Refer to the Oregon Address Data Standard v. 1.0 (2025) for a complete description of the Address Data Standard feature class and domains of acceptable values for many fields. This standard is used for addresses within the State of Oregon.https://www.oregon.gov/eis/geo/Documents/Oregon%20Address%20Point%20Standard%20v1.0.pdf

Dataset

This dataset was created by Derrick Mallison

Contents

Starter guides for participants in the 3D Data Hack Dublin to help them visualise the new open 3D model of Dublin's Docklands, using real-time interactive gaming technology. The guides have been prepared by the Building City Dashboards project, a Science Foundation Ireland funded initiative at Maynooth University, Ireland. We gratefully acknowledge funding from Science Foundation Ireland under the Investigator’s Award Program. Award number: 15/IA/3090

Dataset

This dataset was created by Sovann Tong

Released under Apache 2.0

Contents

This file can be used to manipulate the storage technologies cost data for the Starter Data Kits.

This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.

Dataset Card for [Dataset Name]

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  Source Data… See the full description on the dataset page: https://huggingface.co/datasets/Langame/starter.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Dataset

This dataset was created by Abhishek Kumar Chauhan

Contents

Dataset

This dataset was created by Ayman Eid

Contents

This document contains the formatted but otherwise unmodified data provided by the informants we approached and kindly asked to share their experience on integrating AI-based tools, platforms, and solutions in their teaching processes.

62343 Global import shipment records of Soft Starter with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Historical price and volatility data for X-Starter in US Dollar across different time periods.

A starter data kit for Uganda

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

coastal and river flooding (Ward et al, 2020)

extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)

tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

population (Schiavina et al, 2023)

built-up area (Pesaresi et al, 2023)

roads (OpenStreetMap, 2023)

railways (OpenStreetMap, 2023)

power plants (Global Energy Observatory et al, 2018)

power transmission lines (Arderne et al, 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

snkit helps clean network data



nismod-snail is designed to help implement infrastructure
exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the
global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo.
DOI: 10.5281/zenodo.3628142



Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical
cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI:
10.4121/12705164.v3



Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.;
et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI:
10.4121/14510817.v3



Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources
Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine;
resourcewatch.org/



Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries
– Final Report. Available online:
https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries



Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme
climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI:
10.1029/2020EF001616



Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online:
www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details



OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived
from OpenStreetMap. [Dataset] Available at
global.infrastructureresilience.org



Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and
Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID:
data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA



Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived
from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI:
10.5281/zenodo.8147088



Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal
(1975-2030). European Commission, Joint Research Centre (JRC) PID:
data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE



Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020)
Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at:
www.wri.org/publication/aqueduct-floods-methodology.

348 Global import shipment records of Starter Motor with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This file is the Base SAND file for South America.

This is published as part of the MethodsX paper titled How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.

Nationally representative, longitudinal information from an evaluation where children were randomly assigned to Head Start or community services as usual;direct assessments and observations of children as well as parent and staff interviews were conducted

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.