 Next up, we have Nicholas Alessandrony, who is going to talk about the many, many collaboratively advancing best research practices through cross-taxon victim science. Well, good morning. It's a great pleasure to be here today to introduce the many, many's Collaboratory, a victim science project that aims to revolutionize comparative cognition research and advanced best research practices. So it is no news to anybody here that conducting studies in siloed laboratories with limited communication and collaboration with other units with small sample sizes and underpowered designs has had a devastating effect on the reproducibility, replicability, and robustness of research findings. This has created an urgent need to develop new workflows to enhance the reliability of scientific discovery. Behavioral scientists focusing on single species groups have begun to solve this issue by adopting a form of grassroots collaborative research known as victim science, where large numbers of researchers come together to pull resources toward a common research goal. Examples of these networks include many babies focused on human infant development, many primates investigating primate cognition, many goats working on goat behavior and welfare, and other initiatives like the psychological science accelerator, many birds, many fishes, and many dogs. These large-scale collaborations bring about extensive advantages enabling researchers to do science in ways they could not under the traditional model. For example, victim science projects collect data from hundreds or even thousands of subjects in a single study, which far exceeds the capacity of an individual researcher or team. Victim science projects also embrace and foster open science practices such as preregistration of materials and open data, thus sparing the transparency of research. Despite progress in other areas, creating a large-scale networking comparative cognition remains an unmet, albeit critical challenge. A reason behind this shortcoming is the multidisciplinary nature of comparative cognition, which often leads to differences in the definition of crucial concepts, think for example the definition of learning in psychology versus in biology, and in the implementation of research methods. Indeed, most researchers are specialists in a single species or taxon and comparing subjects with disparate body structures and abilities renders implementation of standardized procedures exceptionally difficult. Many-many's is an unprecedented mega-network of international researchers that aims to overcome these challenges by collaboratively developing and applying innovative methods to measure and compare behavior across animal taxa. It was funded after the Victim Science Conference held virtually last year where representatives from different networks came together to identify challenges to the implementation of victim science and explore solutions. It was during Hackathon that a large team of researchers agreed on the pressing need to create a victim science network to help comparative cognition realize its full potential. Many-many's will foster the convergence of many-fold perspectives and practices from diverse fields and develop extensive knowledge and novel viewpoints on how open replicable and collaborative practices can benefit victim science workflows. Indeed, the field of victim science in the behavioral sciences is relatively nascent and there is a depth of solutions. Our project will bridge this gap by providing insights into the effective execution of a victim science project that's promoting and enhancing the quality of research. I am very excited to announce that through consensus-based decision-making many-many's has decided that its first project named many-many's one will revolve around learning. Learning is a fundamental ability present across species from insects to humans. The study of learning provides an unparalleled approach to understanding how different species acquire, process, and utilize information to adapt and thrive in their environments. Researchers in various fields have developed methods and theories to understand learning with the species they study, but the typically isolated manner of this work has limited valid comparisons across species. To begin to unravel the mysteries of learning and its evolutionary origins, many-many's one will create an implemented task to compare reversal learning abilities across animal taxa. Reversal learning tasks are the gold standard procedure to investigate learning and behavioral flexibility across species. A reversal learning task involves subjects being first reinforced for selecting one of two stimuli, which leads to the formation of a dominant response. Then the reinforcement contingencies are reversed and subjects must shift their response and use the previously unrewarded stimulus as a cue. How long does it take individuals from different species groups to become aware of this shift and adapt their responses? Can they learn how to solve reversal learning tasks with training over time? What other factors affect reversal learning abilities across taxa? Past research on reversal learning has advanced our understanding of learning development, but it's not without limitations. For one thing, there's been substantial variation in the methods used. For example, differences in the cues used and the method and the amount of training, and this is made comparing results daunting, if not impossible. Also, while learning has been investigated in numerous species, each study has typically focused on one or two species. Moreover, within each field, studies have consistently suffered from small sample sizes and low statistical power, and result interpretation has depended on theoretical frameworks whose suitability remains under heated debate. Many-many's one will develop a reversal learning task that can be applied across taxa and collect data from at least eight species groups. Confirmed populations of study include birds, dogs, fish, bovids, human and non-human primates, insects, reptiles, and rodents. Many-many's one will also utilize a victim science approach achieving a sample large enough to ensure adequate statistical power and inference. As a result, many-many's one will help determine the soundness of competing theories through a robust experimental design and a large and diverse dataset. Importantly, many-many's one will pre-register its main empirical study and all research outputs will be published as pre-print, open access peer reviewed journal articles, open datasets, and open materials. Our project, which is now in the initial stages, draws on the expertise of project leads and researchers already engaged in victim science initiatives. Team members have diverse backgrounds in behavioral ecology, comparative psychology, biology, zoology, developmental psychology, computer science, and neurobiology. This will allow us to integrate multiple viewpoints to achieve a comprehensive understanding of reversal learning across taxa. The many-many's one undertaking couldn't possibly be fulfilled by any single research team given limitations in access to subjects, equipment, personnel, and financial resources. Our victim science approach will maximize feasibility by sharing the high cost of data collection, while fostering generalizability and replicability. We believe many-many's one is supposed to be a game changer in comparative cognition and victim science, and we are really excited to invite new collaborators to join our project. And the best part is that joining does not require access to a laboratory, participation in data collection, or prior involvement in victim science initiatives. So if you are interested, make sure to visit our website, many-many's.github.io, and come talk to me later. And if you know somebody else that might be enthused, please share this information with them. Thank you so much for your attention today, and I will now be delighted to address any questions or comments you might have. Thank you. I'm Ryan. I was just interested in making a standardized, like, behavioral paradigm between taxa. How exactly do you plan to do that with, like, different animals in taxa, of course, having different environmental stimuli? Yeah, exactly. So the methodological team within many-many's is currently discussing what is the best approach for that, but we've identified common both individual, I mean, independent and dependent variables that have been used in these kind of studies. So what we are trying to do here is to select the ones that can be applied across taxa. So that is one of the tasks we are currently undertaking. Thank you. Well, yesterday there was a very interesting talk about sample diversity in victim science, and I would say that another big challenge is incorporating equity diversity and inclusion actions within victim science networks. So to achieve that, we have developed a code of conduct and a collaborator agreement that aims to achieve these exact actions, right? So I guess it's important to recognize that sample variation as discussed here yesterday is important, but this is not a victim science problem. It is a science problem. It's more general, that's what I'm trying to say, and we need to address it. And I think victim science networks are doing a great job at addressing these concerns, which are absolutely valid and important. Thank you. Thank you very much.