The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

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As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

columns are individual-id, sex and population

The Real World Data (RWD) Solution in the medical market has emerged as a transformative force, reshaping the landscape of healthcare analytics and decision-making. Defined as the data collected from a variety of sources outside of traditional clinical trials-such as electronic health records, insurance claims, and

Areal statistics will be produced for selected 25 GOP defined areas (http://gop.meteo.uni-koeln.de/gop/)

Statistics MSG for the various areas at 15 min resolution (see below)
Mean, median and standard deviation of brightness temperatures for all 8 IR MSG channels
- probability density function (20 classes, min=180, max=350) for each of the 8 IR MSG channels

Mean cloud coverage and mean cloud area fraction
- probability density function (20 classes, min=0, max=1) for cloud probability

Mean, median and standard deviation of cloud top pressure
- probability density function (20 classes, min=0, max=1020) for cloud top pressure

Mean, median and standard deviation of integrated_water_vapor
- probability density function (20 classes, min=0, max=10) for integrated_water_vapor

Station statistics will be produced for selected 58 GOP defined station (http://gop.meteo.uni-koeln.de/gop/)

Statistics MSG for the various station at 15 min resolution (see below) on 3x3 MSG-grid. It includes atmospheric water vapor content, cloud top pressure, and the brightness temperatures at 15 MSG channels.

Datasets from https://ourworldindata.org/coronavirus

Statistics illustrates consumption, production, prices, and trade of Data processing machines; n.e.s. in heading no. 8471 in the World from 2007 to 2024.

IntroductionOptimise:MS is an observational pharmacovigilance study aimed at characterizing the safety profile of disease-modifying therapies (DMTs) for multiple sclerosis (MS) in a real world population. The study will categorize and quantify the occurrence of serious adverse events (SAEs) in a cohort of MS patients recruited from clinical sites around the UK. The study was motivated particularly by a need to establish the safety profile of newer DMTs, but will also gather data on outcomes among treatment-eligible but untreated patients and those receiving established DMTs (interferons and glatiramer acetate). It will also explore the impact of treatment switching.MethodsCausal pathway confounding between treatment selection and outcomes, together with the variety and complexity of treatment and disease patterns observed among MS patients in the real world, present statistical challenges to be addressed in the analysis plan. We developed an approach for analysis of the Optimise:MS data that will include disproportionality-based signal detection methods adapted to the longitudinal structure of the data and a longitudinal time-series analysis of a cohort of participants receiving second-generation DMT for the first time. The time-series analyses will use a number of exposure definitions in order to identify temporal patterns, carryover effects and interactions with prior treatments. Time-dependent confounding will be allowed for via inverse-probability-of-treatment weighting (IPTW). Additional analyses will examine rates and outcomes of pregnancies and explore interactions of these with treatment type and duration.ResultsTo date 14 hospitals have joined the study and over 2,000 participants have been recruited. A statistical analysis plan has been developed and is described here.ConclusionOptimise:MS is expected to be a rich source of data on the outcomes of DMTs in real-world conditions over several years of follow-up in an inclusive sample of UK MS patients. Analysis is complicated by the influence of confounding factors including complex treatment histories and a highly variable disease course, but the statistical analysis plan includes measures to mitigate the biases such factors can introduce. It will enable us to address key questions that are beyond the reach of randomized controlled trials.

This presentation is aimed at those who are starting up the learning curve on all the international socioeconomic data sources out there. Comparisons of coverage, ease of use, advantages and disadvantages will be presented for services such as World Development Indicators (WDI), International Financial Statistics (IFS), the Economist Intelligence Unit (EIU) WorldDATA, United Nations Data bases, etc. A secondary focus will evaluate what else is worth exploring besides the big, well-known data providers just mentioned. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-220.)

Philippines Exports: Rest of the World data was reported at 378.197 USD mn in Mar 2025. This records an increase from the previous number of 351.864 USD mn for Feb 2025. Philippines Exports: Rest of the World data is updated monthly, averaging 247.437 USD mn from Mar 2019 (Median) to Mar 2025, with 69 observations. The data reached an all-time high of 420.782 USD mn in Jan 2025 and a record low of 95.891 USD mn in Apr 2020. Philippines Exports: Rest of the World data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.JA042: Trade Statistics: Imports and Exports: Value: by Economic Bloc. This refers to exports other than APEC, East Asia, ASEAN and European Union.

The project embrases the simulations with the coupled climate model ECHAM4/OPYC, relevant for the third assessment report (TAR, http://www.ipcc.ch/ipccreports/assessments-reports.htm) of the Intergovernmental Panel on Climate Change (IPCC).The IPCC has been established by WMO and UNEP to assess scientific, technical and socio-economic information, relevant for the understanding of climate change, its potential impacts and options for adaption and mitigation. A more detailed description about the work of the IPCC can be found at the IPCC homepage ( http://www.ipcc.ch ) and at ( www.grida.no/climate/ipcc ). As a further development the Special Report on Emission Scenarios (SRES, http://www.grida.no/Climate/ipcc/emission/) have been constructed, to describe (potential) future developments in the global enviroment with special reference to the production of greenhouse gases and aerosol precursor emissions. A set of four scenarios families (A1, A2, B1, B2) have been developed (see also http://www.grida.no/climate/ipcc/emission/index.htm ) The model output data are available at the World Data Center for Climate, Hamburg.( wdc-climate.de ). Projection of future trends based on selected emission scenarios are provided through this project for a great many model variables of ECHAM4/OPYC. For a selected set of variables the IDCC-Data Distribution Center provides additional data sets from a multitude of models that contribute to the IPCC-TAR report (project: IPCC_DDC_TAR).

The Intergovernmental Panel on Climate Change (IPCC) has been established by WMO und UNEP to assess scientific, technical and socio-economic information, relevant for the understanding of climate change, its potential impacts and option for adaption and migration. Projection of future trends for a number of key variables are provided through this section of the DDC (http://ipcc-data.org/sim/gcm_clim/SRES_TAR ). This information contained in either IS92 emission scenarios (IPCC 1992), the Special Report on Emission Scenarios (IPCC 2000, SRES) or published model studies using data from these scenarios. Six alternative IPCC scenarios (IS92a to f) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumption affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adoped. The SRES scenarios have been constructed to explore future developments in the global enviromental with special reference to the production of greenhouse gases and aerosol precursor emission. A set of four scenario families (A1, A2, B1, B2) have been developed that each of this storylines describes one possible demographic, polito-economic, societal and technological future. Model experiments, also using different forcing scenarios, were calculated at other modeling centres. Emissions Scenarios. 2000 ,Special Report of the Intergovernmental Panel on Climate Change Nebojsa Nakicenovic and Rob Swart (Eds.) Cambridge University Press, UK. pp 570

Classification results on several real-world data sets.

This dataset provides ensemble means, ensemble standard deviations and ensemble minima/maxima for ModE-Sim Set 1420-3. The output of the individual ensemble members and forcings can be found in the other datasets within this dataset group. Information on the experiment design and the variables included in this dataset can be found in the experiment summary and the additional information provided with it. Example run scripts of the simulations can be found in second additional info file at the experiment level. For a detailed description of the ModE-Sim please refer to the documentation paper (reference provided in the summary at the experiment level).

The statistics , AIC, BIC, A⋆, W⋆, D⋆ and p⋆ for D1, D2 and D3.

Philippines Imports: Rest of the World data was reported at 839.753 USD mn in Mar 2025. This records an increase from the previous number of 754.774 USD mn for Feb 2025. Philippines Imports: Rest of the World data is updated monthly, averaging 804.681 USD mn from Mar 2019 (Median) to Mar 2025, with 69 observations. The data reached an all-time high of 1.441 USD bn in Mar 2022 and a record low of 182.709 USD mn in Apr 2020. Philippines Imports: Rest of the World data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.JA042: Trade Statistics: Imports and Exports: Value: by Economic Bloc. This refers to exports other than APEC, East Asia, ASEAN and European Union.

The descriptive statistics related to D1, D2 and D3.

Partial and overall ranks of all the methods of estimation of GD by various values of model parameters.