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.

Northern Virginia was by far the world’s largest data center market by total inventory as of the first quarter of 2024. The region had a reported inventory of over 2,500 MW, which was more than twice that of London, the second-largest market. Northern Virginia remains the world’s most important data center market The Northern Virginia region has established a remarkable position as a data center hub. Data center facilities cluster around Loudon, Prince William, and Fairfax counties, with operators keen to locate close to east coast metropolitan areas and key subsea cables. Vacancy rates in such facilities are notably low, reflecting the continued demand for capacity in the region. However, new rules and standards could see a slowdown in construction moving forward. In addition, while Northern Virginia is not the world’s most expensive market for data center construction, considerably lower construction costs in alternative North American markets could prompt investors to reassess. London is the largest of Europe’s FLAPD markets Europe’s leading data center hubs are often collectively referred to as the FLAPD market, standing for Frankfurt, London, Amsterdam, Paris, and Dublin. These markets are driving forwards a European data center industry forecast to generate almost 98 billion euros in 2024, with newly adopted European Union sustainability reporting requirements set to dominate the agenda.

Northern Virginia had the lowest vacancy rate among leading data center markets in 2024. Northern Virginia is the world's largest data center market by total inventory.

See live version at https://docs.google.com/spreadsheets/d/1ejOJTNTL5ApCuGTUciV0REEEAqvhI2Rd2FCoj7afops/edit#gid=0

As of March 2025, *** data centers were listed as being located in Germany, the most of any European nation. Data centers are facilities housing critical IT infrastructure designed to store, process, and manage vast volumes of data. The United States is home to the largest share of data centers worldwide, with over ***** facilities.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance publicly available dataset has 33 elements for all COVID-19 cases shared with CDC and includes demographics, geography (county and state of residence), any exposure history, disease severity indicators and outcomes, and presence of any underlying medical conditions and risk behaviors. This dataset requires a registration process and a data use agreement.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

Requesting Access to the COVID-19 Case Surveillance Restricted Access Detailed Data Please review the following documents to determine your interest in accessing the COVID-19 Case Surveillance Restricted Access Detailed Data file: 1) CDC COVID-19 Case Surveillance Restricted Access Detailed Data: Summary, Guidance, Limitations Information, and Restricted Access Data Use Agreement Information 2) Data Dictionary for the COVID-19 Case Surveillance Restricted Access Detailed Data The next step is to complete the Registration Information and Data Use Restrictions Agreement (RIDURA). Once complete, CDC will review your agreement. After access is granted, Ask SRRG (eocevent394@cdc.gov) will email you information about how to access the data through GitHub. If you have questions about obtaining access, email eocevent394@cdc.gov.

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

COVID-19 case surveillance data are collected by jurisdictions and are shared voluntarily with CDC. For more information, visit: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.

The deidentified data in the restricted access dataset include demographic characteristics, state and county of residence, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and comorbidities.

All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 case reports have been routinely submitted using standardized case reporting forms.

On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification. All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for lab-confirmed or probable cases.

On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.

Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question "Was the individual hospitalized?" where the possible answer choices include "Yes," "No," or "Unknown," the blank value is recoded to "Missing" because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race, ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<11 COVID-19 case records with a given values). Suppression includes low frequency combinations of case month, geographic characteristics (county and state of residence), and demographic characteristics (sex, age group, race, and ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These and other COVID-19 data are available from multiple public locations:

Culminating more than four years of processing data, NASA and the National Geospatial-Intelligence Agency (NGA) have completed Earth's most extensive global topographic map. The mission is a collaboration among NASA, NGA, and the German and Italian space agencies. For 11 days in February 2000, the space shuttle Endeavour conducted the Shuttle Radar Topography Mission (SRTM) using C-Band and X-Band interferometric synthetic aperture radars to acquire topographic data over 80% of the Earth's land mass, creating the first-ever near-global data set of land elevations. This data was used to produce topographic maps (digital elevation maps) 30 times as precise as the best global maps used today. The SRTM system gathered data at the rate of 40,000 per minute over land. They reveal for the first time large, detailed swaths of Earth's topography previously obscured by persistent cloudiness. The data will benefit scientists, engineers, government agencies and the public with an ever-growing array of uses. The SRTM radar system mapped Earth from 56 degrees south to 60 degrees north of the equator. The resolution of the publicly available data is three arc-seconds (1/1,200th of a degree of latitude and longitude, about 295 feet, at Earth's equator). The final data release covers Australia and New Zealand in unprecedented uniform detail. It also covers more than 1,000 islands comprising much of Polynesia and Melanesia in the South Pacific, as well as islands in the South Indian and Atlantic oceans. SRTM data are being used for applications ranging from land use planning to "virtual" Earth exploration. Currently, the mission's homepage "http://www.jpl.nasa.gov/srtm" provides direct access to recently obtained earth images. The Shuttle Radar Topography Mission C-band data for North America and South America are available to the public. A list of complete public data set is available at "http://www2.jpl.nasa.gov/srtm/dataprod.htm" The data specifications are within the following parameters: 30-meter X 30-meter spatial sampling with 16 meter absolute vertical height accuracy, 10-meter relative vertical height accuracy, and 20-meter absolute horizontal circular accuracy. From the JPL Mission Products Summary, "http://www.jpl.nasa.gov/srtm/dataprelimdescriptions.html". The primary products of the SRTM mission are the digital elevation maps of most of the Earth's surface. Visualized images of these maps are available for viewing online. Below you will find descriptions of the types of images that are being generated:

• Radar Image
• Radar Image with Color as Height
• Radar Image with Color Wrapped Fringes
  -Shaded Relief
• Perspective View with B/W Radar Image Overlaid
• Perspective View with Radar Image Overlaid, Color as Height
• Perspective View of Shaded Relief
• Perspective View with Landsat or other Image Overlaid
• Contour Map - B/W with Contour Lines
• Stereo Pair
• Anaglypgh

The SRTM radar contained two types of antenna panels, C-band and X-band. The near-global topographic maps of Earth called Digital Elevation Models (DEMs) are made from the C-band radar data. These data were processed at the Jet Propulsion Laboratory and are being distributed through the United States Geological Survey's EROS Data Center. Data from the X-band radar are used to create slightly higher resolution DEMs but without the global coverage of the C-band radar. The SRTM X-band radar data are being processed and distributed by the German Aerospace Center, DLR.

The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been requested.

About this dataset

Context

This list ranks the 32 cities in the Centre County, PA by Puerto Rican population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Puerto Rican Population: This column displays the rank of city in the Centre County, PA by their Puerto Rican population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Puerto Rican Population: The Puerto Rican population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Puerto Rican. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Centre County Puerto Rican Population: This tells us how much of the entire Centre County, PA Puerto Rican population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

As of December 31, 2024, Texas-based Digital Realty listed 230 data center buildings across the globe as part of its consolidated portfolio. This was down from 242 the previous year, with the company often selling properties that it does not consider as central to its growth strategy. Though Europe is home to the largest number of individual Digital Realty buildings, the United States accounted for a far greater share of net rentable space.

Context

The CMS National Plan and Provider Enumeration System (NPPES) was developed as part of the Administrative Simplification provisions in the original HIPAA act. The primary purpose of NPPES was to develop a unique identifier for each physician that billed medicare and medicaid. This identifier is now known as the National Provider Identifier Standard (NPI) which is a required 10 digit number that is unique to an individual provider at the national level.

Once an NPI record is assigned to a healthcare provider, parts of the NPI record that have public relevance, including the provider’s name, speciality, and practice address are published in a searchable website as well as downloadable file of zipped data containing all of the FOIA disclosable health care provider data in NPPES and a separate PDF file of code values which documents and lists the descriptions for all of the codes found in the data file.

Content

The dataset contains the latest NPI downloadable file in an easy to query BigQuery table, npi_raw. In addition, there is a second table, npi_optimized which harnesses the power of Big Query’s next-generation columnar storage format to provide an analytical view of the NPI data containing description fields for the codes based on the mappings in Data Dissemination Public File - Code Values documentation as well as external lookups to the healthcare provider taxonomy codes . While this generates hundreds of columns, BigQuery makes it possible to process all this data effectively and have a convenient single lookup table for all provider information.

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:nppes?_ga=2.117120578.-577194880.1523455401

https://console.cloud.google.com/marketplace/details/hhs/nppes?filter=category:science-research

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @rawpixel from Unplash.

Inspiration

What are the top ten most common types of physicians in Mountain View?

What are the names and phone numbers of dentists in California who studied public health?

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 single levels from 1940 to present".

About this dataset

Context

This list ranks the 35 cities in the Centre County, PA by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Black Population: This column displays the rank of cities in the Centre County, PA by their Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Black Population: The Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Centre County Black Population: This tells us how much of the entire Centre County, PA Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Context

This dataset contains Hospital General Information from the U.S. Department of Health & Human Services. This is the BigQuery COVID-19 public dataset. This data contains a list of all hospitals that have been registered with Medicare. This list includes addresses, phone numbers, hospital types and quality of care information. The quality of care data is provided for over 4,000 Medicare-certified hospitals, including over 130 Veterans Administration (VA) medical centers, across the country. You can use this data to find hospitals and compare the quality of their care

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.cms_medicare.hospital_general_info.

Sample Query

How do the hospitals in Mountain View, CA compare to the average hospital in the US? With the hospital compare data you can quickly understand how hospitals in one geographic location compare to another location. In this example query we compare Google’s home in Mountain View, California, to the average hospital in the United States. You can also modify the query to learn how the hospitals in your city compare to the US national average.

“#standardSQL SELECT MTV_AVG_HOSPITAL_RATING, US_AVG_HOSPITAL_RATING FROM ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS MTV_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE city = 'MOUNTAIN VIEW' AND state = 'CA' AND hospital_overall_rating <> 'Not Available') MTV JOIN ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS US_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE hospital_overall_rating <> 'Not Available') ON 1 = 1”

What are the most common diseases treated at hospitals that do well in the category of patient readmissions? For hospitals that achieved “Above the national average” in the category of patient readmissions, it might be interesting to review the types of diagnoses that are treated at those inpatient facilities. While this query won’t provide the granular detail that went into the readmission calculation, it gives us a quick glimpse into the top disease related groups (DRG)
, or classification of inpatient stays that are found at those hospitals. By joining the general hospital information to the inpatient charge data, also provided by CMS, you could quickly identify DRGs that may warrant additional research. You can also modify the query to review the top diagnosis related groups for hospital metrics you might be interested in. “#standardSQL SELECT drg_definition, SUM(total_discharges) total_discharge_per_drg FROM bigquery-public-data.cms_medicare.hospital_general_info gi INNER JOIN bigquery-public-data.cms_medicare.inpatient_charges_2015 ic ON gi.provider_id = ic.provider_id WHERE readmission_national_comparison = 'Above the national average' GROUP BY drg_definition ORDER BY total_discharge_per_drg DESC LIMIT 10;”

This document describes publicly available urban climate data from the DWD Climate Data Center (CDC). The measurements are taken in the center of large cities in order to be able to compare the city and the surrounding area and to document the influence of different urban structures on the meteorological parameters. Due to their urban location, the stations of the special urban climate measurement network cannot meet the usual World Meteorological Organization (WMO) standards, but follow the recommendations of the WMO Instruments and observing methods report no. 81 “Initial guidance to obtain representative meteorological observations at urban sites” (Oke, 2006). The data in the recent/ directory is preliminary data (not yet fully quality checked). Further information: https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate_urban/hourly/precipitation/recent/BESCHREIBUNG_obsgermany_climate_urban_hourly_precipitation_recent_de.pdf

About this dataset

Context

This list ranks the 35 cities in the Centre County, PA by Native Hawaiian and Other Pacific Islander (NHPI) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Pacific Islander Population: This column displays the rank of cities in the Centre County, PA by their Native Hawaiian and Other Pacific Islander (NHPI) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Pacific Islander Population: The Pacific Islander population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Pacific Islander. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Centre County Pacific Islander Population: This tells us how much of the entire Centre County, PA Pacific Islander population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This document describes publicly available urban climate data from the DWD Climate Data Center (CDC). The measurements are taken in the center of large cities in order to be able to compare the city and the surrounding area and to document the influence of different urban structures on the meteorological parameters. Due to their urban location, the stations of the special urban climate measurement network cannot meet the usual World Meteorological Organization (WMO) standards, but follow the recommendations of the WMO Instruments and observing methods report no. 81 “Initial guidance to obtain representative meteorological observations at urban sites” (Oke, 2006). The data in the "recent/" directory is preliminary data (not yet fully quality checked). Further information: https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate_urban/hourly/wind/recent/BESCHREIBUNG_obsgermany_climate_urban_hourly_wind_recent_de.pdf

As of April 2025, there were 449 data centers in China, the most of any country or territory in the Asia-Pacific region. China had the fourth-highest number of data centers worldwide. Data centers in China As the leading market in public cloud in the Asia-Pacific region and an aspiring global leader in artificial intelligence, China has placed considerable weight on data center infrastructure, which underlies most of the advances in internet technology. The country dominates the global data center market in terms of revenue, trailing only the United States. In addition, China accounted for around 16 percent of the worldwide hyperscale data center capacity in the 4th quarter of 2023. The data center segment revenue in China is expected to have an annual growth rate of around 8.3 percent between 2025 and 2029. The outlook of data centers in the Asia-Pacific region The pandemic has accelerated enterprise digitalization across the Asia-Pacific region, driving a surge in demand for computational power. This trend, coupled with advancements in artificial intelligence and the region's significant population growth, points to a promising future for data centers in the region. For instance, the revenue in the data center market in India was forecast to grow further and is set to reach about 11.85 billion U.S. dollars by 2029. Meanwhile, economic growth and increasing internet penetration rates in Southeast Asian countries have been the primary drivers for data center demand growth in the subregion.

Service Records and Retention System (SRRS) is historical digital data set DSI-9949, a collection of products created by the U.S. National Weather Service (NWS) and archived at the National Centers for Environmental Information (NCEI) [formerly National Climatic Data Center (NCDC)]. SRRS was a network of computers and associated hardware whose purpose was to transmit and store a large number of NWS products and make them available as needed. Basic meteorological and hydrological data, analyses, forecasts, and warnings are distributed among NWS offices over the AFOS (Automation of Field Operations and Services) communications system since 1978. These include PIREP (aircraft reports from pilots), AIRMET (aeronautical meteorological bulletins), SIGMET (significant meteorological information), surface and upper air plotted unanalyzed maps, air stagnation, precipitable water, Forecasts such as wind and temperature aloft, thickness and analysis, fire weather, area, local, zone, state, agricultural advisory, and terminal; and Warnings such as marine, severe weather, hurricane and tornado. The AFOS system was developed to increase the productivity and effectiveness of NWS personnel and to increase the timeliness and quality of their warning and forecasting services.

This format version of the SRRS data was archived at NCEI from 1983 to 2001 (when a new format was created). The NCEI can service requests for products from the SRRS; two types of products are available to the user: 1) graphic displays of meteorological analyses and forecast charts (limited), and 2) alphanumeric displays of narrative summaries and meteorological/hydrological data. The following is a partial list of historical SRRS products available through the NCDC: rawinsonde data above 100 MB; AIREPS buoy reports; coastal flood warning; Coast Guard surface report; climatological report (daily and misc, incl monthly reports); weather advisory Coastal Waters Forecast Center (CWSU); weather statement; 3- to 5-day extended forecast; average 6- to 10-day weather outlook (local and national); aviation area forecast winds aloft forecast; flash flood statements, watches and warnings; flood statement; flood warning forecast; medium range guidance; FOUS relative humidity/temperature guidance; FOUS prog max/min temp/POP guidance; FOUS wind/cloud guidance; Great Lakes forecast; hurricane local statement; high seas forecast; international aviation observations; local forecast; local storm report; rawinsonde observation - mandatory levels;, METAR formatted surface weather observation; marine weather statement; short term rorecast; non-precipitation warnings/watches/advisories; nearshore marine forecast (Great Lakes only), offshore aviation area forecast; offshore forecast; other marine products, other surface weather observations, pilot report plain language, ship report, state pilot report, collective recreational report; narrative radar summary radar observation; hydrology-meteorology data report; river summary; river forecast; miscellaneous river product; river recreation statement; ; regional weather summary; surface aviation observation; preliminary notice of watch and canc msg SVR; local storm watch and warning; cancelation msg SELS watch; point information message; state forecast discussion ; state forecast rawinsonde observation - significant levels; surface ship report at intermediate synoptic time; surface ship report at non-synoptic time; surface ship report at synoptic time; special weather statement international; SIGMET severe local storm watch and area outline; special marine warning; intermediate surface synoptic observation; main surface synoptic observation; severe thunderstorm warning; severe weather statement; severe storm outlook; narrative state weather summary; terminal forecast; tropical cyclone discussion; marine/aviation tropical cyclone advisory; public tropical cyclone advisory; tornado warning; transcribed weather broadcast; tropical weather discussion; tropical weather outlook and summary; AIRMET SIGMET zone forecast; terminal forecast (prior to 7/1/96); winter weather warnings, watches, advisories; marine advisory/warning; special marine warning; miscellaneous product convective SIGMET ; local ice forecast; area forecast discussion; public information statement. SRRS (DSI-9949) by the Gateway SRRS (DSI-9957; C00583). NWS products after 2001 can be obtained from those systems, from NCEI.

About this dataset

Context

This list ranks the 35 cities in the Centre County, PA by American Indian and Alaska Native (AIAN) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Native American Population: This column displays the rank of cities in the Centre County, PA by their American Indian and Alaska Native (AIAN) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Native American Population: The Native American population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Native American. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Centre County Native American Population: This tells us how much of the entire Centre County, PA Native American population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.