LOW TRANSPORTATION COST INDEXSummaryThe Low Transportation Cost Index is based on estimates of transportation expenses for a family that meets the following description: a 3-person single-parent family with income at 50% of the median income for renters for the region (i.e. CBSA). The estimates come from the Location Affordability Index (LAI). The data correspond to those for household type 6 (hh_type6_) as noted in the LAI data dictionary. More specifically, among this household type, we model transportation costs as a percent of income for renters (t_rent). Neighborhoods are defined as census tracts. The LAI data do not contain transportation cost information for Puerto Rico.InterpretationValues are inverted and percentile ranked nationally, with values ranging from 0 to 100. The higher the transportation cost index, the lower the cost of transportation in that neighborhood. Transportation costs may be low for a range of reasons, including greater access to public transportation and the density of homes, services, and jobs in the neighborhood and surrounding community.

Data Source: Location Affordability Index (LAI) data, 2012-2016.Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 11.

References: www.locationaffordability.infohttps://lai.locationaffordability.info//lai_data_dictionary.pdf

To learn more about the Low Transportation Cost Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

Note: April 22, 2025: Updates to "CHN by income and HH size_v3". --------------------------------------------------------------------------------------------------------------------------------- Note: April 16, 2025: Updates to the following files have been made on April 9th and 16th: "CHN by income and HH size_v2", "cd_hh_projections_v2", "csd_hh_projections_v2", and "CMAs_all data_v3". --------------------------------------------------------------------------------------------------------------------------------- Note: March 31, 2025 files "Data_Element_1a" & "...1b" updated to v3 to include additional geographies (CDs and PTs) in the calculation of households close to rail transit. --------------------------------------------------------------------------------------------------------------------------------- Note: This dataset as of March 31st, 2025 now contains data on all 12 data elements, including core housing need among "gender diverse" households (formerly called "2SLGBTQ+" households) in table "Data_Element_ 3". That table (i.e. Data_Element_3) now also includes core housing need data on those priority populations reported in HART's HNA Tool. Two other outputs were migrated from that HNA Tool into this Federal HNA Template dataset: Income Categories and Affordable Shelter Costs, Percentage of Households in Core Housing Need by Income Category and Household Size, and 2021 Affordable Housing Deficit. (HICC Section 3.6), and Projected Households by Household Size and Income Category (HICC Section 6.1.1) This Borealis dataset has been updated accordingly to include that data: "AMHI.csv" (2021 AMHI and dollar ranges of income and shelter cost categories) "cd_hh_projections.csv" (Projected households in 2031 for CDs) "csd_hh_projections.csv" (Projected households in 2031 for CSDs) "CHN by income and HH size.csv" (2021 core housing need by income and household size) The geographical scope of the dataset has also been expanded. Before March 31st, only CSDs were included. As of March 31st, data on CDs, provinces/territories, the country of Canada, and CMA/CAs has been added. Not all data is available for all geographies: Data from CMHC's Rental Market Survey and Starts and Completions Survey are reported at the CSD level within CMAs/CAs. Results for provinces/territories/Canada are reported, but data for CDs is not. Since these surveys may not include all CSDs within a given CD, we have not attempted to aggregate this CSD data into CDs. Data from any custom census order by HART does not include CMA/CAs. We are able to aggregate the data by CSD into CMA/CAs, but all income and shelter cost data had been categorized based on the AMHI of the CSD as part of the original order (i.e. whether a household is "Very Low" income or "Low" income depends on the median household income of the CSD that the household lives in). This will lead to some inaccuracy and ambiguity of interpretation for the income or shelter cost data reported for CMAs. Data on "gender diverse" households is only available from Statistics Canada for geographies with a population count greater than 50,000 as of the 2021 census. This represents a total of 239 geographies (incl. Canada and the provinces/territories). Due to the low number of CSDs with this data, we have not attempted to aggregated this to the CMA/CA level. Data for CMAs/CAs will be added to the tool by mid-April 2025, but the source data has been summarized and included in this dataset: "CMAs_all data.csv" (All available data for CMAs and CAs) --------------------------------------------------------------------------------------------------------------------------------- Update (March 14, 2025): Tables "Data_Element_1a" and "...1b" have been updated to exclude some non-rail rapid transit stops that were erroneous included, notably in Winnipeg. --------------------------------------------------------------------------------------------------------------------------------- For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This database was created to accompany the dashboard on HART's website called the "Federal Housing Needs Assessment Template." URL: https://hart.ubc.ca/federal-hna-template/. This dashboard presents housing-related data to help communities complete the Housing Needs Assessment template requested by the Government of Canada as a requirement for certain funding applications. For more information on that template, please visit the Government of Canada's website (https://housing-infrastructure.canada.ca/housing-logement/hna-ebml/template-modele-eng.html). This dataset represents the underlying data used to populate HART's dashboard. The data contains some public and custom data from Canada's Census of Population (author: Statistics Canada), public data from the Canada Mortgage and Housing Corporation (CMHC) regarding it's Rental Market Survey as well as it's Starts and Completions Survey, private...

This is the proportion of children aged under 16 (0-15) living in families in absolute low income during the year. The figures are based on the count of children aged under 16 (0-15) living in the area derived from ONS mid-year population estimates. The count of children refers to the age of the child at 30 June of each year.

Low income is a family whose equivalised income is below 60 per cent of median household incomes. Gross income measure is Before Housing Costs (BHC) and includes contributions from earnings, state support, and pensions. Equivalisation adjusts incomes for household size and composition, taking an adult couple with no children as the reference point. For example, the process of equivalisation would adjust the income of a single person upwards, so their income can be compared directly to the standard of living for a couple.

Absolute low income is income Before Housing Costs (BHC) in the reference year in comparison with incomes in 2010/11 adjusted for inflation. A family must have claimed one or more of Universal Credit, Tax Credits, or Housing Benefit at any point in the year to be classed as low income in these statistics. Children are dependent individuals aged under 16; or aged 16 to 19 in full-time non-advanced education. The count of children refers to the age of the child at 31 March of each year.

Data are calibrated to the Households Below Average Income (HBAI) survey regional estimates of children in low income but provide more granular local area information not available from the HBAI. For further information and methodology on the construction of these statistics, visit this link. Totals may not sum due to rounding.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

Affordability ratios calculated by dividing house prices by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

This dataset includes one dataset which was custom ordered from Statistics Canada.The table includes information on housing suitability and shelter-cost-to-income ratio by number of bedrooms, housing tenure, age of primary household maintainer, household type, and income quartile ranges for census subdivisions in British Columbia. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Non-reserve CSDs in British Columbia - 299 geographies The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. Housing Tenure Including Presence of Mortgage (5) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero by housing tenure 2. Households who own 3. With a mortgage1 4. Without a mortgage 5. Households who rent Notes: 1) Presence of mortgage - Refers to whether the owner households reported mortgage or loan payments for their dwelling. 2015 Before-tax Household Income Quartile Ranges (5) 1. Total – Private households by quartile ranges1, 2, 3 2. Count of households under or at quartile 1 3. Count of households between quartile 1 and quartile 2 (median) (including at quartile 2) 4. Count of households between quartile 2 (median) and quartile 3 (including at quartile 3) 5. Count of households over quartile 3 Notes: 1) A private household will be assigned to a quartile range depending on its CSD-level location and depending on its tenure (owned and rented). Quartile ranges for owned households in a specific CSD are delimited by the 2015 before-tax income quartiles of owned households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. Quartile ranges for rented households in a specific CSD are delimited by the 2015 before-tax income quartiles of rented households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. 2) For the income quartiles dollar values (the delimiters) please refer to Table 1. 3) Quartiles 1 to 3 are suppressed if the number of actual records used in the calculation (not rounded or weighted) is less than 16. For cases in which the renters’ quartiles or the owners’ quartiles (figures from Table 1) of a CSD are suppressed the CSD is assigned to a quartile range depending on the provincial renters’ or owners’ quartile figures. Number of Bedrooms (Unit Size) (6) 1. Total – Private households by number of bedrooms1 2. 0 bedrooms (Bachelor/Studio) 3. 1 bedroom 4. 2 bedrooms 5. 3 bedrooms 6. 4 bedrooms Note: 1) Dwellings with 5 bedrooms or more included in the total count only. Housing Suitability (6) 1. Total - Housing suitability 2. Suitable 3. Not suitable 4. One bedroom shortfall 5. Two bedroom shortfall 6. Three or more bedroom shortfall Note: 1) 'Housing suitability' refers to whether a private household is living in suitable accommodations according to the National Occupancy Standard (NOS); that is, whether the dwelling has enough bedrooms for the size and composition of the household. A household is deemed to be living in suitable accommodations if its dwelling has enough bedrooms, as calculated using the NOS. 'Housing suitability' assesses the required number of bedrooms for a household based on the age, sex, and relationships among household members. An alternative variable, 'persons per room,' considers all rooms in a private dwelling and the number of household members. Housing suitability and the National Occupancy Standard (NOS) on which it is based were developed by Canada Mortgage and Housing Corporation (CMHC) through consultations with provincial housing agencies. Shelter-cost-to-income-ratio (4) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero 2. Spending less than 30% of households total income on shelter costs 3. Spending 30% or more of households total income on shelter costs 4. Spending 50% or more of households total income on shelter costs Note: 'Shelter-cost-to-income ratio' refers to the proportion of average total income of household which is spent on shelter costs. Household Statistics (8) 1....

The StatXplore Children in low-income families' local area statistics (CiLIF) provides information on the number of children living in Relative low income by local area across the United Kingdom.The summary Statistical Release and tables which also show the proportions of children living in low income families are available here: Children in low income families: local area statistics - GOV.UK (www.gov.uk)Statistics on the number of children (by age) in low income families by financial year are published on Stat-Xplore. Figures are calibrated to the Households Below Average Income (HBAI) survey regional estimates of children in low income but provide more granular local area information not available from the HBAI, for example by Local Authority, Westminster Parliamentary Constituency and Ward.

Relative low-income is defined as a family in low income Before Housing Costs (BHC) in the reference year. A family must have claimed Child Benefit and at least one other household benefit (Universal Credit, tax credits, or Housing Benefit) at any point in the year to be classed as low income in these statistics. Gross income measure is Before Housing Costs (BHC) and includes contributions from earnings, state support and pensions.

Understanding the costs of health interventions is critical for generating budgets, planning and managing programs, and conducting economic evaluations to use when allocating scarce resources. Here, we utilize techniques from the hedonic pricing literature to estimate the characteristics of the costs of social and behavior change communication (SBCC) interventions, which aim to improve health-seeking behaviors and important intermediate determinants to behavior change. SBCC encompasses a wide range of interventions including mass media (e.g., radio, television), mid media (e.g., community announcements, live dramas), digital media (e.g., short message service/phone reminders, social media), interpersonal communication (e.g., individual or group counseling), and provider-based SBCC interventions focused on improving provider attitudes and provider-client communication. While studies have reported on the costs of specific SBCC interventions in low- and middle-income countries, little has been done to examine SBCC costs across multiple studies and interventions. We use compiled data across multiple SBCC intervention types, health areas, and low- and middle-income countries to explore the characteristics of the costs of SBCC interventions. Despite the wide variation seen in the unit cost data, we can explain between 63 and 97 percent of total variance and identify a statistically significant set of characteristics (e.g., health area) for media and interpersonal communication interventions. Intervention intensity is an important determinant for both media and interpersonal communication, with costs increasing as intervention intensity increases; other important characteristics for media interventions include intervention subtype, target population group, and country income as measured by per capita Gross National Income. Important characteristics for interpersonal communication interventions include health area, intervention subtype, target population group and geographic scope.

More than 1 million U.S. Department of Housing and Urban Development-supported public housing units provide rental housing for eligible low-income families across the country. These units range from scattered single-family houses to high-rise apartments. In this project, the Advanced Residential Integrated Energy Solutions Collaborative (ARIES) worked with two public housing authorities (PHAs) to develop packages of energy efficiency retrofit measures the PHAs can cost-effectively implement with their own staffs in the normal course of housing operations at the time when units are refurbished between occupancies. ARIES conducted a survey of PHAs to assess their receptiveness to this concept and the applicability of the concept to PHA units. The results of the survey, to which more than 100 PHAs responded, support the proposed approach. The project consisted of a field evaluation in which energy audits were performed on a sample of PHA units at two housing authorities. Energy efficiency turnover protocols were developed for typical units, the protocol was implemented by PHA staff, and the effectiveness of the protocol was quantified through field testing and modeling. The energy efficiency turnover protocols emphasized air infiltration reduction, duct sealing, and measures that improve equipment efficiency. In the 10 housing units in which ARIES documented implementation, reductions in average air leakage of 16%-20% and duct leakage of 38% were obtained. Total source energy consumption savings was estimated at 6%-10% based on Building Energy Optimization modeling with a simple payback of 1.7-2.2 years. Implementation challenges were encountered, mainly related to required operational changes and budgetary constraints. Lack of complete training and inadequate quality control can prevent PHAs from effectively retrofitting units to their full potential. Nevertheless, despite these hurdles, simple improvements, such as caulking and sealing penetrations, windows, and doors; sealing duct boots; and adding pipe insulation into a standardized turnover protocol can feasibly be accomplished by PHA staff at low or no cost. At typical housing unit turnover rates, these measures could impact hundreds of thousands of units per year nationally.

Islip Housing Authority - Single Story Typical home - Raleigh, NC Raleigh Housing Authority - 2 story typical home in Raleigh housing authority - Terrace Park

Affordability ratios calculated by dividing house prices by gross annual workplace-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

Data DescriptionThe CADDI dataset is designed to support research in in-class activity recognition using IMU data from low-cost sensors. It provides multimodal data capturing 19 different activities performed by 12 participants in a classroom environment, utilizing both IMU sensors from a Samsung Galaxy Watch 5 and synchronized stereo camera images. This dataset enables the development and validation of activity recognition models using sensor fusion techniques.Data Generation ProceduresThe data collection process involved recording both continuous and instantaneous activities that typically occur in a classroom setting. The activities were captured using a custom setup, which included:A Samsung Galaxy Watch 5 to collect accelerometer, gyroscope, and rotation vector data at 100Hz.A ZED stereo camera capturing 1080p images at 25-30 fps.A synchronized computer acting as a data hub, receiving IMU data and storing images in real-time.A D-Link DSR-1000AC router for wireless communication between the smartwatch and the computer.Participants were instructed to arrange their workspace as they would in a real classroom, including a laptop, notebook, pens, and a backpack. Data collection was performed under realistic conditions, ensuring that activities were captured naturally.Temporal and Spatial ScopeThe dataset contains a total of 472.03 minutes of recorded data.The IMU sensors operate at 100Hz, while the stereo camera captures images at 25-30Hz.Data was collected from 12 participants, each performing all 19 activities multiple times.The geographical scope of data collection was Alicante, Spain, under controlled indoor conditions.Dataset ComponentsThe dataset is organized into JSON and PNG files, structured hierarchically:IMU Data: Stored in JSON files, containing:Samsung Linear Acceleration Sensor (X, Y, Z values, 100Hz)LSM6DSO Gyroscope (X, Y, Z values, 100Hz)Samsung Rotation Vector (X, Y, Z, W quaternion values, 100Hz)Samsung HR Sensor (heart rate, 1Hz)OPT3007 Light Sensor (ambient light levels, 5Hz)Stereo Camera Images: High-resolution 1920×1080 PNG files from left and right cameras.Synchronization: Each IMU data record and image is timestamped for precise alignment.Data StructureThe dataset is divided into continuous and instantaneous activities:Continuous Activities (e.g., typing, writing, drawing) were recorded for 210 seconds, with the central 200 seconds retained.Instantaneous Activities (e.g., raising a hand, drinking) were repeated 20 times per participant, with data captured only during execution.The dataset is structured as:/continuous/subject_id/activity_name/ /camera_a/ → Left camera images /camera_b/ → Right camera images /sensors/ → JSON files with IMU data

/instantaneous/subject_id/activity_name/repetition_id/ /camera_a/ /camera_b/ /sensors/ Data Quality & Missing DataThe smartwatch buffers 100 readings per second before sending them, ensuring minimal data loss.Synchronization latency between the smartwatch and the computer is negligible.Not all IMU samples have corresponding images due to different recording rates.Outliers and anomalies were handled by discarding incomplete sequences at the start and end of continuous activities.Error Ranges & LimitationsSensor data may contain noise due to minor hand movements.The heart rate sensor operates at 1Hz, limiting its temporal resolution.Camera exposure settings were automatically adjusted, which may introduce slight variations in lighting.File Formats & Software CompatibilityIMU data is stored in JSON format, readable with Python’s json library.Images are in PNG format, compatible with all standard image processing tools.Recommended libraries for data analysis:Python: numpy, pandas, scikit-learn, tensorflow, pytorchVisualization: matplotlib, seabornDeep Learning: Keras, PyTorchPotential ApplicationsDevelopment of activity recognition models in educational settings.Study of student engagement based on movement patterns.Investigation of sensor fusion techniques combining visual and IMU data.This dataset represents a unique contribution to activity recognition research, providing rich multimodal data for developing robust models in real-world educational environments.CitationIf you find this project helpful for your research, please cite our work using the following bibtex entry:@misc{marquezcarpintero2025caddiinclassactivitydetection, title={CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors}, author={Luis Marquez-Carpintero and Sergio Suescun-Ferrandiz and Monica Pina-Navarro and Miguel Cazorla and Francisco Gomez-Donoso}, year={2025}, eprint={2503.02853}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2503.02853}, }

This dataset contains the raw-data (RGB-images) and reconstructed point clouds of 6 different wheat (Triticum aestivum) genotypes. The plants were imaged at two time instances, 14 days and 35 days after planting, and from each genotype 10 instances were imaged, for a total of 120 imaging sessions (6 genotypes x 2 imaging days x 10 pots) resulting in the same number of point clouds.

Data from the first imaging day (14 days after planting) shows 5 individual plants per pot, each pot containing plants from a single genotype. These multiple plants per pot had been thinned to a single plant per pot for the second imaging day (35 days after planting).

The raw data of each imaging session consists of 140 RGB-images, each with a resolution of 4056x3040 pixels. Further, for each imaging session a three-dimensional point cloud of the plant(s) was reconstructed using the method described in https://doi.org/10.48550/arXiv.2504.16840.

In addition to the above data the dataset contains three more csv-files. The first file is an assessment by a plant expert of the genotypes canopy architecture rating that ranges on a scale from 1 (extreme erectophile) to 10 (extreme planophile). The other two csv-files report measurements performed from the point-clouds for each plant, these measurements are defined in https://doi.org/10.48550/arXiv.2504.16840.

The CMS Program Statistics - Medicare Part D tables provide use and Part D drug costs by type of Part D plan (stand-alone prescription drug plan and Medicare Advantage prescription drug plan). For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. These data do not exist in a machine-readable format, so the view data and API options are not available. Please use the download function to access the data. Below is the list of tables: MDCR UTLZN D 1. Medicare Part D Utilization: Average Annual Prescription Drug Fills by Type of Plan, Low Income Subsidy (LIS) Eligibility, and Generic Dispensing Rate, Yearly Trend MDCR UTLZN D 2. Medicare Part D Utilization: Average Annual Gross Drug Costs Per Part D Enrollee, by Type of Plan, Low Income Subsidy (LIS) Eligibility, and Brand/Generic Drug Classification, Yearly Trend MDCR UTLZN D 3. Medicare Part D Utilization: Average Annual Gross Drug Costs Per Part D Enrollee, by Type of Plan, Low Income Subsidy (LIS) Eligibility, and Brand/Generic Drug Classification, Yearly Trend MDCR UTLZN D 4. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Enrollee, by Type of Plan and Demographic Characteristics MDCR UTLZN D 5. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Utilizer, by Type of Plan and Demographic Characteristics MDCR UTLZN D 6. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Enrollee, by Type of Plan, by Area of Residence MDCR UTLZN D 7. Medicare Part D Utilization: Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Utilizer, by Type of Plan, by Area of Residence MDCR UTLZN D 8. Medicare Part D Utilization: Number of Part D Utilizers and Average Annual Prescription Drug Fills by Type of Part D Plan, Low Income Subsidy (LIS) Eligibility, and Part D Coverage Phase, Yearly Trend MDCR UTLZN D 9. Medicare Part D Utilization: Number of Part D Utilizers and Drug Costs by Type of Part D Plan, Low Income Subsidy (LIS) Eligibility, and Part D Coverage Phase, Yearly Trend MDCR UTLZN D 10. Medicare Part D Utilization: Number of Part D Utilizers, Average Annual Prescription Drug Events (Fills) and Average Annual Gross Drug Cost Per Part D Utilizer, by Part D Coverage Phase and Demographic Characteristics MDCR UTLZN D 11. Medicare Part D Utilization: Number of Part D Utilizers, Average Annual Prescription Drug Fills and Average Annual Gross Drug Cost Per Part D Utilizer, by Part D Coverage Phase and Area of Residence

Background: Value of a Statistical Life Year (VSLY) provides an important economic measure of an individual’s trade‑off between health risks and other consumption, and is a widely used policy parameter. Measuring VSLY is complex though, especially in low‑income and low‑literacy communities. Methods: Using a large randomized experiment (N = 3027), we study methodological aspects of stated‑preference elicitation with payment cards (price lists) in an extreme poverty context. In a 2 × 2 design, we systematically vary whether buying or selling prices are measured, crossed with the range of the payment card. Results: We find substantial effects of both the pricing method and the list range on elicited VSLY. Estimates of the gross domestic product per capita multiplier for VSLY range from 3.5 to 33.5 depending on the study design. Importantly, all estimates are economically and statistically significantly larger than the current World Health Organization threshold of 3.0 for cost‑effectiveness analyses. Conclusions: Our results inform design choice in VSLY measurements, and provide insight into the potential variability of these measurements and possibly robustness checks.

This dataset contains school-level expenditures reported by major functional spending category starting with fiscal year 2019. It also includes school-level enrollment, demographic, and performance indicators as well as teacher salary and staffing data.

The dataset shows school-level per pupil expenditures by major functional expenditure categories and funding sources, including state and local funds (general fund and state grants) and federal funds.

School districts only report instructional expenditures by school. This report attributes other costs to each school on a per pupil basis to show a full resource picture. The three cost centers are:

1. Non-instructional per pupil spending reported at the school district level;
2. Instructional per pupil spending reported at the district level; and
3. Instructional per pupil spending reported at the school level

Economically Disadvantaged was used 2015-2021. Low Income was used prior to 2015, and a different version of Low Income has been used since 2022. Please see the DESE Researcher's Guide for more information.

This dataset is one of three containing the same data that is also published in the School Finance Dashboard: District Expenditures by Spending Category District Expenditures by Function Code School Expenditures by Spending Category

List of Indicators by Category

Student Enrollment

• In-District FTE Pupils
• Out-of-District FTE Pupils
• Total FTE Pupils

Student Demographics

• Student Headcount
• Low-Income % Headcount
• English Learner % Headcount
• Students with Disabilities % Headcount

Teacher Salaries

• Teacher FTE
• Teachers per 100 FTE Students
• Average Teacher Salary

Other Staff

• Instructional Coach FTE
• Instructional Support FTE
• Special Education Instructional Support FTE
• Paraprofessional FTE

MCAS Performance

• ELA Grades 3-8 % Meets Exceeds
• Math Grades 3-8 % Meets Exceeds
• ELA Grade 10 % Meets Exceeds
• Math Grade 10 % Meets Exceeds

District-Level FTE Pupils

District-Level State and Local Non-Instructional Expenditures Per Pupil

• Administration
• Benefits and Fixed Costs
• Operations and Maintenance
• Pupil Services

District-Level Federal Non-Instructional Expenditures Per Pupil

• Administration
• Benefits and Fixed Costs
• Operations and Maintenance
• Pupil Services

Sub-total A

District-Level State and Local Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

District-Level Federal Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

Sub-total B

School-Level State and Local Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

School-Level Federal Instructional Expenditures Per Pupil

• Guidance and Psychological Services
• Instructional Leaders
• Instructional Materials
• Other Teaching Services
• Professional Development
• Teachers

Sub-total C

Total A+B+C

Dataset Description

This dataset contains all the tests used for the low-cost sensor development during the iSCAPE project. The dataset is divided in a series of tests, each of them described on a yaml file with the test name. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each test. The tests are described in the comment of the yaml file, and are meant to be self explanatory. The conditions of the test and the purpose vary, and their reports are also included.

Sensors

The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and the Living Lab Station or Smart Citizen Station. These are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The Smart Citizen Station is meant to expand the capabilities of the Smart Citizen Kit, aiming to measure pollutants with more advanced sensors. The hardware is licensed under CERN Open Hardware License V1.2 and is fully described in the HardwareX Open Access publication: https://doi.org/10.1016/j.ohx.2019.e00070. The sensor documentation can be found at https://docs.smartcitizen.me and with this DOI at Zenodo: https://doi.org/10.5281/zenodo.2555029.

In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.

Air temperature (ºC): Sensirion SHT-31 [TEMP]

Relative Humidity (%rh): Sensirion SHT-31 [HUM]

Noise level (dBA): Invensense ICS-434342 [NOISE_A]

Ambient light (lux): Rohm BH1721FVC [LIGHT]

Barometric pressure (kPa): NXP MPL3115A26 [PRESS]

Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]

In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.

Air Temperature (ºC) Sensirion SHT-31 [TEMP]

Relative Humidity (% REL) Sensirion SHT-31 [HUM]

Noise Level (dBA) Invensense ICS-434342 [NOISE_A]

Ambient Light (Lux) Rohm BH1721FVC [LIGHT]

Barometric pressure and AMSL (Pa and Meters) NXP MPL3115A26 [PRESS]

Carbon Monoxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]

Nitrogen Dioxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]

Carbon Monoxide (ppm) Alphasense CO-B4 [GB_1W, GB_1A]

Nitrogen Dioxide (ppb) Alphasense NO2-B43F [GB_2W, GB_2A]

Ozone (ppb) Alphasense OX-B431 [GB_3W, GB_3A]

Gases Board Temperature (ºC) Sensirion SHT-31 [GB_TEMP] or [EXT_TEMP]

Gases Board Rel. Humidity (% REL) Sensirion SHT-31 [GB_HUM] or [EXT_HUM]

PM 1 (µg/m3) Plantower PMS5003 [EXT_PM_1] or [EXT_PM_A_1], [EXT_PM_B_1] for each PM sensor in the case of the Living Lab Station

PM 2.5 (µg/m3) Plantower PMS5003 [EXT_PM_25] or [EXT_PM_A_25], [EXT_PM_B_25] for each PM sensor in the case of the Living Lab Station

PM 10 (µg/m3) Plantower PMS5003 [EXT_PM_10] or [EXT_PM_A_10], [EXT_PM_B_10] for each PM sensor in the case of the Living Lab Station

PN between 0.3um<0.5um particle size (#/l) Plantower PMS5003 [EXT_PN_03] or [EXT_PN_A_03], [EXT_PN_B_03] for each PM sensor in the case of the Living Lab Station

PN between 0.5um<1um particle size (#/l) Plantower PMS5003 [EXT_PN_05] or [EXT_PN_A_05], [EXT_PN_B_05] for each PM sensor in the case of the Living Lab Station

PN between 1m<2.5um particle size (#/l) Plantower PMS5003 [EXT_PN_1] or [EXT_PN_A_1], [EXT_PN_B_1] for each PM sensor in the case of the Living Lab Station

PN between 2.5m<5um particle size (#/l) Plantower PMS5003 [EXT_PN_25] or [EXT_PN_A_25], [EXT_PN_B_25] for each PM sensor in the case of the Living Lab Station

PN between 5m<10um particle size (#/l) Plantower PMS5003 [EXT_PN_5] or [EXT_PN_A_5], [EXT_PN_B_5] for each PM sensor in the case of the Living Lab Station

PN between >10um particle size (#/l) Plantower PMS5003 [EXT_PN_10] or [EXT_PN_A_10], [EXT_PN_B_10] for each PM sensor in the case of the Living Lab Station

How to find the data

Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:

author: who has been in charge of performing the test (internal reference - not relevant)

comment: describing in general terms what was done in the test, and with what purpose

commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only

devices: a descriptor containing different fields for traceability (below)

id: the test name

project: within the test was performed, in this case it is always iscape

report: if there is any report analysing the test

type_test: indoor, oudoor test or other.

Description of devices entry

For each device that was used in the test, two generic types are used:

low cost sensors (type: STATION or KIT)

high end sensors (type: REFERENCE)

For low cost Smart Citizen sensors, the fields are:

alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed

device_id: device id in Smartcitizen API

fileNameInfo: not used

fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv

fileNameRaw: (only if source = csv is used) raw file name

frequency: original recording frequency

location: for timezone correction only, not accurate

max_date: last recording date

min_date: first recording date

name: self-explanatory

pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)

source: api or csv

type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT

version: smartcitizen hardware version

For high end sensors, the fields are:

channels: which channels the device was recording for internal convertion

names: which are the columns in the csv file

pollutants: which pollutants do they respectively refer to

units: the units of these pollutants

equipment: the brand of the analyser

fileNameProc: same as above

fileNameRaw: same as above

index: format in which the timeindex is done, for parsing purposes

format: (example '%Y-%m-%d %H:%M:%S')

frequency: frequency at which the device was recorded

name: column name

location: same as above

name: name of the device

type: REFERENCE (always for these devices)

source: csv

iSCAPE Dataset Reference Numbers:

The datasets here presented are related to the following iSCAPE dataset reference numbers:

DS_TS_054

DS_TS_062

DS_TS_063

DS_TS_065

DS_TS_067

DS_TS_068

DS_TS_069

DS_TS_070

DS_TS_071

DS_TS_072

DS_TS_073

DS_TS_074

DS_TS_075

DS_TS_076

DS_TS_077

DS_TS_078

DS_TS_079

DS_TS_080

DS_TS_081

DS_TS_084

DS_TS_088

DS_TS_089

DS_TS_090

DS_TS_092

Cooking skills play a relevant role in food security, which encompasses the availability, accessibility, utilization, and stability of food. While previous discussions have mainly focused on accessibility, particularly economic access through food prices and income, this article explores the dimension of food utilization by analyzing the relation between food insecurity and cooking-related variables. We conducted a survey of 106 low-income households in an urban area of Santiago, Chile. Food insecurity was measured using the Food Insecurity Experience Scale (FIES) developed by the FAO. Using principal component analysis, we constructed two indexes reflecting subjective perceptions of cooking skills. We then applied probit models to examine how both subjective and objective cooking skills variables are associated with the probability of experiencing food insecurity. Results show that individuals who can prepare six to ten egg preparations have an 8.4 percentage point lower prevalence of experiencing food insecurity, while those who can prepare more than ten such preparations show a 30.5 percentage point lower prevalence compared to those who can prepare five or fewer. Moreover, our results found a positive prevalence between negative subjective perceptions and food insecurity of 8.8 percentage point. For the first time, this study jointly examines subjective perceptions and self-reported objective measures of cooking skills in relation to food insecurity. We hope this work contributes to expanding the food insecurity discussion beyond economic access and supports the design of food security policies focused on improving cooking aspects.

This dataset comprises 19 subsets. Each subset measures a different parameter or is produced by a different sensor provider. The measurement period for this dataset was from October 11, 2024, to October 31, 2024, and the measurement interval depends on the type of parameter being measured, ranging from 1 second to 15 minutes. The dataset includes six indoor low-cost sensor providers with their respective measuring sensors. Three of these providers had only one sensor at the location, while one had 16 sensors, and the other two had 4 and 2 sensors, respectively. Human presence was monitored using a camera and a motion detection sensor. Window and door opening and closing were monitored using Xiaomi Door/Window sensors. In addition to the indoor low-cost sensors, the location was equipped with reference sensing units that were calibrated to the measuring station. Furthermore, outdoor low-cost sensors were also used. Specifically, one was a low-cost sensor, and the other was a mid-range sensor in terms of pricing. This dataset also includes black carbon data and CPC data. A camera was set up on the balcony to monitor the road in front of the house, so traffic data is also included in the dataset. Additionally, on-site measuring data from the Croatian Meteorological and Hydrological Service was made available in this dataset, sourced from the two nearest locations to the measuring site, as well as satellite data from the Climate Data Store. Every single parameter is detailed in the Data.xlsx file, which is integrated into the data.zip archive.

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If you’re a senior with low income, you may qualify for monthly Guaranteed Annual Income System payments.

Maximum payment and allowable private income amounts for the period from July 1, 2025 to June 30, 2026 are:

• $90 monthly for single seniors (maximum monthly payment amount), your annual private income must be less than $4,320
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