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Unwanted, stranded meetings, overlapping with a general one in a channel, can occur when people click on the 'Meet' button, instead of clicking on the same 'Join' button in the chat field. This may especially happen to those who reach the channel first, or who cannot see the 'Join' button in the chat field because this field has been taken up by messages.

This document is part of teaching materials created for the workshop 'Open data and reproducibility v2.1: R Markdown, dashboards and Binder', delivered at the CarpentryCon 2020 conference. The purpose of this specific document is to practise R Markdown, including basic features such as Markdown markup and code chunks, along with more special features such as cross-references for figures, tables, code chunks, etc. Since this conference was originally going to take place in Madison, let's look at some open data from the City of Madison.

There is an increasing number of training courses introducing early career researchers to sustainable software practices but relatively little aimed at Research Group Leaders and Principal Investigators. Expecting group leaders to personally acquire such skills through training such as a two-day Carpentries workshop is unrealistic, as these require a significant time investment and are less directly applicable in the role of research director. In addition, many group leaders would not consider their group as outputting software, or are less aware of the full range of benefits that sustainable practice brings and will thus be less likely to signpost such training to their team members. Even where they do identify benefits, they may have concerns about releasing group software or may feel overwhelmed by the potential scale of the task, especially with respect to legacy projects.

Software is increasingly becoming recognised as fundamental to research. In a 2014 survey of UK researchers undertaken by the Institute, 7 out of 10 researchers supported the view that it would be impossible to conduct research without software. As software continues to underpin more research activities, we must engage a variety of stakeholders to incentivise the uptake of best practice in software development to ensure the quality of research software keeps pace with the research it supports.

This project offers free activities to learn and practise reproducible data presentation. Pablo Bernabeu organises these events in the context of a Software Sustainability Institute Fellowship. Programming languages such as R and Python offer free, powerful resources for data processing, visualisation and analysis. Experience in these programs is highly valued in data-intensive disciplines. Original data has become a public good in many research fields thanks to cultural and technological advances. On the internet, we can find innumerable data sets from sources such as scientific journals and repositories (e.g., OSF), local and national governments, non-governmental organisations (e.g., data.world), etc. Activities comprise free workshops and datathons.

Overview of event-related potentials as a research method, covering electroencephalography fundamentals, ERP definitions and processing, and their application to studying the time course of cognitive processes like conceptual processing.

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set those yourself based on whatever prior information or purposes you had. The 'naive' approach is characterized by a first stage that checks whether the PCA should actually be performed with your current variables, or if some should be removed. The variables that are accepted are taken to a second stage which identifies the number of principal components that seem to underlie your set of variables.

Throughout the 1990s, two opposing theories were used to explain how people understand texts, later bridged by the Landscape Model of reading (van den Broek, Young, Tzeng, & Linderholm, 1999). A review is offered below, including a schematic representation of the Landscape Model. Memory-based view The memory-based view presented reading as an autonomous, unconscious, effortless process. Readers were purported to achieve an understanding of a text as a whole by combining the concepts, and implications readily afforded, in the text with their own background knowledge (Myers & O’Brien, 1998; O’Brien & Myers, 1999).

Het menselijk brein begrijpt de wereld om ons heen op een taalkundige en zintuiglijke manier. Pablo Bernabeu (Language and Communication) onderzocht waarom dat zo is.

The single dependent variable, RT, was accompanied by other variables which could be analyzed as independent variables. These included Group, Trial Number, and a within-subjects Condition. What had to be done first off, in order to take the usual table? The trials!