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What's New, Switzerland? Corpus
No full textThe What's New, Switzerland? Corpus is a dataset of 72 authentic WhatsApp chats between 118 French-speaking users in Switzerland, collected in the framework of the "Evolving Language" NCCR. Chats were donated by users between August and October 2022. The data have been de-identified using a partly automated and partly manual workflow. Each chat is provided in two versions: an XML-TEI version (which includes extensive metadata about chats, users, and messages) and a plain text version. The dataset is available on demand for research purposes, under a restricted license contract
Surface Groups Bolivia 1984-2021
No full textSurface groups for Bolivia (years 1984 - 2021) as georeferenced TIF files.
Classified land cover (surface) of each pixel indicated as:
0 = built-up surfaces: surfaces with buildings of non-natural materials such as concrete, metal, and glass (e.g., residential buildings, industrial plants, roads)
1 = grassy surfaces: surfaces covered by grass or other plants with similar surface reflectance (e.g., natural grassland, city parks)
2 = surfaces with crop fields: surfaces with vegetation for agricultural purposes (e.g., hayfields, vineyards)
3 = forest-covered surfaces: surfaces covered by trees or other plants with similar surface reflectance (e.g., mixed forests, moors)
4 = surfaces without vegetation: surfaces with (almost) no vegetation or buildings (e.g., bare rock, sand plains)
5 = water surfaces: any type of water surface (e.g., rivers, lakes)
9 = missing surface classification, most likely due to cloud cover
If a TIF file for a given year within the observation period is missing, no valid satellite imagery was available for that year (e.g., due to constant cloud cover)
Surface Groups Comoros 1984-2021
No full textSurface groups for Comoros (years 1984 - 2021) as georeferenced TIF files.
Classified land cover (surface) of each pixel indicated as:
0 = built-up surfaces: surfaces with buildings of non-natural materials such as concrete, metal, and glass (e.g., residential buildings, industrial plants, roads)
1 = grassy surfaces: surfaces covered by grass or other plants with similar surface reflectance (e.g., natural grassland, city parks)
2 = surfaces with crop fields: surfaces with vegetation for agricultural purposes (e.g., hayfields, vineyards)
3 = forest-covered surfaces: surfaces covered by trees or other plants with similar surface reflectance (e.g., mixed forests, moors)
4 = surfaces without vegetation: surfaces with (almost) no vegetation or buildings (e.g., bare rock, sand plains)
5 = water surfaces: any type of water surface (e.g., rivers, lakes)
9 = missing surface classification, most likely due to cloud cover
If a TIF file for a given year within the observation period is missing, no valid satellite imagery was available for that year (e.g., due to constant cloud cover)
From implicit to explicit: The processing of forward causal and temporal relations. Data and experimental items
No full textThis dataset resulted from a self-pacedreading experiment, in which we sought to compare the cost of inferring the presence of causal vs. temporal relations in the absence vs. presence of a connective indicating a given relation in French. For the explicit marking, two types of connectives were tested: one specialized for each relation ('donc' for causality and 'puis' for temporality) and one underspecified ('et' in its temporal and causal readings)
Surface Groups Gabon 1984-2021
No full textSurface groups for Gabon (years 1984 - 2021) as georeferenced TIF files.
Classified land cover (surface) of each pixel indicated as:
0 = built-up surfaces: surfaces with buildings of non-natural materials such as concrete, metal, and glass (e.g., residential buildings, industrial plants, roads)
1 = grassy surfaces: surfaces covered by grass or other plants with similar surface reflectance (e.g., natural grassland, city parks)
2 = surfaces with crop fields: surfaces with vegetation for agricultural purposes (e.g., hayfields, vineyards)
3 = forest-covered surfaces: surfaces covered by trees or other plants with similar surface reflectance (e.g., mixed forests, moors)
4 = surfaces without vegetation: surfaces with (almost) no vegetation or buildings (e.g., bare rock, sand plains)
5 = water surfaces: any type of water surface (e.g., rivers, lakes)
9 = missing surface classification, most likely due to cloud cover
If a TIF file for a given year within the observation period is missing, no valid satellite imagery was available for that year (e.g., due to constant cloud cover)
Surface Groups Kenya 1984-2021
No full textSurface groups for Kenya (years 1984 - 2021) as georeferenced TIF files.
Classified land cover (surface) of each pixel indicated as:
0 = built-up surfaces: surfaces with buildings of non-natural materials such as concrete, metal, and glass (e.g., residential buildings, industrial plants, roads)
1 = grassy surfaces: surfaces covered by grass or other plants with similar surface reflectance (e.g., natural grassland, city parks)
2 = surfaces with crop fields: surfaces with vegetation for agricultural purposes (e.g., hayfields, vineyards)
3 = forest-covered surfaces: surfaces covered by trees or other plants with similar surface reflectance (e.g., mixed forests, moors)
4 = surfaces without vegetation: surfaces with (almost) no vegetation or buildings (e.g., bare rock, sand plains)
5 = water surfaces: any type of water surface (e.g., rivers, lakes)
9 = missing surface classification, most likely due to cloud cover
If a TIF file for a given year within the observation period is missing, no valid satellite imagery was available for that year (e.g., due to constant cloud cover)
DDS21, Cross Sectional Survey Wave 1: Popular Vote on 18.06.2023
No full textThe dataset contains the anonymised results of a cross-sectional survey of eligible voters conducted following a popular vote on 18 June 2023. The vote concerned three subjects:
- The OECD/G20 project (FSO vote no. 662; "vote1" in the data set)
- The Climate and Innovation Act (FSO vote no. 663; "vote2" in the data set)
- The COVID 19 Act (FSO vote no. 664; "vote3" in the data set)
Topics covered in the survey include voting decisions, arguments concerning the referenda, political knowledge, values, and beliefs, media consumption, and a survey experiment on political advertisements.
Data is provided in SAV, RDS, and CSV formats. SAV is the primary target format, and researchers are advised to use this version where possible. The SAV file contains user-defined missing variable levels. Users working in the R programming language may wish to ensure that their chosen method of data import does not simply coerce these to simple "NA"s.
Due to anonymity reasons, some variables (e.g., municipality, zipcode) are not included in the downloadable dataset. These variables are only available on request and with prior consent of the authors. Please send an e-mail with your justified request to [email protected]. The DDS21 project was approved by the Ethics Committee of the University Zurich (UZH PhF Ethics Committee, number 23.05.04)
INF-COVID: Longitudinal data - Switzerland German-speaking - T0-T1-T2
No full textThis data set contains raw data from the German-speaking Swiss sample at all time points (T0 to T2)
Recordings in Ruching Palaung from Myanmar 2018
No full textThe Ruching Palaung dataset is the first of a series of original audio and video recordings of Austroasiatic languages of SA and SEA, partly with transcripts and annotations. The present dataset includes a selection of audio recordings and transcripts collected in Shan State, Myanmar.
The data was collected and processed mainly for linguistic purposes, but also contains cultural and social information in the form of interviews and descriptions of everyday life.
Data was collected by the team Mathias Jenny, Hiram Ring, We-Wei Lee, Rachel Weymuth, in cooperation with the Department of Anthropology, Mandalay University, and the Yangon University of Foreign Languages and supported by the SNSF. Data processing was assisted by Alexandra Herdeg
Tableau comparatif des manuels étudiés
No full textTableau comparatif des manuels étudiés selon les critères suivants:
-Titre
-Auteur(s)
-Années parution
-Pays édition
-Ville et maison d'édition
-Rééditions
-Changements dans les rééditions
-Spécialisation auteur
-Auteur-s russophone
-Méthode d'apprentissage
-Causes de création et parution
-Présentation du russ