TopicNavi/Wikipedia-example-data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

Example of modeled customer behavioral data showing user sessions, engagement metrics, and conversion data across multiple platforms and devices

We include Stata syntax (dummy_dataset_create.do) that creates a panel dataset for negative binomial time series regression analyses, as described in our paper "Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer". We also include a sample dataset for clarity (dummy_dataset.dta), and a sample of that data in a spreadsheet (Appendix 2).

The variables contained therein are defined as follows:

case: binary variable for case or control status (takes a value of 0 for controls and 1 for cases).

patid: a unique patient identifier.

time_period: A count variable denoting the time period. In this example, 0 denotes 10 months before diagnosis with cancer, and 9 denotes the month of diagnosis with cancer,

ncons: number of consultations per month.

period0 to period9: 10 unique inflection point variables (one for each month before diagnosis). These are used to test which aggregation period includes the inflection point.

burden: binary variable denoting membership of one of two multimorbidity burden groups.

We also include two Stata do-files for analysing the consultation rate, stratified by burden group, using the Maximum likelihood method (1_menbregpaper.do and 2_menbregpaper_bs.do).

Note: In this example, for demonstration purposes we create a dataset for 10 months leading up to diagnosis. In the paper, we analyse 24 months before diagnosis. Here, we study consultation rates over time, but the method could be used to study any countable event, such as number of prescriptions.

VAPOR is the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers. VAPOR provides an interactive 3D visualization environment that can also produce animations and still frame images. VAPOR runs on most UNIX and Windows systems equipped with modern 3D graphics cards. VAPOR is a product of the National Center for Atmospheric Research's Computational and Information Systems Lab. Support for VAPOR is provided by the U.S. National Science Foundation and by the Korea Institute of Science and Technology Information This dataset contains sample files of model outputs from numerical simulations that VAPOR is capable of directly reading. They are not related to each other aside from being sample data for VAPOR.
To unpack the tar.gz files on Linux/OSX, issue the command tar -xzvf [myFile].tar.gz on the file you've downloaded. On Windows, a program like 7-zip can perform that operation. Once unpacked, the files can be directly imported into VAPOR, or converted to VDC. For more information see the "Getting Data Into VAPOR" Related Link below.

These data are modelled using the OMOP Common Data Model v5.3.Correlated Data SourceNG tube vocabulariesGeneration RulesThe patient’s age should be between 18 and 100 at the moment of the visit.Ethnicity data is using 2021 census data in England and Wales (Census in England and Wales 2021) .Gender is equally distributed between Male and Female (50% each).Every person in the record has a link in procedure_occurrence with the concept “Checking the position of nasogastric tube using X-ray”2% of person records have a link in procedure_occurrence with the concept of “Plain chest X-ray”60% of visit_occurrence has visit concept “Inpatient Visit”, while 40% have “Emergency Room Visit”NotesVersion 0Generated by man-made rule/story generatorStructural correct, all tables linked with the relationshipWe used national ethnicity data to generate a realistic distribution (see below)2011 Race Census figure in England and WalesEthnic Group : Population(%)Asian or Asian British: Bangladeshi - 1.1Asian or Asian British: Chinese - 0.7Asian or Asian British: Indian - 3.1Asian or Asian British: Pakistani - 2.7Asian or Asian British: any other Asian background -1.6Black or African or Caribbean or Black British: African - 2.5Black or African or Caribbean or Black British: Caribbean - 1Black or African or Caribbean or Black British: other Black or African or Caribbean background - 0.5Mixed multiple ethnic groups: White and Asian - 0.8Mixed multiple ethnic groups: White and Black African - 0.4Mixed multiple ethnic groups: White and Black Caribbean - 0.9Mixed multiple ethnic groups: any other Mixed or multiple ethnic background - 0.8White: English or Welsh or Scottish or Northern Irish or British - 74.4White: Irish - 0.9White: Gypsy or Irish Traveller - 0.1White: any other White background - 6.4Other ethnic group: any other ethnic group - 1.6Other ethnic group: Arab - 0.6

A dataset I generated to showcase a sample set of user data for a fictional streaming service. This data is great for practicing SQL, Excel, Tableau, or Power BI.

1000 rows and 25 columns of connected data.

See below for column descriptions.

Enjoy :)

Each R script replicates all of the example code from one chapter from the book. All required data for each script are also uploaded, as are all data used in the practice problems at the end of each chapter. The data are drawn from a wide array of sources, so please cite the original work if you ever use any of these data sets for research purposes.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

N.B. This is not real data. Only here for an example for project templates.

Project Title: Add title here

Project Team: Add contact information for research project team members

Summary: Provide a descriptive summary of the nature of your research project and its aims/focal research questions.

Relevant publications/outputs: When available, add links to the related publications/outputs from this data.

Data availability statement: If your data is not linked on figshare directly, provide links to where it is being hosted here (i.e., Open Science Framework, Github, etc.). If your data is not going to be made publicly available, please provide details here as to the conditions under which interested individuals could gain access to the data and how to go about doing so.

Data collection details: 1. When was your data collected? 2. How were your participants sampled/recruited?

Sample information: How many and who are your participants? Demographic summaries are helpful additions to this section.

Research Project Materials: What materials are necessary to fully reproduce your the contents of your dataset? Include a list of all relevant materials (e.g., surveys, interview questions) with a brief description of what is included in each file that should be uploaded alongside your datasets.

List of relevant datafile(s): If your project produces data that cannot be contained in a single file, list the names of each of the files here with a brief description of what parts of your research project each file is related to.

Data codebook: What is in each column of your dataset? Provide variable names as they are encoded in your data files, verbatim question associated with each response, response options, details of any post-collection coding that has been done on the raw-response (and whether that's encoded in a separate column).

Examples available at: https://www.thearda.com/data-archive?fid=PEWMU17 https://www.thearda.com/data-archive?fid=RELLAND14

A complete list of live websites using the Sample Data technology, compiled through global website indexing conducted by WebTechSurvey.

Despite the wide application of longitudinal studies, they are often plagued by missing data and attrition. The majority of methodological approaches focus on participant retention or modern missing data analysis procedures. This paper, however, takes a new approach by examining how researchers may supplement the sample with additional participants. First, refreshment samples use the same selection criteria as the initial study. Second, replacement samples identify auxiliary variables that may help explain patterns of missingness and select new participants based on those characteristics. A simulation study compares these two strategies for a linear growth model with five measurement occasions. Overall, the results suggest that refreshment samples lead to less relative bias, greater relative efficiency, and more acceptable coverage rates than replacement samples or not supplementing the missing participants in any way. Refreshment samples also have high statistical power. The comparative strengths of the refreshment approach are further illustrated through a real data example. These findings have implications for assessing change over time when researching at-risk samples with high levels of permanent attrition.

Sample data for exercises in Further Adventures in Data Cleaning.

This is an example of a public dataset on the AST Data Repository

This is an auto-generated index table corresponding to a folder of files in this dataset with the same name. This table can be used to extract a subset of files based on their metadata, which can then be used for further analysis. You can view the contents of specific files by navigating to the "cells" tab and clicking on an individual file_kd.

The global big data and business analytics (BDA) market was valued at ***** billion U.S. dollars in 2018 and is forecast to grow to ***** billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around ** billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate **** ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around **** billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

In the way of my journey to earn the google data analytics certificate I will practice real world example by following the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Picking the Bellabeat example.

This file is an example data set from the Central Valley of California from a drought study corresponding to “recent non-drought conditions” (Scenario 1 in Petrie et al., in review). In 2014, following an 8-year period with 7 below-normal to critically-dry water years, the bioenergetic model TRUEMET was used to assess the impacts of drought on wintering waterfowl habitat and bioenergetics in the Central Valley of California. The goal of the study was to assess whether available foraging habitats could provide enough food to support waterfowl populations (ducks and geese) under a variety of climate and population level scenarios. This information could then be used by managers to adapt their waterfowl habitat management plans to drought conditions. The study area spanned the Central Valley and included the Sacramento Valley in the north, the San Joaquin Valley in the south, and Suisun Marsh and Sacramento-San Joaquin River Delta (Delta) east of San Francisco Bay. The data set consists of two foraging guilds (ducks and geese/swans) and five forage types: harvested corn, rice (flooded), rice (unflooded), wetland invertebrates and wetland moist soil seeds. For more background on the data set, see Petrie et al. in review.

This is a textbook, created example for illustration purposes. The System takes inputs of Pt, Ps, and Alt, and calculates the Mach number using the Rayleigh Pitot Tube equation if the plane is flying supersonically. (See Anderson.) The unit calculates Cd given the Ma and Alt. For more details, see the NASA TM, also on this website.

Information on samples submitted for RNAseq

Rows are individual samples

Columns are: ID Sample Name Date sampled Species Sex Tissue Geographic location Date extracted Extracted by Nanodrop Conc. (ng/µl) 260/280 260/230 RIN Plate ID Position Index name Index Seq Qubit BR kit Conc. (ng/ul) BioAnalyzer Conc. (ng/ul) BioAnalyzer bp (region 200-1200) Submission reference Date submitted Conc. (nM) Volume provided PE/SE Number of reads Read length