When conducting animal advocacy research projects, the sheer amount of online material can be overwhelming. Fortunately, there are several research libraries and data repositories that can help you access high-quality, relevant, detailed information. Animal Charity Evaluators (ACE) has compiled a list of such sources that we have found particularly useful. We recommend considering these sources when carrying out your own research, in addition to search tools such as Google Scholar, Elicit, Consensus, Research Rabbit, or Semantic Scholar.

For more information on animal advocacy research and its benefits for animal causes, check out our blog post on the topic.

This is not an exhaustive list, and we are interested to hear what other sources of information you have found especially useful.

OrganizationResourceDescription
Animal Charity EvaluatorsResearch libraryA curated collection of research done by individuals, organizations, and academics in the fields of animal welfare science, psychology, social movements, and other relevant fields.
Animal Charity EvaluatorsResearch newsletterA newsletter including all the empirical studies ACE is aware of from the last month about advocating for farmed animals or providing evidence that may be of interest to farmed animal advocates.
Animal AskResearch databaseIn-depth, cross-comparative research to guide decision-making toward the most promising opportunities for animals.
Animal Welfare LibraryAnimal Welfare LibraryA large collection of high-quality animal welfare resources.
Bryant ResearchInsightsIn-depth original research on meat reduction and alternative proteins.
Charity EntrepreneurshipAnimal welfare reportsReports on animal welfare
published by Charity Entrepreneurship.
EA ForumAnimal welfare postsEffective Altruism-focused forum with many posts on animal welfare.
FaunalyticsOriginal studiesOriginal studies on animal issues and animal advocacy conducted by Faunalytics.
FaunalyticsResearch libraryA large library of research about animal issues and animal advocacy.
Food and Agriculture Organization of the United NationsFAOSTATFood and agriculture data for over 245 countries and territories, dating from 1961.
Food Systems InnovationAnimal Data ProjectCurated resources for topics related to wild animals and animals used for food, products, research, and entertainment.
Impactful Animal AdvocacySlack communityA global online hub where advocates frequently share animal advocacy research.
Impactful Animal AdvocacyNewslettersMonthly newsletter covering a range of animal advocacy updates and resources.
Impactful Animal AdvocacyIAA WikisA collection of Wiki databases on a variety of animal advocacy topics.
Open PhilanthropyFarm animal welfare research reportsOpen Philanthropy’s research reports on farmed animal welfare.
Our World in DataAnimal WelfareData, visualizations, and writing on animal welfare.
Plant Based DataLibrariesAn organization providing studies and summaries on why we need a plant-based food system.
Rethink PrioritiesResearch reportsRethink Priorities’ research reports on animal welfare.
Sentience InstituteSummary of evidence for foundational questions in animal advocacyA summary of the evidence on all sides of important foundational questions in effective animal advocacy.
Tiny Beam FundBeaconA series of key messages from academic works useful for tackling industrial animal agriculture in developing countries.
Tiny Beam FundAcademic Studies Without TearsA series that aims to turn academic research findings into accessible information for advocacy and frontline groups. Readers have to sign up via email.
Comments


No comments on this post yet.
Be the first to respond.
Curated and popular this week
 ·  · 10m read
 · 
I wrote this to try to explain the key thing going on with AI right now to a broader audience. Feedback welcome. Most people think of AI as a pattern-matching chatbot – good at writing emails, terrible at real thinking. They've missed something huge. In 2024, while many declared AI was reaching a plateau, it was actually entering a new paradigm: learning to reason using reinforcement learning. This approach isn’t limited by data, so could deliver beyond-human capabilities in coding and scientific reasoning within two years. Here's a simple introduction to how it works, and why it's the most important development that most people have missed. The new paradigm: reinforcement learning People sometimes say “chatGPT is just next token prediction on the internet”. But that’s never been quite true. Raw next token prediction produces outputs that are regularly crazy. GPT only became useful with the addition of what’s called “reinforcement learning from human feedback” (RLHF): 1. The model produces outputs 2. Humans rate those outputs for helpfulness 3. The model is adjusted in a way expected to get a higher rating A model that’s under RLHF hasn’t been trained only to predict next tokens, it’s been trained to produce whatever output is most helpful to human raters. Think of the initial large language model (LLM) as containing a foundation of knowledge and concepts. Reinforcement learning is what enables that structure to be turned to a specific end. Now AI companies are using reinforcement learning in a powerful new way – training models to reason step-by-step: 1. Show the model a problem like a math puzzle. 2. Ask it to produce a chain of reasoning to solve the problem (“chain of thought”).[1] 3. If the answer is correct, adjust the model to be more like that (“reinforcement”).[2] 4. Repeat thousands of times. Before 2023 this didn’t seem to work. If each step of reasoning is too unreliable, then the chains quickly go wrong. Without getting close to co
 ·  · 1m read
 · 
(Audio version here, or search for "Joe Carlsmith Audio" on your podcast app.) > “There comes a moment when the children who have been playing at burglars hush suddenly: was that a real footstep in the hall?”  > > - C.S. Lewis “The Human Condition,” by René Magritte (Image source here) 1. Introduction Sometimes, my thinking feels more “real” to me; and sometimes, it feels more “fake.” I want to do the real version, so I want to understand this spectrum better. This essay offers some reflections.  I give a bunch of examples of this “fake vs. real” spectrum below -- in AI, philosophy, competitive debate, everyday life, and religion. My current sense is that it brings together a cluster of related dimensions, namely: * Map vs. world: Is my mind directed at an abstraction, or it is trying to see past its model to the world beyond? * Hollow vs. solid: Am I using concepts/premises/frames that I secretly suspect are bullshit, or do I expect them to point at basically real stuff, even if imperfectly? * Rote vs. new: Is the thinking pre-computed, or is new processing occurring? * Soldier vs. scout: Is the thinking trying to defend a pre-chosen position, or is it just trying to get to the truth? * Dry vs. visceral: Does the content feel abstract and heady, or does it grip me at some more gut level? These dimensions aren’t the same. But I think they’re correlated – and I offer some speculations about why. In particular, I speculate about their relationship to the “telos” of thinking – that is, to the thing that thinking is “supposed to” do.  I also describe some tags I’m currently using when I remind myself to “really think.” In particular:  * Going slow * Following curiosity/aliveness * Staying in touch with why I’m thinking about something * Tethering my concepts to referents that feel “real” to me * Reminding myself that “arguments are lenses on the world” * Tuning into a relaxing sense of “helplessness” about the truth * Just actually imagining differ
JamesÖz
 ·  · 3m read
 · 
Why it’s important to fill out this consultation The UK Government is currently consulting on allowing insects to be fed to chickens and pigs. This is worrying as the government explicitly says changes would “enable investment in the insect protein sector”. Given the likely sentience of insects (see this summary of recent research), and that median predictions estimate that 3.9 trillion insects will be killed annually by 2030, we think it’s crucial to try to limit this huge source of animal suffering.  Overview * Link to complete the consultation: HERE. You can see the context of the consultation here. * How long it takes to fill it out: 5-10 minutes (5 questions total with only 1 of them requiring a written answer) * Deadline to respond: April 1st 2025 * What else you can do: Share the consultation document far and wide!  * You can use the UK Voters for Animals GPT to help draft your responses. * If you want to hear about other high-impact ways to use your political voice to help animals, sign up for the UK Voters for Animals newsletter. There is an option to be contacted only for very time-sensitive opportunities like this one, which we expect will happen less than 6 times a year. See guidance on submitting in a Google Doc Questions and suggested responses: It is helpful to have a lot of variation between responses. As such, please feel free to add your own reasoning for your responses or, in addition to animal welfare reasons for opposing insects as feed, include non-animal welfare reasons e.g., health implications, concerns about farming intensification, or the climate implications of using insects for feed.    Question 7 on the consultation: Do you agree with allowing poultry processed animal protein in porcine feed?  Suggested response: No (up to you if you want to elaborate further).  We think it’s useful to say no to all questions in the consultation, particularly as changing these rules means that meat producers can make more profit from sel