The statistic shows the problems caused by poor quality data for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, 44 percent of respondents indicated that having poor quality data can result in extra costs for the business.

The statistic depicts the causes of poor data quality for enterprises in North America, according to a survey of North American IT executives conducted by 451 Research in 2015. As of 2015, 47 percent of respondents indicated that poor data quality at their company was attributable to data migration or conversion projects.

Freshwater harmful algal bloom (HAB) data from the Freshwater Harmful Algal Bloom (FHAB) data system. The FHAB data system is the California State Water Resources Control Board's data system for data and information voluntarily reported to the agency. Bloom reports are voluntary reports submitted by the public or organization to identify a POTENTIAL HAB for evaluation. Bloom Reports may or may not include a report that is confirmed to be a HAB, regardless, all bloom reports are published. Due to the voluntary basis of information and data included in the database, data and information may include: waterbody name and location, potential algal bloom location and observed characteristics, observed field observations and/or analytical sampling results, waterbody and/or land management, general information, recommended advisory status (if any), and updates regarding bloom status. Refer to Data Dictionary and Data Disclaimer for additional information about this dataset. Please visit the Water Boards FHABS web site for more information and data visualizations https://mywaterquality.ca.gov/habs/index.html.

This dataset of historical poor law cases was created as part of a project aiming to assess the implications of the introduction of Artificial Intelligence (AI) into legal systems in Japan and the United Kingdom. The project was jointly funded by the UK’s Economic and Social Research Council, part of UKRI, and the Japanese Society and Technology Agency (JST), and involved collaboration between Cambridge University (the Centre for Business Research, Department of Computer Science and Faculty of Law) and Hitotsubashi University, Tokyo (the Graduate Schools of Law and Business Administration). As part of the project, a dataset of historic poor law cases was created to facilitate the analysis of legal texts using natural language processing methods. The dataset contains judgments of cases which have been annotated to facilitate computational analysis. Specifically, they make it possible to see how legal terms have evolved over time in the area of disputes over the law governing settlement by hiring.

A World Economic Forum meeting at Davos 2019 heralded the dawn of 'Society 5.0' in Japan. Its goal: creating a 'human-centred society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.' Using Artificial Intelligence (AI), robotics and data, 'Society 5.0' proposes to '...enable the provision of only those products and services that are needed to the people that need them at the time they are needed, thereby optimizing the entire social and organizational system.' The Japanese government accepts that realising this vision 'will not be without its difficulties,' but intends 'to face them head-on with the aim of being the first in the world as a country facing challenging issues to present a model future society.' The UK government is similarly committed to investing in AI and likewise views the AI as central to engineering a more profitable economy and prosperous society.

This vision is, however, starting to crystallise in the rhetoric of LegalTech developers who have the data-intensive-and thus target-rich-environment of law in their sights. Buoyed by investment and claims of superior decision-making capabilities over human lawyers and judges, LegalTech is now being deputised to usher in a new era of 'smart' law built on AI and Big Data. While there are a number of bold claims made about the capabilities of these technologies, comparatively little attention has been directed to more fundamental questions about how we might assess the feasibility of using them to replicate core aspects of legal process, and ensuring the public has a meaningful say in the development and implementation.

This innovative and timely research project intends to approach these questions from a number of vectors. At a theoretical level, we consider the likely consequences of this step using a Horizon Scanning methodology developed in collaboration with our Japanese partners and an innovative systemic-evolutionary model of law. Many aspects of legal reasoning have algorithmic features which could lend themselves to automation. However, an evolutionary perspective also points to features of legal reasoning which are inconsistent with ML: including the reflexivity of legal knowledge and the incompleteness of legal rules at the point where they encounter the 'chaotic' and unstructured data generated by other social sub-systems. We will test our theory by developing a hierarchical model (or ontology), derived from our legal expertise and public available datasets, for classifying employment relationships under UK law. This will let us probe the extent to which legal reasoning can be modelled using less computational-intensive methods such as Markov Models and Monte Carlo Trees.

Building upon these theoretical innovations, we will then turn our attention from modelling a legal domain using historical data to exploring whether the outcome of legal cases can be reliably predicted using various technique for optimising datasets. For this we will use a data set comprised of 24,179 cases from the High Court of England and Wales. This will allow us to harness Natural Language Processing (NLP) techniques such as named entity recognition (to identify relevant parties) and sentiment analysis (to analyse opinions and determine the disposition of a party) in addition to identifying the main legal and factual points of the dispute, remedies, costs, and trial durations. By trailing various predictive heuristics and ML techniques against this dataset we hope to develop a more granular understanding as to the feasibility of predicting dispute outcomes and insight to what factors are relevant for legal decision-making. This will allow us to then undertake a comparative analysis with the results of existing studies and shed light on the legal contexts and questions where AI can and cannot be used to produce accurate and repeatable results.

About this dataset

Context

The dataset tabulates the Bad Axe population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Bad Axe. The dataset can be utilized to understand the population distribution of Bad Axe by age. For example, using this dataset, we can identify the largest age group in Bad Axe.

Key observations

The largest age group in Bad Axe, MI was for the group of age 60 to 64 years years with a population of 317 (10.53%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Bad Axe, MI was the 75 to 79 years years with a population of 79 (2.62%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Bad Axe is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Bad Axe total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Bad Axe Population by Age. You can refer the same here

Historical price and volatility data for Bad Idea AI in Taiwan New Dollar across different time periods.

United States CCI: Present Situation: sa: Business Conditions: Bad data was reported at 15.700 % in Feb 2025. This records an increase from the previous number of 15.200 % for Jan 2025. United States CCI: Present Situation: sa: Business Conditions: Bad data is updated monthly, averaging 19.700 % from Feb 1967 (Median) to Feb 2025, with 635 observations. The data reached an all-time high of 57.000 % in Dec 1982 and a record low of 6.000 % in Dec 1968. United States CCI: Present Situation: sa: Business Conditions: Bad data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H042: Consumer Confidence Index. [COVID-19-IMPACT]

The infrastructure database or POI database of the city of Gelsenkirchen offers you extensive information about infrastructures in Gelsenkirchen. You currently have access to over 100 different types of infrastructure, as well as over 7,000 data sets from the areas of family, education, leisure, infrastructure, culture, administration, social affairs and economy. In addition to the spatial location, information on contact details and other specialist information is stored. The offer is constantly being expanded and maintained by the responsible services.

Historical price and volatility data for Bad Idea AI in Japanese Yen across different time periods.

A groundwater-flow model was developed for the Bad River Watershed and surrounding area by using the U.S. Geological Survey (USGS) finite-difference code MODFLOW–NWT. The model simulates steady-state groundwater-flow and base flow in streams by using the streamflow routing (SFR) package. The model was calibrated to groundwater levels and base flows obtained from the USGS National Water Information System (NWIS) database, and groundwater levels obtained from the Wisconsin Department of Natural Resources and Bad River Band well-construction databases.
Calibration was performed via nonlinear regression by using the parameter-estimation software suite PEST.

Abstract copyright UK Data Service and data collection copyright owner.

The paper Udny Yule read to the Royal Statistical Society at the end of the nineteenth century (Yule, 1899) was a landmark in social statistics. He applied multiple regression analysis to a question of social policy, namely reforms to the 19th century system of poverty alleviation in England. To do this, Yule created a dataset from administrative and Census data. Yule’s original dataset was not preserved, but because his data were drawn from public sources, it is possible to reconstruct it, albeit with some slight differences from the original. This report provides a description of how the dataset was reconstructed and how it varies from the one used in the 1899 paper.

United States CSI: Home Buying Conditions: Bad Time: Bad Investment data was reported at 0.000 % in May 2018. This records a decrease from the previous number of 1.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Bad Investment data is updated monthly, averaging 0.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 3.000 % in Feb 2014 and a record low of 0.000 % in May 2018. United States CSI: Home Buying Conditions: Bad Time: Bad Investment data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

This is the replication data for "The Bad Neighbor Problem," written by Ben Jebb and Alisa Laufer. The data includes information on regime type, population, various economic factors, political fragmentation, political freedom, government effectiveness, and susceptibility to terrorism for 157 unique countries between 2002-2021.

This dataset provides information about the number of properties, residents, and average property values for Willis Street cross streets in Bad Axe, MI.

Bad jazz is a book. It was written by Robert Farquhar and published by Josef Weinberger in 2007.

According to INSPIRE transformed development plan “Bad-/Bachstraße” of the city of Kornwestheim based on an XPlanung dataset in version 5.0.

Supplementary material for "Kreutz, A.; Clifton, M.S.; Henderson, W.M.; Smeltz, M.G.; Phillips, M.; Wambaugh, J.F.; Wetmore, B.A. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics 2023, 11, 463. https://doi.org/10.3390/toxics11050463". This dataset is associated with the following publication: Kreutz, A., M. Clifton, W. Henderson, M. Smeltz, M. Phillips, J. Wambaugh, and B. Wetmore. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics. MDPI, Basel, SWITZERLAND, 11(5): 463, (2023).

File represents total mercury (THg) concentrations in hair of nine river otters from the Bad River. Neither the collection of river otter hair samples nor the Hg analyses were performed by the U.S. Geological Survey, New York Water Science Center

This is the replication package for "Bad Repetition," accepted in 2022 by the Journal of Political Economy Microeconomics.

The aerial photographs provide a straightforward depiction of the geographical and cultural landscape of areas in Trinidad and Tobago. The photos are unaltered images taken by the Government of the Republic of Trinidad and Tobago. The Data is provided with the compliments of the Tobago Emergency Management Authority (TEMA). The aerial photos of Bad Rock date for November 2022, comprising one hundred and three (103) images.