Comprehensive dataset containing 33 verified Assignment Help From No1AssignmentHelp.Com locations in Australia with complete contact information, ratings, reviews, and location data.

This statistical information compiles research support performance by year from 2009 to the present. It provides comprehensive information on various aspects of support, including total support and R&D support, and details key information such as research project-specific support details, project year, and language. This data can be utilized for various purposes, including establishing research support policies, effectively allocating budgets, and analyzing research outcomes. It is particularly suitable as a reference for researchers, policymakers, and institutional officials to understand research support trends and develop more effective support strategies.

The data files contain information about the preferences of bachelor 1 and 2 students obtained via a discrete choice experiment (12 choice tasks per respondent), demographic characteristics of the sample and population, experiences with free-riding, attitude towards teamwork, and a measure of individualism/collectivism. Students were presented a different grade weight before each choice task (i.e., 10%, 30%, or 100%). The data was collected from mid-June to mid-July 2021.

Access to the data is subject to the approval of a data sharing agreement due to the personal information contained in the dataset.

A summary of the publication can be found below: Reducing free-riding is an important challenge for educators who use group projects. In this study, we measure students’ preferences for group project characteristics and investigate if characteristics that better help to reduce free-riding become more important for students when stakes increase. We used a discrete choice experiment based on twelve choice tasks in which students chose between two group projects that differed on five characteristics of which each level had its own effect on free-riding. A different group project grade weight was presented before each choice task to manipulate how much there was at stake for students in the group project. Data of 257 student respondents were used in the analysis. Based on random parameter logit model estimates we find that students prefer (in order of importance) assignment based on schedule availability and motivation or self-selection (instead of random assignment), the use of one or two peer process evaluations (instead of zero), a small team size of three or two students (instead of four), a common grade (instead of a divided grade), and a discussion with the course coordinator without a sanction as a method to handle free-riding (instead of member expulsion). Furthermore, we find that the characteristic team formation approach becomes even more important (especially self-selection) when student stakes increase. Educators can use our findings to design group projects that better help to reduce free-riding by (1) avoiding random assignment as team formation approach, (2) using (one or two) peer process evaluations, and (3) creating small(er) teams.

Financial overview and grant giving statistics of Homework Central Help Center

You can use the government-supported job information API to configure government-supported job project information, recruitment information, participant information, and basic organization information. It provides statistical data on the gender, age, and regional characteristics of participants in government-supported job projects. It receives project ID, search year, etc. as parameters and provides results such as project name, gender, gender code, number of participants, by age, age code, number of participants, by region, region code, and number of participants. * Note: In the case of items entered as strings, be careful because search results may not be displayed properly if they contain spaces or typos. Most parameters can be entered optionally, and searches are possible by entering only the necessary conditions.

Comprehensive dataset containing 29 verified Lab Support, a division of On Assignment locations in United States with complete contact information, ratings, reviews, and location data.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

This joint letter from the Children’s Bureau and the Office of Child Support Enforcement of the U.S. Department of Health and Human Services provides recommendations regarding the practice of title IV-E agencies securing an assignment of the rights to child support for a child receiving title IV-E foster care maintenance payments. Metadata-only record linking to the original dataset. Open original dataset below.

pip-installable code to assist with the 2021 NFL Helmet Assignment challenge. Also available via github here.

NFL Helmet Assignment Helpers

A package of code to assist in the 2021 Kaggle NFL Helmet Assignment Task

Install via github

$ git clone https://github.com/RobMulla/helmet-assignment.git
$ cd helmet-assignment
$ pip install .

Install in Kaggle Notebook via dataset.

1. Add this dataset to your kaggle notebook.
2. Install via pip like this: !pip install ../input/helmet-assignment-helpers/helmet-assignment-main/ > /dev/null 2>&1

Scoring

This code can be used to score your predictions in a similar to the offical competition metric.

from helmet_assingment.score import NFLAssignmentScorer
scorer = NFLAssignmentScorer(labels)
scorer.score(submission_df)

or

scorer = NFLAssignmentScorer(labels_csv='labels.csv')
scorer.score(submission_df)

The check_submission can be used as a final check to ensure your submission meets all the requirements of the submission:

check_submission(submission_df)
>> True # If passed otherwise returns False

Videos

Code here can be used to create videos that display your predictions against ground truth boxes.

The video_with_predictions function allows you to combine the results from the NFLAssignmentScorer and overlay the results in video format.

Features

Theo code contains helper functions which add features to the data.

add_track_features adds additional features to the tracking data which can help when attempting to merge this data onto the video frames.

This is data from projects within the scope of the CESAW research project. The data contains detailed logs of software project development at the task level. All project effort, defects, and size have been recorded for each individual task performed. There are 35 projects within this data set. The project objective was to measure the cost and benefits of applying Static analysis to development.

Financial overview and grant giving statistics of Help Each Other Project Inc

Financial overview and grant giving statistics of Project Help

Financial overview and grant giving statistics of Project Help Long Island Corp

The After-School Homework Assistance Services market has emerged as a vital segment of the educational landscape, catering to the increasing need for supplementary academic support among students. As educational pressures escalate and parental involvement strengthens, these services have gained prominence, providing

During a global survey of students conducted in mid-2024, it was found that a whopping ** percent said they were using artificial intelligence tools in their schoolwork. Almost a ****** of them used it on a daily basis.

Descriptive statistics of the health outcome variables before assignment (T1) and after assignment (T2).

📝 Description: This synthetic dataset is designed to help beginners and intermediate learners practice data cleaning and analysis in a realistic setting. It simulates a student tracking system, covering key areas like:

Attendance tracking 📅

Homework completion 📝

Exam performance 🎯

Parent-teacher communication 📢

✅ Why Use This Dataset? While many datasets are pre-cleaned, real-world data is often messy. This dataset includes intentional errors to help you develop essential data cleaning skills before diving into analysis. It’s perfect for building confidence in handling raw data!

🛠️ Cleaning Challenges You’ll Tackle This dataset is packed with real-world issues, including:

Messy data: Names in lowercase, typos in attendance status.

Inconsistent date formats: Mix of MM/DD/YYYY and YYYY-MM-DD.

Incorrect values: Homework completion rates in mixed formats (e.g., 80% and 90).

Missing data: Guardian signatures, teacher comments, and emergency contacts.

Outliers: Exam scores over 100 and negative homework completion rates.

🚀 Your Task: Clean, structure, and analyze this dataset using Python or SQL to uncover meaningful insights!

📌 5. Handle Outliers

Remove exam scores above 100.

Convert homework completion rates to consistent percentages.

📌 6. Generate Insights & Visualizations

What’s the average attendance rate per grade?

Which subjects have the highest performance?

What are the most common topics in parent-teacher communication?

[Government-Supported Job Information API - Participating Organization Support Fund Information] Provides project information, recruitment information, participant information, and basic organization information provided by government-supported financial support job projects (government-supported job projects). The items provided are company name, main organization name, payment date, current amount, and personal serial number. You can configure government-supported job project information, recruitment information, participant information, and basic organization information using the government-supported job information API. Provides a list of participating organization support fund information that matches the search conditions.

NMR spectroscopy and mass spectrometry are the two major analytical platforms for metabolomics, and both generate substantial data with hundreds to thousands of observed peaks for a single sample. Many of these are unknown, and peak assignment is generally complex and time-consuming. Statistical correlations between data types have proven useful in expediting this process, for example, in prioritizing candidate assignments. However, this approach has not been formally assessed for the comparison of direct-infusion mass spectrometry (DIMS) and NMR data. Here, we present a systematic analysis of a sample set (tissue extracts), and the utility of a simple correlation threshold to aid metabolite identification. The correlations were surprisingly successful in linking structurally related signals, with 15 of 26 NMR-detectable metabolites having their highest correlation to a cognate MS ion. However, we found that the distribution of the correlations was highly dependent on the nature of the MS ion, such as the adduct type. This approach should help to alleviate this important bottleneck where both 1D NMR and DIMS data sets have been collected.