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HF4ATS Model Checkpoints
No full textHF4ATS (Human Feedback For Automatic Text Simplification) LLM checkpoints following supervised fine-tuning (SFT) and direct preference optimization (DPO) for German-language automatic news sentence simplification.
The pretrained models we performed SFT on were DiscoLeo-Llama-3-8B-Instruct, Llama-3.1-8B-Instruct, and LeoLM-Mistral-7B-Chat.
The SFT checkpoints resulted from fine-tuning on up to 2,800 data instances from HF4ATS-SFT (the companion dataset also available on SWISSUbase).
The DPO checkpoints resulted from preference alignment on up to 2,280 data instances from HF4ATS-DPO (the companion dataset also available on SWISSUbase).
The number of training instances seen by a given model checkpoint during either SFT or DPO is shown in its filename
CCSA: Survey of Secondary School Teachers, Khmelnytskyi, Zhytomyr, and Sumy Regions, Ukraine (2024-2025)
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, row text responses to open-ended items (textual, in Ukrainian), and numerical responses to open-ended questions (row and numerically-coded). Missing data are pre-coded.
— Core questionnaire data: pre-coded answers to closed-ended questions, pre-coded answers to matrix questions, row text responses to open-ended questions (textual), row 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, Duration, Completion status, Response ID, Percentage of unanswered questions
DDS21, Cross Sectional Survey Wave 4: Popular Vote on 22.09.2024
No full textThe dataset contains the anonymised results of a cross-sectional survey of eligible voters conducted following a popular vote on 22 September 2024. The vote concerned two subjects:
- The popular Initiative "For the future of our nature and our landscape" ("Biodiversity Initiative"), FSO proposal no. 671
- A referendum regarding the "Amendment to the federal law on retirement, survivors’ and disability pension plans (BVG/LPP)" ("Reform of the occupational pension system"), FSO proposal no. 672
Topics covered in the survey include voting decisions, arguments concerning the ballot measures, 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 an R programming environment 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 are not included in the downloadable dataset, or provided with a reduced level of precision. This concerns:
- The place of residence at the municipality level (omitted)
- Client device metadata (omitted; a separate variable indicating whether the survey was filled out on a mobile or a desktop platform is included)
- Variables related to respondents' current or previous occupation (plain text answers omitted; ISCO-08-coded variables have their precision reduced from the unit group level to the major group level)
- Variables related to respondents' country of birth, as well as the countries of birth of their parents (reduced to dummy variables indicating whether or not the country of birth is Switzerland)
We additionally omit variables solely collected for internal purposes of the DDS21 project; this concerns items measuring satisfaction with the survey, as well as free-form comments about the survey.
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)
Individual Survey
No full textThis workpackage aims to gather information in order to measure various degrees of integration of young unemployed, as well as to assess the impact of individual characteristics (ethnicity, gender, age, participation in organizations, etc.) on integration
Strukturdaten 2022 (anonymisiert)
No full textDE:
Die Strukturdaten als Ergebnis der Synthetischen Bevölkerung der Schweiz 2022 sind hier verfügbar. Sie werden momentan für die laufende Aktualisierung des Nationalen Personenverkehrsmodells (NPVM) 2023 verwendet.
- Anonymisierung: Personenbezogene Informationen in Verkehrszonen mit weniger als 30 Einwohnern oder Erwerbstätigen wurden gelöscht.
- Stand Verkehrszonen: Gleich wie NPVM 2017, 2017+ (Download als open data über Zenodo: https://doi.org/10.5281/zenodo.3379492)
- Stand politische Gemeinde: 01.01.2023
- Stand räumliche Daten BFS (Stadt-Land-Typologie 3, Gemeindetypologie 9 Kategorien): 2020
FR:
Les données structurelles résultant de la Population Synthétique de la Suisse 2022 sont disponibles ici. Elles sont actuellement utilisées pour la mise à jour en cours du modèle national de transport de personnes (MNTP) 2023.
- Anonymisation : les informations relatives aux personnes dans les zones de transport comptant moins de 30 habitants ou personnes actives ont été supprimées.
- État des zones de transport : Identique au MNTP 2017, 2017+ (téléchargement en open data via Zenodo : https://doi.org/10.5281/zenodo.3379492)
- Etat des communes politiques : 01.01.2023
- Etat des données spatiales OFS (typologie urbain-rural 3, typologie des communes 9 catégories) : 202
Dataset by Giulia Berchio: Dati orali monologici in italiano, svizzero-tedesco e tedesco di parlanti adulte/i bilingui e monolingui
No full text- un dossier contentant des données orales (données liées à l'étude principal, donc qui concernent le dataset 1) qui comprend à son tour les sous-dossiers suivants :
• un dossier avec les productions orales en italien des locutrices/locuteurs bilingues italien/suisse-allemand
• un dossier avec les productions orales en suisse allemand des locutrices/locuteurs bilingues italien/suisse-allemand
• un dossier avec les productions orales des locuteurs monolingues italophones
• un dossier avec les productions orales des monolingues germanophones
- un dossier avec des données écrites, qui comporte à son tour les sous-dossiers suivants :
• un dossier avec les données concernant l’étude principale sur la production de la structure informationnelle et qui comprend des autres sous-dossiers avec les résultats du questionnaire Bilingual Language Profile (mesure utilisée comme proxy de la dominance langagière), les résultats des tests de vocabulaire (mesure utilisée comme proxy de la compétence linguistique) et un dossier avec les annotations des données orales
• un dossier avec les données concernant l’étude secondaire sur la perception de la structure informationnell
Surface Groups Sierra Leone 1984-2021
No full textSurface groups for Sierra Leone (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 Aruba 1984-2023
No full textSurface groups for Aruba (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 Cayman Islands 1984-2023
No full textSurface groups for Cayman Islands (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 Belize 1984-2023
No full textSurface groups for Belize (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)