SWISSUbase
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Surface Groups Egypt 1984-2021
No full textSurface groups for Egypt (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)
Enquêtes-ménages sur les Perceptions climatiques et sur les Intentions migratoires au nord du Sénégal
No full textSurface Groups Gambia 1984-2021
No full textSurface groups for The Gambia (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 Ethiopia 1984-2021
No full textSurface groups for Ethiopia (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: Longitudinal data - Canada - T0-T1-T2
No full textThis data set contains raw data from the Canadian sample at all time points (T0 to T2)
SWISS100 Phone Study
No full textThe primary aim of this study is to explore the relationship between proxy respondent characteristics and the occurrence of item non-response (INR) in surveys involving centenarians, a particularly hard-to-reach population. Specifically, the study focuses on how different types of proxy respondents—children, other relatives, and non-relatives—impact the quality of survey data, with an emphasis on understanding the factors contributing to INR and enhancing the inclusivity and accuracy of data collection in studies involving the oldest-old individuals
OBELIS Swiss Political Elites
No full textThe data collected on members of the Swiss political elites are part of the more general OBELIS database on Swiss Elites. Currently, the OBELIS database includes elites from four sectors: Economic, Political, Administrative and Academic and covers nine dates: 1890, 1910, 1937, 1957, 1980, 2000, 2010, 2015 and 2020. The elite status of individuals is defined by the position/function held in these four spheres at the date mentioned. A description of all the different samples of the Swiss elites can be consulted on the website. The data allows researchers to understand the elites through a relational analysis (network analysis) to highlight the interrelations between these elites. The data is also suitable to conduct prosopographical analysis.
There are two samples of Switzerland's political elite positions on SWISSUbase:
1/ Swiss Federal political elite : This sample of federal political elites includes members of the Federal Council, the Federal Parliament, National Council and Council of States to 9 dates (1890, 1910, 1937, 1957, 1980, 2000, 2010, 2015 and 2020). This sample includes 2190 positions of political elites at federal level for these 9 dates (n=2190).
2/ Swiss cantonal and main cities governmental elites : This sample includes members of the Cantonal executives and members of the governments of the four largest Swiss cities (Zurich, Bern, Geneva, and Lausanne) which are the largest in terms of population and budget to 9 dates (1890, 1910, 1937, 1957, 1980, 2000, 2010, 2015 and 2020). The number of members of cantonal executives varies between 5 and 10, and sometimes there have been changes during the year. In some cases, two people were appointed in succession during the year. This explains the higher number in some years. Many individuals are present on several dates, particularly in the recent period. This sample includes 1751 positions of political elites at cantonal or main cities level for these 9 dates (n=1751)
OBELIS Swiss Administrative Elites
No full textThere are two samples of Switzerland's administrative elite positions on SWISSUbase:
1/ Senior civil servants and Federal Judges
This sample of federal administrative elites includes members of the chancellery (chancellors and vice-chancellors), secretaries-general and deputy secretaries of federal departments, all directors of offices in federal departments, members of the SNB Executive Board and members of the Federal Supreme Court. In addition, Secretaries of State from 1980 onwards are included under Office Directors since they also hold this position (n= 904 positions in the archived dataset).
2/ Members of the extra-parliamentary committees
This sample includes members of the extra-parliamentary committees, often described in the literature as a "militia administration". These are "meeting places", bringing together representatives from the political, administrative, economic, scientific, and academic spheres, as well as from the cantons. These extra-parliamentary committees began to flourish in the 1930s. It should be noted that the identities of members of extra-parliamentary committees are documented, but the collection of their biographical data and mandates is not as detailed as for senior civil servants and Federal Judges. (n = 12079 positions in the archived dataset)
Synthetische Bevölkerung der Schweiz 2022 (SynPop 2022)
No full textDE:
Die Datendatei ist zu gross und kann nicht in MS-Excel geöffnet werden. In anderen Applikationen wie zum Beispiel R, Python oder simplen Texteditoren können die Daten aber gut eingelesen werden.
FR:
Le fichier de données est trop grand et ne peut pas être ouvert dans MS-Excel. Les données peuvent toutefois être lues dans d'autres applications comme par exemple R, Python ou des éditeurs de texte simples.
EN:
The datafile is too large to be opened in MS-Excel. However, the data can be easily read in other applications such as R, Python or simple text editors
Surface Groups Kuwait 1984-2021
No full textSurface groups for Kuwait (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)