This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.

An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

United States - Net Percentage of Domestic Banks Increasing Spreads of Loan Rates over Banks' Cost of Funds to Large and Middle-Market Firms was -4.80% in April of 2025, according to the United States Federal Reserve. Historically, United States - Net Percentage of Domestic Banks Increasing Spreads of Loan Rates over Banks' Cost of Funds to Large and Middle-Market Firms reached a record high of 98.20 in October of 2008 and a record low of -70.40 in April of 2005. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Net Percentage of Domestic Banks Increasing Spreads of Loan Rates over Banks' Cost of Funds to Large and Middle-Market Firms - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Net Percentage of Large Domestic Banks Increasing Spreads of Loan Rates Over Banks' Cost of Funds to Large and Middle-Market Firms (SUBLPDCILTSLGNQ) from Q2 1990 to Q3 2025 about funds, cost, spread, large, percent, domestic, Net, loans, banks, depository institutions, rate, and USA.

United States LS: Spread of Loan Rates for Large Firms: Tightened: Net data was reported at -26.500 % in Oct 2018. This records an increase from the previous number of -31.900 % for Jul 2018. United States LS: Spread of Loan Rates for Large Firms: Tightened: Net data is updated quarterly, averaging -26.500 % from Apr 1990 (Median) to Oct 2018, with 115 observations. The data reached an all-time high of 98.200 % in Oct 2008 and a record low of -70.400 % in Apr 2005. United States LS: Spread of Loan Rates for Large Firms: Tightened: Net data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S025: Senior Loan Officer Opinion Survey: Lending Policies for Large & Middle Market Firms.

Fifty-seven white mica clasts were separated from five samples taken from near the bases of turbidites ranging in age from early Albian to middle Eocene. Twenty two (39%) of the micas have ages between 260 and 340 Ma and five (9%) have older ages (~400-600 Ma). The former age range is characteristic of the North American Alleghenian orogeny and the Iberian Variscan orogeny. The latter range is characteristic of the North American Acadian orogeny and older basement rocks in the Grand Banks and Newfoundland areas. Both age ranges are present in the middle Eocene sample, but only the younger range occurs in the middle Albian sample. This difference could be a sampling artifact. If this is not the case, then the most likely explanation is that the Acadian-aged micas within the Meguma Zone underlying the Grand Banks were totally reset by Alleghenian reactivation of the zone, a feature which occurs extensively in Nova Scotia. The addition of Acadian-aged micas in the middle Eocene sample may reflect a change in sediment provenance as drainage systems unrelated to rift topography developed. With the exception of one clast dated at 186 Ma, the 12 other micas obtained from the upper Paleocene sample yielded ages between 55 and 74 Ma, with 7 falling within ±2 m.y. of the 57-Ma age of the sample indicated by the biostratigraphic age-depth plot for Site 1276. This, together with the volcaniclastic content of the sample, indicates an input from near-contemporaneous volcanism. The nearest known occurrences of near-contemporaneous late Paleocene volcanism that could have produced white micas are in Greenland and Portugal, some 2000 and 1500 km distant, respectively, from Site 1276 during the Paleocene. However, ages of volcanism in these areas indicate that they could probably not be sources of micas younger than 60 m.y., which suggests some as-yet unknown volcanic source in the North Atlantic area. Accumulation in the Grand Banks area of airborne-transported volcaniclastic material from eruptions of slightly different ages, followed by a single resedimentation event, could account for the spread of dates obtained from the sample. White micas from the lowermost Albian sample show a spread of ages between 37 and 284 Ma that is completely different from the age distribution pattern of the middle Albian and middle Eocene samples. The sample location is between, and at least 25 m above and below, two igneous sills dated at 98 and 105 Ma. The sills have narrow thermal aureoles and ages older than the youngest detrital micas in the sample. It is unlikely, therefore, that the spread of mica ages in the sample is due to partial resetting of ages caused by thermal effects associated with the intrusion of the sills. The resetting may have been associated with a longer lived thermal event.

According to the Cognitive Market Research Report, the Data Center Interconnect Market size in 2023 was XX Million and is projected to have a compounded annual growth rate of XX% from 2024 to 2031. Furthermore, the rising number of data centers and increased use of cloud storage is driving the market expansion. The Datacenter Interconnect market is further segmented by type and end-use. The hardware type and Communications Service Providers dominate their particular segment. Europe accounted for the highest revenue share in the year 2023. The expanding number of data centres, increased investment in cloud technologies, and the development of end-user markets are among the primary reasons driving European data centres' investment in the interconnect industry. Leading market companies are investing extensively in R&D to extend their product lines, which will fuel further growth in the data centre interconnect market. Market participants are also engaged in a variety of strategic initiatives to broaden their worldwide presence, including new product releases, contractual agreements, mergers and acquisitions, increased investments, and collaboration with other organizations.

Market Dynamics of Data Center Interconnect

Key Drivers for Data Center Interconnect Market

Increasing Number of Data Centers to Drive Market Growth for Data Centre Interconnect: Data centres, with house computers for data storage and processing, have expanded fast in response to increasing demand. The United States has more data centres than any other market, as it is home to major data producers and consumers such as Facebook, Amazon, Microsoft, and Google. Data servers and data centres are in high demand due to increased data output and utilization across sectors. According to CloudScene data6 from 110 countries, there were almost 8,000 data centres worldwide. Six nations account for the bulk of data centres which are the United States (33%), the United Kingdom (5.7%), Germany (5.5%), China (5.2%), Canada (3.3%), and the Netherlands (3.4%). OECD member nations account for 77%, while NATO members account for around 64%. Furthermore, data centre service providers are increasing their colocation and cloud offerings. End-user firms (such as telecom and financial organizations) that choose to establish their data centres are primarily responsible for the interconnected data centre sector becoming a worldwide investment hotspot. Due to data centre expansion and spread, enhanced fibre utilization, and low-cost pluggable modules, industries, namely OTT, ISPs, the financial industry, and the public sector, are creating use cases for DCI networks. The proliferation of data centres is also fueling a surge in DCI, which helps businesses to link their data centres, cloud providers, and other data center operators for easier data and resource sharing. Hence with such rise of data centers and the benefits provided drive the market growth.

Increased use of cloud storage and adoption of cloud-based solutions: Cloud-based storage solutions are today's most practical and effective way to keep data online. There are various cloud computing solution vendors. Because this industry is so large, every major technology business now has its own data centre, which dramatically boosts user income. The migration to cloud-based solutions, as well as the increase in organizational data traffic and big data analytics, are expected to drive development in the data centre interconnect market. Backup and storage are becoming increasingly important as the quantity of data created grows.
Data centre interconnect tools to enable communication and information exchange between its linked components, as well as the data centres' internal and external networks. Companies employ these solutions to establish solid connections between data centres and their linked devices, allowing for faster and more secure data transfers. Furthermore, the usability and accessibility of cloud-based apps have contributed to the expansion of the data centre interconnect industry. For instance, Equinix is a leading global provider of digital infrastructure. They link industry-leading organizations in banking, manufacturing, retail, transportation, government, healthcare, and education in a digital-first world. Business leaders use their trusted worldwide platform to safely and sustainably link the core infrastructur...

This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data. The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record.

In the decision support system Fire Risk Forest and Land, fire risk forecasts are calculated and meteorological data are reported to support decisions on fire in vegetation. Once the forecast has ended, a new calculation of the fire risk values is made based on the latest available weather data. This “analysis data” is stored. After each year’s fire season, the analysed historical dataset of the model-calculated boxes of 2.8 km * 2.8 km is saved with the midpoint given in x and y coordinates. Please note that the WMS service only contains the previous year’s data. Each 2.8 km * 2.8 km square has the attributes listed below (some variation may occur from year to year): ID: Internal MSB Task Puncture: Internal MSB task for unique grid box. The data was added in 2015, before there was no value. Time: It was used until 2014 to indicate the date of statistical values. As of 2015, dates will be stored in the “Date” field. Temp: Temperature in degrees Celsius 2 m above ground at 12 UTC according to the calculation model for the current grid. RH: Relative humidity expressed in % 2 m above the ground at 12 UTC according to the calculation model for the current grid grid. Wind speed: Wind speed in m/s (medium wind) 10 m above the ground at 12 UTC according to the calculation model for the current grid pane. Downstairs: Precipitation current day 18 UTC-18 UTC the previous day according to the calculation model for the current grid grid. FFMC: Fine Fuel Moisture Code represents the humidity of leaves and grass. The maximum water storage in this layer is less than 1 mm. Included in the FWI model. DMC: The Duff Moisture Code represents the humidity in a slightly deeper layer than the most superficial in the FWI model, e.g. moss and the upper part of the humus layer. The storage in this layer corresponds to about 15 mm of water. DC: The Drought Code shows the moisture content of thick compact humus layers (about 100 mm water). DC is part of the FWI model. ISI: The initial spread index. A measure of the rate of spread of the fire. An index used to calculate an FWI. Calculated from FFMC and reinforced by wind speed. BUI: Buildup index. Can be seen as a general measure of humidity for the slightly deeper soil layers. An index value used to calculate a fire risk value, FWI. Weighted average of DMC and DC. FWI: Fire Weather Index. Fire risk value calculated from ISI and BUI, which in turn are based on three basic values for moisture content in different layers (FFMC, DMC and DC). The input to the calculation is the daily rainfall as well as temperature, relative humidity and wind speed in the middle of the day. FWI index: Index of spread risk and fire behaviour in forest land. Based on the basic value FWI (see above). Currently divided into six different index levels (1, 2, 3, 4, 5, 5E) where index 1 is the lowest risk and index 5E is the highest risk. Fuel dehydration: Index values for the dehydration of the fuel and the lower layers of soil that are most important for a forest fire. Currently divided into six different index levels (1, 2, 3, 4, 5, 5E) where index 1 is the lowest risk and index 5E is the highest risk. Medium: Daily average temperature 2 m above ground according to SMHI calculation model for the current grid grid. RN: Dispersion rate in m/min for fire in uncut, unclaimed last year weeds. GFI: Fire risk levels for grass fires, the following indications are used: Very large grass fire risk (6) High Grass Fire risk (5) Moderate grass fire risk (4) Small grass fire risk (3) End of Grass Fire Season (2) Snow — Snow-covered land (1) Data missing/Not season (-1) Wind direction: Wind direction in degrees from where the wind comes from (division 360 degrees, 180 degrees equivalent to southern wind) at 12 UTC according to the calculation model for the current grid pane. E: The east-west position (centre) of the grid pane specified in SWEREF 99 TM. NOTE: prior to 2015, this information was entered in box ‘y’. N: The north-south position of the grid (centre point) specified in SWEREF99 TM. NOTE: prior to 2015, this information was entered in box “x”. Municipality: The commune code of the municipality within which the coordinate in the fields “E” and “N” is located.

The size of the US Data Center Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.00% during the forecast period.A data center is a facility that keeps computer systems and networking equipment housed, processing, and transmitting data. It represents the infrastructure on which organizations carry out their IT operations and host websites, email servers, and database servers. Data centers, therefore, are imperative to any size business: small start-ups or large enterprise since they enable digital transformation, thus making business applications available.The US data center industry is one of the largest and most developed in the world. The country boasts robust digital infrastructure, abundant energy resources, and a highly skilled workforce, making it an attractive destination for data center operators. Some of the drivers of the US data center market are the growing trend of cloud computing, internet of things (IoT), and high-performance computing requirements.Top-of-the-line technology companies along with cloud service providers set up major data center footprints in the US, mostly in key regions such as Silicon Valley and Northern Virginia, Dallas, for example. These data centers support applications such as e-commerce-a manner of accessing streaming services-whose development depends on its artificial intelligence financial service type. As demand increases concerning data center capacity, therefore, the US data centre industry will continue to prosper as the world's hub for reliable and scalable solutions. Recent developments include: February 2023: The expansion of Souther Telecom to its data center in Atlanta, Georgia, at 345 Courtland Street, was announced by H5 Data Centers, a colocation and wholesale data center operator. One of the top communication service providers in the southeast is Southern Telecom. Customers in Alabama, Georgia, Florida, and Mississippi will receive better service due to the expansion of this low-latency fiber optic network.December 2022: DigitalBridge Group, Inc. and IFM Investors announced completing their previously announced transaction in which funds affiliated with the investment management platform of DigitalBridge and an affiliate of IFM Investors acquired all outstanding common shares of Switch, Inc. for USD approximately USD 11 billion, including the repayment of outstanding debt.October 2022: Three additional data centers in Charlotte, Nashville, and Louisville have been made available to Flexential's cloud customers, according to the supplier of data center colocation, cloud computing, and connectivity. By the end of the year, clients will have access to more than 220MW of hybrid IT capacity spread across 40 data centers in 19 markets, which is well aligned with Flexential's 2022 ambition to add 33MW of new, sustainable data center development projects.. Key drivers for this market are: , High Mobile penetration, Low Tariff, and Mature Regulatory Authority; Successful Privatization and Liberalization Initiatives. Potential restraints include: , Difficulties in Customization According to Business Needs. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

Snapshot img

Spreads Market Size 2024-2028

The spreads market size is forecast to increase by USD 7.52 billion at a CAGR of 4.2% between 2023 and 2028.

The market is experiencing significant growth, driven primarily by the increasing trend towards on-the-go consumption and the growing popularity of e-commerce channels. The consumers' busy lifestyles have led to a surge in demand for convenient and portable food options, including spreads and sandwiches. Moreover, the rise of e-commerce platforms has made it easier for consumers to access a wide range of spreads from various brands, further fueling market growth. However, the market also faces challenges,One major obstacle is the health concerns associated with spreads, particularly those high in sugar and saturated fats.
As consumers become more health-conscious, there is a growing demand for healthier spread options. Another challenge is the intense competition in the market, with numerous players vying for market share. Companies must differentiate themselves by offering unique and innovative products to meet the evolving needs and preferences of consumers. To capitalize on opportunities and navigate challenges effectively, market participants must stay abreast of consumer trends and respond with agility and innovation.

What will be the Size of the Spreads Market during the forecast period?

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The market continues to evolve, with financial institutions increasingly relying on advanced data analysis techniques to gain insights and make informed decisions. Data quality is paramount, as enterprise solutions implement data warehousing and financial modeling to ensure accurate and reliable information. Data governance and marketing analysis employ machine learning and sales forecasting to identify trends and patterns in big data. Freemium models and artificial intelligence are transforming customer segmentation, enabling businesses to target their offerings more effectively. Cloud computing platforms and spreadsheet software offer user-friendly data dashboards for business process automation and user experience optimization.
Predictive modeling and collaboration tools facilitate real-time data analysis and scenario planning for investment firms. Business intelligence software and data visualization tools provide valuable insights for business users, while risk management and operations optimization rely on prescriptive analytics and data analytics software. Portfolio management and investment analysis benefit from interactive reports and data integration, enabling advanced analytics and mobile accessibility. Data storytelling and user interfaces enhance the value of data, while data security remains a critical concern. Subscription models and project management tools enable data mining and workflow automation for power users. The continuous dynamism of the market underscores the importance of staying informed and adaptable to evolving trends and patterns.

How is this Spreads Industry segmented?

The spreads industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Distribution Channel

  Offline
  Online


End-User

  Households
  Food Service
  Industrial


Product Type

  Jams & Jellies
  Nut Butters
  Cheese Spreads
  Savory Spreads


Packaging

  Jars
  Tubes
  Packets


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Distribution Channel Insights

The offline segment is estimated to witness significant growth during the forecast period.

The market encompasses various retail sectors, including department stores, supermarkets, hypermarkets, convenience stores, and restaurants. Major retail chains, such as Tesco Plc (Tesco) and Walmart Inc. (Walmart), have dedicated sections for spreads, offering a diverse range of butter, fruit, and chocolate spreads. companies employ marketing strategies, like branding through signages and discounts on product packages, to attract consumers. Walmart and Walgreens are long-standing retailers of spreads. Operating in the organized retail sector, companies consider factors like geographical presence, production and inventory management ease, and goods transportation. Businesses utilize enterprise solutions, such as data warehousing, financial modeling, and data governance, to manage their spreads offerings.

Machine learning and predictive analytics enable sales forecasting and customer segmentation. Data visualization tools help in data storytelling and risk management. Cloud-based platforms facilitate business planning

United States LS: Spread of Loan Rates for LF(SRLF): Tightened Considerably data was reported at 0.000 % in Oct 2018. This stayed constant from the previous number of 0.000 % for Jul 2018. United States LS: Spread of Loan Rates for LF(SRLF): Tightened Considerably data is updated quarterly, averaging 0.000 % from Jan 2008 (Median) to Oct 2018, with 44 observations. The data reached an all-time high of 40.000 % in Oct 2008 and a record low of 0.000 % in Oct 2018. United States LS: Spread of Loan Rates for LF(SRLF): Tightened Considerably data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S025: Senior Loan Officer Opinion Survey: Lending Policies for Large & Middle Market Firms. Senior Loan Officer Survey Questionnaire: For applications for C&I loans or credit lines—other than those to be used to finance mergers and acquisitions—from large and middle market firms that your bank currently is willing to approve, how have the Spreads of loan rates over your bank's cost of funds for those loans changed over the past three months?

The social assistance explorer contains a harmonised panel dataset of social assistance indicators spanning 2000-2015. It has been developed to support comparative research on emerging welfare institutions. Comparative analysis of social protection institutions in low and middle income countries is scarce. Yet social assistance accounts for most of the recent expansion of welfare institutions. The project collected data on programme design and objectives, institutionalisation, reach, and financial resources. Key indicators can be aggregated at country and region levels.Since the turn of the century low and middle income countries have introduced or expanded programmes providing direct transfers to families in poverty or extreme poverty as a means of strengthening their capacity to exit poverty. The rationale underpinning these programmes is that stabilising and enhancing family income through transfers in cash and in kind will enable programme participants to improve their nutrition, ensure investment in children's schooling and health, and help overcome economic and social exclusion. The expansion of antipoverty transfer programmes has accelerated. Estimates suggest that around 1 billion people in developing countries reside with someone in receipt of a transfer. As would be expected, the spread of social assistance has been slower and more tentative in low income countries due to implementation and finance constraints and limited elite political support. Antipoverty transfer programmes in developing countries show large variation in design, effectiveness, scale, and objectives. In most countries, there are several interventions running alongside one another with diverse priorities and designs, and often targeting different groups. In many countries social public assistance programmes work alongside social insurance programmes for formal sector workers and humanitarian or emergency assistance. Social assistance focuses on groups in poverty, provides medium term support, and is budget-financed. The spread of social assistance in developing countries has revealed significant gaps in the knowledge, for example as regards their effectiveness, reach, and sustainability. Comparative analysis is essential to fill in these gaps and improve national, regional and global policy. For example, achieving a zero target for extreme poverty, as has been suggested in the context of the post-2015 international development agenda, would require effective and permanent institutions ensuring the benefits from economic growth reach the poorest. Social assistance is essential to achieving this goal. This research project focuses on improving research infrastructure on social assistance, in terms of concepts, indicators and data. This is urgently needed to support comparative analysis of emerging social assistance institutions. The project will identify indicators to assess social assistance programmes and will collect information on these for 2000 to 2015 for all developing countries. The database will be made available online to researchers and policy makers globally. As part of the project, the database will be analysed to examine patterns or configurations in social assistance programmes and institutions. Our interest is in identifying ideal types, broad features of social assistance programmes or institutions which enable reducing the large diversity of programmes and interventions to their core characteristics. These ideal types are social assistance regimes. Further analysis will test for potential combinations of political, demographic, economic and social factors linked to specific social assistance regimes. This analysis will allow us to examine what conditions can help explain the expansion of social assistance in developing countries; what factors influence the specific configuration of social assistance institutions in different countries and regions; and what conditions are needed for their effectiveness and sustainability. This research will throw light on the contribution of social assistance to the reduction of poverty and vulnerability and to economic and social development. The data collection included all countries defined as low and middle income in the 2016 version of the World Bank Country Classification. An inventory of potential social assistance programmes was developed for each country. The definition described above was then applied to identify social assistance programmes. For some countries with a large number of small or localised programmes, the data collection focused on nationwide, large-scale, and/or leading programmes. For example, some states in India have localised programmes. These were excluded from the data collection. In sub-Saharan Africa some programmes are very small in scale but they are significant in leading the expansion of social assistance. They were included. Where programmes consolidate pre-existing programmes, for example Brazil's Bolsa Família, the dataset includes Bolsa Família as well as its component programmes. Data were collected from a variety of sources: global and regional datasets (ASPIRE, ODI, CEPAL, ADB's SPI, IPC-PG); national government websites; programme agency reports; research papers; evaluation reports; policy documents; IFIs project documentation and reports; personal communication with programme agencies. The collection of the data was organised around a codebook, describing each of the variables and the specific coding of the information. The codebook was constructed after extensive consultation with specialist researchers. The codebook is available from the data webpage in the website. Specialist consultants supported data collection in had-to-reach areas. The data collected were checked against alternative sources of information where available.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on pressure levels from 1940 to present".

Amur honeysuckle bush (Lonicera maackii) and Morrow's honeysuckle (Lonicera morrowii) are two of the most aggressively invasive species to become established throughout areas along the Blue River in metropolitan Kansas City, Missouri. These two large, spreading shrubs (locally referred to as bush honeysuckle in the Kansas City metropolitan area) colonize the understory, crowd out native plants, and may be allelopathic, producing a chemical that restricts growth of native species. Removal efforts have been underway for more than a decade by local conservation groups such as Bridging The Gap and Heartland Conservation Alliance, who are concerned with the loss of native species diversity associated with the spread of bush honeysuckle. Bush honeysuckle produces leaves early in the spring before almost all other vegetation and retains leaves late in the fall after almost all other species have lost their leaves. Appropriately timed imagery can be used during early spring and late fall to map the extent of bush honeysuckle. Using multispectral imagery collected in February 2016 and true color aerial imagery collected in March 2016, a coverage map of bush honeysuckle in the study area was made to investigate the extent of bush honeysuckle in a study area along the middle reach of the Blue River in the Kansas City metropolitan area in Jackson County, Missouri. The coverage map was further classified into unlikely, low-, and high-density bush honeysuckle density at a 30-foot cell size. The unlikely density class correctly predicted the absence and approximate density of bush honeysuckle for 86 percent of the field-verification points, the low-density class predicted the presence and approximate density with 73-percent confidence, and the high-density class was predicted with 67-percent confidence. This data was used to support the project work described in: Ellis, J.T., 2018, Remote sensing of bush honeysuckle in the Middle Blue River Basin, Kansas City, Missouri, 2016–17: U.S. Geological Survey Scientific Investigations Map XXXX, 1 sheet., https://doi.org/xxxx.

This zip file contains the R script to simulate the potential spread of the yellow-legged hornet in France and the data needed to do these simulations. This study was conducted in the frame of the project "FRELON" (2012-2014), which was supported by a French regional grant of Region Centre (France).

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

In the decision support system Fire Risk Forest and Mark, fire risk forecasts and weather are calculated as support for decisions on issues related to fire in vegetation. At the end of the forecast, a new calculation of the fire risk values is made on the basis of the latest available weather data. This “analysis data” is stored. After each year’s fire season, the analysed historical data set of the model-calculated boxes of 11*11 km with the midpoint in x and y coordinates is saved. Please note that the WMS service only contains the previous year’s data. Each 11*11 km box has the attributes listed below (some variation may occur from year to year): FFMC: Fine Fuel Moisture Code represents the humidity of leaves and grass. The maximum water storage in this layer is less than 1 mm. Included in the FWI model. DMC: The Duff Moisture Code represents humidity in a slightly deeper layer than the most superficial in the FWI model, such as moss and the superficial soil layer. The storage in this layer corresponds to about 15 mm of water DC: The Drought Code shows the moisture content of thick compact humus layers (about 100 mm water). DC is part of the FWI model. ISI: Initial Spread Index. A measure of fire dispersal speed. An index used to calculate an FWI value. Calculated from FFMC and amplified by wind speed. BUI: Buildup Index. Can be seen as a general measure of humidity for the slightly deeper soil layers. An index value used to calculate a fire risk value, FWI value. Weighted average of DMC and DC. FWI: Fire Weather Index. Fire risk calculated from the ISI and BUI, which in turn are based on three basic values for moisture content in different layers (FFMC, DMC and DC). The input data for the calculation is the daily rainfall as well as temperature, relative humidity and wind speed in the middle of the day. FWI index: Index of spread risk and fire behaviour in forest land. Based on baseline FWI (see above). Currently divided into six different index levels (1, 2, 3, 4, 5, 5E) where index 1 is the lowest risk and index 5E is the highest risk. HBV: Weighted soil moisture value based on the humidity of two layers of soil calculated with HBV-Forest, which is a special adaptation of the hydrological drainage model HBV (Water Balance Department of the Hydrological Agency). The input data for the model are daily rainfall and daily average temperature. HBV island: HBV upper ground layer (see HBV above) HBV-u: HBV lower ground layer (see HBV above) Please note that the View Service displays the midpoints for which values have been collected. Downloading gives access to the actual values as described above. Fire risk forecasts are available in the fire forest and land service during the respective calendar year. The service requires login and authorisation is given primarily to municipal rescue services, county administrative boards, forestry, etc. A simplified information from the system is also presented publicly without a login requirement. Here is the public service: https://www.msb.se/sv/amnesomraden/skydd-mot-olyckor-och-farliga-amnen/naturolyckor-och-klimat/skogsbrand-och-vegetationsbrand/brandriskprognoser/ or https://www.dinsakerhet.se/sakrare-fritid/skog-och-mark/brandriskprognos-for-gras--och-skogsbrander/

Although there are thousands of languages and over a hundred alphabets in use in the world today, most cultures use the same system for displaying numbers. This system is the Arabic numeral system, also called Hindu-Arabic numerals, which uses ten digits (1, 2, 3, 4, 5, 6, 7, 8, 9 and 0) to display numerical values. This system was developed by Indian mathematicians over 1,500 years ago, and it spread west through Persian and Arab cultures. The work of the Italian mathematician Fibonacci made this system known in Europe, and it proved popular with traders as it was much easier to use when making basic calculations. This system then went global through sixteenth century colonialism and globalization, replacing many of the previously-existing numerical systems, such as the Babylonian and Mayan systems (now considered complicated to use when making calculations). Roman Numerals Another system that was replaced by Arabic numerals was the system of Roman numerals. This system was used from as early as 500BCE, until the late-middle ages (which ended around 1500CE). Similarly to the Arabic system, the Roman system used a set number of digits in different combinations to represent different numerical values. The Roman system uses seven individual digits as the foundation of all numbers. The Roman digits with Arabic values are; I = 1, V = 5, X = 10, L = 50, C = 100, D = 500, and M = 1,000. Combinations The major difference between Roman and Arabic numerals is the way in which the digits are combined to create different numbers. In Roman numerology, the display of larger numbers requires more individual digits than in Arabic numerology, making it more complicated. In modern mathematics, the value of a digit depends on its position related to the decimal point, whereas in Roman numerals the digit I can come before or after other digits to show if it is more or less than the other numerals, which means that the digit with the lowest value is not always the furthest to the right (for example, IX = 9, whereas XI = 11). If one of the basic numerals is shown with a line above it, this means that the number has been multiplied by a thousand, for example, X with a line above it is equal to 10,000.

A quality assessment of the CFC-11 (CCl3F), CFC-12 (CCl2F2), HF, and SF6 products from limb-viewing satellite instruments is provided by means of a detailed intercomparison. The climatologies in the form of monthly zonal mean time series are obtained from HALOE, MIPAS, ACE-FTS, and HIRDLS within the time period 1991-2010. The intercomparisons focus on the mean biases of the monthly and annual zonal mean fields and aim to identify their vertical, latitudinal and temporal structure. The CFC evaluations (based on MIPAS, ACE-FTS and HIRDLS) reveal that the uncertainty in our knowledge of the atmospheric CFC-11 and CFC-12 mean state, as given by satellite data sets, is smallest in the tropics and mid-latitudes at altitudes below 50 and 20 hPa, respectively, with a 1sigma multi-instrument spread of up to ±5 %. For HF, the situation is reversed. The two available data sets (HALOE and ACE-FTS) agree well above 100 hPa, with a spread in this region of ±5 to ±10 %, while at altitudes below 100 hPa the HF annual mean state is less well known, with a spread ±30 % and larger. The atmospheric SF6 annual mean states derived from two satellite data sets (MIPAS and ACE-FTS) show only very small differences with a spread of less than ±5 % and often below ±2.5 %. While the overall agreement among the climatological data sets is very good for large parts of the upper troposphere and lower stratosphere (CFCs, SF6) or middle stratosphere (HF), individual discrepancies have been identified. Pronounced deviations between the instrument climatologies exist for particular atmospheric regions which differ from gas to gas. Notable features are differently shaped isopleths in the subtropics, deviations in the vertical gradients in the lower stratosphere and in the meridional gradients in the upper troposphere, and inconsistencies in the seasonal cycle. Additionally, long-term drifts between the instruments have been identified for the CFC-11 and CFC-12 time series. The evaluations as a whole provide guidance on what data sets are the most reliable for applications such as studies of atmospheric transport and variability, model-measurement comparisons and detection of long-term trends.