The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) is the world's most extensive surface marine meteorological data collection. Building on national and international partnerships, ICOADS provides a variety of user communities with easy access to many different data sources in a consistent format. Data sources range from early historical ship observations to more modern, automated measurement systems including moored buoys and surface drifters. Past versions of the ICOADS dataset have been published as monthly files while holding a daily version of the product for internal use only. NCEI has since developed a reformatted daily product of the dataset that now aligns with the monthly, ready for public use. The objective of this initiative is to sustain the quality and usability of this high-profile ICOADS product for stakeholders that have requested the need for an expanded product. ICOADS R3.0.2 Daily is now developed and released.

The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.

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.

The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.

The NOAA Regional Surface Data - 1989 (NCDC) Data Set contains hourly surface meteorological data for the FIFE area. Though the measurements presented in this data set were not taken precisely at the FIFE study area, it is hypothesized that they present a representative horizontal cross-section of meteorological variables and sky conditions in and around the site. It is also realized that many of the variables presented in this data set are somewhat subjective and dependent on the skill (and biases) of the observer, such as estimates of cloud amount and height. This data may be used as input data and/or verification data for numerical simulation models.

Covid-19 Daily metrics at the county level As of 6/1/2023, this data set is no longer being updated. The COVID-19 Data Report is posted on the Open Data Portal every day at 3pm. The report uses data from multiple sources, including external partners; if data from external partners are not received by 3pm, they are not available for inclusion in the report and will not be displayed. Data that are received after 3pm will still be incorporated and published in the next report update. The cumulative number of COVID-19 cases (cumulative_cases) includes all cases of COVID-19 that have ever been reported to DPH. The cumulative number of COVID_19 cases in the last 7 days (cases_7days) only includes cases where the specimen collection date is within the past 7 days. While most cases are reported to DPH within 48 hours of specimen collection, there are a small number of cases that routinely are delayed, and will have specimen collection dates that fall outside of the rolling 7 day reporting window. Additionally, reporting entities may submit correction files to contribute historic data during initial onboarding or to address data quality issues; while this is rare, these correction files may cause a large amount of data from outside of the current reporting window to be uploaded in a single day; this would result in the change in cumulative_cases being much larger than the value of cases_7days. On June 4, 2020, the US Department of Health and Human Services issued guidance requiring the reporting of positive and negative test results for SARS-CoV-2; this guidance expired with the end of the federal PHE on 5/11/2023, and negative SARS-CoV-2 results were removed from the List of Reportable Laboratory Findings. DPH will no longer be reporting metrics that were dependent on the collection of negative test results, specifically total tests performed or percent positivity. Positive antigen and PCR/NAAT results will continue to be reportable.

This data set contains hydrology, soils, radiation, cloud, and vegetation data from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I. The ISLSCP data sets should provide LBA modelers with many of the fields required to describe boundary conditions, and to initialize and force a wide range of land-biosphere-atmosphere models. All of the data have been processed to the same global spatial resolution (1 deg. x 1 deg.), using the same land/sea mask and steps have been taken to ensure spatial and temporal continuity of the data. The data sets cover the period 1987-1988 at 1-month time resolution for most of the seasonally varying quantities. For this pre-LBA data set, the ISLSCP I data are provided as global coverages. The companion file illustrations were subset over the LBA study area, from 35-85 deg. W longitude and 20 deg. S to 10 deg. N latitude, as shown in Figure 1.The data files and illustrations are organized into the three groups listed below.1. Hydrology and Soils2. Radiation and Clouds3. VegetationThe data within each of these areas were acquired from a variety of sources including model output, satellites, and ground measurements. The individual data sets were provided in a variety of forms. In some cases, this required the data publication team to regrid and reformat data sets and in others to produce monthly averages from finer resolution data. The specific processing for each data set is detailed in the documentation. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD-ROMs (Marengo and Victoria, 1998) but are now archived individually.

In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits of a certain assistance type in that calendar year and month. A recipient may have received benefits of multiple types in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enro

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

Despite the critical importance of virus disinfection by chlorine, our fundamental understanding of the relative susceptibility of different viruses to chlorine and robust quantitative relationships between virus disinfection rate constants and environmental parameters remains limited. We conducted a systematic review of virus inactivation by free chlorine and used the resulting data set to develop a linear mixed model that estimates chlorine inactivation rate constants for viruses based on experimental conditions. 570 data points were collected in our systematic review, representing 82 viruses over a broad range of environmental conditions. The harmonized inactivation rate constants under reference conditions (pH = 7.53, T = 20 °C, [Cl–] < 50 mM) spanned 5 orders of magnitude, ranging from 0.0196 to 1150 L mg–1 min–1, and uncovered important trends between viruses. Whereas common surrogate bacteriophage MS2 does not serve as a conservative chlorine disinfection surrogate for many human viruses, CVB5 was one of the most resistant viruses in the data set. The model quantifies the role of pH, temperature, and chloride levels across viruses, and an online tool allows users to estimate rate constants for viruses and conditions of interest. Results from the model identified potential shortcomings in current U.S. EPA drinking water disinfection requirements.

This data set includes daily, population-weighted mean values of various heat metrics for every county in the contiguous United States from 2000-2020. The dataset methodology, usage notes, and additional citations are published in Scientific Data (see reference below for Spangler et al. [2022]). Minimum, maximum, and mean ambient temperature, dew-point temperature, humidex, heat index, net effective temperature, wet-bulb globe temperature, and Universal Thermal Climate Index are included. Note that Monroe County, Florida (FIPS: 12087) and Nantucket County, Massachusetts (FIPS 25019) are missing due to unavailability of ERA5-Land data for Key West, Florida and Nantucket, MA. To use these data, assign the data from the .Rds file to a new data frame in R using the readRDS() function. Please cite the use of this data set with the following reference. Note that additional citations for specific variables can be found in Table 2.

K.R. Spangler, S. Liang, and G.A. Wellenius. "Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000-2020." Scientific Data (2022). doi: 10.1038/s41597-022-01405-3

This data set contains modified Copernicus Climate Change Service information (2022), as described and cited in the manuscript referenced above. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. This data set is provided “as is” with no warranty of any kind.

This archive contains electrophysiological recordings from freely behaving mice, with each 4-second epoch having been annotated with the corresponding vigilance state by multiple sleep experts. The recordings are either 12 or 24 hours long and consist at minimum of a frontal EEG, a parietal EEG and an EMG trace sampled at 256 Hz. Several recordings additionally contain LFP traces and/or unsorted multi-unit activity.

The data comprises four groups:

- a pilot data set,

- a test data set,

- a sleep deprivation data set, and

- an optogenetic stimulation data set.

The recordings in the pilot data set and the test data set have been annotated by 4-10 experienced sleep researchers from the Vyazovskiy group at the University of Oxford. They are ideally suited for benchmarking of automated methods for polysomnography. The recordings in the sleep deprivation data set and the optogenetic stimulation data set exhibit characteristics that are distinct from corresponding baseline recordings and are thus useful to test the resilience of automated methods to experimental manipulations. Sleep deprivation increases the amplitude of slow-wave activity during NREM and thus changes the spectral features used by many automated methods. The manipulation in the optogenetic stimulation data set increased the number of times the animal was (briefly) awake during sleep, resulting in increases in the transition probabilities from NREM or REM to the awake state compared to baseline recordings. Methods that leverage expectations of transition probabilities in their predictions would be expected to be sensitive to these changes.

This benchmark data set was created during the development of Somnotate, an automated vigilance state classifier (available at https://github.com/paulbrodersen/somnotate). The data collection is hence described in the following publications:

Brodersen et al. Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions. PLoS Comput Biol. 2024. DOI: 10.1371/journal.pcbi.1011793

Please consider citing this publication if you use Somnotate or this data set in your academic work.

Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains measurements taken during the Soil Moisture Experiment 2004 (SMEX04) in southern Arizona, USA. The SCAN station houses numerous sensors which were used to automatically record the data.

About this dataset

Context

The dataset tabulates the Many population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Many. The dataset can be utilized to understand the population distribution of Many by age. For example, using this dataset, we can identify the largest age group in Many.

Key observations

The largest age group in Many, LA was for the group of age 10 to 14 years years with a population of 226 (10.26%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Many, LA was the 80 to 84 years years with a population of 45 (2.04%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Many is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Many total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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/.

Recommended for further research

This dataset is a part of the main dataset for Many Population by Age. You can refer the same here

The USGS National Hydrography Dataset (NHD) downloadable data collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP include NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards. The next generation of national hydrography data is the USGS 3D Hydrography Program (3DHP).

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

A joint venture involving the National Atlas programs in Canada (Natural Resources Canada), Mexico (Instituto Nacional de Estadstica Geografa e Informtica), and the United States (U.S. Geological Survey), as well as the North American Commission for Environmental Co-operation, has led to the release (June 2004) of several new products: an updated paper map of North America, and its associated geospatial data sets and their metadata. These data sets are available online from each of the partner countries both for download. This data has been revised and re-released in 2006. The North American Atlas data are standardized geospatial data sets at 1:10,000,000 scale. A variety of basic data layers (e.g. roads, railroads, populated places, political boundaries, hydrography, bathymetry, sea ice and glaciers) have been integrated so that their relative positions are correct. This collection of data sets forms a base with which other North American thematic data may be integrated. Any data outside of Canada, Mexico, and the United States of America included in the North American Atlas data sets is strictly to complete the context of the data. The North American Atlas - Roads data set shows the roads of North America at 1:10,000,000 scale. The roads included in this data set are either those that connect major centres of population or selected frontier roads. Roads under construction are not shown. There are three road classes: Major roads, which are divided, multi-lane, limited access highways; Secondary roads, which are all roads that do not meet the definition of major roads; and Ferries, which are major ferry links which run either year round or through periods when ice conditions permit.

Dataset contains all survey data collected in the research project "Strengthening REsilience in Communities and sOciety through citizeN sciEnce and CiTizenship", which was conducted by a team of researchers from the Kaunas University of Technology. This research was funded by the Research Council of Lithuania (RCL) as part of the competitive program "Strengthening Societal Resilience and Crisis Management in the Context of Contemporary Geopolitical Situation" (S-VIS-23-14). Project leader – prof. dr. Eglė Butkevičienė. The aim of the project – to analyse and make recommendations to local and national public sector institutions on how citizenship and citizen science can strengthen the resilience of Lithuanian society and communities to socio-political threats. Temporary accessibility restrictions apply for this dataset. Data will be made available without restrictions from 2026-07-01.

Chemicals may cause cardiotoxicity by binding to the K+ channel encoded by the human ether-à-go-go-related gene (hERG). Given the ever-increasing number of chemicals, developing in silico models to efficiently fill the hERG binding affinity data gap is more desirable than conducting time-consuming experimental tests. However, previous data sets with limited chemical space hindered the development of models with high prediction accuracy and broad applicability domains (ADs). Herein, an expanded hERG binding affinity data set containing diverse categories of chemicals was constructed and subsequently employed to develop machine learning models. ADs of the constructed models were defined by an innovative structure–activity landscape (SAL)-based AD characterization (ADSAL), which considers activity cliffs within SALs formed by molecules with similar structures but inconsistent bioactivities. The optimal model constrained by the ADSAL achieved a coefficient of determination up to 0.89 on the external-validation set, which significantly outperformed previous models. The model coupled with the ADSAL constraint was applied to predict hERG binding affinities for more than 100,000 chemicals from multiple inventories, identifying over 5,000 potential hERG blockers. The model with ADSAL can serve as an efficient and reliable tool for bridging the hERG-mediated cardiotoxicity data vacancy to support sound chemical management.