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

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

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.

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.

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.

Dataset

This dataset was created by nguyenthanhktdt

Contents

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 :)

The same information is attributed to different data fields in the cell banks we reviewed. This table shows how the two sample terms ‘medium’ and ‘organ’ are handled in the respective databases. The fields marked by * are free text fields. They are also used for other types of data.

Sample data set used in an introductory course on Programming in Python

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

Jerjes/neuro-specter2-sample-data

This dataset contains anchor papers with their top-K most similar (positive) and most dissimilar (negative) papers based on SPECTER2 embeddings.

  Dataset Structure

Each row contains:

anchor_id: Unique identifier for the anchor paper anchor_title: Title of the anchor paper
anchor_abstract: Abstract of the anchor paper positive_pool: List of 5 most similar papers, each as [id, title, abstract] negative_pool: List of 5 most dissimilar… See the full description on the dataset page: https://huggingface.co/datasets/Jerjes/neuro-specter2-sample-data.

Discrete sample data from manual field collection and laboratory analyses taken since 2010. It contains water quality, sediment, biological, air, and soil samples from monitoring locations across the Upper Colorado Subregion of Texas, Hydrologic Unit Code (HUC) 1208.

This part of the data release is a spreadsheet including radiocarbon sample information and calibrated ages of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Radiocarbon.xlsx. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

The Biological Sampling Database (BSD) is an Oracle relational database that is maintained at the NMFS Panama City Laboratory and NOAA NMFS Beaufort Laboratory. Data set includes port samples of reef fish species collected from commercial and recreational fishery landings in the U.S. South Atlantic (NC - FL Keys). The data set serves as an inventory of samples stored at the NMFS Beaufort Labor...

Scientific contributions (lectures and posters) to the American Association of Orthodontists (AAO) annual sessions from 2013 to 2023 were investigated with the aims of analysing the contributions of each country and their efficiency, presentation trends, and gender differences during these years as well as the most frequent topics and their evolution. Official data were requested from and provided by the AAO secretary. The year and type of presentation; the name, country and gender of the first author; and the full title of the presentation were considered. In addition, six national indicators that could determine the quantity and quality of scientific production were obtained from the Our World in Data website with regard to the countries that made the greatest contributions to the AAO annual sessions. The USA featured the largest number of lecturers (69.44%), while the presentations of posters were more balanced among the 4 countries that exhibited the highest levels of production (i.e., Brazil, the USA, Mexico and South Korea). Brazil was the main country to perform above expectations. The COVID-19 pandemic resulted in a significant reduction in the number of poster presentations. The male/female ratio was close to 3:1 in terms of lectures and close to 1:1 in terms of posters. In 2023, women presented more posters than did men. The terms clear/aligners and digital were strongly present, and the terms maxillary, adults, and expansion were used increasingly frequently, while the use of the terms brackets or cephalometry decreased. American lecturers included terms that differentiated them from lecturers in other countries. The nationalities of lecturers are not closely related to those of posters, particularly with regard to the USA, Brazil, Canada, Mexico and Turkey. Research spending and economic level are the most significant factors with respect to the type and number of a country’s contributions. Concerning gender, a clear imbalance in favour of men persists among lecturers. Increased distance from the USA makes it more difficult for women to serve as lecturers. An emergent paradigm shift in current topics towards a focus on the terms clear/aligners and digital in lectures is evident.

Sample data, including nominal and faulty scenarios, for Tier 1 and Tier 2 of the First International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the Support/Documentation section below and the First International Diagnostic Competition project page for more information.

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.

Sample data, including nominal and faulty scenarios, for Diagnostic Problems I and II of the Second International Diagnostic Competition. Three file formats are provided, tab-delimited .txt files, Matlab .mat files, and tab-delimited .scn files. The scenario (.scn) files are read by the DXC framework. See the Second International Diagnostic Competition project page for more information.

Dataset Card for "amazon-product-data-filter"

  Dataset Summary

The Amazon Product Dataset contains product listing data from the Amazon US website. It can be used for various NLP and classification tasks, such as text generation, product type classification, attribute extraction, image recognition and more. NOTICE: This is a sample of the full Amazon Product Dataset, which contains 1K examples. Follow the link to gain access to the full dataset.

  Languages… See the full description on the dataset page: https://huggingface.co/datasets/iarbel/amazon-product-data-sample.

Discrete sample data from manual field collection and laboratory analyses taken since 2000. It contains water quality, sediment, biological, air, and soil samples from monitoring locations across the North Canadian Subregion of Texas, Hydrologic Unit Code (HUC) 1110.