Validation to ensure data and identity integrity. DAS will also ensure security compliant standards are met.

Medicaid Analytic eXtract (MAX) Validation Reports
These documents contain validation reports for all 50 States and Washington D.C..

PDS Validation Tool (1.2.0) and Product Tools (1.2.0)

The files contain the dataset for the thesis "Development and Validation of Explainable Machine-Learning Prediction Systems: A Study of Biomedical and Clinical Data".

Chapter 3 includes a patient dataset with CDI (Clostridioides difficile infection) admissions from 2009-2014 in Hong Kong.

Chapter 4 includes a list of protein structure data derived from UniProt (www.uniprot.org) (release 2021_03) and their corresponding enzyme functions. The protein structure data file can be downloaded from the open-source database Protein Data Bank (www.rcsb.org). Additionally, a list of AlphaFold 2 predicted structures is also included, and the structural data can be downloaded from www.alphafold.com.

Chapter 5 contains a list of PDB structures derived from UniProt (release 2023_01).

What is D-SEPC?D-SPEC is a standard specification for the delivery of digital data of newly constructed stormwater drainage and telecommunication assets to local governments, utilities and water authorities. It aims to streamline the processes of receiving, handling and storing data in GIS and AMIS systems, and to improve the quality and consistency of asset information. D-SPEC is aligned with AS 5488 - 2022, a standard for the classification of subsurface utility infrastructure. D-SPEC also provides guidelines for graphical data construction, attribute data fields, validation rules and code lists for different asset types.For more information about D-Spec Specifications, please visit: https://www.a-specstandards.com.au/d-spec

This research project was about developing and validating a new scale, named the Romance Quotient (RQ), which aimed to measure varying levels of romantic traits. Individuals who were 18-years-old or above and with English literacy were recruited online to complete a survey. The sample size is 812; this is the number of cases in the dataset "FinalData_RQ_SubApril2025". All variables' information is available in the dataset.

PDS Software Release Validation Tool (2.5.0)

Validation of Innovative Exploration Technologies for Newberry Volcano: Temperature Readings from 7 wells drilled to date by SMU 2012

The Committee on Earth Observation Satellites (CEOS) is providing information and data for Calibration (Cal) and Validation (Val) of Earth Observation (EO) data through the Cal/Val Portal. The portal will support worldwide activities on Cal/Val, and specifically ensure that sensor intercalibration is undertaken in a standardised way. The overall goal is to increase measurement accuracy of all EO sensors, so that the community can be served with the best information products available.

Dataset is comprised of hourly air quality data points captured from the District’s air monitoring network sites. The dataset is quality controlled by a process of field data verification and validation.

My research focuses on the role of parental involvement in children's CT learning and development. Study One is a systematic review in which the data is available from online databases. The collected data of Study Two are from 28 families. The collected data of Study Three are from more than 1500 students from primary school students.

The ATDM Trajectory Validation project developed a validation framework and a trajectory computational engine to compare and validate simulated and observed vehicle trajectories and dynamics. The field data were used to demonstrate how on-site instrumented vehicle data can be used to validate simulated vehicle dynamics using the validation framework.

The vehicle trajectory data were collected in a separate task of the Active Transportation Demand Management (ATDM) Trajectory Level Validation project. The primary project objective was to develop a methodology to validate simulated vehicle dynamics at the trajectory level. Microscopic and macroscopic performance measures were calculated from the trajectory data and used in a number of validation tests related to safety, vehicle limits, driver comfort levels, and traffic flow

RANseq data of liver tissues of 98 obese patients for "Omics-based identification and multicentric validation of soluble CUB Domain Containing Protein 1 (sCDCP1) as a robust serological biomarker for personalized risk-stratification of NASH in obese Chinese".

Data and code belonging to the manuscript:

Tracking transformative agreements through open metadata: method and validation using Dutch Research Council NWO funded papers

Abstract

Transformative agreements have become an important strategy in the transition to open access, with almost 1,200 such agreements registered by 2025. Despite their prevalence, these agreements suffer from important transparency limitations, most notably article-level metadata indicating which articles are covered by these agreements. Typically, this data is available to libraries but not openly shared, making it difficult to study the impact of these agreements. In this paper, we present a novel, open, replicable method for analyzing transformative agreements using open metadata, specifically the Journal Checker tool provided by cOAlition S and OpenAlex. To demonstrate its potential, we apply our approach to a subset of publications funded by the Dutch Research Council (NWO) and its health research counterpart ZonMw. In addition, the results of this open method are compared with the actual publisher data reported to the Dutch university library consortium UKB. This validation shows that this open method accurately identified 89% of the publications covered by transformative agreements, while the 11% false positives shed an interesting light on the limitations of this method. In the absence of hard, openly available article-level data on transformative agreements, we provide researchers and institutions with a powerful tool to critically track and evaluate the impact of these agreements.

This dataset contains the following files:

• Dataset.csv - Data set of unique DOIs (n = 6,610) enriched with data from Crossref, Unpaywall, OpenAlex and the Journal Checker Tool.
• Data dictionary.csv - description of the data in the dataset, its type and sources.
• Google_Apps_Script.js - Google Apps Script for retrieving information from the Journal Checker Tool API.
• Data comparison.csv - Data set of DOI's (n= 10,126) retrieved from the UKBsis datahub and used to establish the overlap with the original dataset.
• Data dictionary data comparison.csv - description of the data in the data comparison data set, its type and sources.

N/A

135 views (5 recent) Dataset extent Map data © OpenStreetMap contributors. The European Alien Species Information Network team (EASIN, http://easin.jrc.ec.europa.eu) of the Joint Research Centre (JRC) requests the European member states to provide and verify the baseline distribution data of invasive alien species of Union Concern (Tsiamis et al. 2017) as provided by the EASIN mapping system (Katsanevakis et al. 2012). These are species with documented biodiversity impacts sensu the European Union Regulation on the prevention and management of the introduction and spread of Invasive Alien Species in Europe (IAS Regulation No 1143/2014) (European Union 2014). The dataset provides a shapefile on the baseline distribution of the invasive species of EU concern in Belgium based on an aggregated dataset (ias_belgium_t0_xxxx). Belgium. Data were compiled from various datasets holding invasive species observations such as data from research institutes and research projects (76%), citizen science observatories (23%) and a range of other sources (1%) such as governmental agencies, water managers, invasive species control companies, angling and hunting organizations etc. Data were normalized using a custom mapping of the original data files to Darwin Core (Wieczorek et al. 2012) where possible. Species names were mapped to the GBIF Backbone Taxonomy (GBIF 2016) using the species API (http://www.gbif.org/developer/species). Appropriate selection of records was performed based on predefined cut-off dates (see data range) and record content validation (see validation procedure). Data were then joined with GRID10k layer Belgium based on GRID10k cellcodes (ETRS_1989_LAEA).

Schemas describing the core HXL hashtags and attributes. Starting with version 1.1, the standards documentation listing HXL hashtags and attributes at hxlstandard.org is generated directly from this dataset.

See the documentation on the HXL schema format , and the HXL Proxy validation service. Note that this is just a generic default schema—you can also create your own, project-specific HXL schemas.

CFD Validation of Synthetic Jets and Turbulent Separation Control. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, SD developed a cloud validation dataset from 48 unique Landsat 8 Collection 1 images. These images were selected at random from the Landsat 8 archive from various locations around the world. While these validation images were subjectively designed by a single analyst, they provide useful information for quantifying the accuracy of clouds flagged by various cloud masking algorithms. Each mask is provided in GeoTIFF format, and includes all bands from the original Landsat 8 Level-1 Collection 1 data product (COG GeoTIFF), and its associated Level-1 metadata (MTL.txt file).

Exp: FAITH Hill 3-D Separated Flow. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.