Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

In 2016, non-interpretive streamflow statistics were compiled for streamgages located throughout the Nation and stored in the StreamStatsDB database for use with StreamStats and other applications. Two previously published USGS computer programs that were designed to help calculate streamflow statistics were updated to better support StreamStats as part of this effort. These programs are named “GNWISQ” (Get National Water Information System Streamflow (Q) files) and “QSTATS” (Streamflow (Q) Statistics). Statistics for 20,438 streamgages that had 1 or more complete years of record during water years 1901 through 2015 were calculated from daily mean streamflow data; 19,415 of these streamgages were within the conterminous United States. About 89 percent of the 20,438 streamgages had 3 or more years of record, and 65 percent had 10 or more years of record. Drainage areas of the 20,438 streamgages ranged from 0.01 to 1,144,500 square miles. The magnitude of annual average streamflow yields (streamflow per square mile) for these streamgages varied by almost six orders of magnitude, from 0.000029 to 34 cubic feet per second per square mile. About 64 percent of these streamgages did not have any zero-flow days during their available period of record. The 18,122 streamgages with 3 or more years of record were included in the StreamStatsDB compilation so they would be available via the StreamStats interface for user-selected streamgages.

Dataset

This dataset was created by Will Hore-Lacy

Released under Other (specified in description)

Contents

It contains the following files:

Government Transportation Financial Statistics is no longer being updated by the Bureau of Transportation Statistics as of June 2024! It is being replaced by our new product, Transportation Public Financial Statistics (TPFS) which provides more granularity by expanding the categories of revenues and expenditures. The new dataset can be found: https://data.bts.gov/Research-and-Statistics/Transportation-Public-Financial-Statistics-TPFS-/6aiz-ybqx/about_data Further information about the TPFS can be found at: https://www.bts.gov/tpfs The government plays an important role in the U.S. transportation system, as a provider of transportation infrastructure and as an administrator and regulator of the system. The government spends a large amount of funds on building, rehabilitating, maintaining, operating, and administering the infrastructure system. Government revenue generated from several sources including user fees, taxes from transportation and non-transportation-related activities, borrowing, and grants from federal, state, and local governments primarily supports these activities. Government Transportation Financial Statistics (GTFS) provides a set of maps, charts, and tables with information on transportation-related revenue and expenditures for all levels of government, including federal, state, and local, and for all modes of transportation. Related tables can be found in National Transportation Statistics, Section 3.D - Government Finance (https://www.bts.gov/topics/national-transportation-statistics). For further information, data definitions, and methodology, see https://www.bts.gov/gtfs

Streamflow data and statistics are vitally important for proper protection and management of both the water quality and water quantity of Alabama streams. Such data and statistics are available at U.S. Geological Survey streamflow-gaging stations, also referred to as streamgages or stations, but are often needed at ungaged stream locations. To address this need, the U.S. Geological Survey, in cooperation with numerous Alabama state agencies and organizations, developed regional regression equations for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations in Alabama that are not substantially affected by tides, regulation, diversions, or other anthropogenic influences. This data release comprises the geographic information systems (GIS) layers and tabular data used to create the new low-flow and mean annual flow regression equations and implement them for the U.S. Geological Survey StreamStats application (https://streamstats.usgs.gov).

YouTube Videos and Channels Metadata

Analyze the statistical relation between videos and form a topic tree

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains YouTube video and channel metadata to analyze the statistical relation between videos and form a topic tree. With 9 direct features, 13 more indirect features, it has all that you need to build a deep understanding of how videos are related – including information like total views per unit time, channel views, likes/subscribers ratio, comments/views ratio, dislikes/subscribers ratio etc. This data provides us with a unique opportunity to gain insights on topics such as subscriber count trends over time or calculating the impact of trends on subscriber engagement. We can develop powerful models that show us how different types of content drive viewership and identify the most popular styles or topics within YouTube's vast catalogue. Additionally this data offers an intriguing look into consumer behaviour as we can explore what drives people to watch specific videos at certain times or appreciate certain channels more than others - by analyzing things like likes per subscribers and dislikes per views ratios for example! Finally this dataset is completely open source with an easy-to-understand Github repo making it an invaluable resource for anyone looking to gain better insights into how their audience interacts with their content and how they might improve it in the future

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How to use the dataset

How to Use This Dataset

In general, it is important to understand each parameter in the data set before proceeding with analysis. The parameters included are totalviews/channelelapsedtime, channelViewCount, likes/subscriber, views/subscribers, subscriberCounts, dislikes/views comments/subscriberchannelCommentCounts,, likes/dislikes comments/views dislikes/ subscribers totviewes /totsubsvews /elapsedtime.

To use this dataset for your own analysis:1) Review each parameter’s meaning and purpose in our dataset; 2) Get familiar with basic descriptive statistics such as mean median mode range; 3) Create visualizations or tables based on subsets of our data; 4) Understand correlations between different sets of variables or parameters; 5) Generate meaningful conclusions about specific channels or topics based on organized graph hierarchies or tables.; 6) Analyze trends over time for individual parameters as well as an aggregate reaction from all users when videos are released

Research Ideas

• Predicting the Relative Popularity of Videos: This dataset can be used to build a statistical model that can predict the relative popularity of videos based on various factors such as total views, channel viewers, likes/dislikes ratio, and comments/views ratio. This model could then be used to make recommendations and predict which videos are likely to become popular or go viral.

• Creating Topic Trees: The dataset can also be used to create topic trees or taxonomies by analyzing the content of videos and looking at what topics they cover. For example, one could analyze the most popular YouTube channels in a specific subject area, group together those that discuss similar topics, and then build an organized tree structure around those topics in order to better understand viewer interests in that area.

• Viewer Engagement Analysis: This dataset could also be used for viewer engagement analysis purposes by analyzing factors such as subscriber count, average time spent watching a video per user (elapsed time), comments made per view etc., so as to gain insights into how engaged viewers are with specific content or channels on YouTube. From this information it would be possible to optimize content strategy accordingly in order improve overall engagement rates across various types of video content and channel types

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: YouTubeDataset_withChannelElapsed.csv | Column name | Description | |:----------------------------------|:-------------------------------------------------------| | totalviews/channelelapsedtime | Ratio of total views to channel elapsed time. (Ratio) | | channelViewCount | Total number of views for the channel. (Integer) | | likes/subscriber ...

Abstract

The harmonized data set on health, created and published by the ERF, is a subset of Iraq Household Socio Economic Survey (IHSES) 2012. It was derived from the household, individual and health modules, collected in the context of the above mentioned survey. The sample was then used to create a harmonized health survey, comparable with the Iraq Household Socio Economic Survey (IHSES) 2007 micro data set.

----> Overview of the Iraq Household Socio Economic Survey (IHSES) 2012:

Iraq is considered a leader in household expenditure and income surveys where the first was conducted in 1946 followed by surveys in 1954 and 1961. After the establishment of Central Statistical Organization, household expenditure and income surveys were carried out every 3-5 years in (1971/ 1972, 1976, 1979, 1984/ 1985, 1988, 1993, 2002 / 2007). Implementing the cooperation between CSO and WB, Central Statistical Organization (CSO) and Kurdistan Region Statistics Office (KRSO) launched fieldwork on IHSES on 1/1/2012. The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

The survey has six main objectives. These objectives are:

1. Provide data for poverty analysis and measurement and monitor, evaluate and update the implementation Poverty Reduction National Strategy issued in 2009.
2. Provide comprehensive data system to assess household social and economic conditions and prepare the indicators related to the human development.
3. Provide data that meet the needs and requirements of national accounts.
4. Provide detailed indicators on consumption expenditure that serve making decision related to production, consumption, export and import.
5. Provide detailed indicators on the sources of households and individuals income.
6. Provide data necessary for formulation of a new consumer price index number.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006/2007 Household Socio Economic Survey in Iraq. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Iraq 2007 & 2012- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

Geographic coverage

National coverage: Covering a sample of urban, rural and metropolitan areas in all the governorates including those in Kurdistan Region.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey was carried out over a full year covering all governorates including those in Kurdistan Region.

Kind of data

Sample survey data [ssd]

Sampling procedure

----> Design:

Sample size was (25488) household for the whole Iraq, 216 households for each district of 118 districts, 2832 clusters each of which includes 9 households distributed on districts and governorates for rural and urban.

----> Sample frame:

Listing and numbering results of 2009-2010 Population and Housing Survey were adopted in all the governorates including Kurdistan Region as a frame to select households, the sample was selected in two stages: Stage 1: Primary sampling unit (blocks) within each stratum (district) for urban and rural were systematically selected with probability proportional to size to reach 2832 units (cluster). Stage two: 9 households from each primary sampling unit were selected to create a cluster, thus the sample size of total survey clusters was 25488 households distributed on the governorates, 216 households in each district.

----> Sampling Stages:

In each district, the sample was selected in two stages: Stage 1: based on 2010 listing and numbering frame 24 sample points were selected within each stratum through systematic sampling with probability proportional to size, in addition to the implicit breakdown urban and rural and geographic breakdown (sub-district, quarter, street, county, village and block). Stage 2: Using households as secondary sampling units, 9 households were selected from each sample point using systematic equal probability sampling. Sampling frames of each stages can be developed based on 2010 building listing and numbering without updating household lists. In some small districts, random selection processes of primary sampling may lead to select less than 24 units therefore a sampling unit is selected more than once , the selection may reach two cluster or more from the same enumeration unit when it is necessary.

Mode of data collection

Face-to-face [f2f]

Research instrument

----> Preparation:

The questionnaire of 2006 survey was adopted in designing the questionnaire of 2012 survey on which many revisions were made. Two rounds of pre-test were carried out. Revision were made based on the feedback of field work team, World Bank consultants and others, other revisions were made before final version was implemented in a pilot survey in September 2011. After the pilot survey implemented, other revisions were made in based on the challenges and feedbacks emerged during the implementation to implement the final version in the actual survey.

----> Questionnaire Parts:

The questionnaire consists of four parts each with several sections: Part 1: Socio – Economic Data: - Section 1: Household Roster - Section 2: Emigration - Section 3: Food Rations - Section 4: housing - Section 5: education - Section 6: health - Section 7: Physical measurements - Section 8: job seeking and previous job

Part 2: Monthly, Quarterly and Annual Expenditures: - Section 9: Expenditures on Non – Food Commodities and Services (past 30 days). - Section 10 : Expenditures on Non – Food Commodities and Services (past 90 days). - Section 11: Expenditures on Non – Food Commodities and Services (past 12 months). - Section 12: Expenditures on Non-food Frequent Food Stuff and Commodities (7 days). - Section 12, Table 1: Meals Had Within the Residential Unit. - Section 12, table 2: Number of Persons Participate in the Meals within Household Expenditure Other Than its Members.

Part 3: Income and Other Data: - Section 13: Job - Section 14: paid jobs - Section 15: Agriculture, forestry and fishing - Section 16: Household non – agricultural projects - Section 17: Income from ownership and transfers - Section 18: Durable goods - Section 19: Loans, advances and subsidies - Section 20: Shocks and strategy of dealing in the households - Section 21: Time use - Section 22: Justice - Section 23: Satisfaction in life - Section 24: Food consumption during past 7 days

Part 4: Diary of Daily Expenditures: Diary of expenditure is an essential component of this survey. It is left at the household to record all the daily purchases such as expenditures on food and frequent non-food items such as gasoline, newspapers…etc. during 7 days. Two pages were allocated for recording the expenditures of each day, thus the roster will be consists of 14 pages.

Cleaning operations

----> Raw Data:

Data Editing and Processing: To ensure accuracy and consistency, the data were edited at the following stages: 1. Interviewer: Checks all answers on the household questionnaire, confirming that they are clear and correct. 2. Local Supervisor: Checks to make sure that questions has been correctly completed. 3. Statistical analysis: After exporting data files from excel to SPSS, the Statistical Analysis Unit uses program commands to identify irregular or non-logical values in addition to auditing some variables. 4. World Bank consultants in coordination with the CSO data management team: the World Bank technical consultants use additional programs in SPSS and STAT to examine and correct remaining inconsistencies within the data files. The software detects errors by analyzing questionnaire items according to the expected parameter for each variable.

----> Harmonized Data:

• The SPSS package is used to harmonize the Iraq Household Socio Economic Survey (IHSES) 2007 with Iraq Household Socio Economic Survey (IHSES) 2012.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household and individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Iraq Household Socio Economic Survey (IHSES) reached a total of 25488 households. Number of households refused to response was 305, response rate was 98.6%. The highest interview rates were in Ninevah and Muthanna (100%) while the lowest rates were in Sulaimaniya (92%).

Students and Courses and Apprentices and Trainees: These statistics cover administrative data sets on student enrolments and qualifications attained with approximately 2 million students enrolling on vocation education and training in Australia each year, 400,000 graduates each year, and around 400,000 people in training as part of an apprenticeship or traineeships. Demographic information on students as well as the qualification they are training in and where the training took place are included. Courses are classified by intended occupation on completion, and field of study. Student Outcomes Survey: In addition a graduate destination survey is run capturing information on the quality of training, occupations before and after training, salary, and further education. Under data tab each collection appears and can be selected individually for information excel files and publications, under data data are three resources, Vocstats datacubes, VET Students by Industry, VET Graduates outcomes, salaries and jobs. http://www.ncver.edu.au For an overview of the statistics please see the following publication https://www.ncver.edu.au/publications/publications/all-publications/statistical-standard-software/avetmiss-data-element-definitions-edition-2.2# Datasets to be attributed to National Centre for Vocational Education Research (NCVER). https://www.ncver.edu.au/ Register for VOCSTATS by visiting the website (http://www.ncver.edu.au/wps/portal/vetdataportal/data/menu/vocstats)

The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

Definition Extract of Ordnance Survey's Code-Point® Open product filtered for London Borough of Barnet coverage. Data has been processed from the .csv schema filtering on the administrative district code, with administrative district and ward names appended for ease of use Postcodes do not have an exact match to administrative boundaries, see the Ordnance Survey product support page for full definition. Purpose These datasets have been created as general resource for the council. The information is sourced from Ordnance Survey Open Data products and may be used more widely subject to the Open Government Licence (v3). Disclaimer This dataset is not the primary source and may not reflect the latest version or scope of the original product. You should assess whether using the original product directly is more appropriate for your purpose. Acknowledgements Contains OS data © Crown copyright and database right 2025 Contains Royal Mail data © Royal Mail copyright and Database right 2025 Contains National Statistics data © Crown copyright and database right 2025

The data set contains (1) model data, (2) the corresponding model grid, and (3) the result of a statistical analysis. The model data consist of mean values of an ensemble of 80 members in total. Of these, 40 simulations belong to the reference experiment and 40 simulations belong to the snow-darkening experiment. Variables with the addition "diff" show the mean difference between the simulations of the two experiments. The statistical data is the result of the Wilcoxon signed-rank test at t176.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current State House Districts for New Mexico as posted on the Census Bureau website for 2006.

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. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

dsd/043 dimension sdmx data structure definition exposure type

dsd/045 dimension sdmx data structure definition valuation method

universal resource locator url http://bit.ly/2wFtGw8

dataset

data structure definition

ECB_CBD2 agency

download SDMX 2.1 schema of the ECB_CBD2 DSD http://bit.ly/2ImA7p3

uc health / ucsd health dataset

data structure definition(s)

UCH_CCD1 agency <0000 0001 2107 4242 ucsd health>

access to CCD1 is through the ucsd tableau server

Metrics based on this standard are developed by persons listed in this resource

d/416 2018 19 131 master organisation chart ucsd health patient financial services 0000 0001 2107 4242 ucsd health

Discovery metrics

Beta metrics

CCD Bm 0.0

Alpha metrics

CCD Am 0.0

P...

Data associated with main analysis and figures presented in manuscript titled: "Long-term organic farming and floral diversity promotes stability of bee communities in agroecosystems." Dataset titled: "Bloom_BetaDiverstiy_GPSPointsAndSiteCharacteristics_DataFinal.csv" are associated with the map of site locations shown in Figure 1. These data are also given in Table 2 (note no data are associated with Table 1). GPS points have been jittered to protect the identity of farmer collaborators. Exact locations are available upon request and after consideration by the lead author. No data are associated with Figure 2. The four datasets tilted: "Bloom_BetaDiversity_GeometricRemovalXXX_DataFinal.csv" are files associated with the geometric species removal analysis accompanying Figure 3, where XXX is the scale (local or landscape) and term (species loss or removal) (see manuscript for details). Column definitions include: SiteID - the site for which the statistic was generated (local level only); SiteID1 - the first site involved in the pairwise comparison which generated the statistic (landscape level only); SiteID2 - the second site involved in the pairwise comparison which generated the statistic (landscape level only); year1 - the year (e.g., 20XX) for which the sample was taken at the site given in SiteID1 which then generated the statistic (landscape level only); year2 - the year (e.g., 20XX) for which the sample was taken at the site given in SiteID2 which then generated the statistic (landscape level only); variable - species removed at random; sim (replacement) or sne (loss) - the statistic with no species removed; value - the statistics with the species removed; vec - the color relating the number of species removed (see Figure 3 caption for column species number relationships). The dataset title: "Bloom_BetaDiverstiy_SADS_DataFinal.xlsx" contains three sheets each associated with a sample year (2014, 2015, 2016). Within each sheet are the vectors of abundance values and species names used to create species abundance models plotted in Figure 4. The datasets titled: "Bloom_BetaDiverstiy_LocalLevelRegressions_DataFinal.csv" and "Bloom_BetaDiverstiy_LandscapeLevelRegressions_DataFinal" contain statistics used to create Figure 5. The column name definitions that have not been previously given are as follows: detlaaic - difference in species abundance model fit (for local level); Years.Since.Transition - years since transitioning to organic farming scaled to enhance regression model fitting; sor - the overall Sorenson's beta diversity term for bees across years or sites (depends on scale see file name); sim - the species replacement term from the additive partition of Sorenson's beta diversity for bees across years or sites (depends on scale see file name); nes - the species loss term from the additive partition of Sorenson's beta diversity for bees across years or sites (depends on scale see file name); X.sor/X.sim/S.nes - where X is l and p for landscape and plant beta diversity for each term at the site across years or sites (depends on scale see file name); diff_years - differences in times since transitions to organic agriculture scaled to enhance regression model fitting (landscape level only); diff_do_new - difference in species abundance model fit (for landscape level); p.all - multiplication of the p.sim and l.nes terms for plotting the interaction shown in Figure 5d. The dataset titled: "Bloom_BetaDiverstiy_Jackknife_DataFinal.xlsx" contains 6 sheets corresponding to Figure 6 panels "a-f" in linear order. Column definitions include: pvalue - the pvalue found when the variable was removed from the site by variable matrix and used to create the histograms; variable - the variable that was removed from the site by variable matrix. Variables can be bee species, landscape classes, or plants given by their unique common name. The final 3 datasets are titled: "Bloom_BetaDiverstiy_BeeSiteXSpeciesMatrix_DataFinal.csv", "Bloom_BetaDiverstiy_LandscapeSiteXClassMatrix_DataFinal.csv", and "Bloom_BetaDiverstiy_PlantSiteXCommonNameMatrix_DataFinal.csv." These datasets contain the matrices used to generate these bee, landscape, and plant beta diversity metrics used for our analysis.

The 1990 Census Block Statistics portion of the Archive of Census Related Products (ACRP) contains population and housing data from the U.S. Census Bureau's 1990 Summary Tape File (STF1B). The population data includes total population, age, race, and hispanic origin, while the housing data comprises number of housing Units, tenure, room density, mean contract rent, mean value, and mean number of rooms. Additional data includes land area, water area, centroids, Metropolitan Statistical Area (MSA) codes, place codes, and special area codes. This portion of the ACRP is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Context

Data description

The FAOSTAT Temperature Change domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961–2023. Statistics are available for monthly, seasonal and annual mean temperature anomalies, i.e., temperature change with respect to a baseline climatology, corresponding to the period 1951–1980. The standard deviation of the temperature change of the baseline methodology is also available. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).

Content

Statistical concepts and definitions

Statistical standards: Data in the Temperature Change domain are not an explicit SEEA variable. Nonetheless, country and regional calculations employ a definition of “Land area” consistent with SEEA Land Use definitions, specifically SEEA CF Table 5.11 “Land Use Classification” and SEEA AFF Table 4.8, “Physical asset account for land use.” The Temperature Change domain of the FAOSTAT Agri-Environmental Indicators section is compliant with the Framework for the Development of Environmental Statistics (FDES 2013), contributing to FDES Component 1: Environmental Conditions and Quality, Sub-component 1.1: Physical Conditions, Topic 1.1.1: Atmosphere, climate and weather, Core set/ Tier 1 statistics a.1.

Statistical unit: Countries and Territories.

Statistical population: Countries and Territories.

Reference area: Area of all the Countries and Territories of the world. In 2019: 190 countries and 37 other territorial entities.

Code - reference area: FAOSTAT, M49, ISO2 and ISO3 (http://www.fao.org/faostat/en/#definitions). FAO Global Administrative Unit Layer (GAUL National level – reference year 2014. FAO Geospatial data repository GeoNetwork. Permanent address: http://www.fao.org:80/geonetwork?uuid=f7e7adb0-88fd-11da-a88f-000d939bc5d8.

Code - Number of countries/areas covered: In 2019: 190 countries and 37 other territorial entities.

Time coverage: 1961-2023

Periodicity: Monthly, Seasonal, Yearly

Base period: 1951-1980

Unit of Measure: Celsius degrees °C

Reference period: Months, Seasons, Meteorological year

Acknowledgements

Documentation on methodology: Details on the methodology can be accessed at the Related Documents section of the Temperature Change (ET) domain in the Agri-Environmental Indicators section of FAOSTAT.

Quality documentation: For more information on the methods, coverage, accuracy and limitations of the Temperature Change dataset please refer to the NASA GISTEMP website: https://data.giss.nasa.gov/gistemp/

                                                                          Source: http://www.fao.org/faostat/en/#data/ET/metadata

Inspiration

Climate change is one of the important issues that face the world in this technological era. The best proof of this situation is the historical temperature change. You can investigate if any hope there is for stopping global warming :)

• Can you find any correlation between temperature change and any other variable? (Using ISO3 codes for merging any other countries' data sets possible.)

• Prediction of temperature change: there is also an overall world temperature change in the country list as 'World'.

Use this summary report to properly interpret 2020 NSDUH estimates of substance use and mental health issues. The report accompanies theannual detailed tablesand covers overall methodology, key definitions for measures and terms used in 2020 NSDUH reports and tables, and selected analyses of the measures and how they should be interpreted.The report is organized into six chapters:Introduction.Description of the survey, including information about the sample design, data collection procedures, and key aspects of data processing such as development of the analysis weights.Technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, issues around data accuracy, and measurement issues for selected substance use and mental health measures.Special topics related to prescription psychotherapeutic drugs.A comparison between NSDUH and other sources of data on substance use and mental health issues, including data sources for populations outside the NSDUH target population.A more in-depth view of special methodological issues for the 2020 NSDUH, including those related to methodological changes made because of the Coronavirus 2019 Pandemic.An appendix covers key definitions used in NSDUH reports and tables.