Pen-and-paper homework and project-based learning are both commonly used instructional methods in introductory statistics courses. However, there have been few studies comparing these two methods exclusively. In this case study, each was used in two different sections of the same introductory statistics course at a regional state university. Students’ statistical literacy was measured by exam scores across the course, including the final. The comparison of the two instructional methods includes using descriptive statistics and two-sample t-tests, as well authors’ reflections on the instructional methods. Results indicated that there is no statistically discernible difference between the two instructional methods in the introductory statistics course.

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.

The statistic shows the global market size of the IT project and portfolio management (IT PPM) market from 2014 to 2019 and a forecast for 2024. In 2019, The total market size of the global IT project and portfolio management (IT PPM) was at **** billion U.S. dollars.

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

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.

In 2024, the total number of open source projects taken up was about 3.9 million. Of these, the majority was through JavaScript with about 4.8 million projects, far more than those in any other language.

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.

This dataset is for the Status of Partner led Projects with ratings of Red, Yellow and Green. It provides the monthly count, target and Year to date values for every month.

Annual Statistics of Approved Projects under General Support Programme

Financial overview and grant giving statistics of Kids In Crisis Project Inc

Study 5: Down from the Ivory Tower: Exploring Implementation of the ESTRO Core Curriculum at the National Level. An anonymous, 37-item, survey was designed and distributed to the Presidents of the National Societies who have endorsed the ESTRO Core Curriculum (n=29). The survey addressed perceptions about implementation factors related to context, process and curriculum change. The data was summarized using descriptive statistics.

All Project Request submitted to the Communications Department

Financial overview and grant giving statistics of Kids Project Inc

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.

The achievements of the 110 Year Low Carbon Sustainable Home Project Promotion (Online Document Signing Quantity - Grassroots Agencies).

In a survey conducted in the United States, shoppers were asked if they had started a DIY project in or around the house in the past month, from March 2020 to May 2021. Share of respondents who responded positively were at ** percent at the beginning of the pandemic, and peaked at ** percent on the **** of May 2020. As of June 2021, the DIY activity was as high as ** percent. However, around just over a third of consumers during the same period claimed not having started a DIY project.

Overview This is the WFIP2 event log covering all sites and instruments for the entire project duration. Final Event Log and Common Case Study Set Additional details may be added here.

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

These data were automated to provide an accurate high-resolution historical shoreline of Long Island, New York suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

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.