the Department of Energy’s Enterprise Project Management Organization (EPMO), providing leadership and assistance in developing and implementing DOE-wide policies, procedures, programs, and management systems pertaining to project management, and independently monitors, assesses, and reports on project execution performance. The office validates project performance baselines–scope, cost and schedule–of the Department’s largest construction and environmental clean-up projects prior to budget request to Congress—an active project portfolio totaling over $30 billion. The office also serves as Executive Secretariat for the Department’s Energy Systems Acquisition Advisory Board (ESAAB) and the Project Management Risk Committee (PMRC). In these capacities, the Director is accountable to the Deputy Secretary.

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

The statistic shows the success rate of various big data initiatives as of 2019, according to a survey of industry-leading firms, primarily in the United States. As of that time, **** percent of respondents reported having seen measurable results from big data initiatives to decrease expenses.

Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.

Get data on announced projects funded through the Rural Economic Development (RED) program.

Ontario's RED program funds projects that stimulate economic growth in rural and Indigenous communities.

The data includes:

• name of the organization that requested funding
• county or district where the organization is located
• project name
• date that funding was approved
• amount of funding provided

From 2013 to 2016, the RED program funded projects led by businesses or communities.

Starting in 2017, the RED program only focuses on projects led by:

• not-for-profit organizations
• municipalities
• local service boards
• Indigenous communities and organizations

Learn more about the Rural Economic Development program.

Replacement project detail by school.

Data contains project type, project information, project status and project county, district or region.

This dataset is a collection of:

1. The final SPSS dataset concerning the 171 Open Government Data Initiatives (OGDIs) analysed in this study;

2. The SPSS Output;

3. The Questionnaire used to collect information concerning the OGDIs;

All Project Request submitted to the Communications Department

A complete copy of the Materials Project database as of 10/18/2018. Mp_all files contain structure data for each material while mp_nostruct does not.Available as Monty Encoder encoded JSON and as CSV. Recommended access method for these particular files is with the matminer Python package using the datasets module. Access to the current Materials Project is recommended through their API (good), pymatgen (better), or matminer (best).Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset discussed in:A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002.Dataset sourced from:https://materialsproject.org/Citations for specific material properties available here:https://materialsproject.org/citing

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

This data set contains DOT construction project information. The data is refreshed nightly from multiple data sources, therefore the data becomes stale rather quickly.

C Project

## Overview

C Project is a dataset for object detection tasks - it contains Tooth annotations for 713 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).

Dataset Summary About this data: Planning is responsible for monitoring physical, social and economic factors relevant to the well being of the City. The Office provides consultant services to City departments and community organizations for planning related projects and topics. This layer provides the boundaries of areas where a community or neighborhood plan has been developed, or is in process of development. Each feature provides a link to a website or actual plan for more information. For more information please visit: https://www.cityofrochester.gov/planning.aspx Dictionary: Project Name: The name of the planning project. Project Description: Notes the specific goals of the project. Website: The link to the relevant City of Rochester web page that provides more detail on a given project. Contact: The contact person for a given project. Organization: The group responsible for carrying out the project. Funding Source: Identifies the organization responsible for providing financial resources to cover the expenses of a project. Contact Email: The email for the Contact person (if available). Project Year: The year the project started. Project Status: Identifies the current progress of a given project (In progress, Completed, Ongoing, Not Started). City Lead: Notes if the City of Rochester is the primary project lead for a given project (Yes/No). ACTIVE: Notes whether or not the project is active or completed. COLOR: The color chosen to represent the project on the Development Projects and Plans application. Link Text: Contains “More information” as a hyperlink to the project website. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester’s Office of Planning.

Approved Major IT Projects funded under Computerisation

All major capital infrastructure projects with a committed budget. Financial information and agency schedule details for each project are joined via FMS ID. Only projects in the design phase or later have project schedules displayed. This dataset is part of the Capital Projects Dashboard.

Note: Each row is uniquely identified by its Financial Management Service (FMS) ID. FMS ID is the unique ID that OMB uses for the FMS (Financial Information System). This ID can be universally joined with any OMB dataset that has the same field. The Capital Projects Dashboard is the result of joining OMB’s fiscal data with the agency’s schedule data. FMS IDs and agency projects don't always have a one-to-one relationship (i.e., one project schedule may correlate to multiple FMS IDs, and one FMS may correct to multiple projects with different schedules).

This data layer provides locations and details about select permit applications that have received a lot of public attention.Service is updated as needed and was last updated 1/3/22.For more information or to download layer see https://www.dec.ny.gov/permits/6061.html1. The NYS DEC asks to be credited in derived products. 2. Secondary Distribution of the data is not allowed. 3. Any documentation provided is an integral part of the data set. Failure to use the documentation in conjunction with the digital data constitutes misuse of the data. 4. Although every effort has been made to ensure the accuracy of information, errors may be reflected in the data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, original map scale, collection methodology, currency of data, and other conditions.

Lists all the firms that bid on projects from 2004 to present

This dataset contains capital commitment plan data by managing agency, project identification number and project schedules. The dataset was updated three times a year during the Preliminary, Executive and Adopted Capital Commitment Plans. Starting in January 2024, OMB will no longer update this dataset. It is being replaced by the Capital Projects Dashboard administered by the Mayor's Office of Operations.

Last updated: September 9, 2020Update frequency: ContinualThe Capital Improvements Program (CIP) identifies major funding priorities on a five-year fiscal cycle. Each year, those priorities are updated; projects can be completed, cancelled, or continue to be in a future state. The CIP is composed of 10 major areas: airport, combined, drainage, facilities, fire, parks, streets, traffic, wastewater, water. This layer aggregates all CIP fiscal cycles into a single sourceThe layer is based on polygons, these polygons were digitized from as-builts, parcel boundaries, schematics, and the descriptions of subject matter experts. The attribute information are created from the input of several departments, and their subject matter experts. Updates to project types under the jurisdiction of the Engineering CIP Group are made on automated nightly basis: combined, drainage, streets, traffic, wastewater, and water. Non-Engineering projects are updated on a bi-annual basis.This layer is currently used by the McKinney Active Infrastructure Projects Hub. It can be found at the following location:www.mckinneytexas.org/projectstatus