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QClass: Question Classifier for canonical and some non-canonical questions in German
No full textThis dataset includes python scripts and test data for the QClass question classifier. It was developed by Gunilla Kaibel (Universität Zürich) and Seraina Betschart (Universität Zürich)
TraCiSS: School Teachers Survey-2025, Khmelnytskyi, Zhytomyr, and Sumy Regions, Ukraine
No full textThis dataset is based on a survey of secondary school teachers in Ukraine, conducted between December 2024 and February 2025, and includes:
— Contextual data: pre-coded answers to closed-ended questions, raw text responses to open-ended items (textual, in Ukrainian), and numerical responses to open-ended questions (raw and numerically coded). Missing data are pre-coded.
— Core questionnaire data: pre-coded answers to closed-ended questions, pre-coded answers to matrix questions, raw text responses to open-ended questions (textual), and raw text responses to open-ended items of matrix questions (textual). All textual data is saved in the original (in Ukrainian). Missing data are pre-coded.
— Metadata: Progress, Completion status, Response ID, Percentage of unanswered questions
Institutional Analysis
No full textThis workpackage aims to gather information on state policies and practices towards unemployment. It provides macro-level data, allowing to ascertain the impact of the institutional context on the integration of young unemployed
Etat des lieux de l'enseignement des musiques actuelles de niveau amateur à préprofessionnel en Suisse romande
No full textStorytelling-in-sequence task
No full textThis dataset contains data, procedures and materials from a storytelling-in-sequence task in which participants had to tell a story within a given temporal frame. The stories to be told were presented in the form of comic strips, and the temporal framework was set by a carrier sentence. In total, each participant told 6 stories, 2 for each temporal framework (past, present, future). Participant's productions were audio-recorded and transcribed verbatim. The number of inflected verb forms produced was counted for past, present, and future tenses. Some files are in French
Temporality tasks
No full textThis dataset contains data, procedures and materials from three tasks assessing temporality: a task of assessment of temporality conveyed by temporal adverbs, a task of assessment of the completeness of action, and a questionnaire on temporality
Surface Groups Mauritius 1984-2021
No full textSurface groups for Mauritius (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 Nigeria 1984-2021
No full textSurface groups for Nigeria (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 Togo 1984-2021
No full textSurface groups for Togo (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)