To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

A high-quality, clean dataset simulating global cosmetics and skincare product sales between January and August 2022. This dataset mirrors real-world transactional data, making it perfect for data analysis, Excel training, visualization projects, and machine learning prototypes.

📁 Dataset Overview

🧾 Sample Products

• Hydrating Face Serum
• Vitamin C Cream
• Aloe Vera Gel
• Charcoal Face Wash
• SPF 50 Sunscreen
• Niacinamide Toner
• Anti-Aging Serum
• Face Sheet Masks
• Hair Repair Oil
• Lip Balm Pack
• Body Butter Cream
• Salicylic Acid Cleanser

🌏 Countries Covered

• India
• USA
• UK
• Canada
• Australia
• New Zealand

📊 Quick Stats

• Total Rows: 374
• Date Range: Jan 1, 2022 – Aug 31, 2022
• Revenue Range: Varies from ~$100 to ~$20,000 per order
• Box Quantity Range: 10 – 500 boxes

🎯 Ideal For

• Excel Practice (VLOOKUP, IF, AVERAGEIFS, INDEX-MATCH, etc.)
• Pivot tables & data cleaning tasks
• Power BI / Tableau dashboards
• Sales trend forecasting
• Exploratory Data Analysis (EDA)
• Retail analytics & product demand modeling

📌 Suggested Projects & Questions

• Which salesperson generated the highest revenue overall?
• What’s the average amount per order in each country?
• Which product was most frequently sold?
• What month had the highest total boxes shipped?
• Create a dashboard comparing revenue across countries.

✅ Clean Data Guarantee

• ✅ No missing/null values
• ✅ No duplicates
• ✅ Realistic values
• ✅ Globally relatable product categories
• ✅ Ready for ML, BI, and teaching use cases

Chemistry is increasingly data centric and the undergraduate curriculum needs to adjust to keep up. To address this, we created the Air Quality Activity, a new first-year undergraduate activity where students use Microsoft Excel to analyze a unique subset of atmospheric ozone (O3) and nitrogen dioxide (NO2) measurements from the Canadian National Air Pollution Surveillance (NAPS) program. Through this activity students develop their numeracy, graphicacy, and proficiency with Excel. Moreover, students are equipped with a foundational approach to data analysis they can leverage throughout their studies. To make this activity possible, we developed an open-source webbook detailing pertinent Excel operations for first-year students, and an interactive web-app for the generation, distribution, and exploration of NAPS data. Students were excited by the analysis of real-world chemical phenomena in comparison to traditional first-year lab exercises and appreciated their acquired Excel skills. The Air Quality Activity is readily adaptable for both virtual and in-person implementation, entirely open-source, and readily deployable at any institution wishing to teach data analysis in a chemistry context.

Go Linter Evaluation Dataset

This is a publicly available dataset for 'An empirical evaluation of Golang static code analysis tools for real-world issues.' Please refer to the data according to the names of the spreadsheets.

Authors: Jianwei Wu, James Clause

Collected Survey Data:
- This Excel file contains the collected survey data for the empirical study in details.

R Scripts and Raw Data:
- These scripts are used for data analysis and processing.
- This is the initial data collected from surveys or other sources before any processing or analysis.

Surveys for External Participants:
- This Excel file contains survey data collected for the evaluation of Go linters.
- This folder contains the surveys sent to external participants for collecting their feedback or data.

Recruitment Letter.pdf:
- This PDF contains an example of the recruitment letter sent to potential survey participants, inviting them to take part in the study.

Outputs from Existing Go Linters and Summarized Categories.xlsx:
- This Excel file contains outputs from various Go linters and categorized summaries of these outputs. It helps in comparing the performance and features of different linters.

Selection of Go Linters.xlsx:
- This Excel file lists the Go linters selected for evaluation, along with criteria or reasons for their selection.

UD IRB Exempt Letter.pdf:
- This PDF contains the Institutional Review Board (IRB) exemption letter from the University of Delaware (UD), indicating that the study involving human participants was exempt from full review.

Survey Template.pdf:
- This PDF contains an example of the survey sent to the participants.

govet issues.pdf:
- This PDF contains a list of reported issues about govet. Collected from various pull requests.

Approved linters:
- staticcheck gofmt govet revive gosec deadcode errcheck.

Table 2.jpg:

- A detailed figure to show the technical data in Table 2 of the paper.

🧥 Snitch Fashion Sales (Uncleaned) Dataset 📌 Context This is a synthetic dataset representing sales transactions from Snitch, a fictional Indian clothing brand. The dataset simulates real-world retail sales data with uncleaned records, designed for learners and professionals to practice data cleaning, exploratory data analysis (EDA), and dashboard building using tools like Python, Power BI, or Excel.

📊 What You’ll Find The dataset includes over 2,500 records of fashion product sales across various Indian cities. It contains common data issues such as:

Missing values

Incorrect date formats

Duplicates

Typos in categories and city names

Unrealistic discounts and profit values

🧾 Columns Explained Column --Description Order_ID ------Unique ID for each sale (some duplicates) Customer_Name ------Name of the customer (inconsistent formatting) Product_Category ---Clothing category (e.g., T-Shirts, Jeans — includes typos) Product_Name -----Specific product sold Units_Sold --Quantity sold (some negative or null) Unit_Price --Price per unit (some missing or zero) Discount_% ----Discount applied (some >100% or missing) Sales_Amount ------Total revenue after discount (some miscalculations) Order_Date ---------Order date (multiple formats or missing) City -------Indian city (includes typos like "Hyd", "bengaluru") Segment----- Market segment (B2C, B2B, or missing) Profit ---------Profit made on the sale (some unrealistic/negative)

💡 How to Use This Dataset Clean and standardize messy data

Convert dates and correct formats

Perform EDA to find:

Top-selling categories

Impact of discounts on sales and profits

Monthly/quarterly trends

Segment-based performance

Create dashboards in Power BI or Excel Pivot Table

Document findings in a PDF/Markdown report

🎯 Ideal For Aspiring data analysts and data scientists

Excel / Power BI dashboard learners

Portfolio project creators

Kaggle competitions or practice

📌 License This is a synthetic dataset created for educational use only. No real customer or business data is included.

Real World Evidence Solutions Market size was valued at USD 2.26 billion in 2019 and is poised to grow from USD 2.45 billion in 2023 to USD 4.97 billion by 2031, growing at a CAGR of 8.2% in the forecast period (2024-2031).

This repository contains files related to Sant et al. Nature Genetics 2025 which were too large to include as part of the publication. Below, we describe each file and its contents.

1. Simulated datasets and associated parameters

Simulated_Data_Parameters.xlsx - This file contains the parameters used to create the simulated datasets mentioned below. Briefly, using the R package Splatter, we generated 100 simulated datasets representing 1, 5, 10, or 20 distinct ground-truth cell populations, ranging from 500 to 25,000 cells. To assess how various aspects of snRNA-seq datasets affect CHOIR’s performance, we used five of the simulated datasets produced with Splatter as the baseline to generate 105 additional simulated datasets in which we incrementally reduced the prevalence of rare cell populations, the degree of differential expression, or the library size. Additionally, we generated 10 simulated datasets with multiple batches, with either balanced or imbalanced batch sizes, and 5 simulated datasets using Splatter’s simulation of cell differentiation trajectories. To ensure that our results were not dependent on the software used for data simulation, we also generated 10 datasets with the simulation method scDesign3 from real subsampled PBMC cell populations.

Simulated_Datasets.tar.gz - This tar.gz archive contains the 230 simulated datasets which were used for benchmarking of clustering tools for single-cell analysis in Sant et al. Nature Genetics 2025. The individual datasets have been stored as Seurat objects and combined into a single tar.gz file.

2. Source data and results for real-world datasets

SourceData1_RealData.xlsx - This excel file contains the parameters used, the metrics obtained, the cell labels obtained, and any relevant single-cell-resolution results from the analyses of the following real-world datasets: snMultiome human retina (Wang et al. Cell Genomics 2022), atlas-scale snRNA-seq of human brain (Siletti et al. Science 2023), scRNA-seq of mixed cell lines (Kinker et al. Nature Genetics 2020), CITE-seq of human PBMCs (Hao et al. Cell 2021), and sci-Space of mouse embryo (Srivatsan et al. Science 2021).

3. Source data and results for simulated datasets

SourceData2_SimulatedData.xlsx - This excel file contains the parameters used, the metrics obtained, and the cell labels obtained for all simulated datasets analyzed in Sant et al. Nature Genetics 2025.

This dataset provides information about radiological procedures carried out in public hospitals in the Spanish region of Aragón during a representative month of scheduled (non-emergency) activity. It has been compiled from two main sources:

• The SERAM catalog (Spanish Society of Medical Radiology) from 2004 and 2009, which offers standardized estimated durations for a wide variety of radiological procedures.

• Anonymized hospital scheduling records extracted from the Hospital Information System (HIS), provided by the Aragonese Institute of Health Sciences (IACS).

The resulting dataset is a merged and cleaned version of the SERAM standards and the IACS hospital logs. The original SERAM data was extracted from PDFs and converted to structured format (Excel), followed by a meticulous cleaning process.

However, it is important to clarify several aspects regarding data quality and interpretation:

The so-called “actual duration” (duracion_real) values do not reflect the real execution time of radiological procedures. Instead, they refer to agenda slot durations allocated in the hospital's scheduling system. In other words, they represent planned time blocks, not the actual time a patient enters or exits the radiology room. To obtain real-time clinical execution data, it would be necessary to access the Radiology Information System (RIS), which logs precise timestamps of image acquisition, patient preparation, and post-procedure cleaning. Such access was not available. Therefore, these timing values may be overestimated or generalized, and do not account for operational variability inherent to clinical settings.

Despite these limitations, the dataset remains valuable for several use cases:

• As a foundation for synthetic data generation.
• For workflow modeling, bottleneck analysis, or resource planning studies, especially when complemented with assumptions or artificial enhancements.
• As a baseline structure for healthcare simulation frameworks.

Its utility lies primarily in simulation, modeling, and educational applications for data analysis, rather than precise clinical auditing. So even though this dataset does not fit perfectly fine-grained clinical efficiency analyses, it is a representation and a structured approximation from radiological studies in the real world.

The dataset contains 103.204 rows from different radiology deparments. It is fully anonymized and contains no personal or sensitive information. All fields, including column names are in spanish languaje but a translation and explanation of all fields is given.

Field metadata descriptions is given below in the uploaded file "metadata_description.txt"

This dataset documents a hypothetical, structured timeline for the creation and publication of The Fracture Index™ – Testament IV: The Dying Ether. It spans the period from 28 July 2025 to 8 August 2025, representing the conceptualisation, drafting, editing, formatting, and final publication phases. The narrative process is presented as a project execution dataset, with each day’s entry including the task, category, status, and a reflective note from the fictional narrator, Sonia Verma. Purpose: The dataset is intended for academic research and literary process analysis only. It offers a unique blend of fictionalised narration and structured project tracking, useful for scholars in digital humanities, creative writing studies, narrative process analysis, and project management research. Background: While The Fracture Index™ was originally conceived as a three-part literary series, the creative process evolved to include a fourth instalment. This dataset captures a hypothetical day-by-day execution plan for the final phase, developed under Radhika BuildScience Consultancy as part of a larger narrative-psychological study. Dataset Contents: Excel file (.xlsx) with the following columns: Date (28 July – 8 August 2025) Task Description Category (Concept Development, Writing, Editing, Formatting, Dataset Preparation, Publication) Status (Planned, In Progress, Completed) Remarks ReadMe.txt file explaining dataset scope, methodology, and ethical guidelines. Potential Research Applications: Analysis of hypothetical literary production timelines Study of embedded narration within process documentation Training resource for creative project management simulations Comparative study of fictional and real-world publication workflows Ethical Note (Updated) This dataset is a work of fiction. While “Sonia Verma” is credited as narrator, she is a fictional literary construct created by the author for narrative framing. Any resemblance to real persons is purely coincidental. All entries, dates, and remarks are hypothetical and for research/educational purposes only.

Excel spreadsheet containing, in separate sheets, the underlying numerical values for generating Fig 2A, 2B, 2C, 3A, 3B, 3C, 3D, 4A, 4B, 4C, 4D, 5A, and 5B.