Embark on a transformative journey with our Data Cleaning Project, where we meticulously refine and polish raw data into valuable insights. Our project focuses on streamlining data sets, removing inconsistencies, and ensuring accuracy to unlock its full potential.

Through advanced techniques and rigorous processes, we standardize formats, address missing values, and eliminate duplicates, creating a clean and reliable foundation for analysis. By enhancing data quality, we empower organizations to make informed decisions, drive innovation, and achieve strategic objectives with confidence.

Join us as we embark on this essential phase of data preparation, paving the way for more accurate and actionable insights that fuel success."

Sample data for exercises in Further Adventures in Data Cleaning.

Dataset

This dataset was created by George M122

Contents

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

The MRO (Maintenance, Repair, and Operations) Data Cleansing and Enrichment Service market is experiencing robust growth, driven by the increasing need for accurate and reliable data across various industries. The digital transformation sweeping sectors like manufacturing, oil and gas, and pharmaceuticals is fueling demand for streamlined data management. Businesses are realizing the significant cost savings and operational efficiencies achievable through improved data quality. Specifically, inaccurate or incomplete MRO data can lead to costly downtime, inefficient inventory management, and missed maintenance opportunities. Data cleansing and enrichment services address these challenges by identifying and correcting errors, filling in gaps, and standardizing data formats, ultimately improving decision-making and optimizing resource allocation. The market is segmented by application (chemical, oil & gas, pharmaceutical, mining, transportation, others) and type of service (data cleansing, data enrichment). While precise market size figures are unavailable, considering a moderate CAGR of 15% and a 2025 market value in the hundreds of millions, a reasonable projection is a market size exceeding $500 million in 2025, growing to potentially over $1 billion by 2033. This projection reflects the increasing adoption of digital technologies and the growing awareness of the value proposition of high-quality MRO data. The competitive landscape is fragmented, with numerous companies offering specialized services. Key players include both large established firms and smaller niche providers. The market's geographical distribution is diverse, with North America and Europe currently holding significant market shares, reflecting higher levels of digitalization and data management maturity in these regions. However, Asia-Pacific is emerging as a high-growth region due to rapid industrialization and increasing technological adoption. The long-term growth trajectory of the MRO Data Cleansing and Enrichment Service market will be influenced by factors such as advancements in data analytics, the expanding adoption of cloud-based solutions, and the continued focus on optimizing operational efficiency across industries. Challenges remain, however, including data security concerns and the need for skilled professionals to manage complex data cleansing and enrichment projects.

The Data Quality Tools market is experiencing robust growth, driven by the increasing volume and complexity of data generated across various industries. The expanding adoption of cloud-based solutions, coupled with stringent data regulations like GDPR and CCPA, are key catalysts. Businesses are increasingly recognizing the critical need for accurate, consistent, and reliable data to support strategic decision-making, improve operational efficiency, and enhance customer experiences. This has led to significant investment in data quality tools capable of addressing data cleansing, profiling, and monitoring needs. The market is fragmented, with several established players such as Informatica, IBM, and SAS competing alongside emerging agile companies. The competitive landscape is characterized by continuous innovation, with vendors focusing on enhancing capabilities like AI-powered data quality assessment, automated data remediation, and improved integration with existing data ecosystems. We project a healthy Compound Annual Growth Rate (CAGR) for the market, driven by the ongoing digital transformation across industries and the growing demand for advanced analytics powered by high-quality data. This growth is expected to continue throughout the forecast period. The market segmentation reveals a diverse range of applications, including data integration, master data management, and data governance. Different industry verticals, including finance, healthcare, and retail, exhibit varying levels of adoption and investment based on their unique data management challenges and regulatory requirements. Geographic variations in market penetration reflect differences in digital maturity, regulatory landscapes, and economic conditions. While North America and Europe currently dominate the market, significant growth opportunities exist in emerging markets as digital infrastructure and data literacy improve. Challenges for market participants include the need to deliver comprehensive, user-friendly solutions that address the specific needs of various industries and data volumes, coupled with the pressure to maintain competitive pricing and innovation in a rapidly evolving technological landscape.

Dataset

This dataset was created by Luis Lira

Contents

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million, at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

How To Cite?

Alinaghi, N., Giannopoulos, I., Kattenbeck, M., & Raubal, M. (2025). Decoding wayfinding: analyzing wayfinding processes in the outdoor environment. International Journal of Geographical Information Science, 1–31. https://doi.org/10.1080/13658816.2025.2473599

Link to the paper: https://www.tandfonline.com/doi/full/10.1080/13658816.2025.2473599

Folder Structure

The folder named “submission” contains the following:

1. “pythonProject”: This folder contains all the Python files and subfolders needed for analysis.
2. ijgis.yml: This file lists all the Python libraries and dependencies required to run the code.

Setting Up the Environment

1. Use the ijgis.yml file to create a Python project and environment. Ensure you activate the environment before running the code.
2. The pythonProject folder contains several .py files and subfolders, each with specific functionality as described below.

Subfolders

1. Data_4_IJGIS

• This folder contains the data used for the results reported in the paper.
• Note: The data analysis that we explain in this paper already begins with the synchronization and cleaning of the recorded raw data. The published data is already synchronized and cleaned. Both the cleaned files and the merged files with features extracted for them are given in this directory. If you want to perform the segmentation and feature extraction yourself, you should run the respective Python files yourself. If not, you can use the “merged_…csv” files as input for the training.

2. results_[DateTime] (e.g., results_20240906_15_00_13)

• This folder will be generated when you run the code and will store the output of each step.
• The current folder contains results created during code debugging for the submission.
• When you run the code, a new folder with fresh results will be generated.

Python Files

1. helper_functions.py

• Contains reusable functions used throughout the analysis.
• Each function includes a description of its purpose and the input parameters required.

2. create_sanity_plots.py

• Generates scatter plots like those in Figure 3 of the paper.
• Although the code has been run for all 309 trials, it can be used to check the sample data provided.
• Output: A .png file for each column of the raw gaze and IMU recordings, color-coded with logged events.
• Usage: Run this file to create visualizations similar to Figure 3.

3. overlapping_sliding_window_loop.py

• Implements overlapping sliding window segmentation and generates plots like those in Figure 4.
• Output:
  • Two new subfolders, “Gaze” and “IMU”, will be added to the Data_4_IJGIS folder.
  • Segmented files (default: 2–10 seconds with a 1-second step size) will be saved as .csv files.
  • A visualization of the segments, similar to Figure 4, will be automatically generated.

4. gaze_features.py & imu_features.py (Note: there has been an update to the IDT function implementation in the gaze_features.py on 19.03.2025.)

• These files compute features as explained in Tables 1 and 2 of the paper, respectively.
• They process the segmented recordings generated by the overlapping_sliding_window_loop.py.
• Usage: Just to know how the features are calculated, you can run this code after the segmentation with the sliding window and run these files to calculate the features from the segmented data.

5. training_prediction.py

• This file contains the main machine learning analysis of the paper. This file contains all the code for the training of the model, its evaluation, and its use for the inference of the “monitoring part”. It covers the following steps:

a. Data Preparation (corresponding to Section 5.1.1 of the paper)

• Prepares the data according to the research question (RQ) described in the paper. Since this data was collected with several RQs in mind, we remove parts of the data that are not related to the RQ of this paper.
• A function named plot_labels_comparison(df, save_path, x_label_freq=10, figsize=(15, 5)) in line 116 visualizes the data preparation results. As this visualization is not used in the paper, the line is commented out, but if you want to see visually what has been changed compared to the original data, you can comment out this line.

b. Training/Validation/Test Split

• Splits the data for machine learning experiments (an explanation can be found in Section 5.1.1. Preparation of data for training and inference of the paper).
• Make sure that you follow the instructions in the comments to the code exactly.
• Output: The split data is saved as .csv files in the results folder.

c. Machine and Deep Learning Experiments

This part contains three main code blocks:

iii. One for the XGboost code with correct hyperparameter tuning:
Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically test the confidence threshold of

• MLP Network (Commented Out): This code was used for classification with the MLP network, and the results shown in Table 3 are from this code. If you wish to use this model, please comment out the following blocks accordingly.
• XGBoost without Hyperparameter Tuning: If you want to run the code but do not want to spend time on the full training with hyperparameter tuning (as was done for the paper), just uncomment this part. This will give you a simple, untuned model with which you can achieve at least some results.
• XGBoost with Hyperparameter Tuning: If you want to train the model the way we trained it for the analysis reported in the paper, use this block (the plots in Figure 7 are from this block). We ran this block with different feature sets and different segmentation files and created a simple bar chart from the saved results, shown in Figure 6.

Note: Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically calculated the confidence threshold of the model (explained in the paper in Section 5.2. Part II: Decoding surveillance by sequence analysis) is given in this block in lines 361 to 380.

d. Inference (Monitoring Part)

• Final inference is performed using the monitoring data. This step produces a .csv file containing inferred labels.
• Figure 8 in the paper is generated using this part of the code.

6. sequence_analysis.py

• Performs analysis on the inferred data, producing Figures 9 and 10 from the paper.
• This file reads the inferred data from the previous step and performs sequence analysis as described in Sections 5.2.1 and 5.2.2.

Licenses

The data is licensed under CC-BY, the code is licensed under MIT.

Sam2.1_l Yolo11x United Cleaning Data Version 2

## Overview

Sam2.1_l  Yolo11x  United  Cleaning Data  Version 2 is a dataset for instance segmentation tasks - it contains Car Person Bus Cyclist CGmu 3CqK QRcR annotations for 856 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).

This dataset presents a dual-version representation of employment-related data from India, crafted to highlight the importance of data cleaning and transformation in any real-world data science or analytics project.

🔹 Dataset Composition:

It includes two parallel datasets: 1. Messy Dataset (Raw) – Represents a typical unprocessed dataset often encountered in data collection from surveys, databases, or manual entries. 2. Cleaned Dataset – This version demonstrates how proper data preprocessing can significantly enhance the quality and usability of data for analytical and visualization purposes.

Each record captures multiple attributes related to individuals in the Indian job market, including: - Age Group
- Employment Status (Employed/Unemployed)
- Monthly Salary (INR)
- Education Level
- Industry Sector
- Years of Experience
- Location
- Perceived AI Risk
- Date of Data Recording

Transformations & Cleaning Applied:

The raw dataset underwent comprehensive transformations to convert it into its clean, analysis-ready form: - Missing Values: Identified and handled using either row elimination (where critical data was missing) or imputation techniques. - Duplicate Records: Identified using row comparison and removed to prevent analytical skew. - Inconsistent Formatting: Unified inconsistent naming in columns (like 'monthly_salary_(inr)' → 'Monthly Salary (INR)'), capitalization, and string spacing. - Incorrect Data Types: Converted columns like salary from string/object to float for numerical analysis. - Outliers: Detected and handled based on domain logic and distribution analysis. - Categorization: Converted numeric ages into grouped age categories for comparative analysis. - Standardization: Uniform labels for employment status, industry names, education, and AI risk levels were applied for visualization clarity.

Purpose & Utility:

This dataset is ideal for learners and professionals who want to understand: - The impact of messy data on visualization and insights - How transformation steps can dramatically improve data interpretation - Practical examples of preprocessing techniques before feeding into ML models or BI tools

It's also useful for: - Training ML models with clean inputs
- Data storytelling with visual clarity
- Demonstrating reproducibility in data cleaning pipelines

By examining both the messy and clean datasets, users gain a deeper appreciation for why “garbage in, garbage out” rings true in the world of data science.

This is the raw medical dataset used in my data cleaning project. It contains original, unprocessed data with missing values, inconsistent formatting, and possible duplicates. This dataset is ideal for practicing data cleaning, preprocessing, and exploratory data analysis (EDA).

Note: This dataset is anonymized and intended for educational purposes only.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

Code and data for "Plastic bag bans and fees reduce harmful bag litter on shorelines " by Anna Papp and Kimberly Oremus.Please see included README file for details: This folder includes code and data to fully replicate Figures 1-5. In addition, the folder also includes instructions to rerun data cleaning steps. Last modified: March 6, 2025For any questions, please reach out to ap3907@columbia.edu._Code (replication/code):To replicate main figures, run each file for each main figure: - 1_figure1.R- 1_figure2.R- 1_figure3.R - 1_figure4.R- 1_figure5.R Update the home directory to match where the directory is saved ("replication" folder) in this file before running it. The code will require you to install packages (see note on versions below).To replicate entire data cleaning pipeline:- First download all required data (explained in Data section below). - Run code in code/0_setup folder (refer to separate README file)._ R-Version and Package VersionsThe project was developed and executed using:- R version: 4.0.0 (2024-04-24)- Platform: macOS 13.5 Code was developed and main figures were created using the following versions: - data.table: 1.14.2- dplyr: 1.1.4- readr: 2.1.2- tidyr: 1.2.0- broom: 0.7.12- stringr: 1.5.1- lubridate: 1.7.9- raster: 3.5.15- sf: 1.0.7- readxl: 1.4.0- cobalt: 4.4.1.9002- spdep: 1.2.3- ggplot2: 3.4.4- PNWColors: 0.1.0- grid: 4.0.0- gridExtra: 2.3- ggpubr: 0.4.0- knitr: 1.48- zoo: 1.8.12 - fixest: 0.11.2- lfe: 2.8.7.1 - did: 2.1.2- didimputation: 0.3.0 - DIDmultiplegt: 0.1.0- DIDmultiplegtDYN: 1.0.15- scales: 1.2.1 - usmap: 0.6.1 - tigris: 2.0.1 - dotwhisker: 0.7.4_Data Processed data files are provided to replicate main figures. To replicate from raw data, follow the instructions below.Policies (needs to be recreated or email for version): Compiled from bagtheban.com/in-your-state/, rila.org/retail-compliance-center/consumer-bag-legislation, baglaws.com, nicholasinstitute.duke.edu/plastics-policy-inventory, and wikipedia.org/wiki/Plastic_bag_bans_in_the_United_States; and massgreen.org/plastic-bag-legislation.html and cawrecycles.org/list-of-local-bag-bans to confirm legislation in Massachusetts and California.TIDES (needs to be downloaded for full replication): Download cleanup data for the United States from Ocean Conservancy (coastalcleanupdata.org/reports). Download files for 2000-2009, 2010-2014, and then each separate year from 2015 until 2023. Save files in the data/tides directory, as year.csv (and 2000-2009.csv, 2010-2014.csv) Also download entanglement data for each year (2016-2023) separately in a file called data/tides/entanglement (each file should be called 'entangled-animals-united-states_YEAR.csv').Shapefiles (needs to be downloaded for full replication): Download shapefiles for processing cleanups and policies. Download county shapefiles from the US Census Bureau; save files in the data/shapefiles directory, county shapefile should be in folder called county (files called cb_2018_us_county_500k.shp). Download TIGER Zip Code tabulation areas from the US Census Bureau (through data.gov); save files in the data/shapefiles directory, zip codes shapefile folder and files should be called tl_2019_us_zcta510.Other: Helper files with US county and state fips codes, lists of US counties and zip codes in data/other directory, provided in the directory except as follows. Download zip code list and 2020 IRS population data from United States zip codes and save as uszipcodes.csv in data/other directory. Download demographic characteristics of zip codes from Social Explorer and save as raw_zip_characteristics.csv in data/other directory.Refer to the .txt files in each data folder to ensure all necessary files are downloaded.

Web mapping application containing Project NICE / KEEP Neighborhood Cleanup Areas in Sioux Falls, South Dakota.

Abstract

The Palestinian society's access to information and communication technology tools is one of the main inputs to achieve social development and economic change to the status of Palestinian society; on the basis of its impact on the revolution of information and communications technology that has become a feature of this era. Therefore, and within the scope of the efforts exerted by the Palestinian Central Bureau of Statistics in providing official Palestinian statistics on various areas of life for the Palestinian community, PCBS implemented the household survey for information and communications technology for the year 2019. The main objective of this report is to present the trends of accessing and using information and communication technology by households and individuals in Palestine, and enriching the information and communications technology database with indicators that meet national needs and are in line with international recommendations.

Geographic coverage

Palestine, West Bank, Gaza strip

Analysis unit

Household, Individual

Universe

All Palestinian households and individuals (10 years and above) whose usual place of residence in 2019 was in the state of Palestine.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of master sample which were enumerated in the 2017 census. Each enumeration area consists of buildings and housing units with an average of about 150 households. These enumeration areas are used as primary sampling units (PSUs) in the first stage of the sampling selection.

Sample size The estimated sample size is 8,040 households.

Sample Design The sample is three stages stratified cluster (pps) sample. The design comprised three stages: Stage (1): Selection a stratified sample of 536 enumeration areas with (pps) method. Stage (2): Selection a stratified random sample of 15 households from each enumeration area selected in the first stage. Stage (3): Selection one person of the (10 years and above) age group in a random method by using KISH TABLES.

Sample Strata The population was divided by: 1- Governorate (16 governorates, where Jerusalem was considered as two statistical areas) 2- Type of Locality (urban, rural, refugee camps).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaire The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on Individuals (10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Programming Consistency Check The data collection program was designed in accordance with the questionnaire's design and its skips. The program was examined more than once before the conducting of the training course by the project management where the notes and modifications were reflected on the program by the Data Processing Department after ensuring that it was free of errors before going to the field.

Using PC-tablet devices reduced data processing stages, and fieldworkers collected data and sent it directly to server, and project management withdraw the data at any time.

In order to work in parallel with Jerusalem (J1), a data entry program was developed using the same technology and using the same database used for PC-tablet devices.

Data Cleaning After the completion of data entry and audit phase, data is cleaned by conducting internal tests for the outlier answers and comprehensive audit rules through using SPSS program to extract and modify errors and discrepancies to prepare clean and accurate data ready for tabulation and publishing.

Tabulation After finalizing checking and cleaning data from any errors. Tables extracted according to prepared list of tables.

Response rate

The response rate in the West Bank reached 77.6% while in the Gaza Strip it reached 92.7%.

Sampling error estimates

Sampling Errors Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, There is no problem to disseminate results at the national level and at the level of the West Bank and Gaza Strip.

Non-Sampling Errors Non-Sampling errors are possible at all stages of the project, during data collection or processing. These are referred to non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, as well as practical and theoretical training during the training course.

The implementation of the survey encountered non-response where the case (household was not present at home) during the fieldwork visit become the high percentage of the non response cases. The total non-response rate reached 17.5%. The refusal percentage reached 2.9% which is relatively low percentage compared to the household surveys conducted by PCBS, and the reason is the questionnaire survey is clear.

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EIS: URS Botany groundwater cleanup project

An unclean copy of my GoodReads dataset (as for 2024/02/11) in csv format with 406 entries.

Data types included are integers, floats, strings, data/time and booleans (both in TRUE/FALSE and 0/1 formats).

This is a good dataset to practice cleaning and analysing as it contains missing values, inconsistent formats and outliers.

Disclaimer: Since GoodReads notifies you when there are duplicate entries, which meant I had no duplicate entries, I asked an AI to add 20 random duplicate entries to the data set for the purpose of this project.

For this project, I cleaned data on a data sheet that had some errors within the data. After cleaning this data, I created 2 pivot tables to summarize the number of products for the top suppliers.