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.

DOM Formula Assignment Dataset

Training and Testing Data for Machine Learning-Based Molecular Formula Assignment in Fulvic Acid DOM Mass Spectra

Paper: Under review

  Abstract

Dissolved organic matter (DOM) is a critical component of aquatic ecosystems, with the fulvic acid fraction (FA-DOM) exhibiting high mobility and ready bioavailability to microbial communities. While understanding the molecular composition is a vital area of study, the heterogeneity of the… See the full description on the dataset page: https://huggingface.co/datasets/SaeedLab/dom-formula-assignment-data.

Dataset

This dataset was created by Dhinesh Gupthaa K

Contents

Dataset

This dataset was created by Timothy Tong

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Dataset

This dataset was created by Edward Tran

Contents

It contains the following files:

Financial overview and grant giving statistics of Athens United Immigrant Support Project

Dataset

This dataset was created by James N.E Bockarie

Contents

Financial overview and grant giving statistics of Military Community Support Project Inc.

This data set contains the replication data for the article "Knowing and doing: The development of information literacy measures to assess knowledge and practice." This article was published in the Journal of Information Literacy, in June 2021. The data was collected as part of the contact author's PhD research on information literacy (IL). One goal of this study is to assess students' levels of IL using three measures: 1) a 21-item IL test for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know. 2) a source-evaluation measure to assess students' abilities to critically evaluate information sources in practice. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. 3) a source-use measure to assess students' abilities to use sources correctly when writing. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. The data set contains survey results from 626 Norwegian and international students at three levels of higher education: bachelor, master's and PhD. The data was collected in Qualtrics from fall 2019 to spring 2020. In addition to the data set and this README file, two other files are available here: 1) test questions in the survey, including answer alternatives (IL_knowledge_tests.txt) 2) details of the assignment-based measures for assessing source evaluation and source use (Assignment_based_measures_assessing_IL_skills.txt) Publication abstract: This study touches upon three major themes in the field of information literacy (IL): the assessment of IL, the association between IL knowledge and skills, and the dimensionality of the IL construct. Three quantitative measures were developed and tested with several samples of university students to assess knowledge and skills for core facets of IL. These measures are freely available, applicable across disciplines, and easy to administer. Results indicate they are likely to be reliable and support valid interpretations. By measuring both knowledge and practice, the tools indicated low to moderate correlations between what students know about IL, and what they actually do when evaluating and using sources in authentic, graded assignments. The study is unique in using actual coursework to compare knowing and doing regarding students’ evaluation and use of sources. It provides one of the most thorough documentations of the development and testing of IL assessment measures to date. Results also urge us to ask whether the source-focused components of IL – information seeking, source evaluation and source use – can be considered unidimensional constructs or sets of disparate and more loosely related components, and findings support their heterogeneity.

Comprehensive YouTube channel statistics for The Local Project, featuring 1,370,000 subscribers and 172,862,479 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in AU. Track 502 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Financial overview and grant giving statistics of Helping Paw Project Inc.

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

Replication data for article forthcoming in REStat

This report describes the purpose for developing the National Law Enforcement Accountability Database (NLEAD), a centralized repository of official records documenting instances of law enforcement officer misconduct as well as commendations and awards to help inform hiring, job assignment, and promotion decisions. It also provides statistics on the NLEAD’s records, the federal law enforcement officers included, and its usage. This is the first annual report, and it covers NLEAD records for events occurring in calendar years 2018 to 2023 and usage of the NLEAD from January 1, 2024 to August 31, 2024.Downloaded from BJS website on 2025-02-25.

Dataset

This dataset was created by Twinkle Sarkar

Released under Apache 2.0

Contents

The CreamT project converted the prototype WireWall wave overtopping field measurement system into a ruggedised monitoring system between August 2020 and August 2023. The system was deployed for up to a year in two high-energy coastal environments along the Southwest coast, UK (Dawlish and Penzance). The system was designed to have a 3-month maintenance interval and was programmed to measure overtopping condition ±3hrs either side of predicted high tide. The wave-by-wave overtopping data were telemetered to the British Oceanographic Data Centre (BODC) every 10 minutes. At the time of the project, the coastal structures at these sites comprised a vertical sea wall with small return lip or curve at the top. Both sea walls were fronted by a beach. During the project period the Dawlish beach levels exposed a concreate toe at the base of the wall. In Penzance, the beach covered the sea wall toe and was higher in the southwest monitoring location. The system was designed at the National Oceanography Centre (NOC) and had previously been validated in HR Wallingford’s flume facility and field tested with Sefton Council (https://www.channelcoast.org/northwest/). During CreamT, three different system configurations were deployed: full WireWall systems each with an array of six capacitance sensors; smaller WireWand systems with two capacitance sensors mounted on a single pole to detect overtopping at hazard hotspots; and a WaveWell using a single sensor on the face of the sea wall. Six datasets are available from the CreamT project. These contain delayed mode data from: 1) a WireWall deployed at the crest of the sea wall in Dawlish; 2) a WireWand deployed at the wall just seaward of the railway line in Dawlish; 3) a WireWand deployed at the fence just inland of the railway line in Dawlish; 4) a WaveWell deployed on the face of the sea wall in Dawlish; 5) a WireWall deployed at the crest of the sea wall in Penzance near Queen’s Hotel, and; 6) a WireWall deployed at the crest of the sea wall in Penzance near the Lidal store at Wherrytown. The datasets in Dawlish provide information about the inland distribution of overtopping, and the two datasets in Penzance provide information about the alongshore variability in overtopping hazard. These data can be used alongside the regional monitoring data available from the Southwest Regional Monitoring Programme to investigate the drivers of wave overtopping. All these data can be visualised in a hazard dashboard developed by the BODC and hosted on JASMIN, https://coastalhazards.app.noc.ac.uk/. This project was delivered by the National Oceanography Centre in collaboration with BODC and the University of Plymouth under NERC Grant References NE/V002538/1 and NE/V002589/1. Project partners were Network Rail, Southwest Regional Monitoring Programme, Environment Agency and Channel Coastal Observatory.

This dataset provides a comprehensive collection of project management tasks, designed to enhance efficiency through AI-driven task assignment and skill matching. Each entry includes detailed descriptions of tasks and the required skills for successful completion.

The dataset aims to facilitate intelligent resource allocation by matching tasks to employees based on their skill sets, ensuring that the right people are assigned to the right tasks. It is an invaluable resource for developing applications in project management, predictive analytics, and machine learning.

Key Features:

• Task descriptions categorized by type (e.g., frontend, backend, database etc).
• Skills required for each task.
• Example employee profiles with skill levels for effective matching.
• Structured format for easy integration with machine learning models and project management tools.

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

Financial overview and grant giving statistics of Grandparents As Parents Support Project Inc.