funds and from Grand Valley State University.

New data will continue to flow in from collaborators around the world, many of them affiliated with the Global Lake Ecological Observatory Network, known as GLEON, an international grassroots network of scientists, students, educators, policy makers, and citizens who study and monitor lake ecosystems. High-frequency sensors deployed on buoys and moorings, such as the long-running monitoring buoy on Lake Mendota in Wisconsin, capture the fine-scale fluctuations in oxygen and temperature that underpin modern metabolism estimates, and GLEON’s distributed community of lake observers provides both the instruments and the local expertise. Cary Institute ecologist Kathleen Weathers, who co-created GLEON and serves as senior personnel on the project, described GLAMR as very much an outgrowth of that network, saying she is excited to play a role in guiding and participating in GLAMR’s extension and expansion of networked lake science.

Training the next generation of freshwater scientists is built into the project’s design. Weathers will lead a training program for GLEON graduate fellows that guides early-career researchers in conducting GLAMR-enabled research, ensuring that the repository is not merely a static archive but a living platform that cultivates new analytical approaches and new questions. Data in the repository will be published and made available to interested scientists everywhere, reflecting the project’s open-access ethos. In an era when reproducibility and data sharing are increasingly central to credible science, GLAMR’s commitment to making hundreds of millions of harmonized observations publicly available positions it as infrastructure for the entire discipline rather than the private resource of a single research group.

Once the repository is assembled, Solomon and colleagues will move from curation to synthesis. Their first analytical targets include defining the various lake metabolism archetypes, the recurring patterns of production and respiration that characterize different classes of lakes, and understanding why those archetypes differ in time and space. From there, the team aims to predict how lake metabolism might change in the future as climate warming, land use change, and other pressures continue to reshape inland waters. Such archetype-based classification could ultimately allow researchers to extrapolate from the hundreds of monitored lakes in GLAMR to the millions of lakes worldwide, turning a curated dataset into a globally predictive framework for freshwater carbon cycling and water quality.

The project’s leadership spans four institutions, each bringing complementary strengths. Christopher Solomon of the Cary Institute of Ecosystem Studies serves as lead principal investigator, joined by Isabella Oleksy of the University of Colorado Boulder, Stuart Jones of Grand Valley State University, and Paul Hanson of the University of Wisconsin-Madison, with Kathleen Weathers of Cary Institute serving as senior personnel. The combination of long-term sensor deployments, ecosystem modeling expertise, and network coordination experience mirrors the interdisciplinary character of the question itself, which sits at the intersection of ecology, biogeochemistry, limnology, and data science.

For a field that has watched its study sites warm, stratify differently, and lose ice cover year after year, the arrival of a unified global metabolism database marks a turning point. What individual researchers have observed at their own lakes, sometimes for decades, will finally become comparable, combinable, and visible at planetary scale. If GLAMR succeeds, the metabolic rhythms of the world’s lakes, ponds, and reservoirs will move from the margins of ecosystem science into the same well-mapped territory long occupied by forests, grasslands, and the ocean, giving scientists a fighting chance to understand, and anticipate, how the freshwater systems humanity depends upon will fare in a changing world.

Subject of Research: Creation of a global open-access lake metabolism database to study lake health, carbon cycling, and climate impacts

Article Title: Cary to lead $2.3 million NSF-funded effort to build global lake ecosystem database

Article References: Cary to lead $2.3 million NSF-funded effort to build global lake ecosystem database. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: lake metabolism, GLAMR, Cary Institute, National Science Foundation, carbon cycle, freshwater ecology, water quality, GLEON, dissolved oxygen, primary production, respiration, open-access database