 Welcome to Introducing MetaLab R, a package to facilitate living meta-analyses and dynamic meta-analytic visualisations, which I'm presenting on behalf of the MetaLab consortium. At MetaLab, we are interested in language and cognitive development in infancy and childhood. In early language development research, the overarching question we have is how and when do babies learn language? But language is an incredibly complex system made up of many aspects like sounds, words and grammar. So to answer these questions, we need to break it down by asking questions about the various steps along the way that lead to learning language. Questions such as when can babies tell the difference between two different languages? Or between different vowels like e and u? When do babies figure out when one word ends and the next begins? When do they start recognising words? And when can they recognise or learn words that are embedded in a sentence? So when conducting studies and reading the literature, we're often looking for results that tell us babies do X at age Y. But such statements made on the basis of the results of a single study can be problematic. Significant results could be a false positive or null results could be a false negative, especially in infant research which is notoriously underpowered due to the difficulties of recruiting and testing babies. How do we know if a study's results can be generalised to all babies? They might actually be restricted only to babies learning a particular language. Or perhaps the results only apply in a specific lab setting or when using particular stimuli and methods. Systematic reviews and meta-analyses help bring coherence to a complex evidence base and improve our confidence in results. But meta-analyses are underused in developmental research. To assist developmental researchers in conducting and accessing meta-analyses, my colleagues created MetaLab, a platform for open dynamic meta-analytic data sets. This goes beyond a traditional meta-analysis to what we call community augmented meta-analyses, or CAMAS, which allow for the smooth addition of new data points to ensure that quantitative synthesis give the most up-to-date summaries of the body of literature. In five years, the site has grown to 29 meta-analyses with data from almost 45,000 infants and children. And now on to the exciting part for this presentation, our MetaLab R package, which allows us to take full advantage of the large array of the MetaLab data by conducting quantitative synthesis in R. The three overarching package features I'll be presenting in this presentation are functions for reading live MetaLab data in R, compatibility with running meta-analytic models using Feaked Bower's MetaFor package, and visualization tools. So first, the processes of reading the data. To give you a sense of the data curation process, the user conducting a meta-analysis extracts data from studies into this MetaLab spreadsheet, which has standardized column names regarding methodological details, participant details and the quantitative results for calculating effect sizes. Back in R, the Get MetaLab data function in the MetaLab R package reads the specified data set live from the Google Sheet. If needed, the function can synthesize multiple data sets on different topics. Behind the scenes, the data is validated and cleaned to ensure adherence to the standardized data format. Then the function computes standardized effect sizes from the values provided in the Google Sheet. And finally, the function returns a tidy data frame. Feaked Bower's MetaFor package already has many great functions for running meta-analytic models. So the MetaLab R package has been designed so that the resulting data frame from reading the data is compatible with Feaked Bower's MetaFor package, running various analyses like calculating estimates of effect sizes and assessing the significance of moderators. And finally, the MetaLab R package contains various functions for visualizing effect sizes, such as a forest plot, violin plot and funnel plot. These build on the GBG plot package, but provide by default an APA theme and selected colors as you can see here in these plots. This makes the process of plotting quick and easy and gives the MetaLab plots a standardized look and feel. As well as using the MetaLab R package in R, the package is the basis for the shiny apps that are up on the MetaLab website. These applications are visualizations for exploring the results of existing datasets, power analysis and power simulation tools to help researchers plan their own studies, and data validation tools to assist reviewers to make sure their planned meta-analysis fits the MetaLab structure and format so that their data can eventually be integrated into MetaLab. And this screenshot gives you a sense of the easy user interface of the visualization tool, where you simply select a dataset from the drop-down menu and visualizations are automatically generated. So in this presentation we've seen the functions of MetaLab and MetaLab R that facilitate conducting a meta-analysis on a new topic, that help researchers plan a new study on a topic where a meta-analysis has been done, help them to add new data to an existing data analysis, and to conduct a synthesis of data across multiple topics of research. These synthesis tools can help us to move beyond the results of single studies and studies researching a single level of language to integrate findings from different areas of research so that we can get closer to answering the question that we're all pursuing, how and when do babies learn language. Thank you to the MetaLab team, as well as the authors and participants of original studies and meta-analyses. Thank you.