The Ministry of Transportation and Tourism Bureau collects spatial tourism information published by various government agencies, including data on tourist attractions, activities, dining and accommodation, tourist service stations, trails, bicycle paths, etc., to provide comprehensive tourism GIS basic data for operators to enhance added value applications. For XML field descriptions of each dataset, please refer to the Tourism Data Standard V1.0 at https://media.taiwan.net.tw/Upload/TourismInformationStandardFormatV1.0.pdf; for Tourism Data Standard V2.0 data, please refer to https://media.taiwan.net.tw/Upload/TourismDataStandardV2.0.pdf.

The Ministry of Transportation and Communications Tourism Bureau collects spatial tourism information released by various government agencies, including information on tourist attractions, activities, dining and lodging, tourist service stations, hiking trails, bike paths, and other data, providing comprehensive tourism GIS basic data for operators to add value. The XML field descriptions for each dataset, tourism data standard V1.0 data, please refer to https://media.taiwan.net.tw/Upload/TourismInformationStandardFormatV1.0.pdf; tourism data standard V2.0 data, please refer to https://media.taiwan.net.tw/Upload/TourismDataStandardV2.0.pdf.

The data included in the GIS Traffic Stations Version database have been collected by the FHWA from the State DOTs. Location referencing information was derived from State offices of Transportation The attributes on the point elements of the database are used by FHWA for its Travel Monitoring and Analysis System and by State DOTs. The attributes for these databases have been intentionally limited to location referencing attributes since the core station description attribute data are contained within the Station Description Tables (SDT). here is a separate Station Description Table (SDT) for each of the station types. The attributes in the Station Description Table correspond with the Station Description Record found in Chapter 6 of the latest Traffic Monitoring Guide. The SDT contains the most recent stations available for each state and station type. This table was derived from files provided UTCTR by FHWA. The Station Description Table can be linked to the station shapefile via the STNNKEY field. Some station where not located in the US, and were beyond available geographic extents causing display problems. These were moved to Lat and Long 0,0. This is in recognition that the locations of these stations where in error, but were moved to a less obtusive area.

This layer is sourced from maps.bts.dot.gov.

This dataset was obtained from the National Household Travel Survey. Due the volume of the data, it was divided in two. This dataset shows the commuting time and transport mode to work all over the country.

This dataset was obtained from the National Household Travel Survey. Due the volume of the data, it was divided in two. This dataset shows the commuting time and transport mode to work all over the country.

The data included in the GIS Traffic Stations Version database have been collected by the FHWA from the State DOTs (NTAD). Location referencing information was derived from State Offices of Transportation. The attributes on the point elements of the database are used by FHWA for its Travel Monitoring and Analysis System and by State DOTs. The attributes for these databases have been intentionally limited to location referencing attributes since the core station description attribute data are contained within the Station Description Tables (SDT). There is a separate Station Description Table (SDT) for each of the station types. The attributes in the Station Description Table correspond with the Station Description Record found in Chapter 6 of the 2001 Traffic Monitoring Guide. The SDT contains the most recent stations available for each state and station type. This table was derived from files provided UTCTR by FHWA. The Station Description Table can be linked to the station shapefile via the STNNKEY field.

Database of air travel activity incurred in the execution of DFID business, including individual flights and CO2. 2010 onwards.

Metrics that can be unearthed will be ones contained in the email booking invoice such as Hotel name, type of room, dates, check in and check out times, price paid, duration of stay. We can go back to 5 years of history.

We also have cancellation emails.

Any hotel vendor can be requested too. We will conduct a search in our database to see if it justifies a parser build to extract the data.

Data set from Survey.Three separate surveys questions designed for the students, employees, and participants of Puerto Rico Community Clinic. The student survey was 30 questions, 23 Likert scale style and 7 free response, intended to gauge the perceived benefits of participating in the service trip, and the employee survey was composed of 12 questions, 8 Likert scale style, and 4 free response. Both the employee and participant surveys were translated from English. The Community Center’s participants surveys included 8 questions, 5 Likert scale style and 3 free response. All Likert scale questions ranged from strongly disagree (1) to strongly agree (5). Surveys were voluntarily completed on the last day by employees and members, whereas student surveys were conducted during the students’ trip back and were completed prior to returning home the next day. All surveys were completed anonymously, allowing one to report their true perception without concern of repercussion.

The Travel Monitoring Analysis System (TMAS) dataset is as of June 6, 2017, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The data included in the GIS Traffic Stations Version database have been collected by the FHWA from the State DOTs. Location referencing information was derived from State offices of Transportation The attributes on the point elements of the database are used by FHWA for its Travel Monitoring and Analysis System and by State DOTs. The attributes for these databases have been intentionally limited to location referencing attributes since the core station description attribute data are contained within the Station Description Tables (SDT). here is a separate Station Description Table (SDT) for each of the station types. The attributes in the Station Description Table correspond with the Station Description Record found in Chapter 6 of the latest Traffic Monitoring Guide. The SDT contains the most recent stations available for each state and station type. This table was derived from files provided UTCTR by FHWA. The Station Description Table can be linked to the station shapefile via the STNNKEY field. Some station where not located in the US, and were beyond available geographic extents causing display problems. These were moved to Lat and Long 0,0. This is in recognition that the locations of these stations where in error, but were moved to a less obtusive area.

Comprehensive dataset of 163 Travel agencies in New Hampshire, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

DATAtourisme is an R & D project winner of the Future Investment Program (PIA). It aims to gather in a national platform, tourist information data produced by the Tourist Offices, Departmental Agencies and Regional Committees of Tourism, in order to disseminate them in open-data and thus facilitate the creation of innovative tourism services by start-ups, digital agencies, media and other public or private actors.

This dataset is the daily export of tourist data present on the platform DATAtourism.

It contains: — description of tourist sites — description of the events taking place on these sites

The content is described on https://framagit.org/datatourisme/ontology/tree/master

as well as https://framagit.org/datatourisme/ontology/raw/master/Documentation/Ontologie%20DATAtourisme%20v1.0_Documentation.pdf

A simplified version in CSV format is also available, with the following fields: — ID: event ID (URI) — label: title of the event — type: type of event (separated by/) — theme: theme of the event (separated by/) — StartDate: start date — EndDate: end date — street: address — PostalCode: postal code — city: city — INSEE: INSEE code of the municipality — latitude,longitude: geographical Position (WGS84) — email — website — phone — lastupdate: last update of the data — how: detailed text describing the event

The CSV file generation script is available at https://github.com/cquest/datatourisme

Comprehensive dataset of 23,066 Travel agencies in Brazil as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/

Comprehensive dataset of 3 Travel in Missouri, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Personal Files - HMS Dauntless, Used to track travel warrants applied for and issued.

A manually curated database of protein-protein interactions (PPIs) for mammalian transient receptor potential (TRP) channels. The detailed summary of PPI data, fits into 4 categories: screening, validation, characterization, and functional consequence. These categorizations give answers for four basic questions about PPIs: how to identify PPIs (screening); how to confirm PPIs (validation); what are biochemical properties of PPIs (characterization); what are biological meaning of PPIs (functional consequence). Users can find in-depth information specified in the literature on relevant analytical methods, gene constructs, and cell/tissue types. The database has a user-friendly interface with several helpful features, including a search engine, an interaction map, and a function for cross-referencing useful external databases.

Link Function: information

The Travel Time to Work dataset was compiled using information from December 31, 2023 and updated December 12, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Travel Time to Work table from the 2023 American Community Survey (ACS) 5-year estimates was joined to 2023 tract-level geographies for all 50 States, District of Columbia and Puerto Rico provided by the Census Bureau. A new file was created that combines the demographic variables from the former with the cartographic boundaries of the latter. The national level census tract layer contains data on the number and percentage of commuters (workers 16 years and over who did not work from home) with a range of travel times to work.

Comprehensive dataset of 7 Travel in Wisconsin, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.