https://ckan.publishing.service.gov.uk/feeds/custom.atomdata.gov.uk - Custom query2024-03-29T00:17:01.660149+00:00dgupython-feedgenRecently created or updated datasets on data.gov.uk. Custom query: 'perform easily'https://ckan.publishing.service.gov.uk/dataset/c63e11b6-4bcb-4110-92c8-1c9bf9b5c26fFlood Risk for Extreme Events (FREE): Radiosonde, Wind Profiles Data and Model Output from the Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting project2017-07-17T17:46:37.214091+00:00The Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting Project is a NERC Flood Risk for Extreme Events (FREE) Research Programme project (Round 1 - NE/E002137/1 - Duration January 2007 - April 2010) led by Prof AJ Illingworth, University of Reading. This project investigates possible methods of producing ensemble weather forecasts at high-resolution. These ensembles will be used with raingauge and river flow to improve methods of flood forecasting. The dataset includes radiosonde and wind profiles in England and Wales derived using Doppler radar returns from insects. The radial velocity measurements from insects were converted into VAD profiles by fitting a sinusoid to radial velocities at constant range. All measured profiles have been interpolated to the instrument location.
Model output files from experiments assimilating radial winds from insects are also available.
Floods in the UK are often caused by extreme rainfall events. At present, weather forecasts can give an indication of a threat of severe storms which might cause flash floods, but are unable to say precisely when and where the downpours will occur, due to the complex range of processes and space-time scales involved. The first stage is to predict the air motions leading to convergence and ascent at a certain location where the precipitation will be initiated, then the development of the precipitation needs to be forecast, and hydrological models used to produce accurate, quantitative, probabilistic flood predictions. Data assimilation is a sophisticated mathematical technique that combines observations with model predictions to give an analysis of the current state of the atmosphere. This analysis may be used to initialise a weather forecast. Although precipitation is well observed by weather radar, attempts to assimilate radar data have had little success; by the time the rain develops the forecast model state is too far from the truth and the air motions are inconsistent with the position of the first radar precipitation echo.
We propose to overcome this problem by assimilating new types of data from weather radars. These provide information on the evolving humidity fields and air motions in the lower atmosphere so that the model can accurately track the developing storm before precipitation appears. The model used will be a new Met Office model that can be run with a resolution (i.e., grid-spacing) of order 1-4km. This enables storm-cloud motions to be explicitly calculated, rather than treated as a sub-grid-scale effect. Furthermore, current operational forecast models are only updated with observations every few hours; in the new approach the model will be updated much more frequently. This should yield weather forecasts with improved locations (in space-time) for rainfall events.
Initialisation errors are not the only cause of inaccuracies in storm-scale weather forecasts. Models are often run only for a small region of the world, and the data on the boundaries of this area provided from a larger-scale model. These data are known as lateral boundary conditions. Errors in these lateral boundary conditions and modelling errors also contribute to the errors in the forecast. Even if these errors were reduced, the nonlinear nature of the storm dynamics ensures that there is a limit, beyond which the value of deterministic forecasts becomes questionable. After that point it becomes important to determine the uncertainties in the forecast precipitation, so an ensemble approach is required. (An ensemble is a collection of perturbed forecasts that may be considered as a statistical sample of the forecast probability distribution.)
The appropriate construction of a storm-scale ensemble is an open question. We propose a structured approach where perturbations will be designed on the basis of physical insight into convective forcing mechanisms. The resulting probabilistic rainfall forecasts can be interfaced to hydrological models used for flood forecasting. For the first time, this project will allow different scales of application of these methods to be supported: ranging from localised flash flooding of small catchments, through to indicative first-alert forecasting with UK-coverage and forecasting of river discharges to the sea. The project will also assess the impacts of improvements in numerical weather prediction on flood forecast performance.
In this project we anticipate fruitful interactions between the different disciplines of observations and measurement, meteorology and hydrology. Radar assimilation software development and ensemble forecasts will take place using Met Office models, so improvements can be implemented operationally very easily. The use of operational radars makes this project well placed to take advantage of data from any extreme events occurring during the period of the study.2016-09-12T09:16:58.192362+00:00https://ckan.publishing.service.gov.uk/dataset/c31cd9b6-d5bd-4909-92ef-703fa32cadbbNERC Airborne Research and Survey Facility (ARSF) Remote Sensing Data2017-07-17T17:50:39.758486+00:00The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in the form of synoptic analogue and digital imagery for use in research, survey and monitoring programmes. Data offered by the facility includes:
1) Aerial photography data collected with an analogue camera, the Wild RC-10 visible NIR, in conjunction with CASI and ATM instruments.
2) Airborne Thematic Mapper (ATM): ARSF has flown two ATM instruments over the period 1982 - 2008: the Daedalus 1268 was operated from 1982 until 1998. Since 1996 and until 2008 an upgraded version - the Azimuth Systems AZ-16 was used, along with an improved data acquisition system.
3) LiDAR (Light Detection and Ranging) data from an Optech ALTM 3033 instrument. The sensor is on loan to the ARSF only for some periods of the year from the Unit of Landscape Modelling (ULM) at Cambridge University.
4) High spectral and spatial resolution data from the Compact Airborne Spectrographic Imager (CASI 2). The CASI 2, produced by Itres Research of Canada, is a two-dimensional CCD array-based pushbroom imaging spectrograph operated by ARSF until 2007
5) High spectral and spatial resolution data from the AISA Eagle and Hawk hyperspectral sensors (since 2007). The AISA Eagle is a 12 bit, pushbroom, hyperspectral sensor with a 1000 pixel swath width, covering the visible and near infra-red spectrum 400 - 970nm. The AISA Hawk is a 14 bit sensor able to capture short wave infrared wavelengths, 970 - 2450nm.
The ARSF currently uses a Dornier 228 aircraft. This extensively modified aircraft is not only capable of accommodating the current ARSF core instrumentation, as well as additional experimental optical and geophysical sensors, but is also configured to deploy a range of atmospheric instrumentation and samplers. Such a comprehensive data service cannot be easily achieved by other survey techniques. The operational flying season generally spans from early March until early October. Three elements determine this period: weather, solar zenith angle and vegetation state; maintenance on the aircraft; sensor maintenance as this is performed by the manufacturers between November and January. Every day during this season, the ARSF has to make difficult decisions on whether or not to attempt flying based on weather forecasts, and to prioritise the most important projects based on many parameters. Flying schedule is available from the ARSF website.
The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.2016-09-12T09:18:12.139466+00:00https://ckan.publishing.service.gov.uk/dataset/70037f3f-da94-4728-a02b-d1b5098c1f58National Audit of Inpatient Falls 20152017-09-19T11:36:37.600003+00:00Inpatient falls are common and remain a great challenge for the NHS. Falls in hospital are the most
commonly reported patient safety incidents, with more than 240,000 reported in acute hospitals and
mental health trusts in England and Wales every year (that is over 600 a day). All falls, even those that
do not result in injury, can cause older patients and their family to feel anxious and distressed. For those
who are frail, minor injuries from a fall can affect their physical function, resulting in reduced mobility,
and undermining their confidence and independence. Some falls in hospital result in serious injuries,
such as hip fracture (more than 3,000 per year) and serious head injuries, and these injuries can result in
death. Falls in hospitals are financially expensive, as they increase the length of stay and may require
increased care costs upon discharge. In 2007, inpatient falls were thought to cost trusts alone £15
million, and will be more expensive now.
Tackling the problem of inpatient falls is challenging. There are no single or easily defined interventions
which, when done on their own, are shown to reduce falls. However, research has shown that multiple
interventions performed by the multidisciplinary team and tailored to the individual patient can reduce
falls by 20–30%. These interventions are particularly important for patients with dementia or delirium,
who are at high risk of falls in hospitals. 2015-11-13T09:30:52.236304+00:00https://ckan.publishing.service.gov.uk/dataset/62a80fe4-4afc-4719-8022-a8af68a2982fNERC Airborne Research and Survey Facility (ARSF) Remote Sensing Data2018-06-19T15:46:53.756461+00:00The Airborne Research & Survey Facility (ARSF, formerly Airborne Remote Sensing Facility) is managed by NERC Scientific Services and Programme Management. It provides the UK environmental science community, and other potential users, with the means to obtain remotely-sensed data in the form of synoptic analogue and digital imagery for use in research, survey and monitoring programmes. Data offered by the facility includes:
1) Aerial photography data collected with an analogue camera, the Wild RC-10 visible NIR, in conjunction with CASI and ATM instruments.
2) Airborne Thematic Mapper (ATM): ARSF has flown two ATM instruments over the period 1982 - 2008: the Daedalus 1268 was operated from 1982 until 1998. Since 1996 and until 2008 an upgraded version - the Azimuth Systems AZ-16 was used, along with an improved data acquisition system.
3) LiDAR (Light Detection and Ranging) data from an Optech ALTM 3033 instrument. The sensor is on loan to the ARSF only for some periods of the year from the Unit of Landscape Modelling (ULM) at Cambridge University.
4) High spectral and spatial resolution data from the Compact Airborne Spectrographic Imager (CASI 2). The CASI 2, produced by Itres Research of Canada, is a two-dimensional CCD array-based pushbroom imaging spectrograph operated by ARSF until 2007
5) High spectral and spatial resolution data from the AISA Eagle and Hawk hyperspectral sensors (since 2007). The AISA Eagle is a 12 bit, pushbroom, hyperspectral sensor with a 1000 pixel swath width, covering the visible and near infra-red spectrum 400 - 970nm. The AISA Hawk is a 14 bit sensor able to capture short wave infrared wavelengths, 970 - 2450nm.
The ARSF currently uses a Dornier 228 aircraft. This extensively modified aircraft is not only capable of accommodating the current ARSF core instrumentation, as well as additional experimental optical and geophysical sensors, but is also configured to deploy a range of atmospheric instrumentation and samplers. Such a comprehensive data service cannot be easily achieved by other survey techniques. The operational flying season generally spans from early March until early October. Three elements determine this period: weather, solar zenith angle and vegetation state; maintenance on the aircraft; sensor maintenance as this is performed by the manufacturers between November and January. Every day during this season, the ARSF has to make difficult decisions on whether or not to attempt flying based on weather forecasts, and to prioritise the most important projects based on many parameters. Flying schedule is available from the ARSF website.
The NEODC holds the entire archive of Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) data acquired by the NERC ARSF. High-resolution scanned digital versions of the entire collection of analogue photographs are now also available as well as selected LiDAR-derived elevation and terrain models for selected sites flown using the sensor.2018-06-19T15:46:53.739987+00:00https://ckan.publishing.service.gov.uk/dataset/f9b6f936-026c-4b1f-859d-e536e6b53571Flood Risk for Extreme Events (FREE): Radiosonde, Wind Profiles Data and Model Output from the Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting project2018-06-19T15:48:21.332739+00:00The Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting Project is a NERC Flood Risk for Extreme Events (FREE) Research Programme project (Round 1 - NE/E002137/1 - Duration January 2007 - April 2010) led by Prof AJ Illingworth, University of Reading. This project investigates possible methods of producing ensemble weather forecasts at high-resolution. These ensembles will be used with raingauge and river flow to improve methods of flood forecasting. The dataset includes radiosonde and wind profiles in England and Wales derived using Doppler radar returns from insects. The radial velocity measurements from insects were converted into VAD profiles by fitting a sinusoid to radial velocities at constant range. All measured profiles have been interpolated to the instrument location.
Model output files from experiments assimilating radial winds from insects are also available.
Floods in the UK are often caused by extreme rainfall events. At present, weather forecasts can give an indication of a threat of severe storms which might cause flash floods, but are unable to say precisely when and where the downpours will occur, due to the complex range of processes and space-time scales involved. The first stage is to predict the air motions leading to convergence and ascent at a certain location where the precipitation will be initiated, then the development of the precipitation needs to be forecast, and hydrological models used to produce accurate, quantitative, probabilistic flood predictions. Data assimilation is a sophisticated mathematical technique that combines observations with model predictions to give an analysis of the current state of the atmosphere. This analysis may be used to initialise a weather forecast. Although precipitation is well observed by weather radar, attempts to assimilate radar data have had little success; by the time the rain develops the forecast model state is too far from the truth and the air motions are inconsistent with the position of the first radar precipitation echo.
We propose to overcome this problem by assimilating new types of data from weather radars. These provide information on the evolving humidity fields and air motions in the lower atmosphere so that the model can accurately track the developing storm before precipitation appears. The model used will be a new Met Office model that can be run with a resolution (i.e., grid-spacing) of order 1-4km. This enables storm-cloud motions to be explicitly calculated, rather than treated as a sub-grid-scale effect. Furthermore, current operational forecast models are only updated with observations every few hours; in the new approach the model will be updated much more frequently. This should yield weather forecasts with improved locations (in space-time) for rainfall events.
Initialisation errors are not the only cause of inaccuracies in storm-scale weather forecasts. Models are often run only for a small region of the world, and the data on the boundaries of this area provided from a larger-scale model. These data are known as lateral boundary conditions. Errors in these lateral boundary conditions and modelling errors also contribute to the errors in the forecast. Even if these errors were reduced, the nonlinear nature of the storm dynamics ensures that there is a limit, beyond which the value of deterministic forecasts becomes questionable. After that point it becomes important to determine the uncertainties in the forecast precipitation, so an ensemble approach is required. (An ensemble is a collection of perturbed forecasts that may be considered as a statistical sample of the forecast probability distribution.)
The appropriate construction of a storm-scale ensemble is an open question. We propose a structured approach where perturbations will be designed on the basis of physical insight into convective forcing mechanisms. The resulting probabilistic rainfall forecasts can be interfaced to hydrological models used for flood forecasting. For the first time, this project will allow different scales of application of these methods to be supported: ranging from localised flash flooding of small catchments, through to indicative first-alert forecasting with UK-coverage and forecasting of river discharges to the sea. The project will also assess the impacts of improvements in numerical weather prediction on flood forecast performance.
In this project we anticipate fruitful interactions between the different disciplines of observations and measurement, meteorology and hydrology. Radar assimilation software development and ensemble forecasts will take place using Met Office models, so improvements can be implemented operationally very easily. The use of operational radars makes this project well placed to take advantage of data from any extreme events occurring during the period of the study.2018-06-19T15:48:21.315831+00:00https://ckan.publishing.service.gov.uk/dataset/9e6023c7-4da1-4453-8ceb-82d0736433a4FCA performance scorecard - comparison metrics for personal current accounts 20212021-01-14T13:26:03.316225+00:00The UK Regulators Network (UKRN) have worked with the FCA, the Office of Gas and Electricity Markets (Ofgem), the Office of Communications (Ofcom), the Water Services Regulation Authority (Ofwat), and the Consumer Council for Water (CCW) to produce scorecards on the performance of regulated sectors.
This performance scorecard highlights some of the information available on personal current accounts (PCAs), and can help customers choose their provider by highlighting:
- good and poor performers
- how easily they can carry out day-to-day banking activities
- the reliability of the service they receive
These metrics should also increase the incentive for firms to offer better service, helping customers to get the most out of their personal current accounts.2021-01-14T13:21:12.303387+00:00https://ckan.publishing.service.gov.uk/dataset/505172d4-6b69-46f7-983c-15de3e75a96cNumber of website users who found the information about air quality easily available2021-11-10T20:09:11.476414+00:00Number of website users who found the information about air quality easily available
*This indicator is discontinued
2016-02-16T16:53:43.902467+00:00https://ckan.publishing.service.gov.uk/dataset/d1de2891-3dc4-419f-af61-43db4fa16cd3Employability Performance Rating (EPR) Annual Ratings2023-08-17T01:16:28.125209+00:00The EPR is an annual rating which generates a rating from zero to four stars for grant holders based upon a provider’s performance in three Key Performance Areas (KPAs): **delivery performance, quality and contract compliance.** For each KPA, performance is assessed through one or more performance indicators. The weighting each indicator contributes towards a KPA score has been developed in consultation with providers and the Performance Ratings should place a high emphasis on performance (i.e. outputs and results).
**Key Performance Area (KPA)** **Weighting** **Indicators (KPI)** **Weighting of indicator within KPA** **Contract Performance** 60% 1. Delivery against grant targets 48% 2. Delivery against grant diversity targets 12% **Quality** 30% 3. Conversion Factor 12% 4. Self assessment of quality 9% 5. Client Satisfaction 9% **Contract Compliance** 10% 6. Contract compliance and contractor pro-activity in delivery 10%
The idea behind the ratings is to identify good performance and also highlight where projects may need to improve to deliver better results. The ratings also allows the funding bodies to identify the top performing organisations more easily and make more informed funding decisions.
“The Employability Performance Rating tool is a great way of both demonstrating and measuring the value of European Social Fund funded programmes in London, and we are delighted that London’s ESF co-financing organisations have adopted it.” _Alex Conway, London European Programmes Director._
The 2017/18 ratings are now published. Please see the below document to view the different ratings providers have received. The briefing for the 2017/18 ratings is also available to download. Last year's ratings are still available to download below.
Full Employability Performance Rating details can be found in the charts and tables below:
In chart form, this will be available for this year's ratings soon - [http://data.london.gov.uk/employability-performance-rating-charts/](/employability-performance-rating-charts/)
Or in an excel spreadsheet below:2017-03-23T09:14:46.150448+00:00https://ckan.publishing.service.gov.uk/dataset/ae744375-0518-4b42-b54b-681a20d463d6Characterization results of the synthesis of iron sulfide phases (NERC grant NE/J008745/1)2024-03-22T15:15:58.794230+00:00Data produced from NERC Grant NE/J008745/1. Grant Abstract: Iron sulfides are widespread in the environment, where they regulate and control the global geochemical iron and sulfur cycles. However, despite their application as indicators for seawater anoxia and recorders of early-life isotopic and paleomagnetic data, iron sulfide minerals are still largely unexplored compared to, for example, iron oxide minerals or the silicates or carbonates. Numerous iron sulfide phases are known, but many are highly unstable or only partially stable for a short time in the environment. Even the least reactive iron sulfide, pyrite, is no longer stable once exposed to air at the Earth's surface. Its dissolution leads to the problem of acid mine drainage, where sulfuric acid and any trapped toxic metals are released with devastating effects on the environment near the mine. However, iron sulfides also have beneficial effects on the environment, as they easily incorporate metals within their structure, and thus could be sinks for toxic metals or radioactive elements. An intriguing aspect of iron sulfides is the crucial role they may have played in the Origin of Life. Thin layers of iron-nickel sulfide are believed to have formed in the warm, alkaline springs on the bottom of the oceans on Early Earth. They are increasingly considered to have been the early catalysts for a series of chemical reactions leading to the emergence of life. The oxygen-free production of various organic compounds, including amino acids and nucleic acid bases - the building blocks of DNA - is thought to have been catalyzed by small iron-nickel-sulfur clusters, which are structurally similar to the highly reactive present day iron sulfide minerals greigite and mackinawite, yet we know little about how they form. In view of the likely role of such reactive minerals in the conversion of pre-biotic CO2 on Early Earth, we may well be able to harness iron sulfides as present-day catalysts for the same process, thereby potentially aiding the slowing down of climate change by converting the CO2 we produce into useful chemicals. In today's world, the importance of such iron-nickel-sulfide clusters as catalysts has been confirmed, as several life-essential iron-sulfur proteins help transform volatiles such as H2, CO and CO2 into other useful and less harmful chemicals. In all of the above examples, it is important to understand that the reactions that lead to the formation of all these minerals which are necessary for any of the geologically stable minerals to exist (i.e., pyrite) all rely on our understanding of the nucleation and growth of unstable precursors or of the reaction transforming one phase to another. Furthermore, the structure and reactivity of each of these phase determines its role and potential application in the environment. A few research groups in the UK and abroad have carried out high quality investigations of the properties of a number of iron sulfide minerals, but it is particularly difficult to investigate events early on in the nucleation process, even though they set the scene for all subsequent transformations. In this project we propose to employ a robust combination of state-of-the-art computation and experiment to unravel the nucleation of iron sulfide mineral phases. We aim to follow the reactions from the emergence of the first building block in solution, through agglomeration into larger clusters, their aggregation into nano-particles and the eventual transformation into the final crystal. We anticipate that this project, investigating short-lived processes which are only now accessible to study through the development of high temporal and spatial resolution in-situ characterization techniques and High Performance Computing platforms, will lead to in-depth step-by-step quantitative insight into the iron sulfide formation pathways and enhance our fundamental understanding of how a mineral nucleates in solution.2020-02-26T14:16:31.281991+00:00