## Applied Category Theory Course: Ordered Sets

7 April, 2018

My applied category theory course based on Fong and Spivak’s book Seven Sketches is going well. Over 250 people have registered for the course, which allows them to ask question and discuss things. But even if you don’t register you can read my “lectures”.

Here are all the lectures on Chapter 1, which is about adjoint functors between posets, and how they interact with meets and joins. We study the applications to logic – both classical logic based on subsets, and the nonstandard version of logic based on partitions. And we show how this math can be used to understand “generative effects”: situations where the whole is more than the sum of its parts!

I’m finding this course a great excuse to put my thoughts about category theory into a more organized form, and it’s displaced most of the time I used to spend on Google+ and Twitter. That’s what I wanted: the conversations in the course are more interesting!

## Applied Category Theory Course

26 March, 2018

It just became a lot easier to learn about applied category theory, thanks to this free book:

• Brendan Fong and David Spivak, Seven Sketches in Compositionality: An Invitation to Applied Category Theory.

I’ve started an informal online course based on this book on the Azimuth Forum. I’m getting pretty sick of the superficial quality of my interactions on social media. This could be a way to do something more interesting.

The idea is that you can read chapters of this book, discuss them, try the exercises in the book, ask and answer questions, and maybe team up to create software that implements some of the ideas. I’ll try to keep things moving forward. For example, I’ll explain some stuff and try to help answer questions that people are stuck on. I may also give some talks or run discussions on Google Hangouts or similar software—but only when I have time: I’m more of a text-based guy. I may get really busy some times, and leave the rest of you alone for a while. But I like writing about math for at least 15 minutes a day, and more when I have time. Furthermore, I’m obsessed with applied category theory and plan to stay that way for at least a few more years.

If this sounds interesting, let me know here—and please visit the Azimuth Forum and register! Use your full real name as your username, with no spaces. I will add spaces and that will become your username. Use a real working email address. If you don’t, the registration process may not work.

Over 70 people have registered so far, so this process will take a while.

The main advantage of the Forum over this blog is that you can initiate new threads and edit your comments. Like here you can write equations in LaTeX. Like here, that ability is severely limited: for example you can’t define macros, and you can’t use TikZ. (Maybe someone could fix that.) But equations are better typeset over there—and more importantly, the ability to edit comments makes it a lot easier to correct errors in your LaTeX.

Please let me know what you think.

What follows is the preface to Fong and Spivak’s book, just so you can get an idea of what it’s like.

### Preface

Category theory is becoming a central hub for all of pure mathematics. It is unmatched in its ability to organize and layer abstractions, to find commonalities between structures of all sorts, and to facilitate communication between different mathematical communities. But it has also been branching out into science, informatics, and industry. We believe that it has the potential to be a major cohesive force in the world, building rigorous bridges between disparate worlds, both theoretical and practical. The motto at MIT is mens et manus, Latin for mind and hand. We believe that category theory—and pure math in general—has stayed in the realm of mind for too long; it is ripe to be brought to hand.

#### Purpose and audience

The purpose of this book is to offer a self-contained tour of applied category theory. It is an invitation to discover advanced topics in category theory through concrete real-world examples. Rather than try to give a comprehensive treatment of these topics—which include adjoint functors, enriched categories, proarrow equipments, toposes, and much more–we merely provide a taste. We want to give readers some insight into how it feels to work with these structures as well as some ideas about how they might show up in practice.

The audience for this book is quite diverse: anyone who finds the above description intriguing. This could include a motivated high school student who hasn’t seen calculus yet but has loved reading a weird book on mathematical logic they found at the library. Or a machine learning researcher who wants to understand what vector spaces, design theory, and dynamical systems could possibly have in common. Or a pure mathematician who wants to imagine what sorts of applications their work might have. Or a recently-retired programmer who’s always had an eerie feeling that category theory is what they’ve been looking for to tie it all together, but who’s found the usual books on the subject impenetrable.

For example, we find it something of a travesty that in 2018 there seems to be no introductory material available on monoidal categories. Even beautiful modern introductions to category theory, e.g. by Riehl or Leinster, do not include anything on this rather central topic. The basic idea is certainly not too abstract; modern human intuition seems to include a pre-theoretical understanding of monoidal categories that is just waiting to be formalized. Is there anyone who wouldn’t correctly understand the basic idea being communicated in the following diagram?

Many applied category theory topics seem to take monoidal categories as their jumping off point. So one aim of this book is to provide a reference—even if unconventional—for this important topic.

We hope this book inspires both new visions and new questions. We intend it to be self-contained in the sense that it is approachable with minimal prerequisites, but not in the sense that the complete story is told here. On the contrary, we hope that readers use this as an invitation to further reading, to orient themselves in what is becoming a large literature, and to discover new applications for themselves.

This book is, unashamedly, our take on the subject. While the abstract structures we explore are important to any category theorist, the specific topics have simply been chosen to our personal taste. Our examples are ones that we find simple but powerful, concrete but representative, entertaining but in a way that feels important and expansive at the same time. We hope our readers will enjoy themselves and learn a lot in the process.

#### How to read this book

The basic idea of category theory—which threads through every chapter—is that if one pays careful attention to structures and coherence, the resulting systems will be extremely reliable and interoperable. For example, a category involves several structures: a collection of objects, a collection of morphisms relating objects, and a formula for combining any chain of morphisms into a morphism. But these structures need to cohere or work together in a simple commonsense way: a chain of chains is a chain, so combining a chain of chains should be the same as combining the chain. That’s it!

We will see structures and coherence come up in pretty much every definition we give: “here are some things and here are how they fit together.” We ask the reader to be on the lookout for structures and coherence as they read the book, and to realize that as we layer abstraction on abstraction, it is the coherence that makes everything function like a well-oiled machine.

Each chapter in this book is motivated by a real-world topic, such as electrical circuits, control theory, cascade failures, information integration, and hybrid systems. These motivations lead us into and through various sorts of category-theoretic concepts.

We generally have one motivating idea and one category-theoretic purpose per chapter, and this forms the title of the chapter, e.g. Chapter 4 is “Collaborative design: profunctors, categorification, and monoidal categories.” In many math books, the difficulty is roughly a monotonically-increasing function of the page number. In this book, this occurs in each chapter, but not so much in the book as a whole. The chapters start out fairly easy and progress in difficulty.

The upshot is that if you find the end of a chapter very difficult, hope is certainly not lost: you can start on the next one and make good progress. This format lends itself to giving you a first taste now, but also leaving open the opportunity for you to come back at a later date and get more deeply into it. But by all means, if you have the gumption to work through each chapter to its end, we very much encourage that!

We include many exercises throughout the text. Usually these exercises are fairly straightforward; the only thing they demand is that the reader’s mind changes state from passive to active, rereads the previous paragraphs with intent, and puts the pieces together. A reader becomes a student when they work the exercises; until then they are more of a tourist, riding on a bus and listening off and on to the tour guide. Hey, there’s nothing wrong with that, but we do encourage you to get off the bus and make contact with the natives as often as you can.

## Azimuth Backup Project (Part 5)

5 October, 2017

I haven’t spoken much about the Azimuth Climate Data Backup Project, but it’s going well, and I’ll be speaking about it soon, here:

International Open Access Week, Wednesday 25 October 2017, 9:30–11:00 a.m., University of California, Riverside, Orbach Science Library, Room 240.

“Open in Order to Save Data for Future Research” is the 2017 event theme.

Open Access Week is an opportunity for the academic and research community to learn about the potential benefits of sharing what they’ve learned with colleagues, and to help inspire wider participation in helping to make “open access” a new norm in scholarship, research and data planning and preservation.

The Open Access movement is made of up advocates (librarians, publishers, university repositories, etc.) who promote the free, immediate, and online publication of research.

The program will provide information on issues related to saving open data, including climate change and scientific data. The panelists also will describe open access projects in which they have participated to save climate data and to preserve end-of-term presidential data, information likely to be and utilized by the university community for research and scholarship.

The program includes:

• Brianna Marshall, Director of Research Services, UCR Library: Brianna welcomes guests and introduces panelists.

• John Baez, Professor of Mathematics, UCR: John will describe his activities to save US government climate data through his collaborative effort, the Azimuth Climate Data Backup Project. All of the saved data is now open access for everyone to utilize for research and scholarship.

• Perry Willett, Digital Preservation Projects Manager, California Digital Library: Perry will discuss the open data initiatives in which CDL participates, including the end-of-term presidential web archiving that is done in partnership with the Library of Congress, Internet Archive and University of North Texas.

• Kat Koziar, Data Librarian, UCR Library: Kat will give an overview of DASH, the UC system data repository, and provide suggestions for researchers interested in making their data open.

This will be the eighth International Open Access Week program hosted by the UCR Library.

The event is free and open to the public. Light refreshments will be served.

## Complex Adaptive System Design (Part 4)

22 August, 2017

Last time I introduced typed operads. A typed operad has a bunch of operations for putting together things of various types and getting new things of various types. This is a very general idea! But in the CASCADE project we’re interested in something more specific: networks. So we want operads whose operations are ways to put together networks and get new networks.

That’s what our team came up with: John Foley of Metron, my graduate students Blake Pollard and Joseph Moeller, and myself. We’re writing a couple of papers on this, and I’ll let you know when they’re ready. These blog articles are kind of sneak preview—and a gentle introduction, where you can ask questions.

For example: I’m talking a lot about networks. But what is a ‘network’, exactly?

There are many kinds. At the crudest level, we can model a network as a simple graph, which is something like this:

There are some restrictions on what counts as a simple graph. If the vertices are agents of some sort and the edges are communication channels, these restrictions imply:

• We allow at most one channel between any pair of agents, since there’s at most one edge between any two vertices of our graph.

• The channels do not have a favored direction, since there are no arrows on the edges of our graph.

• We don’t allow a channel from an agent to itself, since an edge can’t start and end at the same vertex.

For other purposes we may want to drop some or all of these restrictions. There is an appalling diversity of options! We might want to allow multiple channels between a pair of agents. For this we could use multigraphs. We might want to allow directed channels, where the sender and receiver have different capabilities: for example, signals may only be able to flow in one direction. For this we could use directed graphs. And so on.

We will also want to consider graphs with colored vertices, to specify different types of agents—or colored edges, to specify different types of channels. Even more complicated variants are likely to become important as we proceed.

To avoid sinking into a mire of special cases, we need the full power of modern mathematics. Instead of separately studying all these various kinds of networks, we need a unified notion that subsumes all of them.

To do this, the Metron team came up with something called a ‘network model’. There is a network model for simple graphs, a network model for multigraphs, a network model for directed graphs, a network model for directed graphs with 3 colors of vertex and 15 colors of edge, and more.

You should think of a network model as a kind of network. Not a specific network, just a kind of network.

Our team proved that for each network model $G$ there is an operad $O_G$ whose operations describe how to put together networks of that kind. We call such operads ‘network operads’.

I want to make all this precise, but today let me just show you one example. Let’s take $G$ to be the network model for simple graphs, and look at the network operad $O_G.$ I won’t tell you what kind of thing $G$ is yet! But I’ll tell you about the operad $O_G$.

Types. Remember from last time that an operad has a set of ‘types’. For $O_G$ this is the set of natural numbers, $\mathbb{N}.$ The reason is that a simple graph can have any number of vertices.

Operations. Remember that an operad has sets of ‘operations’. In our case we have a set of operations $O_G(t_1,\dots,t_n ; t)$ for each choice of $t_1,\dots,t_n, t \in \mathbb{N}.$

An operation $f \in O_G(t_1,\dots,t_n; t)$ is a way of taking a simple graph with $t_1$ vertices, a simple graph with $t_2$ vertices,… and so on, and sticking them together, perhaps adding new edges, to get a simple graph with

$t = t_1 + \cdots + t_n$

vertices.

Let me show you an operation

$f \in O_G(3,4,2;9)$

This will be a way of taking three simple graphs—one with 3 vertices, one with 4, and one with 2—and sticking them together, perhaps adding edges, to get one with 9 vertices.

Here’s what $f$ looks like:

It’s a simple graph with vertices numbered from 1 to 9, with the vertices in bunches: {1,2,3}, {4,5,6,7} and {8,9}. It could be any such graph. This one happens to have an edge from 3 to 6 and an edge from 1 to 2.

Here’s how we can actually use our operation. Say we have three simple graphs like this:

Then we can use our operation to stick them together and get this:

Notice that we added a new edge from 3 to 6, connecting two of our three simple graphs. We also added an edge from 1 to 2… but this had no effect, since there was already an edge there! The reason is that simple graphs have at most one edge between vertices.

But what if we didn’t already have an edge from 1 to 2? What if we applied our operation $f$ to the following simple graphs?

Well, now we’d get this:

This time adding the edge from 1 to 2 had an effect, since there wasn’t already an edge there!

In short, we can use this operad to stick together simple graphs, but also to add new edges within the simple graphs we’re sticking together!

When I’m telling you how we ‘actually use’ our operad to stick together graphs, I’m secretly describing an algebra of our operad. Remember, an operad describes ways of sticking together things together, but an ‘algebra’ of the operad gives a particular specification of these things and describes how we stick them together.

Our operad $O_G$ has lots of interesting algebras, but I’ve just shown you the simplest one. More precisely:

Things. Remember from last time that for each type, an algebra specifies a set of things of that type. In this example our types are natural numbers, and for each natural number $t \in \mathbb{N}$ I’m letting the set of things $A(t)$ consist of all simple graphs with vertices $\{1, \dots, t\}.$

Action. Remember that our operad $O_G$ should have an action on $A$, meaning a bunch of maps

$\alpha : O_G(t_1,...,t_n ; t) \times A(t_1) \times \cdots \times A(t_n) \to A(t)$

I just described how this works in some examples. Some rules should hold… and they do.

To make sure you understand, try these puzzles:

Puzzle 1. In the example I just explained, what is the set $O_G(t_1,\dots,t_n ; t)$ if $t \ne t_1 + \cdots + t_n?$

Puzzle 2. In this example, how many elements does $O_G(1,1;2)$ have?

Puzzle 3. In this example, how many elements does $O_G(1,2;3)$ have?

Puzzle 4. In this example, how many elements does $O_G(1,1,1;3)$ have?

Puzzle 5. In the particular algebra $A$ that I explained, how many elements does $A(3)$ have?

Next time I’ll describe some more interesting algebras of this operad $O_G.$ These let us describe networks of mobile agents with range-limited communication channels!

Some posts in this series:

Part 2. Metron’s software for system design.

Part 3. Operads: the basic idea.

Part 4. Network operads: an easy example.

Part 5. Algebras of network operads: some easy examples.

Part 6. Network models.

Part 7. Step-by-step compositional design and tasking using commitment networks.

## Complex Adaptive System Design (Part 3)

17 August, 2017

It’s been a long time since I’ve blogged about the Complex Adaptive System Composition and Design Environment or CASCADE project run by John Paschkewitz. For a reminder, read these:

Complex adaptive system design (part 1), Azimuth, 2 October 2016.

Complex adaptive system design (part 2), Azimuth, 18 October 2016.

A lot has happened since then, and I want to explain it.

I’m working with Metron Scientific Solutions to develop new techniques for designing complex networks.

The particular problem we began cutting our teeth on is a search and rescue mission where a bunch of boats, planes and drones have to locate and save people who fall overboard during a boat race in the Caribbean Sea. Subsequently the Metron team expanded the scope to other search and rescue tasks. But the real goal is to develop very generally applicable new ideas on designing and ‘tasking’ networks of mobile agents—that is, designing these networks and telling the agents what to do.

We’re using the mathematics of ‘operads’, in part because Spivak’s work on operads has drawn a lot of attention and raised a lot of hopes:

An operad is a bunch of operations for sticking together smaller things to create bigger ones—I’ll explain this in detail later, but that’s the core idea. Spivak described some specific operads called ‘operads of wiring diagrams’ and illustrated some of their potential applications. But when we got going on our project, we wound up using a different class of operads, which I’ll call ‘network operads’.

Here’s our dream, which we’re still trying to make into a reality:

Network operads should make it easy to build a big network from smaller ones and have every agent know what to do. You should be able to ‘slap together’ a network, throwing in more agents and more links between them, and automatically have it do something reasonable. This should be more flexible than an approach where you need to know ahead of time exactly how many agents you have, and how they’re connected, before you can tell them what to do.

You don’t want a network to malfunction horribly because you forgot to hook it up correctly. You want to focus your attention on optimizing the network, not getting it to work at all. And you want everything to work so smoothly that it’s easy for the network to adapt to changing conditions.

To achieve this we’re using network operads, which are certain special ‘typed operads’. So before getting into the details of our approach, I should say a bit about typed operads. And I think that will be enough for today’s post: I don’t want to overwhelm you with too much information at once.

In general, a ‘typed operad’ describes ways of sticking together things of various types to get new things of various types. An ‘algebra’ of the operad gives a particular specification of these things and the results of sticking them together. For now I’ll skip the full definition of a typed operad and only highlight the most important features. A typed operad $O$ has:

• a set $T$ of types.

• collections of operations $O(t_1,...,t_n ; t)$ where $t_i, t \in T$. Here $t_1, \dots, t_n$ are the types of the inputs, while $t$ is the type of the output.

• ways to compose operations. Given an operation
$f \in O(t_1,\dots,t_n ;t)$ and $n$ operations

$g_1 \in O(t_{11},\dots,t_{1 k_1}; t_1),\dots, g_n \in O(t_{n1},\dots,t_{n k_n};t_n)$

we can compose them to get

$f \circ (g_1,\dots,g_n) \in O(t_{11}, \dots, t_{nk_n};t)$

These must obey some rules.

But if you haven’t seen operads before, you’re probably reeling in horror—so I need to rush in and save you by showing you the all-important pictures that help explain what’s going on!

First of all, you should visualize an operation $f \in O(t_1, \dots, t_n; t)$ as a little gizmo like this:

It has $n$ inputs at top and one output at bottom. Each input, and the output, has a ‘type’ taken from the set $T.$ So, for example, if you operation takes two real numbers, adds them and spits out the closest integer, both input types would be ‘real’, while the output type would be ‘integer’.

The main thing we do with operations is compose them. Given an an operation $f \in O(t_1,\dots,t_n ;t),$ we can compose it with $n$ operations

$g_1 \in O(t_{11},\dots,t_{1 k_1}; t_1), \quad \dots, \quad g_n \in O(t_{n1},\dots,t_{n k_n};t_n)$

by feeding their outputs into the inputs of $f,$ like this:

The result is an operation we call

$f \circ (g_1, \dots, g_n)$

Note that the input types of $f$ have to match the output types of the $g_i$ for this to work! This is the whole point of types: they forbid us from composing operations in ways that don’t make sense.

This avoids certain stupid mistakes. For example, you can take the square root of a positive number, but you may not want to take the square root of a negative number, and you definitely don’t want to take the square root of a hamburger. While you can land a plane on an airstrip, you probably don’t want to land a plane on a person.

The operations in an operad are quite abstract: they aren’t really operating on anything. To render them concrete, we need another idea: operads have ‘algebras’.

An algebra $A$ of the operad $O$ specifies a set of things of each type $t \in T$ such that the operations of $O$ act on these sets. A bit more precisely, an algebra consists of:

• for each type $t \in T,$ a set $A(t)$ of things of type $t$

• an action of $O$ on $A,$ that is, a collection of maps

$\alpha : O(t_1,...,t_n ; t) \times A(t_1) \times \cdots \times A(t_n) \to A(t)$

obeying some rules.

In other words, an algebra turns each operation $f \in O(t_1,...,t_n ; t)$ into a function that eats things of types $t_1, \dots, t_n$ and spits out a thing of type $t.$

When we get to designing systems with operads, the fact that the same operad can have many algebras will be useful. Our operad will have operations describing abstractly how to hook up networks to form larger networks. An algebra will give a specific implementation of these operations. We can use one algebra that’s fairly fine-grained and detailed about what the operations actually do, and another that’s less detailed. There will then be a map between from the first algebra to the second, called an ‘algebra homomorphism’, that forgets some fine-grained details.

There’s a lot more to say—all this is just the mathematical equivalent of clearing my throat before a speech—but I’ll stop here for now.

And as I do—since it also takes me time to stop talking—I should make it clear yet again that I haven’t even given the full definition of typed operads and their algebras! Besides the laws I didn’t write down, there’s other stuff I omitted. Most notably, there’s a way to permute the inputs of an operation in an operad, and operads have identity operations, one for each type.

To see the full definition of an ‘untyped’ operad, which is really an operad with just one type, go here:

They just call it an ‘operad’. Note that they first explain ‘non-symmetric operads’, where you can’t permute the inputs of operations, and then explain operads, where you can.

If you’re mathematically sophisticated, you can easily guess the laws obeyed by a typed operad just by looking at this article and inserting the missing types. You can also see the laws written down in Spivak’s paper, but with some different terminology: he calls types ‘objects’, he calls operations ‘morphisms’, and he calls typed operads ‘symmetric colored operads’—or once he gets going, just ‘operads’.

You can also see the definition of a typed operad in Section 2.1 here:

• Donald Yau, Operads of wiring diagrams.

What I would call a typed operad with $S$ as its set of types, he calls an ‘$S$-colored operad’.

I guess it’s already evident, but I’ll warn you that the terminology in this subject varies quite a lot from author to author: for example, a certain community calls typed operads ‘symmetric multicategories’. This is annoying at first but once you understand the subject it’s as ignorable as the fact that mathematicians have many different accents. The main thing to remember is that operads come in four main flavors, since they can either be typed or untyped, and they can either let you permute inputs or not. I’ll always be working with typed operads where you can permute inputs.

Finally, I’ll say that while the definition of operad looks lengthy and cumbersome at first, it becomes lean and elegant if you use more category theory.

Next time I’ll give you an example of an operad: the simplest ‘network

Some posts in this series:

Part 2. Metron’s software for system design.

Part 3. Operads: the basic idea.

Part 4. Network operads: an easy example.

Part 5. Algebras of network operads: some easy examples.

Part 6. Network models.

Part 7. Step-by-step compositional design and tasking using commitment networks.

## Saving Climate Data (Part 6)

23 February, 2017

Scott Pruitt, who filed legal challenges against Environmental Protection Agency rules fourteen times, working hand in hand with oil and gas companies, is now head of that agency. What does that mean about the safety of climate data on the EPA’s websites? Here is an inside report:

• Dawn Reeves, EPA preserves Obama-Era website but climate change data doubts remain, InsideEPA.com, 21 February 2017.

For those of us who are backing up climate data, the really important stuff is in red near the bottom.

The EPA has posted a link to an archived version of its website from Jan. 19, the day before President Donald Trump was inaugurated and the agency began removing climate change-related information from its official site, saying the move comes in response to concerns that it would permanently scrub such data.

However, the archived version notes that links to climate and other environmental databases will go to current versions of them—continuing the fears that the Trump EPA will remove or destroy crucial greenhouse gas and other data.

The archived version was put in place and linked to the main page in response to “numerous [Freedom of Information Act (FOIA)] requests regarding historic versions of the EPA website,” says an email to agency staff shared by the press office. “The Agency is making its best reasonable effort to 1) preserve agency records that are the subject of a request; 2) produce requested agency records in the format requested; and 3) post frequently requested agency records in electronic format for public inspection. To meet these goals, EPA has re-posted a snapshot of the EPA website as it existed on January 19, 2017.”

The email adds that the action is similar to the snapshot taken of the Obama White House website.

For example, it says the page is “not the current EPA website” and that the archive includes “static content, such as webpages and reports in Portable Document Format (PDF), as that content appeared on EPA’s website as of January 19, 2017.”

It cites technical limits for the database exclusions. “For example, many of the links contained on EPA’s website are to databases that are updated with the new information on a regular basis. These databases are not part of the static content that comprises the Web Snapshot.” Searches of the databases from the archive “will take you to the current version of the database,” the agency says.

“In addition, links may have been broken in the website as it appeared” on Jan. 19 and those will remain broken on the snapshot. Links that are no longer active will also appear as broken in the snapshot.

“Finally, certain extremely large collections of content… were not included in the Snapshot due to their size” such as AirNow images, radiation network graphs, historic air technology transfer network information, and EPA’s searchable news releases.”

#### ‘Smart’ Move

One source urging the preservation of the data says the snapshot appears to be a “smart” move on EPA’s behalf, given the FOIA requests it has received, and notes that even though other groups like NextGen Climate and scientists have been working to capture EPA’s online information, having it on EPA’s site makes it official.

But it could also be a signal that big changes are coming to the official Trump EPA site, and it is unclear how long the agency will maintain the archived version.

The source says while it is disappointing that the archive may signal the imminent removal of EPA’s climate site, “at least they are trying to accommodate public concerns” to preserve the information.

A second source adds that while it is good that EPA is seeking “to address the widespread concern” that the information will be removed by an administration that does not believe in human-caused climate change, “on the other hand, it doesn’t address the primary concern of the data. It is snapshots of the web text.” Also, information “not included,” such as climate databases, is what is difficult to capture by outside groups and is what really must be preserved.

“If they take [information] down” that groups have been trying to preserve, then the underlying concern about access to data remains. “Web crawlers and programs can do things that are easy,” such as taking snapshots of text, “but getting the data inside the database is much more challenging,” the source says.

The first source notes that EPA’s searchable databases, such as those maintained by its Clean Air Markets Division, are used by the public “all the time.”

The agency’s Office of General Counsel (OGC) Jan. 25 began a review of the implications of taking down the climate page—a planned wholesale removal that was temporarily suspended to allow for the OGC review.

But EPA did remove some specific climate information, including links to the Clean Power Plan and references to President Barack Obama’s Climate Action Plan. Inside EPA captured this screenshot of the “What EPA Is Doing” page regarding climate change. Those links are missing on the Trump EPA site. The archive includes the same version of the page as captured by our screenshot.

Inside EPA first reported the plans to take down the climate information on Jan. 17.

After the OGC investigation began, a source close to the Trump administration said Jan. 31 that climate “propaganda” would be taken down from the EPA site, but that the agency is not expected to remove databases on GHG emissions or climate science. “Eventually… the propaganda will get removed…. Most of what is there is not data. Most of what is there is interpretation.”

The Sierra Club and Environmental Defense Fund both filed FOIA requests asking the agency to preserve its climate data, while attorneys representing youth plaintiffs in a federal climate change lawsuit against the government have also asked the Department of Justice to ensure the data related to its claims is preserved.

The Azimuth Climate Data Backup Project and other groups are making copies of actual databases, not just the visible portions of websites.

## Azimuth Backup Project (Part 4)

18 February, 2017

The Azimuth Climate Data Backup Project is going well! Our Kickstarter campaign ended on January 31st and the money has recently reached us. Our original goal was $5000. We got$20,427 of donations, and after Kickstarter took its cut we received $18,590.96. Next time I’ll tell you what our project has actually been doing. This time I just want to give a huge “thank you!” to all 627 people who contributed money on Kickstarter! I sent out thank you notes to everyone, updating them on our progress and asking if they wanted their names listed. The blanks in the following list represent people who either didn’t reply, didn’t want their names listed, or backed out and decided not to give money. I’ll list people in chronological order: first contributors first. Only 12 people backed out; the vast majority of blanks on this list are people who haven’t replied to my email. I noticed some interesting but obvious patterns. For example, people who contributed later are less likely to have answered my email yet—I’ll update this list later. People who contributed more money were more likely to answer my email. The magnitude of contributions ranged from$2000 to \$1. A few people offered to help in other ways. The response was international—this was really heartwarming! People from the US were more likely than others to ask not to be listed.

But instead of continuing to list statistical patterns, let me just thank everyone who contributed.

Daniel Estrada
Ahmed Amer
Saeed Masroor
Jodi Kaplan
John Wehrle
Bob Calder
Andrea Borgia
L Gardner

Uche Eke
Keith Warner
Dean Kalahan
James Benson
Dianne Hackborn

Walter Hahn
Thomas Savarino
Noah Friedman
Eric Willisson
Jeffrey Gilmore
John Bennett
Glenn McDavid

Brian Turner

Peter Bagaric

Martin Dahl Nielsen
Broc Stenman

Gabriel Scherer
Roice Nelson
Felipe Pait
Kenneth Hertz

Luis Bruno

Andrew Lottmann
Alex Morse

Noam Zeilberger

Buffy Lyon

Josh Wilcox

Danny Borg

Krishna Bhogaonker
Harald Tveit Alvestrand

Tarek A. Hijaz, MD
Jouni Pohjola
Chavdar Petkov
Markus Jöbstl
Bjørn Borud

Sarah G

William Straub

Frank Harper
Carsten Führmann
Rick Angel
Drew Armstrong

Jesimpson

Valeria de Paiva
Ron Prater
David Tanzer

Rafael Laguna
Miguel Esteves dos Santos
Sophie Dennison-Gibby

Randy Drexler
Peter Haggstrom

Jerzy Michał Pawlak
Santini Basra
Jenny Meyer

John Iskra

Bruce Jones
Māris Ozols
Everett Rubel

Mike D
Manik Uppal
Todd Trimble

Federer Fanatic

Forrest Samuel, Harmos Consulting

Annie Wynn
Norman and Marcia Dresner

Daniel Mattingly
James W. Crosby

Jennifer Booth
Greg Randolph

Dave and Karen Deeter

Sarah Truebe

Tieg Zaharia
Jeffrey Salfen
Birian Abelson

Logan McDonald

Brian Truebe
Jon Leland

Nicole

Sarah Lim

James Turnbull

John Huerta
Katie Mandel Bruce
Bethany Summer

Heather Tilert

Naom Hart
Aaron Riley

Giampiero Campa

Julie A. Sylvia

Pace Willisson

Bangskij

Peter Herschberg

Alaistair Farrugia

Conor Hennessy

Stephanie Mohr

Torinthiel

Lincoln Muri
Anet Ferwerda

Hanna

Michelle Lee Guiney

Ben Doherty
Trace Hagemann

Ryan Mannion

Penni and Terry O'Hearn

Brian Bassham
Caitlin Murphy
John Verran

Susan

Alexander Hawson
Fabrizio Mafessoni
Anita Phagan
Nicolas Acuña
Niklas Brunberg

V. Lazaro Zamora

Branford Werner
Niklas Starck Westerberg
Luca Zenti and Marta Veneziano

Ilja Preuß
Christopher Flint

Courtney Leigh

Katharina Spoerri

Daniel Risse

Hanna
Charles-Etienne Jamme
rhackman41

Jeff Leggett

RKBookman

Aaron Paul
Mike Metzler

Patrick Leiser

Melinda

Ryan Vaughn
Kent Crispin

Michael Teague

Ben

Fabian Bach
Steven Canning

Betsy McCall

John Rees

Mary Peters

Shane Claridge
Thomas Negovan
Tom Grace
Justin Jones

Jason Mitchell

Josh Weber
Rebecca Lynne Hanginger
Kirby

Dawn Conniff

Michael T. Astolfi

Kristeva

Erik
Keith Uber

Elaine Mazerolle
Matthieu Walraet

Linda Penfold

Lujia Liu

Keith

Samar Tareem

Henrik Almén
Michael Deakin
Rutger Ockhorst

Erin Bassett
James Crook

Junior Eluhu
Dan Laufer
Carl
Robert Solovay

Silica Magazine

Leonard Saers
Alfredo Arroyo García

Larry Yu

John Behemonth

Eric Humphrey

Svein Halvor Halvorsen

Karim Issa

Øystein Risan Borgersen
David Anderson Bell III

Ole-Morten Duesend

Robert Biegler

Qu Wenhao

Steffen Dittmar

Shanna Germain

John WS Marvin (Dread Unicorn Games)

Bill Carter
Darth Chronis

Lawrence Stewart

Gareth Hodges

Colin Backhurst
Christopher Metzger

Rachel Gumper

Mariah Thompson

Johnathan Salter

Maggie Unkefer
Shawna Maryanovich

Wilhelm Fitzpatrick
Dylan “ExoByte” Mayo
Lynda Lee

Scott Carpenter

Charles D, Payet
Vince Rostkowski

Tim Brown
Raven Daegmorgan
Zak Brueckner

Christian Page

Steven Greenberg
Chuck Lunney

Natasha Anicich

Bram De Bie
Edward L

Gray Detrick
Robert

Sarah Russell

Sam Leavin

Abilash Pulicken

Isabel Olondriz
James Pierce
James Morrison

April Daniels

José Tremblay Champagne

Chris Edmonds

Hans & Maria Cummings
Bart Gasiewiski

Andy Chamard

Andrew Jackson

Christopher Wright

Crystal Collins

ichimonji10

Alan Stern
Alison W

Dag Henrik Bråtane

Martin Nilsson