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Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 2000-2013 for each pixel.
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The Standardized Precipitation Index (SPI) was generated for certain Environment Canada long-term climate stations in Ontario.
The SPI quantifies the precipitation deficit and surplus for multiple time scales, including:
one month three months six months nine months 12 months 24 months
You can use the SPI to study the impact of dry and wet weather conditions to create comprehensive water management approaches.
The SPI data package is distributed as a Microsoft Access Geodatabase.
This is a legacy dataset that we no longer maintain or support.
The documents referenced in this record may contain URLs (links) that were valid when published, but now link to sites or pages that no longer exist.
Additional Documentation
Standardized Precipitation Index - User Guide (PDF)
Status Completed: production of the data has been completed
Maintenance and Update Frequency
Not planned: there are no plans to update the data
Contact
Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca
Prognostics and health management (PHM) is a maturing system engineering discipline. As with most maturing disciplines, PHM does not yet have a universally accepted research methodology. As a result, most component life estimation efforts are based on ad-hoc experimental methods that lack statistical rigor. In this paper, we provide a critical review of current research methods in PHM and contrast these methods with standard research approaches in a more established discipline (medicine). We summarize the developmental steps required for PHM to reach full maturity and to generate actionable results with true business impact.
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Background: Critical care units (CCUs) with wide use of various monitoring devices generate massive data. To utilize the valuable information of these devices; data are collected and stored using systems like Clinical Information System (CIS), Laboratory Information Management System (LIMS), etc. These systems are proprietary in nature, allow limited access to their database and have vendor specific clinical implementation. In this study we focus on developing an open source web-based meta-data repository for CCU representing stay of patient with relevant details.
Methods: After developing the web-based open source repository we analyzed prospective data from two sites for four months for data quality dimensions (completeness, timeliness, validity, accuracy and consistency), morbidity and clinical outcomes. We used a regression model to highlight the significance of practice variations linked with various quality indicators. Results: Data dictionary (DD) with 1447 fields (90.39% categorical and 9.6% text fields) is presented to cover clinical workflow of NICU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 82% completeness, 97% accuracy, 91% timeliness and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicator and practice variations are strongly correlated (p-value < 0.05).
Results: Data dictionary (DD) with 1555 fields (89.6% categorical and 11.4% text fields) is presented to cover clinical workflow of a CCU. The overall quality of 1795 patient days data with respect to standard quality dimensions is 87%. The data exhibit 82% completeness, 97% accuracy, 91% timeliness and 94% validity in terms of representing CCU processes. The data scores only 67% in terms of consistency. Furthermore, quality indicators and practice variations are strongly correlated (p-value < 0.05).
Conclusion: This study documents DD for standardized data collection in CCU. This provides robust data and insights for audit purposes and pathways for CCU to target practice improvements leading to specific quality improvements.
A variance is required when an application has submitted a proposed project to the Department of Permitting Services and it is determined that the construction, alteration or extension does not conform to the development standards (in the zoning ordinance) for the zone in which the subject property is located. A variance may be required in any zone and includes accessory structures as well as primary buildings or dwellings. Update Frequency : Daily
https://www.icpsr.umich.edu/web/ICPSR/studies/7023/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/7023/terms
Of the 14 nations included in the original study, these data cover the following ten: Brazil, Cuba, Dominican Republic, India, Israel, Nigeria, Panama, United States, West Germany, and Yugoslavia. (The data for Egypt, Japan, the Philippines, and Poland are not available through ICPSR.) In India and Israel the interviews were conducted in two waves, with different samples. Besides ascertaining the usual personal information, the study employed a "Self-Anchoring Striving Scale," an open-ended scale asking the respondent to define hopes and fears for self and the nation, to determine the two extremes of a self-defined spectrum on each of several variables. After these subjective ratings were obtained, the respondents indicated their perceptions of where they and their nations stood on a hypothetical ladder at three different points in time. Demographic variables include the respondents' age, gender, marital status, and level of education. For more information on the samples, coding, and the means of measurement, see the related publication listed below.
Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality datasets: greater coverage across countries and over time is available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to overcome these limitations. A custom missing-data algorithm was used to standardize the United Nations University's World Income Inequality Database and data from other sources; data collected by the Luxembourg Income Study served as the standard. The SWIID provides comparable Gini indices of gross and net income inequality for 192 countries for as many years as possible from 1960 to the present along with estimates of uncertainty in these statistics. By maximizing comparability for the largest possible sample of countries and years, the SWIID is better suited to broadly cross-national research on income inequality than previously available sources: it offers coverage double that of the next largest income inequality dataset, and its record of comparability is three to eight times better than those of alternate datasets.
Reproducibility data for the AntiBody Sequence Database (ABSD) article. This dataset contains the raw data (antibody sequences) extracted on June 20, 2024, from various databases, as well as the several scripts, to ensure the reproducibility of our results. External databases used: ABDB, AbPDB, CoV-AbDab, Genbank, IMGT, PDB, SACS, SAbDab, TheraSAbDab, UniProt, KABAT Scripts usage: each external database has a corresponding script to format all antibody sequences extracted from it. A last script enable merging all extracted antibody sequences while removing redundancy, standardizing and cleaning data.
http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence
Standard deviation of responses for 'Life Satisfaction' in the First ONS Annual Experimental Subjective Wellbeing survey.
The Office for National Statistics has included the four subjective well-being questions below on the Annual Population Survey (APS), the largest of their household surveys.
This dataset presents results from the first of these questions, "Overall, how satisfied are you with your life nowadays?". Respondents answer these questions on an 11 point scale from 0 to 10 where 0 is ‘not at all’ and 10 is ‘completely’. The well-being questions were asked of adults aged 16 and older.
Well-being estimates for each unitary authority or county are derived using data from those respondents who live in that place. Responses are weighted to the estimated population of adults (aged 16 and older) as at end of September 2011.
The data cabinet also makes available the proportion of people in each county and unitary authority that answer with ‘low wellbeing’ values. For the ‘life satisfaction’ question answers in the range 0-6 are taken to be low wellbeing.
This dataset contains the standard deviation of the responses, alongside the corresponding sample size.
The ONS survey covers the whole of the UK, but this dataset only includes results for counties and unitary authorities in England, for consistency with other statistics available at this website.
At this stage the estimates are considered ‘experimental statistics’, published at an early stage to involve users in their development and to allow feedback. Feedback can be provided to the ONS via this email address.
The APS is a continuous household survey administered by the Office for National Statistics. It covers the UK, with the chief aim of providing between-census estimates of key social and labour market variables at a local area level. Apart from employment and unemployment, the topics covered in the survey include housing, ethnicity, religion, health and education. When a household is surveyed all adults (aged 16+) are asked the four subjective well-being questions.
The 12 month Subjective Well-being APS dataset is a sub-set of the general APS as the well-being questions are only asked of persons aged 16 and above, who gave a personal interview and proxy answers are not accepted. This reduces the size of the achieved sample to approximately 120,000 adult respondents in England.
The original data is available from the ONS website.
Detailed information on the APS and the Subjective Wellbeing dataset is available here.
As well as collecting data on well-being, the Office for National Statistics has published widely on the topic of wellbeing. Papers and further information can be found here.
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Here, we present FLiPPR, or FragPipe LiP (limited proteolysis) Processor, a tool that facilitates the analysis of data from limited proteolysis mass spectrometry (LiP-MS) experiments following primary search and quantification in FragPipe. LiP-MS has emerged as a method that can provide proteome-wide information on protein structure and has been applied to a range of biological and biophysical questions. Although LiP-MS can be carried out with standard laboratory reagents and mass spectrometers, analyzing the data can be slow and poses unique challenges compared to typical quantitative proteomics workflows. To address this, we leverage FragPipe and then process its output in FLiPPR. FLiPPR formalizes a specific data imputation heuristic that carefully uses missing data in LiP-MS experiments to report on the most significant structural changes. Moreover, FLiPPR introduces a data merging scheme and a protein-centric multiple hypothesis correction scheme, enabling processed LiP-MS data sets to be more robust and less redundant. These improvements strengthen statistical trends when previously published data are reanalyzed with the FragPipe/FLiPPR workflow. We hope that FLiPPR will lower the barrier for more users to adopt LiP-MS, standardize statistical procedures for LiP-MS data analysis, and systematize output to facilitate eventual larger-scale integration of LiP-MS data.
GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.
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:-The present study is an attempt to develop and standardize a multiple intelligences inventorywith an aim to its cultural adaptation. The original inventory, found on line, was in American Englishlanguage and hence not suitable to apply on Indian Bengalee population. Therefore, the test wasculturally adapted with the intention to assess the nature of eight multiple intelligences of schoolchildren, particularly adolescents, who study higher secondary level curriculum under West BengalCouncil of Higher Secondary Education. The inventory consists of 80 items in total, divided into eightsub-scales, each containing ten items. After try-out (N=20) and pilot study (N=50), the final study wasconducted with a sample of 100 higher secondary level students from exclusively Bengali mediumschools. Besides split-half reliability of the test, construct validity and internal consistency of the testitems were determined following conventional procedure.
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This document contains a selection of standard terms and definitions relevant to the quality assurance of Essential Climate Variable (ECVs) data records. It reproduces appropriate terms and definitions published by normalization bodies, mainly by BIPM/JCGM/ISO in their International Vocabulary of Metrology (VIM) and Guide to the Expression of Uncertainties (GUM). It also reproduces selected terms and definitions related to the quality assurance and validation of Earth Observation (EO) data, available publicly on the ISO website and on the Cal/Val portal of the Committee on Earth Observation Satellites (CEOS).
Several of those terms have been recommended by CEOS in the GEO-CEOS Quality Assurance framework for Earth Observation (QA4EO) and, as such, are applicable to virtually all Copernicus data sets of EO origin. Terms and definitions are expected to evolve as normalization organisations regularly update their standards.
The Fish Pathology (F013) data set contains data from examinations of diseased fishes. Although these data may be from field observations, they derive primarily from laboratory analyses. Data include: location, and fishing duration, distance, and gear; catch statistics (e.g., total weight, number of individuals, age group, identity of diseases, and number of diseased individuals) by species for any number of species; and biological condition of selected specimens. The size, affected organ, location, and frequency of lesions may be reported for individual specimens. Specimens are identified with the NODC Taxonomic Code. These data may be characteristics of individual lesions or average lesion statistics.
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Context
The dataset tabulates the Standard City 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 Standard City. The dataset can be utilized to understand the population distribution of Standard City by age. For example, using this dataset, we can identify the largest age group in Standard City.
Key observations
The largest age group in Standard City, IL was for the group of age 65 to 69 years years with a population of 35 (18.72%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Standard City, IL was the 40 to 44 years years with a population of 1 (0.53%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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 dataset is a part of the main dataset for Standard City Population by Age. You can refer the same here
This data corresponds to the 2024 Standard Scenarios report, which contains a suite of forward-looking scenarios of the possible evolution of the U.S. electricity sector through 2050. These files contain modeled projections of the future. Although we strive to capture relevant phenomena as comprehensively as possible, the models used to create this data are unavoidably imperfect, and the future is highly uncertain. Consequentially, this data should not be the sole basis for making decisions. In addition to drawing from multiple scenarios within this set, we encourage analysts to also draw on projections from other sources, to benefit from diverse analytical frameworks and perspectives when forming their conclusions about the future of the power sector. For further discussions about the limitations of the models underlying this data, see section 1.4 of the "ReEDS Documentation" linked below. For scenario descriptions, input assumptions, and metric definitions for the data in these files, see the "2024 Standard Scenarios Report" linked below.
This dataset provides information about the number of properties, residents, and average property values for Standard Street cross streets in El Segundo, CA.
Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, sea surface density product forversion 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.
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Standardize Data