we proposed an automated approach for discovering and aligning these consistent fields

The Building Assessment Survey and Evaluation (BASE) study was a five year study to characterize determinants of indoor air quality and occupant perceptions in representative public and commercial office buildings across the U.S. This data source is the raw data from this study about the indoor air quality.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This repository presents the evaluation data used for ASMv2. Please refer to this document for more details about the repository.

The Database Security Evaluation System market is experiencing robust growth, driven by the increasing prevalence of cyber threats targeting sensitive data stored in databases. The market's expansion is fueled by the rising adoption of cloud-based databases, the growing need for regulatory compliance (e.g., GDPR, CCPA), and the increasing sophistication of cyberattacks. Small and Medium-sized Enterprises (SMEs) are a significant growth segment, recognizing the critical need for robust database security solutions despite limited budgets. The market is segmented by deployment type (Cloud-Based and On-Premise), with Cloud-Based solutions gaining significant traction due to their scalability, cost-effectiveness, and ease of management. Key players such as Oracle, IBM, and specialized security firms like TechCERT and Xiarch are actively competing in this space, offering a diverse range of solutions tailored to specific industry needs. While the initial investment in database security evaluation systems can be a restraint for some organizations, the long-term cost savings associated with preventing data breaches far outweigh the initial expense. The market's geographic distribution shows a strong presence in North America and Europe, driven by advanced technological infrastructure and stringent data protection regulations. However, growth opportunities are emerging in Asia-Pacific and other developing regions, fueled by increasing digitalization and government initiatives promoting cybersecurity. By 2033, the market is projected to reach substantial size, reflecting a consistent compound annual growth rate (CAGR) driven by the continuous demand for robust database security measures. The competitive landscape is characterized by both established players offering comprehensive security suites and specialized firms focusing on niche solutions. Strategic partnerships, mergers, and acquisitions are expected to shape the market dynamics in the coming years. Continuous innovation in areas such as artificial intelligence (AI) and machine learning (ML) for threat detection and prevention is further driving the adoption of advanced database security evaluation systems. The market is likely to see an increasing focus on integrating security solutions directly within database management systems (DBMS), offering seamless protection and streamlined management. The demand for skilled professionals capable of implementing and managing these systems is also likely to grow, creating opportunities for training and certification programs.

The Car Evaluation Database was created from a simple hierarchical decision model originally developed to demonstrate DEX, as detailed in M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990. The model evaluates cars based on the following structure:

• CAR: car acceptability
  • PRICE: overall price
  • buying: buying price
  • maint: maintenance price
  • TECH: technical characteristics
  • COMFORT: comfort
    • doors: number of doors
    • persons: capacity in terms of persons
    • lug_boot: size of luggage boot
  • safety: estimated safety of the car

The input attributes are in lowercase. Besides the main concept (CAR), the model includes three intermediate concepts: PRICE, TECH, and COMFORT. In the original model, each concept is related to its lower-level attributes by a set of examples (for these example sets, see here).

The Car Evaluation Database includes examples without the structural information, directly relating CAR to the six input attributes: buying, maint, doors, persons, lug_boot, and safety.

Because of the known concept structure, this database can be particularly useful for testing methods of constructive induction and structure discovery.

**Additional Variable Information **buying: vhigh, high, med, low. maint: vhigh, high, med, low. doors: 2, 3, 4, 5more. persons: 2, 4, more. lug_boot: small, med, big. safety: low, med, high.

Does it have missing values? No

Introductory Paper: Knowledge acquisition and explanation for multi-attribute decision making By M. Bohanec, V. Rajkovic. 1988

Published in the 8th Intl Workshop on Expert Systems and their Applications, Avignon, France.

Does it have missing values? No

Introductory Paper: Knowledge acquisition and explanation for multi-attribute decision making By M. Bohanec, V. Rajkovic. 1988

Published in the 8th Intl Workshop on Expert Systems and their Applications, Avignon, France.

WangResearchLab/verification-evaluation-data dataset hosted on Hugging Face and contributed by the HF Datasets community

These data were collected to evaluate the Partnership for Long-Term Care (PLTC), a project in which the Robert Wood Johnson Foundation awarded grants to four states -- California, Connecticut, Indiana, and New York -- to work with private insurers to create long-term care insurance policies that were more affordable and provided better protection against impoverishment than those generally available. PLTC policies combine private long-term care insurance with special Medicaid eligibility standards that protect assets of the insured once private insurance benefits are exhausted. This collection was extracted from a database compiled from data submitted by three of the PLTC states: California, Connecticut, and Indiana (New York refused participation). It comprises seven parts, which can be linked together using common identifying variables. Part 1, Insured, describes the characteristics of each issued policy and includes variables covering the effective policy date, policy type, elimination periods, maximum benefits, inflation protection mode, and annualized premium, as well as the year of birth, sex, marital status, and state of residence of the insured. Each insured person is represented by one or more records: one record for the initial PLTC policy, plus a separate record for each change to the policy, if any. Part 2, Changes, consists of policy change records used to update the policies in Part 1. Assessments for benefits are recorded in Part 3. This file includes variables on the assessment date, whether the insured met policy criteria at the time of the assessment, disability date, deficiencies in activities of daily living, and MSQ and Folstein test scores. Parts 4-6 describe service payments and utilization: reporting period (quarter), type of service received by the insured, service amount billed, days of service rendered, and amount of remaining benefits (dollars and days). Part 7 contains information on persons denied application to PLTC policies, including date of denial, type and amount of coverage sought, reason for denial, and the sex, year of birth, and marital status of the applicant.

The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection (PADEP), conducted an evaluation of data used by the PADEP to identify groundwater sources under the direct influence of surface water (GUDI) in Pennsylvania (Gross and others, 2022). The data used in this evaluation and the processes used to compile them from multiple sources are described and provided herein. Data were compiled primarily but not exclusively from PADEP resources, including (1) source-information for public water-supply systems and Microscopic Particulate Analysis (MPA) results for public water-supply system groundwater sources from the agency’s Pennsylvania Drinking Water Information System (PADWIS) database (Pennsylvania Department of Environmental Protection, 2016), and (2) results associated with MPA testing from the PADEP Bureau of Laboratories (BOL) files and water-quality analyses obtained from the PADEP BOL, Sample Information System (Pennsylvania ...

This MS Excel Stocktake Database contains a list of EBA tools, as sourced from online and hardcopy sources (Worksheet 'EBA Tools Database'). It also includes a number of projects that may be categoriesed as Ecosystem Based Adaptation Projects (Worksheet 'Profiling of EBA Projects') as well as a list of tools to evaluate adaptation projects (Worksheet 'Evaluation Tools'). The database was compiled as input into a project that developed a Decision Support Framework for Ecosystem Based Adaptation for the United Nations Environment Program (UNEP). The outputs presented here are interim deliverables for this project.

We evaluated CANOPUS on two MS/MS reference datasets: The SVM training dataset, which was also used for training CSI:FingerID (in 10-fold cross-validation), and the Agilent MassHunter library, used as indepenent dataset.The SVM training dataset contains spectra from GNPS, MassBank, and NIST17. As NIST17 is a commercial library, we can only provide the spectra from GNPS and MassBank. Here, we provide the public part of the SVM training dataset (svm_training_data.zip).For training the deep neural network we used a subset of PubChem with 1,106,938 structures for which we downloaded ClassyFire annotations (Feunang et al 2016) and another set of 2,997,933 compounds from the ClassyFire database. The PubChem structures, together with ClassyFire annotations for the evaluation data are available as "structures.csv.gz".With CANOPUS, we analyzed data from two biological studies; the mzML and mzXML files are available at MassIVE (https://massive.ucsd.edu/) with the accession numbers MSV000079949 (mice data, Quinn et al 2020) and MSV000081082 (Euphorbia plant data, Ernst et al 2019).The network visualization of the mice data was done using Cytoscape (Shannon et al 2003). Here, we provide the Cytoscape file (mice_multiple_classes.cys).The source code of CANOPUS is part of the SIRIUS GitHub repository (https://github.com/boecker-lab/sirius-libs). The scripts we used for analyzing and visualizing the data are available at the GitHub repository (https://github.com/kaibioinfo/canopus_treemap).See the LICENSE.txt for further licensing information on Classyfire annotations and mass spectra.

The global Database Security Evaluation System market is projected to reach a valuation of USD 12.5 billion by 2033, growing at a compound annual growth rate (CAGR) of 14.2% from 2025 to 2033.

These data sets contain evaluation data for the evaluation objectives performance, scalability and availability of different cloud-hosted, distributed DBMS. The data sets contain the raw performance metrics, monitoring data and evaluation metadata. For the higher-level evaluation objectives scalability and availability, supportive plots are provided. The performance and scalability evaluations have been carried out with the Mowgli framework and the availability evaluation have been carried out with the King Louie framework.

Database of the process evaluation questionnaire

Data sets as well as R and Python code of the use cases in the book "Impact evaluation in firms and organizations"

Car Evaluation Database was derived from a simple hierarchical decision model

This noise database was developed to provide researchers and other interested stakeholders with noise measurement results that the National Institute for Occupational Safety and Health (NIOSH) has collected during health hazard evaluation (HHE) surveys from 1996 through 2013. HHEs are requested by employees or their representatives, or employers, to help learn whether health hazards are present at their workplace. The scope of HHEs varies based on the requestors’ concerns and the NIOSH project officers’ professional judgment. Only noise measurement results are included in this database; however, many HHEs include evaluation of exposures other than noise. Individual HHE reports are published on the NIOSH website. When available, the database provides a direct link to the HHE report for each of the noise measurement results.

The noise database contains workplace noise measurement results from 77 HHE reports, including over 808 personal noise exposure measurements and 582 area noise measurements. It also includes the following information: U.S. state or territory; Occupational Safety and Health Administration (OSHA) region; National Occupational Research Agenda (NORA) sector; North American Industry Classification System (NAICS) code; facility description; type of dosimeter or sound level meter used; whether a hearing conservation program was in place; whether a hearing protection was used; whether octave band data was collected; job title; noise-generating activities; location of noise measurements; start and end date for site visit; type (full-shift, partial-shift, or task-based) and duration of noise measurement; type of noise (continuous, impulsive, or intermittent); exposure to ototoxic chemicals; and results in decibels A-weighted (dBA) and percent dose according to OSHA and NIOSH noise measurement criteria. This database is an ongoing project and will be updated at least yearly to add the most recent HHE noise measurement data.

The Aurora project was originally set up to establish a world wide standard for the feature extraction software which forms the core of the front-end of a DSR (Distributed Speech Recognition) system. ETSI formally adopted this activity as work items 007 and 008.The two work items within ETSI are :- ETSI DES/STQ WI007 : Distributed Speech Recognition - Front-End Feature Extraction Algorithm & Compression Algorithm- ETSI DES/STQ WI008 : Distributed Speech Recognition - Advanced Feature Extraction Algorithm. The Aurora project is releasing a revised version of the Noisy TI digits database to follow on the work of ETSI. This CD set is a replacement for the previous set (version 1.0 consisted of 2 CDs while version 2.0 now consists of 4 CDs) . This database is intended for the evaluation of algorithms for front-end feature extraction algorithms in background noise but may also be used more widely by speech researchers to evaluate and compare the performance of noise robust speech recognition algorithms. Compared to version 1.0 the changes are as follows : The files are restored to the energy level of the original speech in the TI digits database. One of the noise types added to the speech has been changed (the babble one) There is an additional test sets where the noises are mismatched to those used in the training set. There is a convolutional distortion test. There is a clean training set The CD ROM will be used for the next round of ETSI Aurora standards evaluation. Two original copies of the contract must be sent to ELDA. To be valid these contracts must be initialled and signed. The user should annex to the contract the proof that he obtained the right to use the TI digits from LDC (ref. LDC93S10). This may be a signed licence agreement or a proof of membership payment for 1993.For further details, please check the following website: http://aurora.hsnr.de/

The Database Security Evaluation System market is booming, projected to reach $7.66 billion by 2033 with a 15% CAGR. Learn about market drivers, trends, key players (Oracle, IBM, TechCERT), and regional breakdowns in this comprehensive analysis. Secure your data – explore the future of database security today!

The CEED2016 is newly developed image database dedicated to contrast enhancement evaluation. The database contains 30 original color images and 180 enhanced images obtained using six different CE methods. The database is built with our own captured images and some common pictures used by the image processing community.
The subjective experiments were performed at Universite Paris 13, Sorbonne Paris Cité at Laboratoire de Traitement et Transport de l’Information (L2TI). The images were displayed on a calibrated LCD monitor in a dark room environment to avoid any problem with the illumination adaptation of background. Twenty-three observers, 10 experts, and 13 non-experts, from different age groups, gender, and background participated in the experiments. To obtain the ranking scores, we adopted a balanced pairwise preference based ranking protocol. The interface for the subjective experiments was developed in Matlab, where, for each original image, we randomly displayed all possible pair combinations of enhanced images to the observers. We also showed the original image in the center of the screen (a pair of enhanced images are displayed to its left and right), to facilitate the analysis of after effects of CE. The observers had the choice to rank equally similar stimuli. In the PC ranking protocol, each enhanced image is compared with the others in pairs and ranking results are stored in a preference matrix.