Demographic data used as an input of the modified leslie model was obtained from a city in JangSu province, China. Demographic data consist of three parts, annual fertility rate of different age groups, annual mortality rate of different age groups and the basic population of different age groups.
Data accompanying "When did the Lhasa and Qiantang Terranes “divorce”: Constraints from Middle-Late Triassic Alkaline Trachytic Lavas" by Yang Song, Haifeng Li, Carl Guilmette, Ming Zheng, Douwe J.J. van Hinsbergen, Zhenyu Li, Faqiao Li. Geochemistry, Geophysics, Geosystems, 2020
Contributors:Neus Rotger, Teresa López-Pellisa, Fernando Rodriguez-Gallego
This database provides the evaluation rubric used in the Teaching Innovation Project: "Writing an academic review in a blended learning environment" (PID 181960), carried out at the Universitat de les Illes Balears (UIB) with the collaboration of the Universitat Oberta de Catalunya (UOC) during the academic year 2018-2019. We have used some of the information obtained from these interviews in the article: "Collaborative Writing at Work: Peer Feedback in a Blended Learning Environment"
Article Summary: This exploratory study aims to analyse the nature of peer feedback during a collaborative writing assignment, and to identify the possible effects feedback has on the revision of a text written by university students in a blended learning environment. Under analysis are two different graduate’s courses in academic writing, during which, over a period of a whole semester, the students (n = 85) were divided into 25 work groups to carry out a co-evaluation assignment with the support of a technology platform. The results obtained indicate that, when collaborative writing includes peer feedback, instead of unidirectional corrections from the teacher, the students respond more reflectively and constructively, they discuss the content they are working with, and, as a result, they effect significant changes in their own writing.
Contributors:Kerem Özkap, Ertan Peksen, Ismail Kaplanvural, Deniz Çaka
This data and code are associated with the article "3D Scanner Technology Implementation to Numerical Modeling of GPR" by the same authors. The 3D scanner data and Matlab code used in the article are provided with other necessary files. The Readme file comprises detailed descriptions of the data files and formats.
Please see the publication for more information about this data set.
Contributors:Javier Pastor-Galindo, Mattia Zago, Pantaleone Nespoli, Sergio Lopez, Alberto Huertas Celdrán, Manuel Gil Pérez, José A. Ruipérez-Valiente, Gregorio Martinez Perez, Felix Gomez Marmol
While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.
The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
Data have been exported in three formats to provide the maximum flexibility
- MongoDB Dump BSONs: To import these data, please refer to the official MongoDB documentation.
- JSON Exports: Both the users and the tweets collections have been exported as canonical JSON files.
- CSV Exports (only tweets): The tweet collection has been exported as plain CSV file with comma separators.