This dataset contains information on all projects funded under the School Facility Program. The data is provided by the Office of Public School Construction under the authority of the Department of General Services. As staff to the State Allocation Board (SAB), the Office of Public School Construction (OPSC) implements and administers the $42 billion voter-approved school facilities construction program, known as the School Facility Program.

The Open Database of Buildings (ODB) is a collection of open data on buildings made available under the Open Government License - Canada. The ODB brings together 530 datasets originating from 107 government sources of open data. The database aims to enhance access to a harmonized collection of building features across Canada.

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

This dataset contains information on building permit applications in the Town of Cary.This file is created from the Town of Cary permit application data. It has been created to conform to the BLDS open data specification for building permit data (permitdata.org). In the Town of Cary, a permit application may result in the creation of several permits. Rows in this table represent applications for permits, not individual permits. Individual permits may be released as a separate dataset. With the exception of a few fields, we have applied all of the required and preferred fields of the required dataset for permits.For additional information check out our Applications & Forms webpage.

This is the pre-print version of a paper accepted in Open Repository Conference in Brisbane, Australia, June 2017.Abstract Research Graph is an open collaborative project that builds the capability for connecting researchers, publications, research grants and research datasets (data in research). VIVO is an open source, semantic web platform and a set of ontologies for representing scholarship. To provide interoperability between Research Graph data and VIVO systems we modelled the Research Graph metamodel using the VIVO Integrated Semantic Framework. To evaluate the mapping, we used the model to connect figshare RDF records to data collections in Research Data Australia using Research Graph API. In addition, we are working toward loading Research Graph data into a VIVO instance. VIVO provides a search capability, and pages for first class entities in the Research Graph model -- researcher, dataset, grant, and publication. The result provides a visualisation solution for co-authors, co-funding, timeline, and a capability map for finding expertise related to concepts of interest. The resulting linked open data will be made freely available and can be used in other tools for additional discovery.

The City requires permits for commercial and residential development, such as new single-family homes, commercial construction, remodels, additions and related activity like trade (mechanical, electrical, and plumbing) work. City review ensures that construction projects adhere to the City’s adopted Building Codes and the Unified Development Code to enhance the health and safety for you, your family and our community.

The datasets below are provided as-is as a record of building activity in San Antonio. For any additional information not contained below, or for information and documentation related to building activity, a request for information (“open records request”) is needed; please refer to the City of San Antonio Open Government Request site at https://www.sa.gov/Directory/Departments/CE/Open-Records-Request to submit a request.

Zebra Open Dataset

## Overview

Zebra Open Dataset is a dataset for object detection tasks - it contains Zebra annotations for 1,532 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The BuildingsBench datasets consist of:

• Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock.
• 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF.

Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB).

BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below:

1. ElectricityLoadDiagrams20112014
2. Building Data Genome Project-2
3. Individual household electric power consumption (Sceaux)
4. Borealis
5. SMART
6. IDEAL
7. Low Carbon London

A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

On August 25th, 2022, Metro Council Passed Open Data Ordinance; previously open data reports were published on Mayor Fischer's Executive Order, You can find here both the Open Data Ordinance, 2022 (PDF) and the Mayor's Open Data Executive Order, 2013 Open Data Annual ReportsPage 6 of the Open Data Ordinance, Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council an annual Open Data Report.The Open Data Management team (also known as the Data Governance Team is currently led by the city's Data Officer Andrew McKinney in the Office of Civic Innovation and Technology. Previously, it was led by the former Data Officer, Michael Schnuerle and prior to that by Director of IT.Open Data Ordinance O-243-22 TextLouisville Metro GovernmentLegislation TextFile #: O-243-22, Version: 3ORDINANCE NO._, SERIES 2022AN ORDINANCE CREATING A NEW CHAPTER OF THE LOUISVILLE/JEFFERSONCOUNTY METRO CODE OF ORDINANCES CREATING AN OPEN DATA POLICYAND REVIEW. (AMENDMENT BY SUBSTITUTION)(AS AMENDED).SPONSORED BY: COUNCIL MEMBERS ARTHUR, WINKLER, CHAMBERS ARMSTRONG,PIAGENTINI, DORSEY, AND PRESIDENT JAMESWHEREAS, Metro Government is the catalyst for creating a world-class city that provides itscitizens with safe and vibrant neighborhoods, great jobs, a strong system of education and innovationand a high quality of life;WHEREAS, it should be easy to do business with Metro Government. Online governmentinteractions mean more convenient services for citizens and businesses and online governmentinteractions improve the cost effectiveness and accuracy of government operations;WHEREAS, an open government also makes certain that every aspect of the builtenvironment also has reliable digital descriptions available to citizens and entrepreneurs for deepengagement mediated by smart devices;WHEREAS, every citizen has the right to prompt, efficient service from Metro Government;WHEREAS, the adoption of open standards improves transparency, access to publicinformation and improved coordination and efficiencies among Departments and partnerorganizations across the public, non-profit and private sectors;WHEREAS, by publishing structured standardized data in machine readable formats, MetroGovernment seeks to encourage the local technology community to develop software applicationsand tools to display, organize, analyze, and share public record data in new and innovative ways;WHEREAS, Metro Government’s ability to review data and datasets will facilitate a betterUnderstanding of the obstacles the city faces with regard to equity;WHEREAS, Metro Government’s understanding of inequities, through data and datasets, willassist in creating better policies to tackle inequities in the city;WHEREAS, through this Ordinance, Metro Government desires to maintain its continuousimprovement in open data and transparency that it initiated via Mayoral Executive Order No. 1,Series 2013;WHEREAS, Metro Government’s open data work has repeatedly been recognized asevidenced by its achieving What Works Cities Silver (2018), Gold (2019), and Platinum (2020)certifications. What Works Cities recognizes and celebrates local governments for their exceptionaluse of data to inform policy and funding decisions, improve services, create operational efficiencies,and engage residents. The Certification program assesses cities on their data-driven decisionmakingpractices, such as whether they are using data to set goals and track progress, allocatefunding, evaluate the effectiveness of programs, and achieve desired outcomes. These datainformedstrategies enable Certified Cities to be more resilient, respond in crisis situations, increaseeconomic mobility, protect public health, and increase resident satisfaction; andWHEREAS, in commitment to the spirit of Open Government, Metro Government will considerpublic information to be open by default and will proactively publish data and data containinginformation, consistent with the Kentucky Open Meetings and Open Records Act.NOW, THEREFORE, BE IT ORDAINED BY THE COUNCIL OF THELOUISVILLE/JEFFERSON COUNTY METRO GOVERNMENT AS FOLLOWS:SECTION I: A new chapter of the Louisville Metro Code of Ordinances (“LMCO”) mandatingan Open Data Policy and review process is hereby created as follows:§ XXX.01 DEFINITIONS. For the purpose of this Chapter, the following definitions shall apply unlessthe context clearly indicates or requires a different meaning.OPEN DATA. Any public record as defined by the Kentucky Open Records Act, which could bemade available online using Open Format data, as well as best practice Open Data structures andformats when possible, that is not Protected Information or Sensitive Information, with no legalrestrictions on use or reuse. Open Data is not information that is treated as exempt under KRS61.878 by Metro Government.OPEN DATA REPORT. The annual report of the Open Data Management Team, which shall (i)summarize and comment on the state of Open Data availability in Metro Government Departmentsfrom the previous year, including, but not limited to, the progress toward achieving the goals of MetroGovernment’s Open Data portal, an assessment of the current scope of compliance, a list of datasetscurrently available on the Open Data portal and a description and publication timeline for datasetsenvisioned to be published on the portal in the following year; and (ii) provide a plan for the next yearto improve online public access to Open Data and maintain data quality.OPEN DATA MANAGEMENT TEAM. A group consisting of representatives from each Departmentwithin Metro Government and chaired by the Data Officer who is responsible for coordinatingimplementation of an Open Data Policy and creating the Open Data Report.DATA COORDINATORS. The members of an Open Data Management Team facilitated by theData Officer and the Office of Civic Innovation and Technology.DEPARTMENT. Any Metro Government department, office, administrative unit, commission, board,advisory committee, or other division of Metro Government.DATA OFFICER. The staff person designated by the city to coordinate and implement the city’sopen data program and policy.DATA. The statistical, factual, quantitative or qualitative information that is maintained or created byor on behalf of Metro Government.DATASET. A named collection of related records, with the collection containing data organized orformatted in a specific or prescribed way.METADATA. Contextual information that makes the Open Data easier to understand and use.OPEN DATA PORTAL. The internet site established and maintained by or on behalf of MetroGovernment located at https://data.louisvilleky.gov/ or its successor website.OPEN FORMAT. Any widely accepted, nonproprietary, searchable, platform-independent, machinereadablemethod for formatting data which permits automated processes.PROTECTED INFORMATION. Any Dataset or portion thereof to which the Department may denyaccess pursuant to any law, rule or regulation.SENSITIVE INFORMATION. Any Data which, if published on the Open Data Portal, could raiseprivacy, confidentiality or security concerns or have the potential to jeopardize public health, safety orwelfare to an extent that is greater than the potential public benefit of publishing that data.§ XXX.02 OPEN DATA PORTAL(A) The Open Data Portal shall serve as the authoritative source for Open Data provided by MetroGovernment.(B) Any Open Data made accessible on Metro Government’s Open Data Portal shall use an OpenFormat.(C) In the event a successor website is used, the Data Officer shall notify the Metro Council andshall provide notice to the public on the main city website.§ XXX.03 OPEN DATA MANAGEMENT TEAM(A) The Data Officer of Metro Government will work with the head of each Department to identify aData Coordinator in each Department. The Open Data Management Team will work to establish arobust, nationally recognized, platform that addresses digital infrastructure and Open Data.(B) The Open Data Management Team will develop an Open Data Policy that will adopt prevailingOpen Format standards for Open Data and develop agreements with regional partners to publish andmaintain Open Data that is open and freely available while respecting exemptions allowed by theKentucky Open Records Act or other federal or state law.§ XXX.04 DEPARTMENT OPEN DATA CATALOGUE(A) Each Department shall retain ownership over the Datasets they submit to the Open DataPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityof the Dataset contents, including updating its Data and associated Metadata.(B) Each Department shall be responsible for creating an Open Data catalogue which shall includecomprehensive inventories of information possessed and/or managed by the Department.(C) Each Department’s Open Data catalogue will classify information holdings as currently “public”or “not yet public;” Departments will work with the Office of Civic Innovation and Technology todevelop strategies and timelines for publishing Open Data containing information in a way that iscomplete, reliable and has a high level of detail.§ XXX.05 OPEN DATA REPORT AND POLICY REVIEW(A) Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council anannual Open Data Report.(B) Metro Council may request a specific Department to report on any data or dataset that may bebeneficial or pertinent in implementing policy and legislation.(C) In acknowledgment that technology changes rapidly, in the future, the Open Data Policy shouldshall be reviewed annually and considered for revisions or additions that will continue to positionMetro Government as a leader on issues of

Content

This dataset provides the public with the value of individual payments made to vendors for Professional Services or Construction contacts, by State Contract number. Data includes individual payments recorded in the specified time frame to vendors for the County in which the work was performed. Statewide payments are also included that are not County specific.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Kelvin Ang on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The integration of renewable energy sources and advanced technologies has led to the emergence of prosumer buildings

A supervised learning task involves constructing a mapping from an input data space (normally described by several features) to an output space. A set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output consists of one or more classes to which the corresponding input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. In this chapter, we explain several basic classification algorithms.

Thinking Machines Data Science is releasing TM Open Buildings, a dataset of manually-drawn building outlines covering 12 Philippine cities with detailed annotations on building and roof attributes as seen over satellite imagery. We contribute the buildings in OpenStreetMap and also made available for download in Kaggle. This is made possible with the support from the Lacuna Fund.

Producing the dataset

The team has consulted HOTOSM Asia Pacific and community architects from the Philippine Action for Community-led Shelter Initiatives (PACSII) to validate our attributes and to ensure that our contributions are documented properly. We also looked at street-level views to check tags whenever available. We will take into consideration the feedback from local mappers as local knowledge always precedes, and will always provide changeset comments that are in compliance with OSM guidelines.

You may view more details of our process in our wiki page. Kindly use our Github Issues tab to file any specific concerns about the dataset.

License

This TM Open Buildings dataset is made available by Thinking Machines under the Open Database License (ODbL). Any rights in individual contents of the database are licensed under the Database Contents License.

Building Definitions

We define the buildings we mapped, as well as the attributes included, in the table below. Please refer to our wiki page for more details. | Building Type |Subtype | Definition | Mapped Attributes |
|----------------|--------|----------------------------------------------------------------------------------------|---------------------------------------------------------| | Settlement | Single | Residential houses that are individually distinct from surrounding structures | Roof material, Roof layout, Is within gated community? |
| | Dense | Tight clusters of small residential houses that do not have distinguishable boundaries | - |
| Non-settlement | | Commercial, industrial, or institutional buildings | Building height |

Coverage

The dataset covers selected 250m x 250m tiles in 12 Philippine cities, namely Dagupan City, Palayan City, City of Navotas, City of Mandaluyong, City of Muntinlupa, Legazpi City, Tacloban City, Iloilo City, Mandaue City, Cagayan de Oro City, Davao City, and Zamboanga City. The tiles are chosen to focus on residential areas that lie on a wide variety of terrains (urban, coastal, riparian, agricultural, etc.). All settlements and non-settlements within each tile are drawn manually. Data on the locations of the tiles are given in the following table.

Attributes

The following table contains the definitions of the attributes and how it is tagged in OSM. | Attribute | Type | Characteristics | OSM Key and Tag | |------------------------|------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------| | Roof Material | Natural/Galvanized Iron (GI)/Mixed | Looks rusty when old, silver/gray when new, lines and patches are usually evident. | roof:material = metal_sheet | | | Metal/Tiled | Whole roof is usually one solid color, tiled roofs have texture. | roof:material = roof_tiles | | | Concrete | Flat, usually has raised white edges, no visible roof “folds”, may be smooth or have objects on top. ...

Deer Open Dataset

## Overview

Deer Open Dataset is a dataset for object detection tasks - it contains Deer annotations for 1,289 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

One of the key problems that arises in many areas is to estimate a potentially nonlinear function [tex] G(x, \theta)[/tex] given input and output samples tex [/tex] so that [tex]y approx G(x, \theta)[/tex]. There are many approaches to addressing this regression problem. Neural networks, regression trees, and many other methods have been developed to estimate [tex]$G$[/tex] given the input output pair tex [/tex]. One method that I have worked with is called Gaussian process regression. There many good texts and papers on the subject. For more technical information on the method and its applications see: http://www.gaussianprocess.org/ A key problem that arises in developing these models on very large data sets is that it ends up requiring an [tex]O(N^3)[/tex] computation where N is the number of data points and the training sample. Obviously this becomes very problematic when N is large. I discussed this problem with Leslie Foster, a mathematics professor at San Jose State University. He, along with some of his students, developed a method to address this problem based on Cholesky decomposition and pivoting. He also shows that this leads to a numerically stable result. If ou're interested in some light reading, I’d suggest you take a look at his recent paper (which was accepted in the Journal of Machine Learning Research) posted on dashlink. We've also posted code for you to try it out. Let us know how it goes. If you are interested in applications of this method in the area of prognostics, check out our new paper on the subject which was published in IEEE Transactions on Systems, Man, and Cybernetics.

The Texas Open Data Portal Resource Guide 2025 is produced by the Texas Department of Information Resources to assist publishing organizations in their use of the Open Data Portal. While not exhaustive, this document serves as a guide in establishing an open data governance framework, creating an open data inventory, and publishing open data in an efficient and standardized manner.

The original dataset was based on 1992 B/W orthophotography at 200" and 400" scales. In 2009, Bedford County contracted with Sanborn Map Company, Inc. to develop planimetric data based on the 2007 photography. The dataset was once again updated in 2018 and 2022 through the VBMP.

GIS Staff is in the continual process of attributing building features with the name of the structure (e.g., church name, business name, etc.). This attribute information is valuable as a Landmark feature for Dispatchers in the PSAP, to show common, well-known structures and places within the County.

This dataset contains data for all Commercial Demolition Permit applications issued in the last five years, including status and work performed.

This data shows the digitized building footprints of buildings located within the City of Winchester, Virginia. This data was collected off Eagleview 2017 aerial imagery and was provided to the City after the flight.

The dataset provides information on the preparing permits for works on plantations in Vilnius city, indicating their type, condition, type and nature of the works.