This is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

This is dummy data

Dummy Dataset for AutoTrain Benchmark

This dataset contains dummy data that's needed to create AutoTrain projects for benchmarks like RAFT. See here for more details.

This data is purely random and created for learning purpose.

In situations where data is not readily available but needed, you'll have to resort to building up the data yourself. There are many methods you can use to acquire this data from web scraping to APIs. But sometimes, you'll end up needing to create fake or “dummy” data. Dummy data can be useful in times where you know the exact features you’ll be using and the data types included but, you just don’t have the data itself.

Features Description

• ID — a unique string of characters to identify each user.
• Gender — string data type of three choices.
• Subscriber — a binary True/False choice of their subscription status.
• Name — string data type of the first and last name of the user.
• Email —string data type of the email address of the user.
• Last Login — string data type of the last login time.
• Date of Birth — string format of year-month-day.
• Education — current education level as a string data type.
• Bio — short string descriptions of random words.
• Rating — integer type of a 1 through 5 rating of something.

Note - This Data is Purely Random (Dummy Data). if you wish, you can perform some data visualization and model building part into it.

Reference - https://towardsdatascience.com/build-a-your-own-custom-dataset-using-python-9296540a0178

The spearfish sample database is being distributed to provide users with a solid database on which to work for learning the tools of GRASS. This document provides some general information about the database and the map layers available. With the release of GRASS 4.1, the GRASS development staff is pleased to announce that the sample data set spearfish is also being distributed. The spearfish data set covers two topographic 1:24,000 quads in western South Dakota. The names of the quads are Spearfish and Deadwood North, SD. The area covered by the data set is in the vicinity of Spearfish, SD and includes a majority of the Black Hills National Forest (i.e., Mount Rushmore). It is anticipated that enough data layers will be provided to allow users to use nearly all of the GRASS tools on the spearfish data set. A majority of this spearfish database was initially provided to USACERL by the EROS Data Center (EDC) in Sioux Falls, SD. The GRASS Development staff expresses acknowledgement and thanks to: the U.S. Geological Survey (USGS) and EROS Data Center for allowing us to distribute this data with our release of GRASS software; and to the U.S. Census Bureau for their samples of TIGER/Line data and the STF1 data which were used in the development of the TIGER programs and tutorials. Thanks also to SPOT Image Corporation for providing multispectral and panchromatic satellite imagery for a portion of the spearfish data set and for allowing us to distribute this imagery with GRASS software. In addition to the data provided by the EDC and SPOT, researchers at USACERL have dev eloped several new layers, thus enhancing the spearfish data set. To use the spearfish data, when entering GRASS, enter spearfish as your choice for the current location.

This is the classical GRASS GIS dataset from 1993 covering a part of Spearfish, South Dakota, USA, with raster, vector and point data. The Spearfish data base covers two 7.5 minute topographic sheets in the northern Black Hills of South Dakota, USA. It is in the Universal Transverse Mercator Projection. It was originally created by Larry Batten while he was with the U. S. Geological Survey's EROS Data Center in South Dakota. The data base was enhanced by USA/CERL and cooperators.

Dataset

This dataset was created by Mehul Damani

Contents

In 1968, the Missouri Geological Survey (MGS) established the Operation Basement program to address three objectives: a) to obtain drill hole and underground mining data relative to the structure and composition of the buried Precambrian basement; b) to expand mapping in the Precambrian outcrop area and conduct research related to Precambrian geology and mineral resources; and c) to eventually publish the results of the first two objectives in the Contribution to Precambrian Geology series (Kisvarsanyi, 1976). The database presented here represents the first of those objectives, and it includes more data that was gathered after the third objective was accomplished. It was originally compiled in close cooperation with exploration and mining companies operating in Missouri, who provided drillhole data, core and rock samples to MGS. These data enabled geologists to study otherwise unexposed basement rocks from a large area of the state for the first time, allowing better classification and understanding of the Precambrian basement across the state. MGS is continuing data collection and database compilation today as information becomes available, furthering our knowledge of the Missouri Precambrian basement. This effort was supported through a cooperative agreement with the Mineral Resource Program of the U.S. Geological Survey. There is no plan to update this Data Release product.

This database was prepared using a combination of materials that include aerial photographs, topographic maps (1:24,000 and 1:250,000), field notes, and a sample catalog. Our goal was to translate sample collection site locations at Yellowstone National Park and surrounding areas into a GIS database. This was achieved by transferring site locations from aerial photographs and topographic maps into layers in ArcMap. Each field site is located based on field notes describing where a sample was collected. Locations were marked on the photograph or topographic map by a pinhole or dot, respectively, with the corresponding station or site numbers. Station and site numbers were then referenced in the notes to determine the appropriate prefix for the station. Each point on the aerial photograph or topographic map was relocated on the screen in ArcMap, on a digital topographic map, or an aerial photograph. Several samples are present in the field notes and in the catalog but do not correspond to an aerial photograph or could not be found on the topographic maps. These samples are marked with “No” under the LocationFound field and do not have a corresponding point in the SampleSites feature class. Each point represents a field station or collection site with information that was entered into an attributes table (explained in detail in the entity and attribute metadata sections). Tabular information on hand samples, thin sections, and mineral separates were entered by hand. The Samples table includes everything transferred from the paper records and relates to the other tables using the SampleID and to the SampleSites feature class using the SampleSite field.

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%.

This dataset contains data of biological samples which were collected during scientific missions of JAMSTEC ships (NATSUSHIMA, KAIYO, YOKOSUKA, KAIREI and MIRAI) and submersibles.Data of this dataset is derived from the Marine Biological Sample Database of JAMSTEC. At the original database, you may search sample information such as number of individuals, preservation methods, sex, life stages, identification, collecting information and related literatures.

These are just dummy data for running the analysis script. They will be replaced by the actual study data later on.: Dummy Data (to be replaced by the actual study data)

A list of complaints received and associated data. Prior monthly reports are archived at DOB and are not available on NYC Open Data.

Age and length frequency data for finfish and invertebrate species collected during commercial fishing vessels. Samples are collected by fisheries reporting specialist from fish dealers in ports along the northwest Atlantic Ocean from Maine to North Carolina.

This sample study dataset contains dummy CDISC ADaM formatted data files created for demo purposes. It can be used by anyone interested in a CDISC ADaM formatted dataset. Contact me if you would like more dummy ADaM datasets to be published.

This database archives genetic tissue samples from marine mammals collected primarily from the U.S. east coast. The collection includes samples from field programs, fisheries bycatch, and stranding data. A range of researchers have contributed samples to this archive, so some of the data records are confidential. Data includes field identification numbers, location and date information, collection information, and disposition of samples. These samples are primarily intended to support analyses of the genetic relationships and phylogeny of cetaceans in U.S. and Caribbean waters.

Dataset Card for "medsam-vit-base-cancer-dummy-data"

More Information needed

Dataset

This dataset was created by Ravi Kolluru

Contents

108965 Global import shipment records of Dummy with prices, volume & current Buyer’s suppliers relationships based on actual Global import trade database.

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one best value determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database Version 2.0 before it, the AGDB3 was created and designed to compile and integrate geochemical data from Alaska to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, element concentrations and associations, environmental impact assessments, and studies in public health associated with geology. This relational database, created from databases and published datasets of the U.S. Geological Survey (USGS), Atomic Energy Commission National Uranium Resource Evaluation (NURE), Alaska Division of Geological & Geophysical Surveys (DGGS), U.S. Bureau of Mines, and U.S. Bureau of Land Management serves as a data archive in support of Alaskan geologic and geochemical projects and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 112 laboratory and field analytical methods on 396,343 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. Most samples were collected by personnel of these agencies and analyzed in agency laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various agency programs and projects from 1938 through 2017. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB3 includes historical geochemical data archived in the USGS National Geochemical Database (NGDB) and NURE National Uranium Resource Evaluation-Hydrogeochemical and Stream Sediment Reconnaissance databases, and in the DGGS Geochemistry database. Retrievals from these databases were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. In other words, the data of the AGDB3 supersedes data in the AGDB and the AGDB2, but the background about the data in these two earlier versions are needed by users of the current AGDB3 to understand what has been done to amend, clean up, correct and format this data. Corrections were entered, resulting in a significantly improved Alaska geochemical dataset, the AGDB3. Data that were not previously in these databases because the data predate the earliest agency geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB3 and will be added to the NGDB and Alaska Geochemistry. The AGDB3 data provided here are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. The AGDB3 data provided in the online version of the database may be updated or changed periodically.

cha7ura/dummy-data-1000 dataset hosted on Hugging Face and contributed by the HF Datasets community