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Surface Groups Jamaica 1984-2021
No full textSurface groups for Jamaica (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 Seychelles 1984-2021
No full textSurface groups for Seychelles (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 Thailand 1984-2021
No full textSurface groups for Thailand (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 Curaçao 1984-2023
No full textSurface groups for Curaçao (years 1984 - 2023) 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 Myanmar 1984-2023
No full textSurface groups for Myanmar (years 1984 - 2023) 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)
Exposure of Applied Sciences Bachelor Students to Stressors, Relationship with their Mental Health, and exploration of potential protective factors - T0.5-Nursing
No full textData from the intermediary data collection of the HEalStud study involving nursing students only (T0.5; September 2020). Data is available upon presentation of the agreement of an ethical committee about their secondary use
COINS: Gesamtdatensatz zur schriftlichen Befragung der Schülerinnen und Schüler (Schweiz & Deutschland)
No full textHow does language shape formation of concepts? Data and experimental items
No full textThe dataset consists of data and experimental items for the three experiments reported in Blochowiak J., Grisot C. & Sander E. (2025) How does language shape formation of concepts? Empirical investigation of generics and conditionals in French. Lingua. Volume 324. https://doi.org/10.1016/j.lingua.2025.103959
Analyse des débats parlementaires fédéraux et cantonaux (Vaud) sur l'éducation numérique
No full textHF4ATS Data
No full textHF4ATS-SFT (Human Feedback For Automatic Text Simplification - Supervised Fine-Tuning) is a German-language collection of automatic news sentence simplifications suitable for supervised fine-tuning. It is composed of 5,200 complex-simple sentence pairs (3,600 train, 800 development, 800 test) drawn from DEplain-APA data (https://zenodo.org/records/8304430) and paired with randomly assigned automatic text simplification prompts.
HF4ATS-DPO (Human Feedback For Automatic Text Simplification - Direct Preference Optimization) is a German-language collection of news sentence simplification preferences suitable for preference alignment.
HF4ATS-DPO is composed of 6,018 preference annotations expressed on 3,009 ATS pairs (preferences_all.jsonl) as well as several subsets of these preferences. Each ATS pair was annotated at least once by an individual with an intellectual disability (indicated by "ta" in the dataset) and at least once by a text simplification professional (indicated by "ea" in the dataset). The checkpoints used to generate these preference pairs were 7-8B parameter LLMs tuned using HF4ATS-SFT data (these checkpoints as well as the SFT data are also available on SWISSUbase).
The preference pair subsets are as follows:
1. preferences_ie.jsonl: all preferences on ATS pairs with information equality according to pair creators.
2. preferences_iter.jsonl: all preferences from the four target annotators or two expert annotators with the highest inter-annotation agreement.
3. preferences_itra.jsonl: all preferences from the four target annotators or two expert annotators with the highest intra-annotation agreement.
4. preferences_.jsonl: all preferences on ATS pairs generated by the checkpoint (these checkpoints result from supervised fine-tuning on HF4ATS-SFT).
Additionally, we include all raw annotation files collected during the course of this project. These files include duplicated ATS pairs and pairs that had not been annotated at time of submission. They are aggregated and de-duplicated at the annotator-day level inside preferences_raw.jsonl. preferences_raw.jsonl also contains a field indicating a given annotation's reason for exclusion from preferences_all.jsonl (as well as all subsequent subsets)