Description:
The Global Food Insecurity dataset used in this study was constructed by harmonizing data from multiple public sources, in order to obtain a consistent and continuous time series covering the period 2000-2022 at the global (World) level. As no single international source provides complete and uninterrupted food insecurity data for this entire time range, the dataset was compiled by carefully integrating information from authoritative reports and databases, as follows:

1. 2000–2014 period:

For this period, data were manually extracted from The State of Food Insecurity in the World (SOFI) reports published annually by the Food and Agriculture Organization (FAO).
The indicator used was the Prevalence of Undernourishment (PoU), defined as the estimated percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels required to maintain a normal, active, and healthy life.
Although the PoU focuses primarily on chronic caloric undernourishment, it has been widely used in the literature as a key indicator of global food insecurity prior to the development of experiential-based measures.

Relevant data points from each annual SOFI report were manually compiled, cross-checked for consistency across editions, and transformed into an annual time series from 2000 to 2014. In cases where FAO reported multi-year moving averages (e.g., 3-year averages), the values were assigned to the central year of the corresponding period.

1. 2015–2022 period:

From 2015 onwards, the dataset incorporates data from the Our World in Data platform, specifically the indicator “Share of population with severe food insecurity” derived from the FAO’s Food Insecurity Experience Scale (FIES).

FIES is a direct, experiential measure of food insecurity based on household surveys, and it corresponds to SDG indicator 2.1.2. This metric complements and updates the previous PoU measure by capturing the severity and experience of food access problems.
The OWID dataset, in turn, is based on FAO’s FIES data and was used for the period 2015–2022 as reported by FAO and OWID.

Harmonization:
As the underlying indicators (PoU vs. FIES severe insecurity) are conceptually different but related (both representing serious forms of food insecurity), the entire series was harmonized into a common scale of “% of population food insecure” to allow for temporal comparisons. This was done by cross-validating overlapping years and adjusting for indicator definitions based on FAO and SDG metadata documentation.

Limitations:
The constructed series represents a best-effort harmonization of available public data. While PoU and FIES severe insecurity do not capture identical dimensions of food insecurity, their trends and values are broadly comparable and widely used in global monitoring. The adapted dataset is suitable for trend analysis and causal modeling as conducted in this study, but caution should be exercised when comparing absolute levels across time segments.

Table 1. Food Insecurity Data Sources (2000–2022, adapted dataset)

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

Portugal PT: New Business Density: New Registrations per 1000 People Aged 15 to 64 data was reported at 5.014 Number in 2016. This records a decrease from the previous number of 5.056 Number for 2015. Portugal PT: New Business Density: New Registrations per 1000 People Aged 15 to 64 data is updated yearly, averaging 4.529 Number from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 5.056 Number in 2015 and a record low of 3.971 Number in 2012. Portugal PT: New Business Density: New Registrations per 1000 People Aged 15 to 64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Portugal – Table PT.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations registered in the calendar year.; ; World Bank's Entrepreneurship Survey and database (http://www.doingbusiness.org/data/exploretopics/entrepreneurship).; Unweighted average; For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52 degrees N and 52 degrees S latitudes. ECO3ETPTJPL Version 1 is a Level 3 (L3) product that provides evapotranspiration (ET) generated from data acquired by the ECOSTRESS radiometer instrument according to the Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm described in the Algorithm Theoretical Basis Document (ATBD). The ET product is generated from the Level 2 data products for surface temperature and emissivity, the Level 1 geolocation information, and a significant number of ancillary data inputs from other sources. ET is set by various controls, including radiative and atmospheric demand, and environmental sensitivity, productivity, vegetation physiology, and phenology. PT-JPL is best utilized for natural ecosystems. The L3 ET product is used for creating the Level 4 products, Evaporative Stress Index (ESI) and Water Use Efficiency (WUE).The ECO3ETPTJPL Version 1 data product contains variables of instantaneous ET, daily ET, canopy transpiration, soil evaporation, ET uncertainty, and interception evaporation. Known Issues Data acquisition gaps: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented and science acquisitions resumed. To optimize the new acquisition approach TIR bands 2, 4 and 5 are being downloaded. The data products are as previously, except the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected.* Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods.

# FiN-2 Large-Scale Real-World PLC-Dataset

## About
#### FiN-2 dataset in a nutshell:
FiN-2 is the first large-scale real-world dataset on data collected in a powerline communication infrastructure. Since the electricity grid is inherently a graph, our dataset could be interpreted as a graph dataset. Therefore, we use the word node to describe points (cable distribution cabinets) of measurement within the low-voltage electricity grid and the word edge to describe connections (cables) in between them. However, since these are PLC connections, an edge does not necessarily have to correspond to a real cable; more on this in our paper.
FiN-2 shows measurements that relate to the nodes (voltage, total harmonic distortion) as well as to the edges (signal-to-noise ratio spectrum, tonemap). In total, FiN-2 is distributed across three different sites with a total of 1,930,762,116 node measurements each for the individual features and 638,394,025 edge measurements each for all 917 PLC channels. All data was collected over a 25-month period from mid-2020 to the end of 2022.
We propose this dataset to foster research in the domain of grid automation and smart grid. Therefore, we provide different example use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection. For more decent information on this dataset, please see our [paper](https://arxiv.org/abs/2209.12693).

* * *
## Content
FiN-2 dataset splits up into two compressed `csv-Files`: *nodes.csv* and *edges.csv*.

All files are provided as a compressed ZIP file and are divided into four parts. The first part can be found in this repo, while the remaining parts can be found in the following:
- https://zenodo.org/record/8328105
- https://zenodo.org/record/8328108
- https://zenodo.org/record/8328111

### Node data

| id | ts | v1 | v2 | v3 | thd1 | thd2 | thd3 | phase_angle1 | phase_angle2 | phase_angle3 | temp |
|----|----|----|----|----|----|----|----|----|----|----|----|----|----|
|112|1605530460|236.5|236.4|236.0|2.9|2.5|2.4|120.0|119.8|120.0|35.3|
|112|1605530520|236.9|236.6|236.6|3.1|2.7|2.5|120.1|119.8|120.0|35.3|
|112|1605530580|236.2|236.4|236.0|3.1|2.7|2.5|120.0|120.0|119.9|35.5|

- id / ts: Unique identifier of the node that is measured and timestemp of the measurement
- v1/v2/v3: Voltage measurements of all three phases
- thd1/thd2/thd3: Total harmonic distortion of all three phases
- phase_angle1/2/3: Phase angle of all three phases
- temp: Temperature in-circuit of the sensor inside a cable distribution unit (in °C)

### Edge data
| src | dst | ts | snr0 | snr1 | snr2 | ... | snr916 |
|----|----|----|----|----|----|----|----|
|62|94|1605528900|70|72|45|...|-53|
|62|32|1605529800|16|24|13|...|-51|
|17|94|1605530700|37|25|24|...|-55|

- src & dst & ts: Unique identifier of the source and target nodes where the spectrum is measured and time of measurement
- snr0/snr1/.../snr916: 917 SNR measurements in tenths of a decibel (e.g. 50 --> 5dB).

### Metadata
Metadata that is provided along with the data covers:

- Number of cable joints
- Cable properties (length, type, number of sections)
- Relative position of the nodes (location, zero-centered gps)
- Adjacent PV or wallbox installations
- Year of installation w.r.t. the nodes and cables

Since the electricity grid is part of the critical infrastructure, it is not possible to provide exact GPS locations.

* * *
## Usage
Simple data access using pandas:

```
import pandas as pd

nodes_file = "nodes.csv.gz" # /path/to/nodes.csv.gz
edges_file = "edges.csv.gz" # /path/to/edges.csv.gz

# read the first 10 rows
data = pd.read_csv(nodes_file, nrows=10, compression='gzip')

# read the row number 5 to 15
data = pd.read_csv(nodes_file, nrows=10, skiprows=[i for i in range(1,6)], compression='gzip')

# ... same for the edges
```

Compressed csv-data format was used to make sharing as easy as possible, however it comes with significant drawbacks for machine learning. Due to the inherent graph structure, a single snapshot of the whole graph consists of a set of node and edge measurements. But due to timeouts, noise and other disturbances, nodes sometimes fail in collecting the data, wherefore the number of measurements for a specific timestamp differs. This, plus the high sparsity of the graph, leads to a high inefficiency when using the csv-format for an ML training.
To utilize the data in an ML pipeline, we recommend other data formats like [datadings](https://datadings.readthedocs.io/en/latest/) or specialized database solutions like [VictoriaMetrics](https://victoriametrics.com/).

### Example use case (voltage forecasting)

Forecasting of the voltage is one potential use cases. The Jupyter notebook provided in the repository gives an overview of how the dataset can be loaded, preprocessed and used for ML training. Thereby, a MinMax scaling was used as simple preprocessing and a PyTorch dataset class was created to handle the data. Furthermore, a vanilla autoencoder is utilized to process and forecast the voltage into the future.

This dataset provides monthly summaries of evapotranspiration (ET) data from OpenET v2.0 image collections for the period 2008-2023 for all National Watershed Boundary Dataset subwatersheds (12-digit hydrologic unit codes [HUC12s]) in the US that overlap the spatial extent of OpenET datasets. For each HUC12, this dataset contains spatial aggregation statistics (minimum, mean, median, and maximum) for each of the ET variables from each of the publicly available image collections from OpenET for the six available models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop) and the Ensemble image collection, which is a pixel-wise ensemble of all 6 individual models after filtering and removal of outliers according to the median absolute deviation approach (Melton and others, 2022). Data are available in this data release in two different formats: comma-separated values (CSV) and parquet, a high-performance format that is optimized for storage and processing of columnar data. CSV files containing data for each 4-digit HUC are grouped by 2-digit HUCs for easier access of regional data, and the single parquet file provides convenient access to the entire dataset. For each of the ET models (DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, SSEBop), variables in the model-specific CSV data files include: -huc12: The 12-digit hydrologic unit code -ET: Actual evapotranspiration (in millimeters) over the HUC12 area in the month calculated as the sum of daily ET interpolated between Landsat overpasses -statistic: Max, mean, median, or min. Statistic used in the spatial aggregation within each HUC12. For example, maximum ET is the maximum monthly pixel ET value occurring within the HUC12 boundary after summing daily ET in the month -year: 4-digit year -month: 2-digit month -count: Number of Landsat overpasses included in the ET calculation in the month -et_coverage_pct: Integer percentage of the HUC12 with ET data, which can be used to determine how representative the ET statistic is of the entire HUC12 -count_coverage_pct: Integer percentage of the HUC12 with count data, which can be different than the et_coverage_pct value because the “count” band in the source image collection extends beyond the “et” band in the eastern portion of the image collection extent For the Ensemble data, these additional variables are included in the CSV files: -et_mad: Ensemble ET value, computed as the mean of the ensemble after filtering outliers using the median absolute deviation (MAD) -et_mad_count: The number of models used to compute the ensemble ET value after filtering for outliers using the MAD -et_mad_max: The maximum value in the ensemble range, after filtering for outliers using the MAD -et_mad_min: The minimum value in the ensemble range, after filtering for outliers using the MAD -et_sam: A simple arithmetic mean (across the 6 models) of actual ET average without outlier removal Below are the locations of each OpenET image collection used in this summary: DisALEXI: https://developers.google.com/earth-engine/datasets/catalog/OpenET_DISALEXI_CONUS_GRIDMET_MONTHLY_v2_0 eeMETRIC: https://developers.google.com/earth-engine/datasets/catalog/OpenET_EEMETRIC_CONUS_GRIDMET_MONTHLY_v2_0 geeSEBAL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_GEESEBAL_CONUS_GRIDMET_MONTHLY_v2_0 PT-JPL: https://developers.google.com/earth-engine/datasets/catalog/OpenET_PTJPL_CONUS_GRIDMET_MONTHLY_v2_0 SIMS: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SIMS_CONUS_GRIDMET_MONTHLY_v2_0 SSEBop: https://developers.google.com/earth-engine/datasets/catalog/OpenET_SSEBOP_CONUS_GRIDMET_MONTHLY_v2_0 Ensemble: https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0

Portugal PT: Current Health Expenditure Per Capita: Current Price data was reported at 0.002 USD mn in 2015. This records a decrease from the previous number of 0.002 USD mn for 2014. Portugal PT: Current Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.002 USD mn from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 0.002 USD mn in 2008 and a record low of 0.001 USD mn in 2000. Portugal PT: Current Health Expenditure Per Capita: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Portugal – Table PT.World Bank: Health Statistics. Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Black Earth town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Black Earth town, the median income for all workers aged 15 years and older, regardless of work hours, was $68,125 for males and $58,750 for females.

Based on these incomes, we observe a gender gap percentage of approximately 14%, indicating a significant disparity between the median incomes of males and females in Black Earth town. Women, regardless of work hours, still earn 86 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Black Earth town offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth town median household income by race. You can refer the same here

This dataset is openly accessible and includes nine ZIP archives, each representing a distinct geographic region. Each ZIP archive contains multiple GPKG files, with each capped at 5,000 features to ensure efficient loading. Each GPKG file’s attribute table includes an internal identifier unique within the file (fid); a unique identifier across all files (index_n); a land use/land cover label (class_name); the area in square metres (area_m2); and the classification year (year).

Definitions for each land use/land cover class are as follows:

Land use/land cover class definitions

Portugal PT: Women Business and the Law Index Score: scale 1-100 data was reported at 100.000 NA in 2023. This stayed constant from the previous number of 100.000 NA for 2022. Portugal PT: Women Business and the Law Index Score: scale 1-100 data is updated yearly, averaging 76.875 NA from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 100.000 NA in 2023 and a record low of 39.375 NA in 1977. Portugal PT: Women Business and the Law Index Score: scale 1-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Portugal – Table PT.World Bank.WDI: Governance: Policy and Institutions. The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.;World Bank: Women, Business and the Law. https://wbl.worldbank.org/;;1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases. 2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0. This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org.

NoSQL Database Market Outlook 2032

The global NoSQL database market size was USD 5.9 Billion in 2023 and is likely to reach USD 36.6 Billion by 2032, expanding at a CAGR of 30% during 2024–2032. The market growth is attributed to the rising adoption of NoSQL databases by industries to manage large amounts of data efficiently.



Increasing adoption of digital solutions by businesses is augmenting the NoSQL database industry. Businesses continue using the unique capabilities that NoSQL databases bring to their data management strategies. The NoSQL solutions work without any predefined schemas, thus, offering more flexibility to businesses that need to handle and manage ever-evolving data types and formats.

The factors behind the accelerating growth of the NoSQL database market include the omnipresence of internet-related activities, a surge in big data, and others. NoSQL database solutions present exceptional scalability and offer superior performance while managing extensive datasets. Moreover, the shift from conventional SQL databases to NoSQL databases to handle big-data and real-time web application data augmented the market.

Impact of Artificial Intelligence (AI) on the NoSQL Database Market

Artificial Intelligence (AI) has a significant impact on the NoSQL databases market by creating a surge in data volume and variety. AI technologies, including machine learning and deep learning, generate and process vast amounts of data, necessitating efficient data management solutions. The integration of AI with NoSQL databases further enhances data analysis capabilities and enables businesses to acquire valuable insights and make informed decisions. Therefore, the rise of AI technologies is propelling the market.

Non-Relational Databases, commonly referred to as NoSQL databases, have gained significant traction in recent years due to their ability to handle diverse data types and structures. Unlike traditional relational databases, non-relational databases do not rely on a fixed schema, which allows for greater flexibility and scalability. This adaptability is particularly beneficial for businesses dealing with large volumes of unstructured data, such as social media content, customer reviews, and multimedia files. As organizations continue to embrace digital transformation, the demand for non-relational databases is expected to rise, further driving the growth of the NoSQL database market.

<li style="margin-left: 8px; text-align: justi

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers:

1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332

2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344

3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567

The file with the database is available in excel.

DATA SOURCES

The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas.

With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index.

To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted:

Eurostat [3]

Directorate-General for Mobility and Transport (DG MOVE). European Union [4]

The World Bank [5]

World Health Organization (WHO) [6]

European Transport Safety Council (ETSC) [7]

European Road Safety Observatory (ERSO) [8]

European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9]

EU BestPoint-Project [10]

Ministerstvo dopravy, República Checa [11]

Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12]

Ministerie van Infrastructuur en Waterstaat, Países Bajos [13]

National Statistics Office, Malta [14]

Ministério da Economia e Transição Digital, Portugal [15]

Ministerio de Fomento, España [16]

Trafikverket, Suecia [17]

Ministère de l’environnement de l’énergie et de la mer, Francia [18]

Ministero delle Infrastrutture e dei Trasporti, Italia [19–25]

Statistisk sentralbyrå, Noruega [26-29]

Instituto Nacional de Estatística, Portugal [30]

Infraestruturas de Portugal S.A., Portugal [31–35]

Road Safety Authority (RSA), Ireland [36]

DATA BASE DESCRIPTION

The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure.

Table. Database metadata

Code

Variable and unit

fatal_pc_km

Fatalities per billion passenger-km

fatal_mIn

Fatalities per million inhabitants

accid_adj_pc_km

Accidents per billion passenger-km

p_km

Billions of passenger-km

croad_inv_km

Investment in roads construction per kilometer, €/km (2015 constant prices)

croad_maint_km

Expenditure on roads maintenance per kilometer €/km (2015 constant prices)

prop_motorwa

Proportion of motorways over the total road network (%)

populat

Population, in millions of inhabitants

unemploy

Unemployment rate (%)

petro_car

Consumption of gasolina and petrol derivatives (tons), per tourism

alcohol

Alcohol consumption, in liters per capita (age > 15)

mot_index

Motorization index, in cars per 1,000 inhabitants

den_populat

Population density, inhabitants/km2

cgdp

Gross Domestic Product (GDP), in € (2015 constant prices)

cgdp_cap

GDP per capita, in € (2015 constant prices)

precipit

Average depth of rain water during a year (mm)

prop_elder

Proportion of people over 65 years (%)

dps

Demerit Point System, dummy variable (0: no; 1: yes)

freight

Freight transport, in billions of ton-km

ACKNOWLEDGEMENTS

This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges.

Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study.

REFERENCES

1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance.

2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020).

3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021).

4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021).

5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021).

6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021).

7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011;

8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021).

9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237.

10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic;

11. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2003/2004; Hamburg, Germany, 2004; ISBN 3871542946.

12. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2018/2019. In Verkehrsdynamik; Flensburg, Germany, 2018 ISBN 9783000612947.

13. Ministerie van Infrastructuur en Waterstaat Rijksjaarverslag 2018 a Infrastructuurfonds; The Hague, Netherlands, 2019; ISBN 0921-7371.

14. Ministerie van Infrastructuur en Milieu Rijksjaarverslag 2014 a Infrastructuurfonds; The Hague, Netherlands, 2015; ISBN 0921- 7371.

15. Ministério da Economia e Transição Digital Base de Dados de Infraestruturas - GEE Available online: https://www.gee.gov.pt/pt/publicacoes/indicadores-e-estatisticas/base-de-dados-de-infraestruturas (accessed on Apr 29, 2021).

16. Ministerio de Fomento. Dirección General de Programación Económica y Presupuestos. Subdirección General de Estudios Económicos y Estadísticas Serie: Anuario estadístico; NIPO 161-13-171-0; Centro de Publicaciones. Secretaría General Técnica. Ministerio de Fomento: Madrid, Spain;

17. Trafikverket The Swedish Transport Administration Annual report: 2017; 2018; ISBN 978-91-7725-272-6.

18. Ministère de l’Équipement, du T. et de la M. Mémento de statistiques des transports 2003; Ministère de l’environnement de l’énergie et de la mer, 2005;

19. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle

The physical therapy software market was estimated at USD 1.08 billion in 2022 and is projected to reach USD 2.27 billion in 2030, growing at a CAGR of 9.8 % during the forecast year. Factors Affecting Physical Therapy Software Market Growth

The increasing prevalence of osteoporosis will propel the physical therapy software market

The market for physical therapy software is expanding due to the rising incidence of osteoporosis. Osteoporosis is a bone disease that develops when bone quality or structure changes or when bone mineral density and mass drop. Low calcium consumption increases the risk of developing osteoporosis in a person. Information about treatment plans, claims, invoices, or home exercise advice is provided to patients using physical therapy software during their clinical process. For instance, on 24 May 2022, Amgen, a US-based biotechnology company claimed that every year, osteoporosis results in around 1.5 million fractures in the United States, with associated costs of $19 billion. In addition, it is predicted that from 2018 to 2040, there will be a 68% increase in the number of fractures caused by osteoporosis every year, from 1.9 million to 3.2 million. The physical therapy software industry will therefore be driven by an increase in the prevalence of osteoporosis.

The Restraining Factor of Physical Therapy Software:

The high investment restricts the growth of the physical therapy software market

The physical assets, such as tools, equipment, and rehabilitation services, as well as software investments involving practice management, patient relationship management, telehealth, database e information, and task automation, the market growth for the healthcare industry has been constrained by increased investments and the adoption of advanced software technologies in hospitals and clinics.

Impact of the COVID-19 Pandemic on the physical therapy software market

Governments all across the world have been forced to impose a lockdown, including specialty clinics and wellness centers, due to the pandemic However, due to an increase in patient preference toward online therapy, the market for physical therapy software is experiencing an enormous increase. To boost their consumer base, businesses have started creating a variety of applications and online services. For instance, Meditab made it possible for symptomatic COVID-19 patients to receive free television services. Similarly, to this, patients may check their health profiles and schedule online doctor consultations with the IMS Patient App & Patient Care Portal. Introduction of Physical Therapy Software

Physical therapy software is a component of electronic health record software that is designed for health professional services. Physical therapy software is used to provide seamless care to patients dealing with conditions including osteoporosis, post-operative care, and accidents, among others. Numerous services are provided by the program, including customer relationship management, scheduling, online assistance, reducing billing errors, creating a consolidated database of patient data, improved record keeping, task automation, and improved quality control. Government programs and financing from the public sector also increased demand for physical therapy software in hospitals and clinical trials.

Portugal PT: Current Health Expenditure: % of GDP data was reported at 8.972 % in 2015. This records a decrease from the previous number of 9.022 % for 2014. Portugal PT: Current Health Expenditure: % of GDP data is updated yearly, averaging 9.113 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 9.879 % in 2009 and a record low of 8.375 % in 2000. Portugal PT: Current Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Portugal – Table PT.World Bank: Health Statistics. Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Blue Earth City township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Blue Earth City township, the median income for all workers aged 15 years and older, regardless of work hours, was $51,875 for males and $38,036 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 27% between the median incomes of males and females in Blue Earth City township. With women, regardless of work hours, earning 73 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetownship of Blue Earth City township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Blue Earth City township.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blue Earth City township median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in White Earth township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In White Earth township, the median income for all workers aged 15 years and older, regardless of work hours, was $36,250 for males and $25,250 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in White Earth township. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetownship of White Earth township.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.06 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth township median household income by race. You can refer the same here

Context

Here, I collected data wearing a smart watch. Synchronizing GPS requires longitude, latitude and altitude above sea level information. Several times, we practice breastyle or even butterflystyle. In those kind of strokes, GPS loses signal and stops collecting distance’s data. Therefore, altitude above sea level is fundamental. It's easy to fix this: just raise the arm (wearing the watch) above the water. Signal will return enabling to update your distance score. Such tools are built thinking about better results. However, most part of customers disregard the altitude factor.

Content

When we are analysing data, everything is relevant, until it's not. A smart watch can record data from several activities. I deleted four columns, where there was no data (only dashes). I deleted most of columns with zeros (helps not spending cloud resources). Probably, those missing values are not recorded because they aren't used in the activity I practice. Or even worst, I don' t know how to use all the watch's features! I've started collecting this data in 2017, and save my data once a week.

Acknowledgements

Photo by Tanguy Sauvin on Unsplash. Thanks GAR for munging my data and MIN for prepping it. Thanks Rodrigo and Felipe, teachers from "Minerando Dados", for introducing me to Kaggle and DS. https://minerandodados.com.br/ *** Thanks Equipe 15, my open-water-swimming teachers and collegues. https://pt-br.facebook.com/equipe15natacaonomar/

Inspiration

The most remarcable and awful experience I have is that I don' t see many fishes while I'm swimming. Instead of this I colide with a lot of trash and plastic. So, how Data Scientists can make our world a better place? Yet? "Failure is not fatal. It's the courage to continue that value_counts()." - Churchill adapted. Feel free to work with this Dataset. Or, just ignore it.

Data Collection and Labeling Market Outlook 2032

The global data collection and labeling market size was USD 27.1 Billion in 2023 and is likely to reach USD 133.3 Billion by 2032, expanding at a CAGR of 22.4 % during 2024–2032. The market growth is attributed to the increasing demand for high-quality labeled datasets to train artificial intelligence and machine learning algorithms across various industries.



Growing adoption of AI in e-commerce is projected to drive the market in the assessment year. E-commerce platforms rely on high-quality images to showcase products effectively and improve the online shopping experience for customers. Accurately labeled images enable better product categorization and search optimization, driving higher conversion rates and customer engagement.



Rising adoption of AI in the financial sector is a significant factor boosting the need for data collection and labeling services for tasks such as fraud detection, risk assessment, and algorithmic trading. Financial institutions leverage labeled datasets to train AI models to analyze vast amounts of transactional data, identify patterns, and detect anomalies indicative of fraudulent activity.

Impact of Artificial Intelligence (AI) in Data Collection and Labeling Market

The use of artificial intelligence is revolutionizing the way labeled datasets are created and utilized. With the advancements in AI technologies, such as computer vision and natural language processing, the demand for accurately labeled datasets has surged across various industries.



AI algorithms are increasingly being leveraged to automate and streamline the data labeling process, reducing the manual effort required and improving efficiency. For instance,

• 
  

  In April 2022, Encord, a startup, introduced its beta version of CordVision, an AI-assisted labeling application that inten

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Black Earth. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Black Earth, the median income for all workers aged 15 years and older, regardless of work hours, was $37,083 for males and $36,528 for females.

Based on these incomes, we observe a gender gap percentage of approximately 1%, indicating a significant disparity between the median incomes of males and females in Black Earth. Women, regardless of work hours, still earn 99 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Black Earth, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Blue Earth County. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Blue Earth County, the median income for all workers aged 15 years and older, regardless of work hours, was $42,075 for males and $27,496 for females.

These income figures highlight a substantial gender-based income gap in Blue Earth County. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the county of Blue Earth County.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Blue Earth County.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blue Earth County median household income by race. You can refer the same here