This dataset is a polygon coverage of counties limited to the extent of the Pittsburgh coal bed resource areas and attributed with statistics on the thickness of the Pittsburgh coal bed, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

Teachers' Use of Educational Technology in U.S. Public Schools, 2009 (FRSS 95), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 95 (https://nces.ed.gov/surveys/frss/) is a sample survey that provides national estimates on the availability and use of educational technology among teachers in public elementary and secondary schools during 2009. This is one of a set of three surveys (at the district, school, and teacher levels) that collected data on a range of educational technology resources. The study was conducted using surveys via the web or by mail. Telephone follow-up for survey non-response and data clarification was also used. Questionnaires and cover letters for the teacher survey were mailed to sampled teachers at their schools. Public schools and teachers within those schools were sampled. The weighted response rate for schools providing lists of teachers for sampling was 81 percent, and the weighted response rate for sampled teachers completing questionnaires was 79 percent. Key statistics produced from FRSS 95 were information on the use of computers and internet access in the classroom; availability and use of computing devices, software, and school or district networks (including remote access) by teachers; students' use of educational technology; teachers' preparation to use educational technology for instruction; and technology-related professional development activities.

Statistics Canada, in collaboration with the Public Health Agency of Canada and Natural Resources Canada, is presenting selected Census data to help inform Canadians on the public health risk of the COVID-19 pandemic and to be used for modelling analysis. The data provided here show the counts of the population in nursing homes and/or residences for senior citizens by broad age groups (0 to 79 years and 80 years and over) and sex, from the 2016 Census. Nursing homes and/or residences for senior citizens are facilities for elderly residents that provide accommodations with health care services or personal support or assisted living care. Health care services include professional health monitoring and skilled nursing care and supervision 24 hours a day, 7 days a week, for people who are not independent in most activities of daily living. Support or assisted living care services include meals, housekeeping, laundry, medication supervision, assistance in bathing or dressing, etc., for people who are independent in most activities of daily living. Included are nursing homes, residences for senior citizens, and facilities that are a mix of both a nursing home and a residence for senior citizens. Excluded are facilities licensed as hospitals, and facilities that do not provide any services (which are considered private dwellings).

Self-reported data from approximately 380 public libraries, First Nation public libraries and contracting organizations. The data includes: general information including address financial information holdings information staffing information facilities information activities information including typical week data partnership information (2011 onwards) Data from 2011 and onwards is from a refreshed database. New fields were added for: provincial funding types project grant types special collections holdings circulation of E-resources including E-books lending laptops program types readers advisory transactions information technology support In 2012, new fields were added for: E-readers requests for accessible format materials business and economic partnerships. In 2013 more fields were added for social media visits and other professional staff. In 2016 a field was added for indigenous language training and retention, while circulating and reference holdings information was combined. In 2017 fields were added for e-learning services, students hired for a summer or semester, circulating wireless hot spots, and library service visits to residence-bound people. In 2019 fields were added for Facility Rentals and Bookings, ‘Pop-up’ Libraries, Extended Services and Facilities, Government Services Partnerships, and Business and Economic Sector Partnerships. The database uses the common name "LibStats".

The INE (Instituto Nacional de Estadística) Statistical Inventory is a vital resource offering detailed and up-to-date statistical data on various aspects of Spain's real estate market and broader economic indicators. It includes advanced search capabilities, historical consultations, and methodological insights, making it an indispensable tool for researchers, policymakers, and real estate professionals. The inventory covers a wide range of data points, including property statistics, classifications, and ongoing plans, providing a holistic view of the market dynamics in Spain.

Users of the INE Statistical Inventory range from government agencies and urban planners to private sector analysts and academic researchers. The data supports informed decision-making, market forecasting, and regulatory compliance, helping stakeholders understand trends and patterns in property development, ownership, and usage. Its comprehensive nature and official backing ensure high reliability and accuracy.

In the Spanish market, where real estate plays a crucial role in the economy, having access to such a centralized and authoritative data source is invaluable. The inventory aids in transparency, supports investment decisions, and enhances the efficiency of the property market by providing clear, accessible, and well-structured statistical information. Its integration with other national statistical operations further enriches its value for demographic and economic analysis.

Through its Employment and Financial Services (EFS) division, Assisted Living and Social Services (ALSS) programs form a strong foundation of support to help many Albertans find and keep jobs. The ministry provides financial support, employment services, career resources, referrals, information on job fairs and workshops, and local labor market information. The goal is to help individuals and families gain independence by providing opportunities to enhance their skills to get jobs. The alis.alberta.ca website provides employment resources to help Albertans enhance their employability, plan for education and training, make informed career choices, and connect to and be successful in the labour market. This dataset provides information on web traffic statistics for the alis website, including information on pageviews and web sessions, demographic information for web sessions, and traffic information for the alis YouTube channel at: https://www.youtube.com/user/ALISwebsite.

This dataset is a polygon coverage of counties limited to the extent of the Pocahontas No. 3 coal bed resource areas and attributed with statistics on the thickness of the Pocahontas No. 3 coal bed, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

This sub-domain contains data on agriculture producer prices and the producer price index. Agriculture producer prices are prices received by farmers for primary crops, live animals and livestock primary products as collected at the point of initial sale (prices paid at the farm gate). Annual data are provided from 1991 (while mothly data start in January 2010) for 180 countries and 212 products. The producer price index is the index of agricultural producer prices that measures the average annual change over time in the selling prices received by farmers (prices at the farm gate or at the first point of sale). The three categories of producer price index available in FAOSTAT comprise: single-item price index, commodity group index and the agriculture producer price index.

Data revision: 2025-12-04

Supplemental Information:

Coverage: Most crop and livestock products under agricultural activity

Unit of measure:

• Producer Price: Local Currency Unit per tonne [(LCU)/t]; Standard Local Currency per tonne [(SLC)/t]; US Dollars per tonne [USD/t]

• Producer Price Index: (2014-2016 = 100)

Time coverage: 1991-2025

More detailed information on this dataset is provided in the FAOSTAT metadata.

Citation:

FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0

Contact points:

Resource Contact: FAO Statistics Division (ESS), Social and Economic Statistics Team

Metadata Contact: FAO Statistics Division (ESS), Social and Economic Statistics Team

Data lineage:

Source data

The main source is official statistics from FAO member countries. Most countries report annual prices from surveys, in some cases administered prices are provided. The questionnaire metadata section requests information on the type of source and data collection frequency. No information is requested, and therefore available, on sample sizes at country level.

Data collection method:

Mostly through annual questionnaires complemented by internet data (Eurostat, some developed countries) and CountrySTAT data. Imputation of missing Price data. Forecast of index data for t period.

Base period: Base period: average 2014-2016 only for index

More information on data validation, imputation, regional aggregation, use of statistical classifications, data revision policies and practices are provided in the FAOSTAT metadata.

Resource constraints:

User access: In line with FAO's Statistics Code of Practice data are disseminated for free consultation on FAO's website respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. Users are informed that the data are being released in the FAOSTAT Home Page through the Latest News box. |FAO Statistical Database Terms of Use This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets.

Online resources:

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

Ready-reference guide for human resources (HR) professionals. Contains demographic statistics along with other valuable employee data for full time permanent (FTP) and part time permanent (PTP) SSA employees.

Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.

Data revision: 2020-07-29

Supplemental Information:

Coverage: nan

Unit of measure: Domestic supply quantity [1000 t]Export Quantity [1000 t]Fat supply quantity [g/capita/day]Feed [1000 t]Food [1000 t]Food supply [kcal/capita/day]Food supply quantity [kg/capita/year]Import Quantity [1000 t]Other Util [1000 t]Processed [1000 t]Production Quantity [1000 t]Protein supply quantity [g/capita/day]Seed [1000 t]Stock Variation [1000 t]Total Population - Both sexes [1000]

Time coverage: 1961 - 2017

More detailed information on this dataset is provided in the FAOSTAT metadata.

Contact points:

Resource Contact: FAO Statistics Division (ESS), Crops, Livestock and Food Statistics Team

Metadata Contact: FAO Statistics Division (ESS), Crops, Livestock and Food Statistics Team

Data lineage:

Source data

The main source is official statistics from FAO member countries. Exceptionally, unofficial data are also used as well as estimated/imputed data. In both cases this is "flagged". Data are recorded as countries report them, except for eliminating obvious errors. The source data can originate from surveys, administrative data and estimates based on expert observations. Which type of source is used by countries affect significantly reliability and comparability of data..

Data collection method: Sample surveys are generally used but there are also cases where administrative records are used, see further country specific metadata.

Base period: N.a.

More information on data validation, imputation, regional aggregation, use of statistical classifications, data revision policies and practices are provided in the FAOSTAT metadata.

Resource constraints:

User access: In line with FAO's Statistics Code of Practice data are disseminated on FAO's website respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. | Terms of use: https://www.fao.org/contact-us/terms/db-terms-of-use/en/ This work is made available under the Creative Commons Attribution 4.0 International license. In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets.

Online resources:

Download data from FAOSTAT

FAOSTAT metadata source

Food Balance Sheet presents a comprehensive picture of the pattern of a country's food supply during a specified reference period. The food balance sheet shows for each food item - i.e. each primary commodity and a number of processed commodities potentially available for human consumption - the sources of supply and its utilization. The total quantity of foodstuffs produced in a country added to the total quantity imported and adjusted to any change in stocks that may have occurred since the beginning of the reference period gives the supply available during that period. On the utilization side a distinction is made between the quantities exported, fed to livestock, used for seed, put to manufacture for food use and non-food uses, losses during storage and transportation, and food supplies available for human consumption. The per caput supply of each such food item available for human consumption is then obtained by dividing the respective quantity by the related data on the population actually partaking of it. Data on per caput food supplies are expressed in terms of quantity and - by applying appropriate food composition factors for all primary and processed products - also in terms of caloric value and protein and fat content.

Data revision: 2020-07-29

Supplemental Information:

Reference area: All countries of the world – with some minor exceptions – and geographical aggregates according to the United Nations M49 list.

Notes on geographical coverage:

(1) Data of Iraq do not include Kurdistan region.

(2) Since 2007 France data include French Guiana, Martinique, Guadeloupe, Reunion territories but they exclude French Polynesia and New Caledonia territories.

(3) Information provided by the Russian Federation includes statistical data for the Autonomous Republic of Crimea and the city of Sevastopol, Ukraine, temporarily occupied by the Russian Federation and is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine. Information provided by Ukraine excludes statistical data concerning the Autonomous Republic of Crimea, the city of Sevastopol and certain areas of the Donetsk and Luhansk regions. The information is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine.

Unit of measure:

• Domestic supply quantity: 1000 tonnes [1000 t]

• Export Quantity: 1000 tonnes [1000 t]

• Fat supply quantity; Grams per capita per day [g/cap/d]

• Feed: 1000 tonnes [1000 t]

• Food: 1000 tonnes [1000 t]

• Food supply: Kilograms per capita per day [kcal/cap/d]

• Food supply quantity: Kilograms per capita per year [kg/cap/y]

• Import Quantity: 1000 tonnes [1000 t]

• Other Util: 1000 tonnes [1000 t]

• Processed: 1000 tonnes [1000 t]

• Production Quantity: 1000 tonnes [1000 t]

• Protein supply quantity: Grams per capita per day [g/cap/d]

• Seed: 1000 tonnes [1000 t]

• Stock Variation: 1000 tonnes [1000 t]

• Total Population - Both sexes [1000]

Time coverage: 1961 - 2017

More detailed information on this dataset is provided in the FAOSTAT metadata.

Citation:

FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0

Contact points:

Resource Contact: FAO Statistics Division (ESS), Economic Statistics Team

Metadata Contact: FAO Statistics Division (ESS), Economic Statistics Team

Data lineage:

Source data

The main source is official statistics from FAO member countries. Exceptionally, unofficial data are also used as well as estimated/imputed data. In both cases this is "flagged". Data are recorded as countries report them, except for eliminating obvious errors. The source data can originate from surveys, administrative data and estimates based on expert observations. Which type of source is used by countries affect significantly reliability and comparability of data..

Data collection method: Sample surveys are generally used but there are also cases where administrative records are used, see further country specific metadata.

Base period: N.a.

More information on data validation, imputation, regional aggregation, use of statistical classifications, data revision policies and practices are provided in the FAOSTAT metadata.

Resource constraints:

User access: In line with FAO's Statistics Code of Practice data are disseminated on FAO's website respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. Terms of use:. This work is made available under the Creative Commons Attribution 4.0 International license. In addition to this license, some database specific terms of use are listed: Terms of Use of Datasets.

Online resources:

Download data from FAOSTAT

FAOSTAT metadata source

ServiceNow, Inc. specializes in delivering cloud-based solutions designed to streamline and automate critical business services for organizations across the globe. Its flagship "Now Platform" serves as the foundation, leveraging technologies such as workflow automation, artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA). This platform also incorporates robust features like performance analytics, electronic service catalogs, configuration management systems, data benchmarking, encryption capabilities, and various collaboration and development tools. ServiceNow offers a comprehensive suite of applications built on this platform, catering to diverse enterprise needs. Key offerings include IT Service Management (ITSM), which streamlines support for employees, customers, and partners; IT Business Management (ITBM); IT Operations Management (ITOM), designed to integrate and manage both physical and cloud-based IT infrastructure; and IT Asset Management (ITAM) for automating asset lifecycles. Its Security Operations solution facilitates seamless integration between internal systems and third-party security tools. Beyond IT, the company provides solutions for Governance, Risk, and Compliance (GRC) to enhance organizational resilience, along with tools for Human Resources, Legal, and general workplace service delivery, including dedicated safe workplace applications. Other specialized applications cover Customer Service Management (CSM) and Field Service Management (FSM). To further extend functionality, ServiceNow offers App Engine for custom development and IntegrationHub to connect workflows across various applications. The company also provides a range of professional services, industry-specific solutions, and comprehensive customer support. ServiceNow's diverse client base spans critical sectors such as government, financial services, healthcare, telecommunications, manufacturing, and education, alongside various IT services, technology, oil and gas, and consumer product industries. The company reaches these customers through a combination of its direct sales force and a network of resale partners. Notably, a strategic alliance with Celonis assists clients in pinpointing and prioritizing business processes ripe for automation. Established in 2004 and headquartered in Santa Clara, California, the company originally operated as Service-now.com before rebranding to ServiceNow, Inc. in May 2012.

This dataset is a polygon coverage of counties limited to the extent of the Pond Creek coal bed resource areas and attributed with statistics on the thickness of the Pond Creek coal zone, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

Educational Technology in Public School Districts, 2008 (FRSS 93), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 93 (https://nces.ed.gov/surveys/frss/) is a sample survey that provides national estimates on the availability and use of educational technology in public school districts during Fall 2008. This is one of a set of three surveys (at the district, school, and teacher levels) that collected data on a range of educational technology resources. The study was conducted by having school superintendents fill out surveys via the web or by mail. Public school districts were sampled. The study's weighted response rate was 90 percent. Key statistics produced from FRSS 93 were information on networks and internet capacity, technology policies, district-provided resources, teacher professional development, and district-level leadership for technology. Respondents reported the number of schools in the district with a local area network and the number of schools with each type of district network connection. The survey collected information on written district policies on acceptable student use of various technologies. Other survey topics included employment of staff responsible for educational technology leadership and the type of teacher professional development offered or required by districts for educational technology. Respondents gave their opinions on statements related to the use of educational technology in the instructional programs in their districts.

This dataset is a polygon coverage of counties limited to the extent of the Fire Clay coal zone resource areas and attributed with statistics on these coal quality parameters: ash yield (percent), sulfur (percent), SO2 (lbs per million Btu), calorific value (Btu/lb), arsenic (ppm) content and mercury (ppm) content. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C. The attributes were generated from public data found in the geochemical dataset found in Chap. F, Appendix 7, Disc 1. Please see the metadata file found in Chap. F, Appendix 8, Disc 1, for more detailed information on the geochemical attributes. The county statistical data used for this data set are found in Tables 2-5 and 17-18, Chap. F, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 6-16, Chap. F, Disc 1.

Abstract

The Annual Agriculture Sample Survey (AASS 2023/24) was conducted to generate up-to-date and precise data on crops, livestock and aquaculture activities. Accurate crop production figures are essential for a wide range of stakeholders in the agriculture sector. The data from this survey will provide critical insights for farmers, agricultural businesses, government policymakers, and other key players to inform their decisions in both the short and long term.

The specific objectives of the AASS 2023/24 include:

1. To collect timely data on agricultural production and productivity at both national and regional levels;

2. To gather core data to help develop and review agricultural policies and to guide the implementation of agricultural plans at national and regional levels between agricultural census periods;

3. To compile fundamental statistics that facilitate comparisons in the development of the agriculture sector across the country; and

4. To collect data on agricultural machinery, equipment, and structures, as well as information on women’s empowerment and nutrition.

The Women's empowerment and nutrition was an additional module that was integrated into the AASS 2023/24 to generate nationally representative statistics on empowerment and women's dietary diversity among agricultural households. This module is useful in generating the Women Empowerment Metric for National Statistical Systems (WEMNS) indicator (https://weai.ifpri.info/wemns/) and the Women's Dietary Diversity (MDD-W) indicator.

Geographic coverage

National, Mainland Tanzania and Zanzibar, Regions

Analysis unit

Households for Smallholder Farmers and Farm for Large Scale Farms

Universe

The survey covered agricultural households and large-scale farms.

Agricultural households are those that meet one or more of the following two conditions: a) Have or operate at least 25 square meters of arable land, b) Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agriculture year.

Large-scale farms are those farms with at least 20 hectares of cultivated land, or 50 herds of cattle, or 100 goats/sheep/pigs, or 1,000 chickens. In addition to this, they should fulfill all of the following four conditions: i) The greater part of the produce should go to the market, ii) Operation of farm should be continuous, iii) There should be application of machinery / implements on the farm, and iv) There should be at least one permanent employee.

Kind of data

Sample survey data [ssd]

Sampling procedure

The frame used to extract the sample for the Annual Agricultural Sample Survey (AASS 2023/24) in Tanzania was derived from the 2022 Population and Housing Census (PHC-2022) Frame that lists all the Enumeration Areas (EAs/Hamlets) of the country. The AASS 2023/24 used a stratified two-stage sampling design which allows to produce reliable estimates at regional level for both Mainland Tanzania and Zanzibar.

In the first stage, 1,504 EAs were selected by using a systematic sampling procedure with probability proportional to size (PPS), where the measure of size is the number of agricultural households in the EA. Before the selection, within each stratum and domain (region), the Enumeration Areas (EAs) were ordered according to the District and Council codes which reflect the geographical proximity, and then ordered according to the codes of Constituency, Division, Wards, and Village. An implicit stratification was also performed, ordering by Urban/Rural type at Ward level.

In the second stage, a simple random sampling selection without replacement was conducted, for the selection of 12 SSUs (agricultural households) in each selected EAs. A total sample of 18,048 agricultural holdings across 1504 EAs.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2023/24 Annual Agricultural Survey used two main questionnaires, Smallholder Farmers and Large-Scale Farms Questionnaire, consolidated into a single questionnaire within the CAPI System. Smallholder Farmers questionnaire captured information at household level while Large Scale Farms questionnaire captured information at establishment/holding level. These questionnaires were used for data collection that covered core agricultural activities (crops, livestock, and fish farming) in both short and long rainy seasons. The Questionnaire is attached as an external resource in the downloads tab.

Cleaning operations

The data processing and data editing phases were critical components of the Annual Agriculture Sample Survey for the agricultural year 2023/24. These phases ensure that the collected data is of high quality, consistent, coherent, and ready for analysis and reporting. The technical team responsible for these tasks included members from the National Bureau of Statistics (NBS), the Office of the Chief Government Statistician (OCGS), Agricultural Sector Lead Ministries (ASLMs), and academia, with technical support from FAO experts at various levels.

A. Data Processing

A.1. Data Entry: - Enumerators entered data directly into tablets during interviews, eliminating the need for a separate data entry activity. This method minimized errors associated with manual data entry. Data collected in the field was periodically synchronized with a central database, ensuring that the information was securely stored and readily accessible for processing.

A.2. Data Cleaning: - Upon synchronization, the data underwent initial automated checks to identify and flag obvious errors, such as missing values, out-of-range responses, and inconsistencies. - Technical staff conducted a manual review of flagged entries, correcting errors based on predefined rules and protocols. This step ensured that all data was accurate and complete before further processing.

A.3. Data Integration: - Data from different sections of the questionnaire (e.g., household information, crop production, livestock data) were integrated into a unified dataset. This process involved matching and merging records to ensure consistency across all sections by data scientists/ data programmers. - The technical team harmonized data formats and units of measurement to ensure consistency. This step was important for maintaining coherence in subsequent analyses.

B. Data Editing

B.1. Consistency Checks: - The data editing phase included rigorous checks for internal consistency within the dataset. This involved ensuring that related variables were logically consistent (e.g., the number of chicken reported matched the eggs production data). - The team conducted cross-sectional checks to verify consistency across different sections of the questionnaire. For example, crop production data were cross-referenced with input use and labor data to identify and correct discrepancies.

B.2. Outlier Detection and Treatment: - Statistical techniques were employed to identify outliers in the dataset. Outliers could indicate data entry errors or exceptional cases that required further investigation. - Identified outliers were validated through additional checks by using STATA program or, if necessary, follow-up with the respondents. This ensured that the outliers were genuine and not due to errors.

B.3. Imputation of Missing Data: - For instances where data was missing, the team used imputation techniques to estimate the missing values. Imputation methods included statistical techniques such as mean substitution, regression imputation, or hot-deck imputation, where necessary. All imputed values were documented by do files (STATA files). This transparency ensured that subsequent analyses accounted for the imputed data appropriately.

B.4. Data Validation: - The dataset was validated against external data sources, such as previous surveys, administrative records, and satellite imagery (limited), to ensure accuracy and reliability. - The validation process included a feedback loop where any identified issues were communicated back to the data collection teams for clarification and correction. - Technical online meetings between FAO, NBS, OCGS and ASLMs related to data validation were conducted professionally to ensure accountability of data along the value chain.

C. Continuous Improvement - After the completion of the survey, the entire process was reviewed to identify areas for improvement. Feedback from all team members and stakeholders was gathered to refine the methodologies and protocols for future agriculture surveys in series under 50x20230 initiatives. - Detailed documentation of all processes, decisions, and methodologies was maintained. This documentation served as a reference for future surveys and contributed to the transparency and reproducibility of the survey process.

STATISTICAL DISCLOSURE CONTROL (SDC)

Microdata are disseminated as Public Use Files under the terms indicated in Appendix A of the NBS Dissemination and Pricing Policy (https://www.nbs.go.tz/publications/policies-and-legislations). These access conditions are also indicated in the "data access" section below.

Statistical Disclosure Control (SDC) methods have been applied to the microdata, to protect the confidentiality of the individuals that data was collected from. These methods include: i) removal of information that may directly identify a respondent (name, address, etc.), ii) grouping values of some variables into categories (e.g. age), iii) limiting geographical information to the region level or higher, iv) suppression of some data points for variables that, in combination with others, may pose a relevant risk of identification of a statistical unit, v) adding noise to continuous

Educational Technology in Public Schools, 2008 (FRSS 92), is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 92 (https://nces.ed.gov/surveys/frss/) is a sample survey that provides national estimates on the availability and use of educational technology in public elementary and secondary schools during fall 2008. This is one of a set of three surveys (at the district, school, and teacher levels) that collected data on a range of educational technology resources. The study was conducted using mailed questionnaires and respondents had the option of completing the survey via the web. Schools were sampled. The study's weighted response rate was 79 percent. Key statistics produced from FRSS 92 were information on computer hardware and internet access, availability of staff to help integrate technology into instruction and provide timely technical support, and perceptions of educational technology issues at the school and district levels. Respondents reported the number of instructional computers within their schools, by type, mobility, and location. The survey also asked respondents about the types of operating systems or platforms used on instructional computers. Data on the number of handheld devices provided to school personnel and students, and the number of other technology devices provided for instructional purposes were also collected. Respondents indicated the extent to which technology staff provided assistance with technology support and integration and the response times for obtaining such support. Respondents gave opinions on statements related to using educational technology in their schools.

This dataset is a polygon coverage of counties limited to the extent of the Lower Kittanning coal bed resource areas and attributed with statistics on these coal quality parameters: ash yield (percent), sulfur (percent), SO2 (lbs per million Btu), calorific value (Btu/lb), arsenic (ppm) content and mercury (ppm) content. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C. The attributes were generated from public data found in the geochemical dataset found in Chap. E, Appendix 2, Disc 1, as well as some additional proprietary data. Please see the metadata file found in Chap. E, Appendix 3, Disc 1, for more detailed information on the geochemical attributes. The county statistical data used for this data set are found in Tables 2-3, 12-13 and 25-26 in Chap. E, Disc 1. Additional county geochemical statistics for other parameters are found in Tables 14-24, Chap. E, Disc 1.