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Meta data for this meta-analysis from 55 papers

Materials for two studies on the impact of an “aha” moment on gender biases.

This directory features data that is discussed in the paper: F. Kunneman, A. Hürriyetoglu, N. Oostdijk and A. Van den Bosch (2014), Timely identification of event start dates from Twitter, Computational Linguistics in the Netherlands Journal, 4, pp. 39-52, http://hdl.handle.net/2066/135169 This paper describes a study to automatically identify the date of a social event based on tweets that refer to it in anticipation, as early as possible. This data set comprises of the tweetids that refer to 60 football events and 5 events of other types by means of a hashtag or the name of the event. These tweets were used to train and test different approaches to identifying the event date long before the event starts. In addition to the tweetids, we give the number of days until the event at the time when each tweet is posted, and the time references that could be extracted from each tweet.

JFMA cases and the covariates tested and used in our modelThis data has all variables used in the statistical model as they entered the generalized linear model and the generalized linear mixed model. The variables included are (in the order they appear): year, kebeleID, JFMA total cases, log expected cases, scaled log ratio of SOND cases to the expected SOND cases, scaled DJF mean temperature in degree Celsius, scaled DJF total rainfall in mm, scaled population density from overlapping circles of 5km radius, scaled population density from overlapping circles of 10km radius, scaled weighted distance to roads, scaled inverse square distance to perennial water bodies, scaled average soil water holding capacity, scaled average slope, scaled average NDVI, scaled SST anomalies from the Nino 3.4 region, and IRS status (0/1).covariates_std.csvCount of neighboring kebelesThis data set contains the count of kebeles neighboring each kebele. This file should be used in combination with the Nieghborhood.csv. For example the first kebele (ID=1) has 4 neighbors. Thus, the first four numbers in neighborhood.csv are kebele ID's of those kebeles neighboring kebele 1. Similarly, the second kebele has 3 neighbors, and thus the next three number in neighborhood.csv are IDs of its three neighbors. The remaining neighbors are identified by matching them with corresponding kebele IDs in the file neighborhood.csv in this manner.num_neighbors.csvneighborhoodThis data set contains the IDs of kebeles neighboring each kebele in the file named "Count of neighboring kebeles". This file should be used in combination with the "Count of neighboring kebeles". For example the first kebele (ID=1) has 4 neighbors. The first four number in this data set are the kebele ID's of those kebeles neighboring kebele 1. Similarly, the second kebele has 3 neighbors, and the next three number in this data set are IDs of its three neighbors. The remaining neighbors are identified by matching them with corresponding kebele IDs in similar manner.neighbor_geogID.csv A better understanding of malaria persistence in highly seasonal environments such as highlands and desert fringes requires identifying the factors behind the spatial reservoir of the pathogen in the low season. In these ‘unstable’ malaria regions, such reservoirs play a critical role by allowing persistence during the low transmission season and therefore, between seasonal outbreaks. In the highlands of East Africa, the most populated epidemic regions in Africa, temperature is expected to be intimately connected to where in space the disease is able to persist because of pronounced altitudinal gradients. Here, we explore other environmental and demographic factors that may contribute to malaria's highland reservoir. We use an extensive spatio-temporal dataset of confirmed monthly Plasmodium falciparum cases from 1995 to 2005 that finely resolves space in an Ethiopian highland. With a Bayesian approach for parameter estimation and a generalized linear mixed model that includes a spatially structured random effect, we demonstrate that population density is important to disease persistence during the low transmission season. This population effect is not accounted for in typical models for the transmission dynamics of the disease, but is consistent in part with a more complex functional form of the force of infection proposed by theory for vector-borne infections, only during the low season as we discuss. As malaria risk usually decreases in more urban environments with increased human densities, the opposite counterintuitive finding identifies novel control targets during the low transmission season in African highlands.