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Surface Groups Djibouti 1984-2021
No full textSurface groups for Djibouti (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 Iran 1984-2021
No full textSurface groups for Iran (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)
INF-COVID: Cross-sectional data - Belgium - T0
No full textThis data set contains raw data from the Belgian sample. No longitudinal data, the participants answered the survey only once
OBELIS Swiss Academic Elites
No full textThere are three samples of Switzerland's academic elite made available on SWISSUbase:
1/ All University Professors
Universities full and associate professors represent the stable members of the academic community. They exert a considerable institutional and scientific power within the academic field and in certain cases enjoy a relatively high social prestige as experts or intellectuals. Therefore, the sample of Swiss University Professors consists of full and associate professors at Swiss universities and Federal Institutes of Technology at six dates: 1890, 1910, 1937, 1957, 1980, 2000. All professors from the eight cantonal universities (Zurich, Basel, Bern, Geneva, Lausanne, Fribourg, Neuchâtel, and St. Gall) and the two Swiss Federal Institutes of Technology (Zurich (ETHZ) and Lausanne (EPFL)) were selected for 1910, 1937 and 1957. For 1980 and 2000, considering the large increase in the number of professors, we carried out a stratified sampling. The stratified samples for 1980 (1152 individuals) and 2000 (1135 individuals) are representative of the existing gender, university, and discipline proportions of the general population of Swiss university professors on those dates (2027 professors in 1980 and 2471 in 2000).
2/ Professors of power disciplines
Previous studies on Swiss elites showed that Swiss political and economic elites are frequently trained in law, natural sciences and economics and that professors in these disciplines are particularly well-connected to other social spheres (extra-parliamentary commissions, boards of directors of companies or political mandates). Therefore, a second sample includes all full and associate professors (in 1910, 1937, 1957, 1980 and 2000) in the following disciplines with a close link to the field of power:
1. Law
2. Biology, life sciences & chemistry
3. Economics and management.
3/ Members of the top academic elite
The third sample represents all the individuals - in 1890, 1910, 1937, 1957, 1980, 2000 and 2010 - occupying the most powerful positions within the Swiss academic field. All individuals who had one of the following functions were included, even if they were not university professors:
o Rectors, vice-rectors, and deans
o SNSF: Foundation Council (only committee as of 2010)
o SNSF : National Research Councils
o SNSF: Research Commission of the individual universities
o Members of the ETH Board, from 1969 onwards ETH Board
o Members of the Swiss Science and Innovation Council
o Members of the Commission for Technology and Innovation (CTI)
o Member of the committees of the four main scientific academies: Swiss Academy of Humanities and Social Sciences, Swiss Academy of Medical Sciences, Swiss Academy of Natural Sciences and Swiss Academy of Engineering Sciences + Swiss Association of University Teachers
o Members of the committees of certain scientific disciplinary associations: Swiss Society of Jurists, Swiss Society of Statistics and Political Economy
The purpose of this narrower sample of academic elite was also to make it more comparable with the economic, political, and administrative elites, whose numbers are smaller. In addition, this sample captures particularly well the scientific prestige (Academies and SNSF) and the institutional power of professors (rectors and deans)
Formatives Feedback zum mathematischen Argumentieren (FEMAR): Survey mit Lehrpersonen und Schüler
No full textDer vorliegende Datensatz enthält auch die Daten aus dem Vorgängerprojekt LERU (SWISSUbase Ref. 12172). Die ersten 45 Fälle (Lehrpersonen mit Schüler:innen) wurden 2015 erhoben. Die weiteren 26 Fälle wurden 2019 erhoben
PISA 2022 in Switzerland: Ticino Sample
No full textThis dataset is restricted to the schools sampled within the canton of Ticino.
It combines data from the following data files:
* Student questionnaire data file, made available by OECD (https://webfs.oecd.org/pisa2022/STU_QQQ_SPSS.zip), restricted to students of the canton of Ticino.
* School questionnaire data file, made available by OECD (https://webfs.oecd.org/pisa2022/SCH_QQQ_SPSS.zip), restricted to schools of the canton of Ticino.
* PISA 2022 in Switzerland, add-on to the international dataset: Swiss specific variable, restricted to students from the canton of Ticino, available at SwissUbase: https://doi.org/10.48573/cdez-k248.
* In the second release, one Ticino specific variable has been added: sp_t_pieno_parziale indicates whether a particular vocational education and training programme is completed with full-time or part-time schooling.
IMPORTANT: This dataset does not include the international IDs for students or schools; it includes only Ticino specific IDs. Any research project involving data linkage therefore requires further authorisation. Contact for requesting such further authorisation: EDK General Secretariat, [email protected]
Daten von Berner und Deutschschweizer Lehrpersonen und sonderpädagogischen Fachpersonen zu Einstellungen, Bedenken und Selbstwirksamkeit gegenüber schulischer Inklusion (ISASI) (2019-2022)
No full textDer Datensatz besteht aus Befragungen von Lehr- und sonderpädagogischen Fachpersonen an Berner- und Deutschschweizer Schulen sowie von Studierenden an Pädagogischen Hochschulen über den Zeitraum von 2019 bis 2022, die im Rahmen des Projekts Internationale Skalen zu Einstellungen und Selbstwirksamkeitserwartung von Lehrpersonen gegenüber Inklusion (ISASI) der PHBern gesammelt wurden.
Neben standardisierten Fragebogenskalen zu Einstellungen, Bedenken und Selbstwirksamkeit gegenüber schulischer Inklusion beinhaltet der Datensatz auch offene Fragen (z.B. Nennungen von förderlichen und hinderlichen Aspekten bei der Umsetzung schulischer Integration)
Normality in Medicine
No full textfive categories:
(a) demographics (e.g. age, profession),
(b) personality-psychological constructs (e.g. self-esteem, Big Five),
(c) morality-related psychological constructs (e.g. injustice sensitivity, authority),
(d) quality of life (e.g. critical life events, general health),
(e) the dependent variable, namely normality in medicine (with 2 subscales: mental/physical health
Surface Groups Central African Republic 1984-2021
No full textSurface groups for Central African Republic (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)
