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Reflection on the process towards and around the publication 'Ebifananyi VII, Staying Alive - documenting the Uganda Cancer Institute' by medical historian Marissa Mika

This data collection comprises a set of test files developed in the CoDoSiS project of the CLARIAH program. The project ‘Combining Data on slavery in Surinam’ (CoDoSiS) aimed to develop a strategy to convert existing datasets on Surinam slavery into Linked Data by using the CLARIAH wp4-tool Cows and to combine them into one database network with relevant connections using the CLARIN-tool TICCL. This collection contains the test files that were used in the project. The test files are subsets of the complete datasets of the complete registers focussed around the year 1846 and were used to test the two issues defined above in the CoDoSiS project. Included in the collection are: - Slavenregisters_Picturae_plantages_1846 (TICCL and LOD) - Slavenregisters_Everaert_1846 (TICCL and LOD) - Monsterrol_Catharina_Sophia_1846 (TICCL and LOD) - Slavenregisters_Picturae_eigenaren_1846 (TICCL) - Wijkregisters_Paramaribo_1846 (TICCL) - Surinaamsche_almanak_plantages_1846 (LOD) - Surinaamsche_almanak_personen_1846 (TICCL)

Rede Wageningen

This dataset features the training models, emotion classifications and emotion patterns before and after events, related to the paper: F. Kunneman, M. van Mulken and A. Van den Bosch, Anticipointment detection in event tweets (under review) Abstract of the study: We developed a system to detect positive expectation, disappointment, and satisfaction in tweets that refer to events automatically discovered in the Twitter stream. The emotional content shared on Twitter when referring to public events can provide insights into the presumed and experienced quality of the event. We expected to find a connection between positive expectation and disappointment, a succession that is sometimes referred to as anticipointment. The application of computational approaches makes it possible to detect the presence and strength of this hypothetical relation for a large number of events. We extracted events from a longitudinal data set of Dutch Twitter posts, and modeled classifiers to recognize emotion in the tweets related to those events by means of hashtag-labeled training data. After classifying all tweets before and after the events in our data set, we summarized the collective emotions by calculating the percentage of tweets classified with an emotion as well as ranking tweets based on the classifier confidence score for an emotion and selecting the 90th percentile. Only a weak correlation of around 0.2 was found between positive expectation and disappointment, while a higher correlation of 0.6 was found between positive expectation and satisfaction. The most anticipointing events were events with a clear loss, such as a canceled event or when the favored sports team had lost. We conclude that senders of Twitter posts might be more inclined to share satisfaction than disappointment after a much anticipated event. Subject period: January 1st 2011 until October 31st 2015

Cultural heritage institutes often make use of tags to facilitate searching their collections. While professionals associated with these institutes are able to add high quality descriptions to objects in the collections, both their time and their areas of expertise are limited. As a result, online tagging by non-professional users is more frequently becoming deployed to increase the number of tags. When these users are asked to tag objects in the collection, they can be confronted with tags submitted by other users. These tags may be of varying quality and present in differing numbers, both of which may influence users' tagging behavior. We report on a study on the impact of presenting different types of tags on the quality and quantity of tags added by users. We conclude that there is no difference in the quality and quantity of added tags in all experimental conditions, with the exception of the condition in which incorrect tags were presented. In this condition, the quality of the tags added by users decreased. We discuss the implications of these findings on the design of tagging interfaces.

The book was original published in French: Putain de guerre! (2009)

Data on individual survival, growth and reproduction of Daphnia magna individuals, as collected during a laboratory experiment. The aim of this study was to quantify effects of temperature, genetic background and population density on the dynamics of Daphnia magna populations. In this experiment, 40 populations of Daphnia magna, starting with 20 individuals, were followed during 80 days. Twice a week, three individuals were arbitrarily picked from each population, and isolated for three or four days in transparent tubes that were placed inside each aquarium. Results of this study have been published in ‘Population-level responses to temperature, density and clonal differences in Daphnia magna as revealed by Integral Projection Modeling’ (Bruijning et al., Functional Ecology 2018, forthcoming).

This is part 4 of the Rchon statistics course. It continues the basics of statistical testing in R. In this tutorial, we will treat the following statistical testing methods: Mann-Whitney test Kruskal-Wallis test Kolmogorov-Smirnov test Follow the instructions in Instructions Tutorial 4.pdf to start the tutorial. This course was originally created for Archon Research School of Archaeology by Philip Verhagen (Vrije Universiteit Amsterdam) and Bjørn P. Bartholdy (University of Leiden), and consists of an instruction, a tutorial, a test and two datafiles. All content is CC BY-NC-SA: it can be freely distributed and modified under the condition of proper attribution and non-commercial use. How to cite: Verhagen, P. & B.P. Bartholdy, 2022. "Rchon statistics course, part 3". Amsterdam, ARCHON Research School of Archaeology. https://doi.org/10.5281/zenodo.7458108