Abstract

The Department of Education commissioned the Human Sciences Research Council (HSRC) to conduct a national survey on the locality and other information linked to all schools in South Africa. The 2000 version was intended to update the 1996 version of the register of needs database, include 3000 institutions that were previously excluded, provide accurate data on geolocality of schools, school conditions and the availability of resources, and measure progress and trends betwee 1996 and 2000.

Geographic coverage

National coverage

Analysis unit

Schools

Universe

All schools in South Africa

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

The NIST Registry of Adsorbent Materials is a free, web-based catalog of adsorbent materials and metadata describing those adsorbent materials. Each adsorbent material in the registry is assigned a registry ID to 1) allow unique identification adsorbents independent of arbitrary naming schemes (e.g., HKUST-1, CuBTC, Basolate C300® are the same material and have the same registry ID) and 2) enable cross referencing information about each material from outside databases and material registries. The registry ID is based on a cryptographic hash, to prevent ID collisions as the registry grows in content. This web application also includes a mechanism for users to provide feedback regarding entries in the registry, to facilitate growth and correction of the database contents. Current feedback options, available through the "User Feedback" menu item are 1) general comments, 2) revision of a database entry (e.g., addition of an external data resource about a specific material), 3) propose a new material for the registry, and 4) propose the merger of two materials already in the registry.

Four files:

1. Database of variables associated with improper trial registration
2. Database of registration deficiencies and publication status
3. Database of authors' claims of proper registration at submission
4. R code for statistical analysis

READ is EPA's authoritative source for information about Agency information resources, including applications/systems, datasets and models. READ is one component of the System of Registries (SoR).

The FMCSA Safety and Fitness Electronic Records (SAFER) System offers company safety data and related services to industry and the public over the Internet. Users can search FMCSA databases, register for a USDOT number, pay fines online, order company safety profiles, challenge FMCSA data using the DataQs system, access the Hazardous Material Route registry, obtain National Crash and Out of Service rates for Hazmat Permit Registration, get printable registration forms and find information about other FMCSA Information Systems.

1.0 Introduction

Deep Shape From Template Dataset(DSfTD) a multimodal database(depth, registration and rgb data) of recordings synthetically created, monitoring in frontal position, objects being deformed, and it was designed to fulfil the following objetives:

• Allow evaluation and fine tuning of DNN systems in registration, reconstruction and SfT task.
• Provide quality data to the research community in registration, reconstruction and SfT task.

The reconstruction and registration task can also be extended to practical applications such as augmented reality, retail or non invasive surgery.

To give you an idea on what to expect, you can have a look at the following video we prepared from similar data(https://www.youtube.com/watch?v=VvYj-FnuVp0).

2.0 Database Info

FI3S is composed from sequences comprising a broad variety of conditions:

• Thin shell and volumetric templates
• High variety of deformations
• Different level of rotation, translation and light changes over the database objects.

The RGB info is stored in 8 bit images(.png) with each pixel between 0-255 value.

The depth and warps(registration) information is stored in general 16 bit images(.png), with each pixel normalized with three different normalizations, that are provided in the image code example of the database.

File naming conventions:

To ease adapting the experimental setup for specific tasks, we have designed a (verbose) naming conven- tion for the file names and folders.

• The depth images are saved in the folder depth, as images with the name imageX.png
• The RGB images are saved in the folder input, as images with the name imageX_texturaY.png
• The registration images are saved in the folders warpu and warpv, as images with the name imageX.png

Filename extensions: The distributed filenames have an extension of PNG images(.png), to provide an extended and generic use filetipe.

Depht Camera Specifications:

1. The first camera used in our emulations is a Kinect 2 for device, with the following intrinsic parameters:

   cx_K = 947.64 / 4;
   cy_K = 530.38 / 4;
   fy_K = 1064 / 4;
   fx_K = 1057.8 / 4;
   

All the images of the database are resized to 270x480, which imply a resize of the intrinsic parameters too, dividing by a factor of 4.

If you make use of this databases and/or its related documentation, you are kindly requested to cite the paper:

Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image, David Fuentes-Jimenez, David Casillas-Perez, Daniel Pizarro, Toby Collins, Adrien Bartoli, 2018, (https://arxiv.org/abs/1811.07791).

Bibtext: @misc{fuentesjimenez2018deep, title={Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image}, author={David Fuentes-Jimenez and David Casillas-Perez and Daniel Pizarro and Toby Collins and Adrien Bartoli}, year={2018}, eprint={1811.07791}, archivePrefix={arXiv}, primaryClass={cs.CV} }

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

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With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

The Register of Public Sector Bodies in Ireland provides the basis for the preparation of Government Finance Statistics (GFS) and Excessive Deficit Procedure (EDP) reporting for Ireland. The Register lists all the organisations in the State which are classified as “general government” bodies for the purposes of national and government accounts. It also lists organisations which, while under public control, are not part of the general government sector. The Register is based on a number of sources including government publications, annual reports, academic databases and data collection undertaken by the CSO through the Department of Public Expenditure and Reform and the Department of Housing, Local Government and Heritage .hidden { display: none }

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

PcBaSe Sweden is a data base for clinical epidemiological prostate cancer research based on linkages between the National Prostate Cancer Register (NPCR) of Sweden, a nationwide population-based quality database and other nationwide registries. In the period 1996-2009, 110 000 cases have been registered in NPCR with detailed data on tumour characteristics and primary treatment available. In addition, there are five controls per case.

By use of the individually unique person identity number, the NPCR has been linked to the Swedish National Cancer Register, the Cause of Death Register, the Prescribed Drug Register, the National Patient Register, and the Acute Myocardial Infarction Register, the Register of the Total Population, the Longitudinal Integration database for health insurance and labour market studies (LISA), the Multi-Generation Register and several other population-based registers.

Purpose:

To provide a platform for prostate cancer research. The data base allows for population-based observational studies with case-control, cohort, or longitudinal case only design that can be used for studies of pertinent issues of clinical importance.

This database contains fish occurence data from the Hungarian part of the Danube River Basin.

BackgroundFinancial incentive interventions have been suggested as one method of promoting healthy behaviour change.ObjectivesTo conduct a systematic review of the effectiveness of financial incentive interventions for encouraging healthy behaviour change; to explore whether effects vary according to the type of behaviour incentivised, post-intervention follow-up time, or incentive value.Data SourcesSearches were of relevant electronic databases, research registers, www.google.com, and the reference lists of previous reviews; and requests for information sent to relevant mailing lists.Eligibility CriteriaControlled evaluations of the effectiveness of financial incentive interventions, compared to no intervention or usual care, to encourage healthy behaviour change, in non-clinical adult populations, living in high-income countries, were included.Study Appraisal and SynthesisThe Cochrane Risk of Bias tool was used to assess all included studies. Meta-analysis was used to explore the effect of financial incentive interventions within groups of similar behaviours and overall. Meta-regression was used to determine if effect varied according to post-intervention follow up time, or incentive value.ResultsSeventeen papers reporting on 16 studies on smoking cessation (n = 10), attendance for vaccination or screening (n = 5), and physical activity (n = 1) were included. In meta-analyses, the average effect of incentive interventions was greater than control for short-term (≤six months) smoking cessation (relative risk (95% confidence intervals): 2.48 (1.77 to 3.46); long-term (>six months) smoking cessation (1.50 (1.05 to 2.14)); attendance for vaccination or screening (1.92 (1.46 to 2.53)); and for all behaviours combined (1.62 (1.38 to 1.91)). There was not convincing evidence that effects were different between different groups of behaviours. Meta-regression found some, limited, evidence that effect sizes decreased as post-intervention follow-up period and incentive value increased. However, the latter effect may be confounded by the former.ConclusionsThe available evidence suggests that financial incentive interventions are more effective than usual care or no intervention for encouraging healthy behaviour change.Trial RegistrationPROSPERO CRD42012002393

1. Overview

This repository contains datasets used to evaluate potential improvements to flood detectability afforded by combining data collected by Landsat, Sentinel-2, and Sentinel-1 for the first time globally. The datasets were produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review.

2. Dataset Descriptions

There are two datasets included here.

(a) A global grid of revisit periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their combination [GlobalMedianRevisits.zip]

A global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid.
Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2 and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020.
The following equation is used to calculate revisit periods:

Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured)

Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times.

(b) Average revisit periods of satellites for flood records in the DFO database [FloodInfo.zip]

Average Revisit Times of Landsat, Sentinel-1, Sentinel-2 and their ensemble are calculated for 5130 flood records in the Dartmouth Flood Observatory's (DFO) flood record database. These were appended to the already existing attributes of the database.

Context:

This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2015. It is the third update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 1.88 million geo-referenced wildfire records, representing a total of 140 million acres burned during the 24-year period.

Content:

This dataset is an SQLite database that contains the following information:

• Fires: Table including wildfire data for the period of 1992-2015 compiled from US federal, state, and local reporting systems.
• FOD_ID = Global unique identifier.
• FPA_ID = Unique identifier that contains information necessary to track back to the original record in the source dataset.
• SOURCE_SYSTEM_TYPE = Type of source database or system that the record was drawn from (federal, nonfederal, or interagency).
• SOURCE_SYSTEM = Name of or other identifier for source database or system that the record was drawn from. See Table 1 in Short (2014), or \Supplements\FPA_FOD_source_list.pdf, for a list of sources and their identifier.
• NWCG_REPORTING_AGENCY = Active National Wildlife Coordinating Group (NWCG) Unit Identifier for the agency preparing the fire report (BIA = Bureau of Indian Affairs, BLM = Bureau of Land Management, BOR = Bureau of Reclamation, DOD = Department of Defense, DOE = Department of Energy, FS = Forest Service, FWS = Fish and Wildlife Service, IA = Interagency Organization, NPS = National Park Service, ST/C&L = State, County, or Local Organization, and TRIBE = Tribal Organization).
• NWCG_REPORTING_UNIT_ID = Active NWCG Unit Identifier for the unit preparing the fire report.
• NWCG_REPORTING_UNIT_NAME = Active NWCG Unit Name for the unit preparing the fire report.
• SOURCE_REPORTING_UNIT = Code for the agency unit preparing the fire report, based on code/name in the source dataset.
• SOURCE_REPORTING_UNIT_NAME = Name of reporting agency unit preparing the fire report, based on code/name in the source dataset.
• LOCAL_FIRE_REPORT_ID = Number or code that uniquely identifies an incident report for a particular reporting unit and a particular calendar year.
• LOCAL_INCIDENT_ID = Number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.
• FIRE_CODE = Code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression (https://www.firecode.gov/).
• FIRE_NAME = Name of the incident, from the fire report (primary) or ICS-209 report (secondary).
• ICS_209_INCIDENT_NUMBER = Incident (event) identifier, from the ICS-209 report.
• ICS_209_NAME = Name of the incident, from the ICS-209 report.
• MTBS_ID = Incident identifier, from the MTBS perimeter dataset.
• MTBS_FIRE_NAME = Name of the incident, from the MTBS perimeter dataset.
• COMPLEX_NAME = Name of the complex under which the fire was ultimately managed, when discernible.
• FIRE_YEAR = Calendar year in which the fire was discovered or confirmed to exist.
• DISCOVERY_DATE = Date on which the fire was discovered or confirmed to exist.
• DISCOVERY_DOY = Day of year on which the fire was discovered or confirmed to exist.
• DISCOVERY_TIME = Time of day that the fire was discovered or confirmed to exist.
• STAT_CAUSE_CODE = Code for the (statistical) cause of the fire.
• STAT_CAUSE_DESCR = Description of the (statistical) cause of the fire.
• CONT_DATE = Date on which the fire was declared contained or otherwise controlled (mm/dd/yyyy where mm=month, dd=day, and yyyy=year).
• CONT_DOY = Day of year on which the fire was declared contained or otherwise controlled.
• CONT_TIME = Time of day that the fire was declared contained or otherwise controlled (hhmm where hh=hour, mm=minutes).
• FIRE_SIZE = Estimate of acres within the final perimeter of the fire.
• FIRE_SIZE_CLASS = Code for fire size based on the number of acres within the final fire perimeter expenditures (A=greater than 0 but less than or equal to 0.25 acres, B=0.26-9.9 acres, C=10.0-99.9 acres, D=100-299 acres, E=300 to 999 acres, F=1000 to 4999 acres, and G=5000+ acres).
• LATITUDE = Latitude (NAD83) for p...