ACC/AHA = American College of Cardiology/American Heart Association; ATS = American Thoracic Society; CDISC = Clinical Data Interchange Standards Consortium; NCI = National Cancer Institute; SF-36 = Short-form 36 questionnaire.

In 2019, the Global Indigenous Data Alliance (GIDA) developed and published the CARE Principles for Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, Ethics) to complement the FAIR principles for open scientific data management (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al., 2016; Carroll et al., 2020, Carroll S. R. et al., 2021). FAIR are data-centric, focusing on the attributes of data objects themselves. The CARE Principles serve as high-level guidance toward more equitable creation, collection, use, and storage of Indigenous data that focuses on the people to whom data relate, and the purpose for which those data are collected, analyzed, and used. In the six years since their original publication, the CARE Principles have garnered significant interest and induced uptake, informing policy and processes across many institutions, governments, organizations, communities, Tribal Nations, and other data-related entities. There has also been interest in applying the principles and framework beyond Indigenous contexts (Lipphardt et al 2021; Suchikova and Nazarovets 2025).

For a quick summary of the case study, please click "US Economy Powerpoint" and download the Powerpoint.

This dataset was inspired by rising prices for essential goods, the abnormally high inflation rate in March of 7.9 percent of this year, and the 30 trillion-dollar debt that we have. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. This dataset was inspired by rising prices for essential goods and the abnormally high inflation rate in March of 7.9 percent of this year. I was extremely curious to see how sustainable this is for the average American and if wages are increasing at the same rate to help combat this inflation. This is not politically driven in the slightest nor was this made to put the blame on Americans. All of the datasets were obtained from third party sources websites such as https://dqydj.com/household-income-by-year/ and https://www.usinflationcalculator.com/inflation/historical-inflation-rates/ and only excluding https://fred.stlouisfed.org/series/ASPUS, which is first-party data.

I labeled all of the datasets to be self-explanatory based off of the title of the datasets. The US Economy Notebook has most of the code that I used as well as the four of the six phases of data analysis. The last two phases are in the US Economy Powerpoint. The "US Historical Inflation Rates" dataset could have also been labeled "The Inflation Of The US Dollar Month By Month". Lastly, the Average Sales of Houses in Jan is just a filtered version of "Average Sales of Houses in the US" dataset.

The Diverse Community Excerpts are a set of tabular demographic data of households in the United States drawn from real records released in the American Community Survey, a product of the US Census Bureau. The data contain 24 features and are partitioned into three geographic regions: Boston area (7634 records), Dallas-Forth Worth area (9276 records), and US national (27254 records). The feature set is identical for all partitions, but the demographics vary radically between the geographic regions. Therefore, these data are well suited for comparisons of synthetic demographic data generator performance. Detailed documentation for usage, design, and purpose of the data are included in the repository including brief descriptions of localities that the data represent. These data are incorporated into the "SDNist: Synthetic Data Report Tool", a package for evaluating synthetic data generators: https://github.com/usnistgov/SDNist

Overview

The Daily Travel data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland.

The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed.

Notes

These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Data in the charts and graphs above is updated weekly on Mondays. The data lags one week behind the current date.

Data analysis is conducted at the aggregate national, state, and county levels. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day.

Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips.

Key Features

1.Level : Indicates National, State, or County level metrics.

2.Date : The date when the data was recorded.

3.State FIPS : A two-digit code representing the FIPS state code.

4.State Postal Code : State postal code.

5.County FIPS : Five-digit FIPS county code.

6.County Name : County name.

7.Population Staying at Home : Number of residents staying at home, i.e., persons who make no trips with a trip end more than one mile away from home.

8.Population Not Staying at Home : Number of residents not staying at home.

9.Number of Trips : Number of trips made by residents, i.e., movements that include a stay of longer than 10 minutes at an anonymized location away from home.

10.Number of Trips <1 : Number of trips by residents shorter than one mile.

11.Number of Trips 1-3 : Number of trips by residents greater than one mile and shorter than 3 miles (1 ≤ trip distance < 3 miles).

12.Number of Trips 3-5 : Number of trips by residents greater than 3 miles and shorter than 5 miles (3 ≤ trip distance < 5 miles).

13.Number of Trips 5-10 : Number of trips by residents greater than 5 miles and shorter than 10 miles (5 ≤ trip distance < 10 miles).

14.Number of Trips 10-25 : Number of trips by residents greater than 10 miles and shorter than 25 miles (10 ≤ trip distance < 25 miles).

15.Number of Trips 25-50 : Number of trips by residents greater than 25 miles and shorter than 50 miles (25 ≤ trip distance < 50 miles).

16.Number of Trips 50-100 : Number of trips by residents greater than 50 miles and shorter than 100 miles (50 ≤ trip distance < 100 miles).

17.Number of Trips 100-250 : Number of trips by residents greater than 100 miles and shorter than 250 miles (100 ≤ trip distance < 250 miles).

18.Number of Trips 250-500 : Number of trips by residents greater than 250 miles and shorter than 500 miles (250 ≤ trip distance < 500 miles).

19.Number of Trips >=500 : Number of trips by residents greater than 500 miles (trip distance ≥ 500 miles).

20.Row ID : Unique row identifier.

21.Week : The week number corresponding to the recorded date.

22.Month : The month number corresponding to the recorded date.

If this was helpful, a vote is appreciated 😄!

Snapshot img

AI Policy And Standards Market Size 2025-2029

The AI policy and standards market size is valued to increase by USD 377.8 million, at a CAGR of 38.6% from 2024 to 2029. Proliferation of generative AI and management of societal risks will drive the ai policy and standards market.

Major Market Trends & Insights

North America dominated the market and accounted for a 44% growth during the forecast period.
By Component - Solutions segment was valued at USD 14.50 million in 2023
By Deployment - Cloud-based segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 377.80 million
CAGR from 2024 to 2029 : 38.6%

Market Summary

The market is experiencing significant growth as the global community grapples with the societal implications of artificial intelligence (AI). Ethical considerations and risk management have emerged as critical priorities, leading to a shift from abstract principles to practical governance mechanisms. According to recent estimates, the global AI ethics market is projected to reach USD 10.3 billion by 2026, underscoring the market's expanding importance. The operationalization of ethics in AI policy and standards poses unique challenges. The rapid technological evolution of AI outpaces deliberative policy cycles, necessitating a more agile and adaptive approach to governance. Proactive engagement from stakeholders, including governments, industry leaders, and civil society, is essential to ensure that AI is developed and deployed responsibly.
The proliferation of generative AI and its potential impact on society necessitates a robust regulatory framework. Ethical guidelines and standards are necessary to mitigate risks, protect privacy, and promote transparency. However, creating and enforcing these standards requires a collaborative effort from all stakeholders, including AI developers, policymakers, and the public. The market represents a dynamic and complex landscape. Its evolution reflects the ongoing dialogue between technological innovation and societal values. As AI continues to reshape industries and transform our world, the importance of effective policy and standards will only grow.

What will be the Size of the AI Policy And Standards Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the AI Policy And Standards Market Segmented ?

The AI policy and standards industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


Deployment

  Cloud-based
  On-premises


Application

  Risk and compliance management
  Bias detection and mitigation
  Model explainability
  Fairness and accountability tools
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

The market continues to evolve as organizations grapple with the complexities of implementing and managing artificial intelligence (AI) systems. This market encompasses solutions for AI training datasets, system design, policy implementation, fairness metrics, and more. With the increasing deployment of AI systems across various application domains, the need for accountability mechanisms, ethical education, and robust governance structures has become paramount. These solutions include AI policy frameworks, bias detection methods, model validation techniques, interpretability methods, and safety guidelines. According to recent estimates, over 75% of enterprises plan to increase their investment in AI governance and compliance solutions in the next year.

These tools enable automated AI system evaluation, robustness testing, impact assessment, and compliance with data privacy regulations and security protocols. They also provide explainable AI techniques, ethical AI frameworks, and model transparency standards to ensure responsible AI development and decision-making. Overall, the market is a critical enabler for organizations seeking to deploy AI systems in a responsible, ethical, and compliant manner.

Request Free Sample

The Solutions segment was valued at USD 14.50 million in 2019 and showed a gradual increase during the forecast period.

Request Free Sample

Regional Analysis

North America is estimated to contribute 44% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market

This dataset contains links to ThermoML files, which represent experimental thermophysical and thermochemical property data reported in the corresponding articles published by major journals in the field. These files are posted here through cooperation between the Thermodynamics Research Center (TRC) at the National Institute of Standards and Technology (NIST) and American Chemical Society. The ThermoML files corresponding to articles in the journals are available here with permission of the journal publishers.

STUDY PURPOSE: The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) provides a widely accepted system for determining level and severity of a human spinal cord injury (SCI). The ISNCSCI is widely used for clinical purposes (communication of level and severity, monitor changes over time, establish rehabilitation goals and therapy programs and to predict neurological recovery on a group level) and in research (characterization, outcome measures as well as inclusion/exclusion criteria and (sub-)grouping criteria). Its successful application demands accuracy in both the examination and classification, of which the latter is the focus of this work. ISNCSCI classification involves precise rules and nuances, and inherent challenges have been described. The heterogeneity of SCI adds further complexity. A comprehensive dataset of representative ISNCSCI cases with annotated classifications is not yet available within the field. Therefore, the purpose of this dataset is to provide such a workbook to illustrate important classification rules, definitions, and nuances for a wide range of spinal cord injuries. DATA COLLECTED: Twenty-six hypothetical ISNCSCI cases were created by the authors to illustrate important classification rules, definitions, and nuances. Each case contains all 134 examined scores (2 body sides times 28 dermatomes light touch scores; 2 times 28 pin prick scores, 2 times 10 myotomes motor scores as well as voluntary anal contraction and deep anal pressure sensation) as well as all 11 classifications components: right and left sensory levels, right and level motor levels, neurological level of injury, completeness, American Spinal Injury Association (ASIA) Impairment Scale, right/left sensory zone of partial preservation, right/left motor zone of partial preservation. Each case additionally contains detailed explanations of the process for classifying each variable. The cases are documented and classified according to the eighth edition of the ISNCSCI revised in 2019 (https://doi.org/10.46292/sci2702-1).

The cases cover a wide range of topics such as: - New ISNCSCI concepts introduced with the 2019 revision like the -- Non-SCI taxonomy for documentation of non-SCI related conditions superimposed to the SCI that may influence the examination of motor/sensory scores and impact the classification components (e.g., amputations, peripheral nerve lesions, pain, tendon transfers) -- Broadened ZPP applicability not only for sensorimotor complete, but also for a subset of incomplete lesions - Inherent classification challenges -- Motor incompleteness due to sparing of motor function more than three segments below the motor level -- Use of non-key muscle functions in the determination of motor incompleteness -- Motor levels in the high cervical and thoracic regions, where the motor level follows the sensory level -- The correct classification of levels, completeness and zones of partial preservation for ASIA Impairment Scale E classifications DATA USAGE NOTES: