This dataset describes survey data collected for the Bureau of Reclamation (Reclamation), the agency in charge of regulating Colorado River water control operations impounding the Lake Powell reservoir. Additional intent of the collected data was to assure consistencies among gaging elevations at Glen Canyon Dam near Page, Arizona as well as verification and alignment of a recently published topobathymetric digital elevation model for Lake Powell. Glen Canyon Dam is a concrete arch-gravity dam on the Colorado River in northern Arizona and is the second largest man-made reservoir in the United States. The location was chosen to survey due to uncertainty in the local datum used by the Reclamation as well as uncertainties regarding elevation consistencies among the local United States Geological Survey (USGS) gaging operation 09379900 Lake Powell at Glen Canyon Dam, Arizona. The primary component of the survey involved a differential leveling campaign derived from fiducial benchmarks used to perpetuate elevation to a variety of objective points. Additionally, the survey consisted of a Global Navigation Satellite System (GNSS) (Rydlund and Densmore, 2012) campaign constrained to fiducial benchmarks that were used to develop network solutions at the same objective points derived by leveling. This Level I static network GNSS campaign was conducted to quality assure the leveling campaign as well as integrate ellipsoid and geoid height characteristics tied to active monumentation. A third GNSS campaign involved a level III single-base static survey of Lake Powell water-surface elevations that were conducted at marina locations of Antelope and Wahweap, Arizona, along with a location at Bullfrog, Utah to provide comparison and assure alignment of the topobathymetric digital elevation model used to develop a current area capacity table at Lake Powell. Six items containing the survey data and the relevant information are available for download. They are GCD_USBR_LEVEL_SUMMARY.csv, GCD_USBR_LEVEL_SUMMARY.zip, GCD_USBR_MARK_RECOVERY.zip, GCD_USBR_STATIC_NETWORK.csv, GCD_USBR_WSE.csv, and GCD_USBR_LAKE_SURVEY.zip. Differential leveling final elevation for selected objective points are located in GCD_USBR_LEVEL_SUMMARY.csv. Field notes and details representing fiducial benchmarks and objective points within the differential leveling campaign are located in GCD_USBR_LEVEL_SUMMARY.zip. Fiducial marks recovery photographs and integration of USGS recovery forms are located in GCD_USBR_MARK_RECOVERY.zip. GNSS survey solutions referenced in Arizona State Plane Central Zone 0202, Universal Transverse Mercator 12 North, and Geographic (Decimal Degrees) are located in GCD_USBR_STATIC_NETWORK.csv. Orthometric heights in both Geoid 18 and Geoid 12b along with comparisons to differential leveling surveys are also located in GCD_USBR_STATIC_NETWORK.csv. The Lake Powell survey solutions are in the same format as GCD_USBR_STATIC_NETWORK.csv but located in GCD_USBR_WSE.csv. Photographs and USGS GNSS Level IV static observation forms of the lake survey are located in GCD_USBR_LAKE_SURVEY.zip. References Cited: Rydlund, P.H., Jr., and Densmore, B.K., 2012, Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D1, 102 p. with appendixes., https://doi.org/10.3133/tm11D1.

This dataset describes the Survey Data collected for the Planning Assistance to the States (PAS) study along Little Sugar Creek and selected tributaries, near Bella Vista, Arkansas, and Pineville, Missouri, December 2019. Little Sugar Creek is a tributary to the Elk River in Missouri that commences in Benton County, Arkansas and terminates in McDonald County, Missouri. The stream headwaters are located southeast of Garfield, Arkansas. Little Sugar Creek flows through Bella Vista, Arkansas, and runs north to its confluence with the Big Sugar Creek just south of Pineville, Missouri where it forms the Elk River. Browning Creek, Blowing Spring Creek, Spanker Creek and McKisic Creek are all tributaries to the Little Sugar Creek between Bella Vista, Arkansas, and Pineville, Missouri. These streams were selected for bathymetric and specified structure survey by the U.S. Army Corps of Engineers (USACE) PAS study. The survey consisted of channel cross-sections, bridge/culvert cross-sections, and high-water locations along Little Sugar Creek, Browning, Blowing Spring, Spanker Creek, and McKisic Creek in the town of Bella Vista, Benton County, Arkansas to the town of Pineville, McDonald County, Missouri. The surveys included 38 channel cross-sections, 35 bridge/culvert cross-sections, 1 dam outlet works, 1 dam spillway, 1 dam road, and 3 high-water locations. Topographic data and supplemental photographic data were collected for each survey section. These data were collected using a surveying total station, Trimble R10, and a Trimble R8. Trimble R10 and Trimble R8 are the real-time kinematic (RTK) Global Navigation Satellite System (GNSS) receivers. The GNSS receivers were connected to the Arkansas Department of Transportation (ARDOT) or the Missouri Department of Transportation (MODOT) real-time network (RTN), which provided real-time survey grade horizontal and vertical positioning, and were used to obtain Northing, Easting, and the elevation _location information for one control point in the survey area. Supplemental photographic data were collected using cellular telephone cameras. The survey was conducted by the U.S. Geological Survey during a two-week period in December 2019. Six items containing the survey data and the relevant information are available for download. They are LittleSugarCreekPAS_Culverts.xlsx, LittleSugarCreek_PAS_Bridges.xlsx, LittleSugarCreek_PAS_XS.xlsx, LittleSugarCreekPASmoshp.zip, LittleSugarPASarshp.zip, LittleSugarCreek_PAS_pictures_Summary.pptx, LittleSugarCreek_PAS_Field_Pictures.pdf, and LittleSugarCreek_PAS_Field_Sheets.pdf. The topographic points with centerline stationing are available in Microsoft Excel file format (LittleSugarCreekPAS_Culverts.xlsx, LittleSugarCreek_PAS_Bridges.xlsx, LittleSugarCreek_PAS_XS.xlsx). Field notes, which describe bridge/culvert details, are available in pdf format (LittleSugarCreek_PAS_Field_Sheets.pdf). Supplemental photographic data were compiled in a Microsoft PowerPoint file and .pdf as requested (LittleSugarPASpictures.pptx, LittleSugarCreek_PAS_Field_Pictures.pdf). Topographic points are also available in the Environmental Systems Research Institute (ESRI) ArcGIS Shapefile format (LittleSugarPASmoshp.zip & LittleSugarPASarshp.zip). All files in the shapefile group must be retrieved to be useable.

The global survey-grade UAV market is experiencing robust growth, driven by increasing demand for precise and efficient data acquisition across various sectors. This surge is fueled by advancements in sensor technology, improved flight autonomy, and the decreasing cost of UAVs. Applications spanning forestry, construction, mining, and topographic/hydrological surveys are key contributors to market expansion. While precise market sizing data wasn't provided, considering the growth trajectory of the drone industry and the increasing adoption of UAVs for surveying, a reasonable estimate for the 2025 market size could be placed around $2.5 billion. Assuming a conservative Compound Annual Growth Rate (CAGR) of 15% based on industry reports showcasing similar technological advancements, the market is projected to reach approximately $7 billion by 2033. This growth is further propelled by the integration of advanced technologies such as LiDAR, photogrammetry, and multispectral sensors, enabling the generation of highly accurate 3D models and geospatial data. Several factors contribute to market growth. The ability of survey-grade UAVs to provide cost-effective solutions compared to traditional surveying methods is a major driver. Furthermore, the increased accessibility of user-friendly software and data processing tools is lowering the barrier to entry for numerous businesses and organizations. However, regulatory hurdles surrounding UAV operations, concerns regarding data security, and the potential for adverse weather conditions to impact operations remain as constraints. Market segmentation highlights the dominance of fixed-wing UAVs in larger-scale projects, while multi-rotor UAVs maintain their appeal for precise, detailed surveys in confined spaces. The North American and European markets currently hold significant shares, but the Asia-Pacific region is expected to witness rapid growth in the coming years due to increasing infrastructure development and investment in surveying technologies.

Survey Grade UAV Market Outlook

The global survey grade UAV market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach a staggering USD 3.5 billion by 2032, growing at an impressive CAGR of 12.5% during the forecast period. This remarkable growth can be attributed to the increasing adoption of UAV technology across various industries for enhanced precision, efficiency, and cost savings. The market is thriving due to technological advancements in UAV systems, coupled with the growing demand for high-resolution aerial data in industries like agriculture, construction, and mining.

One of the primary growth factors propelling the survey grade UAV market is the technological advancements in UAV systems, including improvements in payload capacity, endurance, and real-time data processing capabilities. These enhancements allow UAVs to perform complex survey tasks with greater accuracy and reliability. The miniaturization of sensors and the integration of advanced data analytics capabilities have further improved the efficiency of UAVs, making them indispensable tools for precise data collection and analysis in sectors such as agriculture and construction.

Another significant growth driver is the increasing regulatory support and favorable policies by governments worldwide, encouraging the adoption of UAV technology across diverse industries. Regulatory bodies are increasingly recognizing the potential of UAVs to enhance operational efficiency and safety. This has led to the establishment of clear guidelines and frameworks for UAV operations, thus reducing the barriers to entry for businesses looking to leverage UAV technology. Furthermore, government initiatives promoting the use of UAVs in infrastructure development and environmental monitoring are also bolstering market growth.

The rising need for efficient and cost-effective data acquisition in industries like mining, oil & gas, and utilities is also contributing to the market's expansion. UAVs offer a unique advantage by providing high-resolution aerial data that is crucial for decision-making processes in these sectors. They enable rapid data collection over large areas, reducing the time and resources traditionally required for ground surveys. The ability to access remote and hazardous areas safely and efficiently is particularly valuable in industries such as oil & gas, where UAVs are used for pipeline inspection and monitoring.

Regionally, North America holds a significant share in the survey grade UAV market, driven by the presence of major UAV manufacturers and technology providers. The region's advanced infrastructure and strong regulatory framework have facilitated the rapid adoption of UAV technology across various sectors. Europe is another key region witnessing substantial growth, with countries like Germany, France, and the UK investing heavily in UAV technology for applications in agriculture and construction. The Asia Pacific region is anticipated to exhibit the highest CAGR during the forecast period, fueled by the increasing demand for UAVs in emerging economies such as China and India, where industries are undergoing rapid digital transformation.

Product Type Analysis

The survey grade UAV market is segmented by product type into fixed-wing, rotary-wing, and hybrid UAVs. Fixed-wing UAVs are characterized by their ability to cover large areas and endurance, making them ideal for applications such as mapping and surveying. These UAVs are particularly favored in industries like agriculture and mining where extensive aerial coverage is required. The demand for fixed-wing UAVs is increasing as more industries recognize their efficiency in long-duration flights and the ability to carry larger payloads, which is crucial for comprehensive data collection tasks.

Rotary-wing UAVs, on the other hand, are known for their versatility and ability to hover in place, making them suited for applications requiring detailed inspections and close-up data capture. These UAVs are widely used in infrastructure inspections, including bridges, wind turbines, and power lines, where precise and stable positioning is necessary. The flexibility offered by rotary-wing UAVs in maneuvering through tight spaces and their ease of deployment make them an attractive choice for real-time data acquisition tasks in urban environments and confined areas.

Hybrid UAVs combine the features of both fixed-wing and rotary-wing UAVs, offering the benefits of long-range coverage with the capability to hover and capture detailed data. These UAVs are gaining tr

Topographic survey data were collected along planned lines from the river water surface to the top of the bank in the study area. Topographic survey data collection began on May 22 and concluded on June 10, 2014. A real-time kinematic (RTK) GNSS network provided real-time survey grade horizontal and vertical positioning.

Abstract

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Geographic coverage

Urban and rural areas of Papua New Guinea

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The initial sample was drawn through Random Digit Dialing (RDD) with geographic stratification from a large random sample of Digicel’s subscribers. As an objective of the survey was to measure changes in household economic wellbeing over time, the HFPS sought to contact a consistent number of households across each province month to month. This was initially a repeated cross section from April 2023-Dec 2023. The resulting overall sample has a probability-based weighted design, with a proportionate stratification to achieve a proper geographical representation. More information on sampling for the cross-sectional monthly sample can be found in previous documentation for the PNG HFPS data.

A monthly panel was established in October 2023, that is ongoing as of March 2025. In each subsequent round of data collection after October 2024, the survey firm would first attempt to contact all households from the previous month, and then attempt to contact households from earlier months that had dropped out. After previous numbers were exhausted, RDD with geographic stratification was used for replacement households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

he questionnaire, which can be found in the External Resources of this documentation, is in English with a Pidgin translation.

The survey instrument for Q1 2025 consists of the following modules: -1. Basic Household information, -2. Household Roster, -3. Labor, -4a Food security, -4b Food prices -5. Household income, -6. Agriculture, -8. Access to services, -9. Assets -10. Wellbeing and shocks -10a. WASH

Cleaning operations

The raw data were cleaned by the World Bank team using STATA. This included formatting and correcting errors identified through the survey’s monitoring and quality control process. The data are presented in two datasets: a household dataset and an individual dataset. The individual dataset contains information on individual demographics and labor market outcomes of all household members aged 15 and above, and the household data set contains information about household demographics, education, food security, food prices, household income, agriculture activities, social protection, access to services, and durable asset ownership. The household identifier (hhid) is available in both the household dataset and the individual dataset. The individual identifier (id_member) can be found in the individual dataset.

Oblique aerial photography is an airborne mapping technique, which uses a professional grade DSLR camera to capture images out the side of our aircraft. Images are geo-referenced using our GPS systems to provide the position of the plane for each image. The Environment Agency has been capturing oblique aerial photography during incident response since 2010, and for bespoke surveys such as cliff line monitoring. Images can be captured in all survey conditions which can have a large influence on the quality of the imagery.

The imagery is available as a JPEG image. Contained within the EXIF metadata for each image is a geo-referenced GPS coordinate of the plane during exposure. These coordinates are in WGS1984 latitude, longitude.

When requesting download of aerial obliques all imagery within a 5km OS Grid is retuned for each type and year of survey. The 'types' of survey available are 'Incident Response' (data captured in varying lighting conditions usually for assessment of flood extent) and 'Other' (bespoke monitoring surveys such as cliff line assessments).

Please refer to the metadata index catalgoues which provde the date and time each image was taken and the location of the plane. The direction the plane was travelling along with the the image view angle is also provided. The image view angle is an approximate direction the camera was pointing for each image with all images captured out the left hand side of the plane. Attribution statement: © Environment Agency copyright and/or database right 2019. All rights reserved.

Access to up-to-date socio-economic data is a widespread challenge in Solomon Islands and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For Solmon Islands, after five rounds of data collection from 2020-2020, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. Fieldwork took place in two non-consecutive weeks of each month. Data for April 2023-December 2023 were a repeated cross section, while January 2024 established the first month of a panel, the was continued to September 2024. Each month has approximately 550 households in the sample and is representative of urban and rural areas, but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in Solomon Islands. There is one date file for household level data with a unique household ID. and a separate file for individual level data within each household data, that can be matched to the household file using the household ID, and which also has a unique individual ID within the household data which can be used to track individuals over time within households, where the data is panel data.

Cleaned, labelled and anonymized version of the master file provided by the World Bank.

• Collection start: 2023
• Collection end: 2024

Survey Grade GNSS Receiver Market Outlook

The global Survey Grade GNSS Receiver market size was valued at approximately USD 4.5 billion in 2023 and is projected to reach USD 8.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% during the forecast period from 2024 to 2032. This significant growth can be attributed to the increasing demand for high-precision navigation and positioning solutions across various industries. The accelerated adoption of advanced GNSS technology driven by modern applications such as autonomous vehicles, smart agriculture, and the Internet of Things (IoT) is further propelling the market's expansion.

A key growth factor of the Survey Grade GNSS Receiver market is the burgeoning development in infrastructure and construction activities globally. With rapid urbanization and the need for efficient land management, high precision in surveying activities has become crucial. GNSS receivers provide an accurate and efficient solution for land surveying needs, facilitating better planning and execution of construction projects. Furthermore, with governments across the world investing heavily in infrastructure development, the demand for reliable and high-precision GNSS receivers is set to rise, thereby contributing significantly to market growth.

Another driving factor is the increasing application of GNSS receivers in agriculture. Precision agriculture requires accurate data collection and mapping to optimize crop yields and manage land resources efficiently. GNSS technology enables farmers to conduct detailed analysis and monitoring, thereby enhancing productivity and reducing input costs. As the global population continues to rise, the pressure on agricultural productivity intensifies, leading to an increased adoption of technology-driven solutions such as survey-grade GNSS receivers to meet food supply demands efficiently. This trend significantly boosts the growth of the GNSS receiver market within the agricultural sector.

The growing adoption of GNSS technology in the transportation and logistics sector is also a major factor propelling market growth. As the demand for real-time tracking and navigation systems intensifies, GNSS receivers are becoming integral components in transportation networks, aiding in route optimization and efficient fleet management. The ability of GNSS technology to provide precise location data enhances the efficiency and reliability of transportation systems, which is particularly critical as e-commerce and global trade continue to expand. Consequently, this has led to a surge in demand for GNSS receivers in the transportation industry.

The integration of Gnss Rtk Receiver technology is revolutionizing the way industries approach precision tasks. By utilizing real-time kinematic (RTK) positioning, these receivers offer centimeter-level accuracy, which is crucial for applications requiring high precision. This technology is particularly beneficial in sectors such as construction and agriculture, where precise measurements and data collection are essential for efficiency and productivity. The ability to provide accurate and reliable data in real-time makes Gnss Rtk Receivers an invaluable tool in modern industry, supporting the growing demand for advanced surveying and mapping solutions.

Regionally, the Asia Pacific market is expected to witness substantial growth, driven by significant investments in infrastructure development and the rapid adoption of GNSS technology across various sectors. Countries like China and India are focusing on enhancing their surveying capabilities as they undergo massive urbanization and industrialization. Additionally, the integration of GNSS in modern technologies such as autonomous vehicles and smart cities is further propelling the market in this region. North America and Europe also represent significant markets for GNSS receivers, supported by technological advancements and a strong emphasis on precision agriculture and transportation logistics.

Component Analysis

The component analysis of the Survey Grade GNSS Receiver market is segmented into hardware, software, and services. The hardware segment holds a dominant share in the market due to the rising demand for advanced GNSS receiver devices. These devices are essential for providing precise location data, which is critical across various applications including surveying, agriculture, and transportation. The constant evolution of hardware components such as antennas and signal processors

Abstract copyright UK Data Service and data collection copyright owner.The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.Further information about the survey, including publications, can be downloaded from the Young Lives website. School Survey: A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children's experiences of schooling, and to improve our understanding of:the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groupsschool effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systemsequity issues (including gender) in relation to learning outcomes and the evolution of inequalities within educationThe survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives. A further round of school surveys took place during the 2016-2017 school year. The key focus areas for these were:benchmarking levels of student attainment and progress in key learning domainseffects of school and teacher quality, and school effectivenesseducational transitions at age 15The 2016-2017 school surveys focused on the level of schooling accessed by 15-year-olds in each country, so including Grade 7 and 8 students in Ethiopia (upper primary level), Grade 9 students in India (lower secondary level), and Grade 10 students in Vietnam (upper secondary level). The School Survey data are held separately for each country. The Ethiopia data are available from the UK Data Archive under SN 7823 and SN 8359, the India data are available from SN 7478 and SN 8359, and the Peru data have been archived under SN 7479 (no 2016-2017 survey). Further information is available from the Young Lives School Survey webpages. Main Topics: The Vietnam survey included data collection at the school, class and pupil level, and involved the Principal / Head teacher, the Maths and English teachers, and the Young Lives child. The instruments included in the survey were:Principal questionnaire - collected background data on the principal and the school (including school management practices)Teacher questionnaire - collected background data on Grade 10 Maths and English teachers (including teacher motivation, and class-level information)Student questionnaire - collected background data on Grade 10 students (including academic support within and beyond school, and psychosocial measures)Maths test - repeated measures, administered at the beginning and end of Grade 10. Assessing students’ curriculum knowledge, and ability to apply curriculum knowledge in less familiar contextsFunctional English test - repeated measures, administered at the beginning and end of Grade 10. Assessing students' English reading skills relevant to the contexts in which they use (or will use) the languageTransferable Skills test - cross-sectional measure, administered at the end of Grade 10. Assessing problem solving and critical thinking skillsSchool facilities observation - collected data on school infrastructure Multi-stage stratified random sample See documentation for details Face-to-face interview Self-completion Educational measurements

These data are the survey results from a five-mile section of the Platte River at, and upstream of the Nebraska Army National Guard Camp Ashland Training Site including the side channel chutes on the east bank. All survey data were collected along planned transect lines that were spaced 492.125 US survey feet apart beginning near the mouth of the Elkhorn River and ending near the U.S. Highway 6 bridge. An effort was made to get complete elevation data for each transect from top of bank to top of bank. Survey grade Global Navigation and Satellite Systems (GNSS) receiving antennas connected to a real time network (RTK high precision network https://hprtk.net) were used to measure elevation along the transects, at the top of banks, along the slope of the banks, at control structures, on islands and sandbars and on the streambed in areas of the wetted channel that were wadable. GNSS data collection methods followed level 3, RTN procedures as described by (Rydlund and Densmore, 2012). An acoustic Doppler current profiler (ADCP) was used to measure streambed elevation in areas of the wetted channel that were not wadable. ADCP data were processed using Velocity Mapping Toolbox (Parsons and others, 2013) to convert measured depths to elevation. This data release contains two comma separated value files. The CSV file named PlatteRiver_GNSS_SurveyData_20200924-20210402.csv contains the GNSS survey data. The CSV file named Bathy_ADCP_final_data_SPCS.csv contains bathymetric survey data.

Abstract copyright UK Data Service and data collection copyright owner.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.
Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.

The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).
The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

School Survey:
A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children's experiences of schooling, and to improve our understanding of:

• the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groups
• school effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systems
• equity issues (including gender) in relation to learning outcomes and the evolution of inequalities within education

The survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives.

A further round of school surveys took place during the 2016-2017 school year. The key focus areas for these were:

• benchmarking levels of student attainment and progress in key learning domains
• effects of school and teacher quality, and school effectiveness
• educational transitions at age 15

The 2016-2017 school surveys focused on the level of schooling accessed by 15-year-olds in each country, so including Grade 7 and 8 students in Ethiopia (upper primary level), Grade 9 students in India (lower secondary level), and Grade 10 students in Vietnam (upper secondary level).

The School Survey data are held separately for each country. The Ethiopia data are available from the UK Data Archive under SN 7823 and SN 8359, the Vietnam data are available from SN 7663 and SN 8360, and the Peru data have been archived under SN 7479 (no 2016-2017 survey).

Further information is available from the Young Lives School Survey webpages.


Main Topics:

The India survey included data collection at the school, class and pupil level, and involved the Director / Head teacher, the Maths and English teachers, and the Young Lives child. The instruments included in the survey were:

• Principal questionnaire - collected background data on the principal and the school (including school management practices)
• Teacher questionnaire - collected background data on Class 9 Maths and English teachers (including teacher motivation and class-level information)
• Student questionnaire - collected background data on Grade 10 students (including academic support within and beyond school, and psychosocial measures)
• School facilities observation - collected data on school infrastructure
• Teacher professional knowledge questionnaire - collected...

The Annual Survey of Jails (ASJ) is the only data collection effort that provides an annual source of data on local jails and jail inmates. Data on the size of the jail population and selected inmate characteristics are obtained every five to six years from the Census of Jails. In each of the years between the full censuses, a sample survey of jails is conducted to estimate baseline characteristics of the nation's jails and inmates housed in these jails. The 2011 Annual Survey of Jails is the 24th such survey in a series begun in 1982. The ASJ supplies data on characteristics of jails such as admissions and releases, growth in the number of jail facilities, changes in their rated capacities and level of occupancy, growth in the population supervised in the community, changes in methods of community supervision, and crowding issues. The ASJ also provides information on changes in the demographics of the jail population, supervision status of persons held, and a count of non-citizens in custody. Starting in 2010, BJS enhanced the ASJ survey instruments to address topics on the number of convicted inmates that are unsentenced or sentenced and the number of unconvicted inmates awaiting trial/arraignment, or transfers/holds for other authorities. In order to reduce respondent burden, the ASJ no longer collects data on conviction status by sex. Also new to the survey, data are collected on jails' operational capacity and design capacity. Incorporating enhanced capacity measurements enables BJS to describe more accurately the variation and volatility of inmate bed space and crowding, especially as they relate to safety and security in jails. To address more directly issues related to overcrowding and safety and security in jails, BJS started collecting data on staff and assaults against staff from the largest jails. In the modifications to the ASJ, starting in 2010, 335 jail jurisdictions (370 respondents) included with certainty in the ASJ sample survey were asked to provide additional information (forms CJ-5D or CJ-5DA) on the flow of inmates going through jails and the distribution of time served, staff characteristics and assaults on staff resulting in death, and inmate misconduct. The data presented in this study were collected in the Annual Survey of Jails, 2011. These data are used to track growth in the number of jails and the capacities nationally, changes in the demographics of the jail population and supervision status of persons held, the prevalence of crowding issues, and a count of non-United States citizens within the jail population. The data are intended for a variety of users, including federal and state agencies, local officials in conjunction with jail administrators, researchers, planners, and the public. The reference date for the survey is June 30, 2011.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

All formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency, Public Services Agency in the case of Moldova.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Moldova 2024 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

Response rate

Overall survey response rate was 45.1%.

Abstract

Millennium Challenge Corporation hired Mathematica Policy Research to conduct an independent evaluation of the BRIGHT II program. The three main research questions of interest are: • What was the impact of the program on school enrollment, attendance, and retention? • What was the impact of the program on test scores? • Are the impacts different for girls than for boys?

Mathematica will compare data collected from the 132 communities served by BRIGHT II (the "treatment group") with that collected from the 161 communities that applied but were not selected for the program (the "comparison group"). Using a statistical technique called regression discontinuity, Mathematica will compare the outcomes of the treatment villages just above the cutoff point to the outcomes of the comparison villages just below the cutoff point. If the intervention had an impact, we will observe a "jump" in outcomes at the point of discontinuity.

Mathematica will perform additional analyses to estimate the overall merit of the BRIGHT investment. By conducting a cost-benefit analysis and a cost-effectiveness analysis and calculating the economic rate of return, Mathematica will be able to answer questions related to the sustainability of the program, and compare the program to interventions and social investments in other sectors. The household survey is designed to capture household-level data rather than community-level data; however, questions have been included to measure head-of-household expectations of educational attainment. These questions ask the head of household what grade level he hopes each child will attain; and what grade level he thinks the child will be capable of achieving in reality.

Geographic coverage

132 rural villages throughout the 10 provinces of Burkina Faso in which girls' enrollment rates were lowest

Analysis unit

Households

Universe

Households, students, and educators in the 287 villages surveyed

Kind of data

Sample survey data [ssd]

Sampling procedure

The BRIGHT II program was implemented in the same 132 villages that received the BRIGHT I interventions. These 132 villages were originally selected using a scoring process, with eligibility scores based on the villages’ potential to improve girls’ educational outcomes. A total of 293 villages applied to receive a BRIGHT school; the Burkina Faso Ministry of Basic Education (MEBA) selected the 132 villages with scores that were above a certain cutoff point. Whenever possible, the survey will be conducted with the same children in the same households and schools surveyed during the BRIGHT I evaluation. By visiting the same households and schools, the evaluator will be able to better assess the longer-term impacts of the BRIGHT project.

Research instrument

Mathematica has developed two surveys, a household survey and a school survey, to collect relevant data from villages in both the treatment and comparison groups. The household survey was administered to a new cross-section of households compared to the BRIGHT I evaluation. Data will be collected on the attendance and educational attainment of school-age children in the household, attitudes towards girls' education, and parental assessment of the extent to which the complementary interventions influenced school enrollment decisions. It will also assess the performance of all household children on basic tests of French and math. The school survey, to be administered to all local schools in the 293 villages, gathers data on school characteristics, personnel, and physical structure, and collects enrollment and attendance records. Data will be gathered by a local data collection firm selected by MCA-Burkina Faso, with Mathematica providing technical assistance and oversight.

Cleaning operations

Following data collection, Mathematica will work with BERD to ensure that the data are correctly entered and are complete and clean. This will include a review of all frequencies for out-of-range responses, missing data, or other problems, as well as a comparison between the data and paper copies for a random selection of variables.

Water supply lakes are the primary source of water for many communities in northern and western Missouri. Therefore, accurate and up-to-date estimates of lake capacity are important for managing and predicting adequate water supply. Many of the water supply lakes in Missouri were previously surveyed by the U.S. Geological Survey (USGS) in the early 2000s (Richards, 2013) and in 2013 (Huizinga, 2014); however, years of potential sedimentation may have resulted in reduced water storage capacity. Periodic bathymetric surveys are useful to update the area/capacity table and to determine changes in the bathymetric surface. In April and May 2022, the USGS, in cooperation with the Missouri Department of Natural Resources (MoDNR) and in collaboration with the cities of Cameron, Springfield, and Unionville, Missouri, completed bathymetric surveys of seven (7) lakes using a marine-based mobile mapping unit, which consists of a multibeam echosounder (MBES) and an inertial navigation system (INS) mounted on a marine survey vessel. Bathymetric data were collected as the vessel traversed longitudinal transects to provide nearly complete coverage of the lake. The MBES was electronically tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than about 2.0 meters deep (the practical limit of reasonable and safe data collection with the MBES). At some lakes, supplemental data were collected in shallow areas using an acoustic Doppler current profiler (ADCP) mounted on a remote-controlled vessel equipped with a differential global positioning system (DGPS). Bathymetric quality-assurance data also were collected at each lake to evaluate the vertical accuracy of the gridded bathymetric point data from the MBES. As part of the survey at each of these lakes, one or more reference marks or temporary bench marks were established to provide a point of known _location and elevation from which the water surface could be measured or another survey could be referenced at a later date. In addition, the elevation of a primary spillway or intake was surveyed, when present. These points were surveyed using a real-time kinematic (RTK) Global Navigation Satellite System (GNSS) receiver connected to the Missouri Department of Transportation real-time network (RTN), which provided real-time survey-grade horizontal and vertical positioning, using field procedures as described in Rydlund and Densmore (2012) for a Level II real-time positioning survey. The MBES data can be combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table for each lake. These data also can be used to compare the current bathymetric surface with any previous bathymetric surface. Data from each of the surveys are provided in ESRI Shapefile format (ESRI, 2023). Each of the seven lakes surveyed in 2022 has a child page containing the metadata and two zip files, one for the bathymetric data, and the other for the bathymetric quality-assurance data. The zip files follow the format of "####2022_bathy_pts.zip" or ####2022_QA_raw.zip," where "####" is the lake name. Each of these zip files contains a shapefile with an attribute table. Attribute/column labels of each table are described in the "Entity and attribute" section of the metadata file. The various reference marks and additional points from all the lake surveys are provided in ESRI Shapefile format (ESRI, 2023) with an attribute table on the main landing page. Attribute/column labels of this table are described in the "Entity and attribute" section of the metadata file. References Cited: Environmental Systems Research Institute, 2023, ArcGIS: accessed July 12, 2023, at https://www.esri.com/en-us/arcgis/about-arcgis/overview Huizinga, R.J., 2014, Bathymetric surveys and area/capacity tables of water-supply reservoirs for the city of Cameron, Missouri, July 2013: U.S. Geological Survey Open-File Report 2014–1005, 15 p., https://doi.org/10.3133/ofr20141005. Richards, J.M., 2013, Bathymetric surveys of selected lakes in Missouri—2000–2008: U.S. Geological Survey Open-File Report 2013–1101, 9 p. with appendix, https://doi.org/10.3133/ofr20131101. Rydlund, P.H., Jr., and Densmore, B.K., 2012, Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D1, 102 p. with appendixes, https://doi.org/10.3133/tm11D1. First posted November 29, 2023 Revised July 31, 2024, ver. 1.1

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). Data collection was carried out in Iraq from 8 to 21 June 2023 using telephone interviews (CATI) in 18 governorates during the harvest season of main crops, such as wheat and barley. The sample size is representative at national and at governorate-level. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Data collection took place in the 18 governorates of Iraq using telephone interviews (CATI). A total of 2,513 households were interviewed (1,510 non-agricultural households and 1,003 agricultural households) using a panel list of agricultural households (interviewed in the previous data collection rounds) and the Random Digit Dialing (RDD) approach to complete the target. The sample target of 136 households per governorate was reached in all governorates. This sample size is representative at national and at governorate-level with a 95% confidence level and a 10% margin of error.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

A link to the questionnaire has been provided in the documentations tab.

Cleaning operations

The datasets have been edited and processed for analysis by the DIEM team at the Office of Emergencies and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). This third-round survey was representative at national level, covering Liberia’s 15 counties. Data were collected through face-to-face interviews conducted between 9 September and 4 October 2021. The sampling approach was based on random sampling for household questionnaires. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

This round 3 survey was representative at national level, covering Liberia's 15 counties. Data were collected through face-to-face interviews conducted between 9 September and 4 October 2021. The sampling approach was based on random sampling for household questionnaires. The overall sampling included 1 800 households, 45 key informants, 45 agro-input vendors and 45 agri-input traders, totalling 1 935 interviews.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

A link to the questionnaire has been provided in the documentations tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Needs Assessment team at the Office of Emergencies and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries

Access to up-to-date socio-economic data is a widespread challenge in Papua New Guinea and other Pacific Island Countries. To increase data availability and promote evidence-based policymaking, the Pacific Observatory provides innovative solutions and data sources to complement existing survey data and analysis. One of these data sources is a series of High Frequency Phone Surveys (HFPS), which began in 2020 as a way to monitor the socio-economic impacts of the COVID-19 Pandemic, and since 2023 has grown into a series of continuous surveys for socio-economic monitoring. See https://www.worldbank.org/en/country/pacificislands/brief/the-pacific-observatory for further details.

For PNG, after five rounds of data collection from 2020-2022, in April 2023 a monthly HFPS data collection commenced and continued for 18 months (ending September 2024) –on topics including employment, income, food security, health, food prices, assets and well-being. This followed an initial pilot of the data collection from January 2023-March 2023. Data for April 2023-September 2023 were a repeated cross section, while October 2023 established the first month of a panel, which is ongoing as of March 2025. For each month, approximately 550-1000 households were interviewed. The sample is representative of urban and rural areas but is not representative at the province level. This dataset contains combined monthly survey data for all months of the continuous HFPS in PNG. There is one date file for household level data with a unique household ID, and separate files for individual level data within each household data, and household food price data, that can be matched to the household file using the household ID. A unique individual ID within the household data which can be used to track individuals over time within households.

Cleaned, labelled and anonymized version of the master file.

• Collection start: 2023
• Collection end: 2025

Abstract copyright UK Data Service and data collection copyright owner.The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.Further information about the survey, including publications, can be downloaded from the Young Lives website. School Survey: A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children’s experiences of schooling. It addressed two main research questions:how do the relationships between poverty and child development manifest themselves and impact upon children's educational experiences and outcomes?to what extent does children’s experience of school reinforce or compensate for disadvantage in terms of child development and poverty? The survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. A wide range of stakeholders, including government representatives at national and sub-national levels, NGOs and donor organisations were involved in the design of the school survey, so the researchers could be sure that the ‘right questions’ were being asked to address major policy concerns. This consultation process means that policymakers already understand the context and potential of the Young Lives research and are interested to utilise the data and analysis to inform their policy decisions. The survey provides policy-relevant information on the relationship between child development (and its determinants) and children’s experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of the Young Lives study. The School Survey data are held separately for each country. The India data are available from the UK Data Archive under SN 7478, the Peru data have been archived under SN 7479, and the Ethiopia data are available from SN 7823. A further round of school surveys took place during the 2016-2017 school year. The Ethiopia survey is available under SN 8358, the India survey under SN 8359 and the Vietnam survey under SN 8360. Further information is available from the Young Lives School Survey webpages. Main Topics: The survey included data collection at the school, class and pupil level, and involved the Principal, the teacher of Grade 5 class, and pupil. The instruments included in the survey are: Principal questionnaire. Administered individually by fieldworkers to principals.School site observation. Fieldworker completed through observation of school site during their time in the school.Teacher questionnaire. Administered individually by fieldworkers to teacher of YL child's class.Pupil questionnaire. Administered to the whole class. Fieldworker led and directed. Collected background data on the Pupil, as well as information on attitudes to school.Pupil assessments in Maths and Vietnamese. Administered to the whole class. Fieldworker led and directed. Multi-stage stratified random sample Face-to-face interview Self-completion Educational measurements Observation