SWISS-MODEL homology models mapping to UniProtKB Proteome UP000001584 (Mycobacterium tuberculosis)

Introduction

This dataset contains detailed data on 42,207 apartments (242,257 rooms) in 3,093 buildings including their geometries, room typology as well as their visual, acoustical, topological and daylight characteristics.

Procurement

The data is sourced from commercial clients of Archilyse AG specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).

Geometries

The dataset contains a file geometries.csv which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.

In total the datasets contains the 2D geometry of ~1.2 million separators (walls, railings), ~550,000 openings (windows, doors), ca. 400,000 areas (rooms, bathrooms, kitchens, etc.) and ~240,000 features (sinks, toilets, bathtubs, etc.).

Each row contains:

• entity_type: The entity type (area, separator, opening, feature)
• entity_subtype: The entity’s sub type (e.g. WALL)
• geometry: The element’s geometry as a WKT geometry. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.
• area_id: The ID of the area in which the element is spatially contained (for features)
• unit_id: The ID of the unit in which the element is spatially contained (for features, areas)
• apartment_id: The ID of the apartment (for features, areas)
• floor_id: The ID of the floor
• building_id: The ID of the building
• site_id: The ID of the site

An example:

column
entity_type	area
entity_subtype	ROOM
geometry	POLYGON ((-2.10406 4.02039…
site_id	127
building_id	164
floor_id	12864
apartment_id	d4438f2129b30290845ce7eef98a5ba7
unit_id	76643
area_id	684674

Simulations

Beside the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout and connectivity-related characteristics of the apartments. The file simulations.csv contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns area_id, unit_id, apartment_id, floor_id, building_id, site_id as defined above as well as 367 simulation columns. Each simulation column is formatted as:

For instance. the column view_buildings_median describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.

Layout

The layout features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.

Area Basics / Geometry

dimension	description
layout_area_type	The area’s area type
layout_net_area	The area’s share of the apartment’s net area (e.g. 0 for a balcony)
layout_area	The area’s actual area
layout_perimeter	The area’s perimeter
layout_compactness	The area’s compactness (the Polsby–Popper score)
layout_room_count	The area’s share to the apartment’s room count
layout_is_navigable	True if the area is navigable by a wheelchair

Area Features

dimension	description
layout_has_sink	True if the area has a sink
layout_has_shower	True if the area has a shower
layout_has_bathtub	True if the area has a bathtub
layout_has_toilet	True if the area has a toilet
layout_has_stairs	True if the area has stairs
layout_has_entrance_door	True if the area is directly leading to an exit of the apartment

Area Windows / Doors

dimension	description
layout_number_of_doors	The number of doors directly leading to the area
layout_number_of_windows	The number of windows of the area
layout_door_perimeter	The sum of all door lengths directly leading to the area
layout_window_perimeter	The sum of all window lengths of the area

Area Walls / Railings

dimension	description
layout_open_perimeter	The sum of all of the areas boundaries that are neither walls nor railings
layout_railing_perimeter	The sum of all of the areas boundaries that are railings
layout_mean_walllengths	The mean length of the area’s sides
layout_std_walllengths	The standard deviation of the lengths of the area’s sides

Area Adjecency

dimension	description
layout_connects_to_bathroom	True if the area connects to a bathroom
layout_connects_to_private_outdoor	True if the area connects to an outside area that is private to the apartment

View

The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a certain object category occupies in the spherical field of view.

Each of the following dimension is provided using the room-wise aggregations min, max, mean, std, median, p20 and p80. For instance, the column view_greenery_p20 describes the amount of greenery that can be seen from at least 20% of the positions in the area.

dimension	description
view_buildings	The amount of visible buildings
view_greenery	The amount of visible greenery
view_ground	The amount of visible ground
view_isovist	The amount of visible isovist
view_mountains_class_2	The amount of visible mountains of UN mountain class 2
view_mountains_class_3	The amount of visible mountains of UN mountain class 3
view_mountains_class_4	The amount of visible mountains of UN mountain class 4
view_mountains_class_5	The amount of visible mountains of UN mountain class 5
view_mountains_class_6	The amount of visible mountains of UN mountain class 6
view_railway_tracks	The amount of visible railway_tracks
view_site	The amount of visible site
view_sky	The amount of visible sky
view_tertiary_streets	The amount of visible tertiary_streets
view_secondary_streets	The amount of visible secondary_streets
view_primary_streets	The amount of visible primary_streets
view_pedestrians	The amount of visible pedestrians
view_highways	The amount of visible highways
view_water	The amount of visible water

Sun

Sun simulations help to understand the impact of the solar radiation on the object. The outcome of the sun simulations helps to

This dataset includes Amphibian occurrence records from Switzerland and adjacent areas. Data sources are literature, museum collections and field observations. The latter and most important resource is provided by a large network of volunteer collaborators, regional representatives of the Swiss Coordination Office for Amphibian and Reptile Conservation (https://www.infofauna.ch/de/beratungsstellen/amphibien-karch) as well as environmental impact studies, national and regional inventories, monitoring, field work for Red Lists and academic work. The period covered by the data extends from 1820 to the present day. All data provided have been subject to a validation procedure.

Investasi:% dari PDB Swiss dilaporkan sebesar 26.8 % pada 2024-12. Rekor ini turun dibanding sebelumnya yaitu 28.1 % untuk 2024-09. Data Investasi:% dari PDB Swiss diperbarui triwulanan, dengan rata-rata 27.7 % dari 1980-03 sampai 2024-12, dengan 180 observasi. Data ini mencapai angka tertinggi sebesar 38.1 % pada 1980-03 dan rekor terendah sebesar 20.9 % pada 2022-09. Data Investasi:% dari PDB Swiss tetap berstatus aktif di CEIC dan dilaporkan oleh CEIC Data. Data dikategorikan dalam Global Economic Monitor World Trend Plus – Table: Investment: % of Nominal GDP: Quarterly.

The Swiss Drone data set was recorded around Cheseaux-sur-Lausanne in Switzerland using a senseFly eBee Classic in 2014 (SenseFly, 2020). The 100 images were captured from a top-down perspective at a flight height of approximately 80 m above the ground at a resolution of 4608  x 3456 pixels. The Okutama Drone data set was recorded and annotated by NII (Laurmaa, 2016) in 2016 using a DJI Phantom 4 at a resolution of 3840 x 2160 pixels. The 91 images were captured over Okutama, west of Tokyo, Japan, from a drone at a flight height of approximately 90 m above the ground. Here, the flight height may have varied more as Okutama is located in a narrow valley with uneven ground.

*Swiss images captured with a senseFly eBee Classic in 2014 (16MP 4608 x 3456 resolution) *Okutama images captured with a DJI Phantom 4 in June 2016 (4K 3840 x 2160 resolution) *Labels are provided in PNG pixel-wise mask files *9 different classes

Utang Pemerintah:% dari PDB Swiss dilaporkan sebesar 23.3 % pada 2028. Rekor ini turun dibanding sebelumnya yaitu 23.8 % untuk 2027. Data Utang Pemerintah:% dari PDB Swiss diperbarui tahunan, dengan rata-rata 29.2 % dari 1990 sampai 2028, dengan 39 observasi. Data ini mencapai angka tertinggi sebesar 47.2 % pada 1998 dan rekor terendah sebesar 23.3 % pada 2028. Data Utang Pemerintah:% dari PDB Swiss tetap berstatus aktif di CEIC dan dilaporkan oleh Federal Finance Administration. Data dikategorikan dalam Swiss Global Database – Table CH.F004: ESA 2010: Public Debt.

Total Impor dari USA Swiss dilaporkan sebesar 2.445 USD bn pada 2025-01. Rekor ini naik dibanding sebelumnya yaitu 2.130 USD bn untuk 2024-12. Data Total Impor dari USA Swiss diperbarui bulanan, dengan rata-rata 334.575 USD mn dari 1960-01 sampai 2025-01, dengan 781 observasi. Data ini mencapai angka tertinggi sebesar 4.631 USD bn pada 2022-10 dan rekor terendah sebesar 14.000 USD mn pada 1963-02. Data Total Impor dari USA Swiss tetap berstatus aktif di CEIC dan dilaporkan oleh CEIC Data. Data dikategorikan dalam Global Economic Monitor World Trend Plus – Table: Total Imports from USA: USD: Monthly.

SWISS-MODEL homology models mapping to UniProtKB Proteome UP000002311 (Saccharomyces cerevisiae)

This dataset tracks annual total students amount from 1987 to 2023 for Swiss Hills Career Center

ZEW Economic Sentiment Index in Switzerland increased to -22 points in May from -51.60 points in April of 2025. This dataset provides - Switzerland Zew Economic Sentiment Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset tracks annual black student percentage from 1991 to 2023 for Swiss Hills Career Center vs. Ohio and Switzerland Of Ohio Local School District

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Residential building including single family home, mutiple family home, farm, and apartment building; other buildings (e.g. factory or commercial buildings) if they contain at least one unit for residential purposes; other accommodations (e.g., barracks, mountain farms, wagons) if they are occupied on the census day. - Group quarters: Collective households are groups of persons who live in hotels, boarding homes, care homes, boarding schools, hospitals, company dormitories. Other collective households include staff members and company workers who live in a common accommodation but do not keep house and are neither connected to another household.

Universe

All persons residing in Switzerland, except foreign diplomats stationed in Switzerland and their families.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Federal Statistical Office

SAMPLE DESIGN: Systematic sample of every 20th household, drawn by the Federal Statistical Office

SAMPLE UNIT: Household

SAMPLE FRACTION: 5%

SAMPLE SIZE (person records): 317,803

Mode of data collection

Face-to-face [f2f]

Research instrument

There are three sets of questionnaires: (i) person questionnaire, (ii) household questionnaire, and (iii) building questionnaire

This dataset includes records of the Ephemeroptera, Trichoptera and Plecoptera from Switzerland and the adjacent area. Data sources include museum collections, field observations provided by a small network of volunteer collaborators, cantonal and federal monitoring programs (water quality, river restoration effects) as well as national inventories (Red list strategy). All data provided have been subject to a validation procedure.

The graph presents the number of Swiss watches exported worldwide from 2016 to 2024, by material. In 2024, about 8.4 million units of steel Swiss watches were exported worldwide, which was considerably more than any other category.

Harmonised Inflation Rate MoM in Switzerland increased to 0.70 percent in April from -0.10 percent in March of 2025. This dataset includes a chart with historical data for Switzerland Harmonised Inflation Rate MoM.

About this dataset

Context

The dataset tabulates the Swiss town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Swiss town. The dataset can be utilized to understand the population distribution of Swiss town by age. For example, using this dataset, we can identify the largest age group in Swiss town.

Key observations

The largest age group in Swiss, Wisconsin was for the group of age 65-69 years with a population of 100 (13.64%), according to the 2021 American Community Survey. At the same time, the smallest age group in Swiss, Wisconsin was the 85+ years with a population of 14 (1.91%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Swiss town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Swiss town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Swiss town Population by Age. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Swiss Trail cross streets in Calera, OK.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Swiss, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Swiss town did not include 4, 5, 6, or 7-person households. Across the different household sizes in Swiss town the mean income is $67,482, and the standard deviation is $45,324. The coefficient of variation (CV) is 67.16%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $23,529. It then further increased to $114,063 for 3-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Swiss town median household income. You can refer the same here

This dataset consist of simulated hourly power production from an Enercon E82 Turbine at 100 m hub-height. It describes the hourly power output a 1MW turbine would produce in each 0.01° grid cell for the years 2016 and 2017. 100 m wind speed data was taken from the COSMO-1 model (Consortium for Small-scale Modeling 2017), which has a 0.01° horizontal resolution. The domain covered is the whole of Switzerland, with the exclusion of lakes. As such, the number of 0.01◦ pixels within Switzerland amounts to 48657. Conversion to power output was done based on the power curve of the Enercon E82 Turbine. As power output is lower at altitude due to lower air density, we corrected for this effect as described in (Kruyt et al. 2017). Please cite the following paper in connection with the dataset: Paper Citation: - Bert Kruyt, Jérôme Dujardin, and Michael Lehning: Improvement of wind power assessment in complex terrain: The case of COSMO-1 in the Swiss Alps, Front. Energy Res., doi:10.3389/fenrg.2018.00102

Graph and download economic data for Real Broad Effective Exchange Rate for Switzerland (RBCHBIS) from Jan 1994 to Apr 2025 about Switzerland, broad, exchange rate, currency, real, and rate.