Administrative information about individuals in Wales that use NHS services; such as address and practice registration history. It replaced the NHS Wales Administrative Register (NHSAR) in 2009.

Data drawn from GP practices via Exeter System.

This dataset provides linkage from anonymous individual to anonymous residences, thus enable to group households of individuals.

The single views are now provisioned to new projects and described here, the metadata for the old three-view WDSD version can be found in a separate legacy metadata entry.

This dataset contains the data for Individuals serving custodial sentences in England & Wales who appear within records from the prison data source, p-NOMIS.

Individuals interacting with the Probation Service in England and Wales.

A register of children diagnosed with type 1 diabetes, collected from Paediatric diabetes clinics in Wales. Maintained by the Brecon Group. Data has been collected since 1995 and is complete since then, but some people diagnosed earlier are also included.

This dataset provides detailed information about daily educational attendance within Wales.

Attendance data in the EDUW schema was discontinued after 2019 and the Education Daily Attendance Dataset (EDAD) schema replaced it. This dataset contains more detailed information on attendance than was previously available in EDUW.

The UK Cystic Fibrosis Registry is a national, secure, centralized database sponsored and managed by the Cystic Fibrosis Trust, with UK National Health Service (NHS) research ethics approval and consent from each person for whom data are collected. First established in 1995, it records longitudinal health data on all people with cystic fibrosis (CF) in England, Wales, Scotland and Northern Ireland, and to date has captured data on over 12,000 individuals.

If you are interested in using the CYFI dataset in the SAIL Databank, please contact SAIL via the website, along with also discussing your project with the Cystic Fibrosis Registry team for further advice via email at: registry@cysticfibrosis.org.uk

This dataset is an extract and collation of 4 months of data from the Craft Tracking System run by the Australian Maritime Safety Authority (AMSA). This dataset shows the location of cargo ships, fishing vessels, passenger ships, pilot vessels, sailing boats, tankers and other vessel types at 1 hour intervals. The Craft Tracking System (CTS) and Mariweb are AMSA’s vessel traffic databases. They collect vessel traffic data from a variety of sources, including terrestrial and satellite shipborne Automatic Identification System (AIS) data sources. This dataset has been built from AIS data extracted from CTS, and it contains vessel traffic data for January - April 2023. The dataset covers the extents of Australia’s Search and Rescue Region. Each point within the dataset represents a vessel position report and is spatially and temporally defined by geographic coordinates and a Universal Time Coordinate (UTC) timestamp respectively. This dataset is a derivative of the monthly Craft Tracking System data available from https://www.operations.amsa.gov.au/Spatial/DataServices/DigitalData. As such this record is not authoritative about the source data. If you have any queries about the Craft Tracking System data please contact AMSA.
Description of the data: This data shows a high volume of cargo ships and tankers traveling between international destinations and the ports of Australia, as well as significant cargo traffic between domestic ports. These vessels tend to travel in straight lines along designated shipping lanes, or along paths that maximize their efficiency on route to their destination. Fishing activities are prominent in international waters, particularly in the Indian Ocean, Coral Sea, and Arafura Seas. The tracking of fishing vessels drops dramatically at the boundary of the Australian Exclusive Economic Zone (EEZ). Most domestic fishing activities appear to be closer to the Australian coast, often concentrating on the edge of the continental shelf. However, the data does not specifically indicate whether the vessels are domestic or international. Western Australia exhibits a great deal of vessel activity associated with the oil and gas industry. Each of these platforms is serviced by tugboats and tankers. At large ports, dozens of cargo ships wait in grid patterns to transit into the port. Shipping traffic in most of the Gulf of Carpentaria is relatively sparse, as the majority of cargo vessels travel from Torres Strait west into the Arafura Sea, bypassing the gulf. However, there is a noticeable concentration of fishing activity along the coast around Karumba and the Wellesley Islands, presumably associated with the prawning industry. Along the Queensland coastline, vessel traffic is dominated by cargo ships, which travel in designated shipping areas between the Great Barrier Reef and the mainland. There are three passages through the reef off Hay Point (Hydrographers Passage), north of Townsville (Palm Passage), and off Cairns (Grafton Passage). The Great Barrier Reef (GBR) region is frequented by pleasure crafts, sailing vessels, and passenger ships. Pleasure crafts mainly seem to visit the islands and outer reefs, while sailing vessels tend to stay within the GBR lagoon, traversing its length. Passenger ships ferry people to popular reef destinations such as reefs off the Whitsundays, Cairns, and Port Douglas, as well as Heron Island and Lady Musgrave Island. Many large passenger ships, presumably cruise vessels, travel between major ports and international destinations. These ships tend to travel 20 km further offshore than the majority of sailing boats. eAtlas Processing: The following is the processing that was applied to create this derivative dataset. This processing was functionally just a collation of 4 months of data, and a file format change (to GeoPackage) and a trimming of the length of the text attributes (which should not affect their values). Four months of data was used as this was the maximum practical limit of the rendering performance of QGIS and GeoServer.

The monthly CTS data was downloaded from https://www.operations.amsa.gov.au/Spatial/DataServices/DigitalData and unzipped. This data was then loaded into QGIS.

The Vector / Data Management Tools / Merge Vector Layers... tool was used to combine the 4 months of data: Input layers: cts_srr_04_2023_pt, cts_srr_03_2023_pt, cts_srr_02_2023_pt, cts_srr_01_2023_pt Save to GeoPackage: AU_AMSA_Craft-tracking-system_Jan-Apr-2023 Layername: AU_AMSA_Craft-tracking-system_Jan-Apr-2023

To reduce the size of the dataset the text attributes were trimmed to the length needed to store the attribute data. Processing Toolbox > Vector table > Refactor fields Input layer: AU_AMSA_Craft-tracking-sytem_Jan-Apr-2023 Remove attributes: layer, path (these were created by the Merge Vector Layers tool) Change: Source Expression, Original Length, New Length TYPE, 254, 80 SUBTYPE, 254, 20 TIMESTAMP, 50, 25 Refactored: AU_AMSA_Craft-tracking-system_Jan-Apr-2023_Trim.gpkg Layer name: au_amsa_craft_tracking_system_jan_apr_2023

Data dictionary:

CRAFT_ID: Double Unique identifier for each vessel LON: Double Longitude in decimal degrees LAT: Double Latitude in decimal degrees COURSE: Double Course over ground in decimal degrees SPEED: Double Speed over ground in knots TYPE: Text Vessel type NULL 'Cargo ship - All' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Cargo ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category D' 'Cargo ship - No additional info' 'Cargo ship - Reserved 5' 'Cargo ship - Reserved 6' 'Cargo ship - Reserved 7' 'Cargo ship - Reserved 8' 'Engaged in diving operations' 'Engaged in dredging or underwater operations' 'Engaged in military operations' 'Fishing' 'HSC - All' 'HSC - No additional info' 'HSC - Reserved 7' 'Law enforcement' 'Local 56' 'Local 57' 'Medical transport' 'Other - All' 'Other - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Other - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Other - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Other - No additional info' 'Other - Reserved 5' 'Other - Reserved 6' 'Other - Reserved 7' 'Other - Reserved 8' 'Passenger ship - All' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Passenger ship - Carrying DG, HS, or MP, IMO Hazard or pollutant category D' 'Passenger ship - No additional info' 'Passenger ship - Reserved 5' 'Passenger ship - Reserved 6' 'Passenger ship - Reserved 7' 'Pilot vessel' 'Pleasure craft' 'Port tender' 'Reserved' 'Reserved - All' 'Reserved - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Reserved - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Reserved - Reserved 6' 'Reserved - Reserved 7' 'Sailing' 'SAR' 'Ship according to RR Resolution No. 18 (Mob-83)' 'Tanker - All' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'Tanker - Carrying DG, HS, or MP, IMO Hazard or pollutant category D' 'Tanker - No additional info' 'Tanker - Reserved 5' 'Tanker - Reserved 6' 'Tanker - Reserved 7' 'Tanker - Reserved 8' 'Towing' 'Towing Long/Large' 'Tug' 'unknown code 0' 'unknown code 1' 'unknown code 100' 'unknown code 104' 'unknown code 106' 'unknown code 111' 'unknown code 117' 'unknown code 123' 'unknown code 125' 'unknown code 140' 'unknown code 150' 'unknown code 158' 'unknown code 2' 'unknown code 200' 'unknown code 207' 'unknown code 209' 'unknown code 223''unknown code 230' 'unknown code 253' 'unknown code 255' 'unknown code 4' 'unknown code 5' 'unknown code 6''unknown code 9' 'Vessel with anti-pollution facilities or equipment' 'WIG - All' 'WIG - Carrying DG, HS, or MP, IMO Hazard or pollutant category A' 'WIG - Carrying DG, HS, or MP, IMO Hazard or pollutant category B' 'WIG - Carrying DG, HS, or MP, IMO Hazard or pollutant category C' 'WIG - No additional info' 'WIG - Reserved 6' 'WIG - Reserved 7' SUBTYPE: Text Vessel sub-type NULL 'Fishing Vessel' 'Powerboat' LENGTH: Short integer Vessel length in metres BEAM: Short integer Vessel beam in metres DRAUGHT: Double Draught of the vessel, in metres. TIMESTAMP: Text Vessel position report UTC timestamp in dd/mm/yyyy hh:mm:ss AM/PM format

eAtlas notes: Fishing vessels are encoded as, TYPE: Fishing or TYPE: NULL, SUBTYPE: Fishing Vessel or TYPE: unknown code X. A lot of the vessels with and unknown code appeared to be predominately fishing vessels based on their behaviour. Location of the data: This dataset is filed in the eAtlas enduring data repository at: data on-custodian\ongoing\AU_AMSA_Craft-tracking-system

Individuals appearing as defendants in criminal cases dealt with by the magistrates' court in England and Wales (including Youth Courts). Companies appearing as defendants have been excluded.

Here are a few use cases for this project:

1. Maritime Surveillance and Security: The "Object Detection" model can classify different types of ships and boats to identify potential security threats, illegal activities, or unauthorized boats in monitored areas.

2. Navigation Assistance: The model can be used in navigation systems to help sailors and captains identify other types of vessels in their proximity. This can help avoid collisions and provide safer navigation in crowded waters.

3. Search and Rescue Operations: During search and rescue operations, the identification and classification of objects like boats or buoys could help pinpoint the location of missing or stranded individuals.

4. Fishing Industry: The model can be used to monitor fishery areas, distinguish between different types of vessels, track movements, and enforce regulations in protected zones.

5. Water Sports and Recreation: Useful in managing water sports activities, like kayaking or sailing races, ensuring routes are clear and tracking participants for safety purposes.

The National Survey for Wales (NSW) is commissioned by the Welsh Government, Sport Wales, Natural Resources Wales, and the Arts Council of Wales. It is used in decision-making by those organisations and other public-sector bodies across Wales.

The survey covers a broad range of topics including education, exercise, health, social care, use of the internet, community cohesion, wellbeing, employment, and finances. The topics change regularly in order to keep up with changing needs for information. Some topics are only included periodically, where the results are slow-changing; and some topics are only asked of a random subsample of respondents, which allows more topics to be included.

The survey sample is adults aged 16+ living in private households. The survey does not cover people living in communal establishments (e.g. care homes, residential youth offender homes, hostels, and student halls). A range of demographic questions is included, to allow for detailed cross-analysis of the results.

Fieldwork runs continuously, with topics updated each April. Each year’s data (from April to the following March) is deposited around six months later at the UK Data Archive so that the data is widely accessible for research purposes. The data collected is also linked with other datasets via the SAIL Databank (excluding any respondents who have asked for their data to not be linked). Respondents are able to opt out of having their results linked if they wish.

From 2016-17 onwards, the National Survey for Wales replaced the Welsh Health Survey by incorporating questions on health conditions, physical activity, alcohol consumption and smoking.

Attendance and clinical information for all general practice interactions: includes patients symptoms, investigations, diagnoses, prescribed medication and referrals to tertiary care.

This dataset covers 83% of the population of Wales and 80% of GP practices in Wales. It is linkable with anonymised fields for individuals and GPs to other datasets, including bespoke project specific cohorts. Each GP practice uses a clinical information system to maintain an electronic health record for each of their patients; capturing the signs, symptoms, test results, diagnoses, prescribed treatment, referrals for specialist treatment and social aspects relating to the patients home environment.

The majority of the data is entered by the clinician during the patient consultation. Test results are electronically transferred from secondary care systems.

There are no standard rules for recording data within primary care clinical information systems. Therefore, each individual clinician can record information in their own way. The majority use Read Code Terminology, however, sometimes this is applied behind the scenes by the clinical system and sometimes local codes are used. Read codes are not as precise as ICD 10 or OPCS codes.

Coding standards have been agreed on for conditions monitored by the QOF (Quality Outcomes Framework) returns. Since the implementation of QOF these conditions have been coded in a more consistent way.

Time coverage varies between each practice.

This dataset covers people involved in family court cases in England and Wales.

This dataset is a population-based electronic cohort containing health-related information on people with and without diagnosed dementia. It was developed by applying coding algorithms to linked routinely-collected datasets.

Analysis of ‘Boat Launch Sites by State Parks or Marine Facility’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ab17cc49-40ac-442e-95c2-522c8d793008 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The New York State Office of Parks, Recreation and Historic Preservation (OPRHP) oversees more than 250 state parks, historic sites, recreational trails, golf courses, boat launches and more, encompassing nearly 350,000 acres, that are visited by 74 million people annually. These facilities contribute to the economic vitality and quality of life of local communities and directly support New York’s tourism industry. Parks also provide a place for families and children to be active and exercise, promoting healthy lifestyles. The agency is responsible for the operation and stewardship of the state park system as well as advancing a statewide parks, historic preservation, and open space mission. The New York State Office of Parks, Recreation, and Historic Preservation operates marinas and boat launching sites across the state. For more information about boating in New York State parks, visit http://nysparks.com/recreation/boating/

--- Original source retains full ownership of the source dataset ---

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Scorpio Tanker's Sailing into Deep Waters (SBBA)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

NHS Wales hospital admissions (Inpatients and daycases) dataset comprising of attendance and clinical information for all hospital admissions: includes diagnoses and operations performed. Includes spell and episode level data.

The data are collected and coded at each hospital. Administrative information is collected from the central PAS (Patient Administrative System), such as specialty of care, admission and discharge dates. After the patient is discharged the handwritten patient notes are transcribed by clinical coder into medical coding terminology (ICD10 and OPCS).

The data held in PEDW is of interest to public health services since it can provide information regarding both health service utilisation and also the incidence and prevalence of disease. However, since PEDW was created to track hospital activity from the point of view of payments for services, rather than epidemiological analysis, the use of PEDW for public health work is not straightforward. For example:

Counts will vary depending on the number of diagnosis fields used e.g. primary only, all fields; There are a number of different things that can be counted in PEDW e.g. individual episodes of care, admissions, discharges, periods of continuous care (group of episodes), patients or procedures. When looking at diagnosis or procedures the number will vary depending on whether you look at only in the primary diagnosis / procedure field or if the secondary fields are also included. Coding practices vary. In particular, coding practices for recording secondary diagnoses is likely to vary for different hospitals. This makes regional variations more difficult to interpret. The validation process led by the Corporate Health Improvement Programme and implemented by Digital Health and Care Wales (DHCW) is aiming to address some of these inconsistencies.

Due to the complexity and pitfalls of PEDW it is recommended that any PEDW requests for public health purposes are discussed with a member of the SAIL team. In turn the SAIL will seek advice from DHCW if required.

This dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Euronav (EURN) - Sailing into Uncharted Waters?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Overview

• Introduction
• Object Classes
  • Human
  • Wind-Sup-Board

Introduction

This dataset is designed to aid in the detection of humans and wind-sup-boards in aquatic environments. The goal is to accurately identify and annotate these objects within images captured from an aerial perspective. The following classes are included:

• Human: Individuals present in the water.
• Wind-Sup-Board: Stand-up paddleboards, with or without sails.

Object Classes

Human

Description

The human class includes all visible parts of a person in the water. Typically, these appear as small figures often partially submerged or floating, with limbs or heads protruding above/below the water.

Instructions

• Annotate any visible part of the human body, including heads, arms, and legs.
• If a person is only partially visible due to water coverage, include visible part and infer the rest for the bounding box.
• Ensure the bounding box encompasses all visible body parts without extending into the water unnecessarily.
• Disambiguate from floating debris or equipment by focusing on the shape and form of human limbs.
• Humans can be on wind-sup-boards, still label them.

Wind-Sup-Board

Description

Wind-sup-boards are elongated, often oval-shaped boards used for stand-up paddling, sometimes equipped with a sail. They appear as larger floating objects in the water.

Instructions

• Annotate the full extent of the board, from tip to tail, including any visible sail.
• Include the paddle if it is laying on the board.
• Boards almost always have human's on them, so ensure to create separate human bounding boxes too.

Basketball, sailing, football, athletics, archery, swimming, kayaking, judo, horse riding, cycling... in total, nearly sixty activities are listed.
Rich with 126 places of practice spread over 42 municipalities, this dataset developed in close collaboration with the Departmental House of Persons with Disabilities and the departmental committees handisport and adapted sport lists the Costa Rican structures offering activities adapted to people with disabilities in order to promote their sports practices.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Sailing into Success: Can HCVI Stock Navigate Market Tides?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data