Bad Data

## Overview

Bad Data is a dataset for object detection tasks - it contains 1 YqnK annotations for 473 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Project Overview For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders – their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed a novel method to elicit bystander responses to human and robot errors. Data Overview This project introduces the Bystander Affect Detection (BAD) dataset – a dataset of videos of bystander reactions to videos of failures. This dataset includes 2,452 human reactions to failure, collected in contexts that approximate “in-the-wild” data collection – including natural variances in webcam quality, lighting, and background. The BAD dataset may be requested for use in related research projects. As the dataset contains facial video data of participants, access can be requested along with the presentation of a research protocol and data use agreement that protects participants. Data Collection Overview and Access Conditions Using 46 different stimulus videos featuring a variety of human and machine task failures, we collected a total of 2,452 webcam videos of human reactions from 54 participants. Recruitment happened through the online behavioral research platform Prolific (https://www.prolific.co/about), where the options were selected to recruit a gender-balanced sample across all countries available. Participants had to use a laptop or desktop. Compensation was set at the Prolific rate of $12/hr, which came down to about $8 per participant for about 40 minutes of participation. Participants agreed that their data can be shared for future research projects and the data were approved to be shared publicly by IRB review. However, considering the fact that this is a machine-learning dataset containing identifiable crowdsourced human subjects data, the research team has decided that potential secondary users of the data must meet the following criteria for the access request to be granted: 1. Agreement to three usage terms: - I will not redistribute the contents of the BAD Dataset - I will not use videos for purposes outside of human interaction research (broadly defined as any project that aims to study or develop improvements to human interactions with technology to result in a better user experience) - I will not use the videos to identify, defame, or otherwise negatively impact the health, welfare, employment or reputation of human participants 2. A description of what you want to use the BAD dataset for, indicating any applicable human subjects protection measures that are in place. (For instance, "Me and my fellow researchers at University of X, lab of Y, will use the BAD dataset to train a model to detect when our Nao robot interrupts people at awkward times. The PI is Professor Z. Our protocol was approved under IRB #.") 3. A copy of the IRB record or ethics approval document, confirming the research protocol and institutional approval. Data Analysis To test the viability of the collected data, we used the Bystander Reaction Dataset as input to a deep-learning model, BADNet, to predict failure occurrence. We tested different data labeling methods and learned how they affect model performance, achieving precisions above 90%. Shared Data Organization This data project consists of 54 zipped folders of recorded video data organized by participant, totaling 2,452 videos. The accompanying documentation includes a file containing the text of the consent form used for the research project, an inventory of the stimulus videos used, aggregate survey data, this data narrative, and an administrative readme file. Special Notes The data were approved to be shared publicly by IRB review. However, considering the fact that this is a machine-learning dataset containing identifiable crowdsourced human subjects data, the research team has decided that potential secondary users of the data must meet specific criteria before they qualify for access. Please consult the Terms tab below for more details and follow the instructions there if interested in requesting access.

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, ** 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.

This release includes eight data files that provide concentrations of mercury (Hg) chemical species and ancillary chemical and physical data that quantify and document aspects of the Hg cycle in stream and rivers located on tribal lands of the Bad River Band of Lake Superior Chippewa. These files were transferred to the U.S. Geological Survey by Lacey Hill Kastern, Natural Resources Department, Bad River Band of Lake Superior Chippewa in May, 2018. Files listed as Child Items in this release include data on Hg concentrations in: bald eagle feathers (Mercury Concentrations in Bald Eagle Feathers, Bad River Watershed, Wisconsin, 2014 - 2016.csv), bed sediment (Mercury Concentrations in Bed Sediment, Bad River Watershed, 2006-2015.csv), fish tissue (Mercury Concentrations in Fish Tissue, Bad River Watershed, Wisconsin, 2004-2013.csv), green frog tissue (Mercury Concentrations in Green Frog Tissue, Bad River Watershed, Wisconsin, 2012-2013.csv), litterfall (Mercury Concentrations in Litterfall, Bad River Watershed, Wisconsin, 2012-2018.csv), river otter hair (Mercury Concentrations in River Otter Hair, Bad River Watershed, Wisconsin.csv), surface waters (Mercury Concentrations in Surface Waters, Bad River Watershed, Wisconsin, 2006-2016.csv), and wild rice (Mercury Concentrations in Wild Rice, Bad River Watershed, Wisconsin, 2006.csv). Attributes describing the contents of each of these data files are provided in the .xml file that accompanies each Child Item. Note that documentation of some data sets was incomplete as received from the Natural Resources Department. Any data items in the .csv files that were not available are marked with "NA" indicating not available. This data release includes as much documentation as was provided to the U.S. Geological Survey. Neither sample collection nor Hg analyses were performed by the U.S. Geological Survey, New York Water Science Center.

This paper explains the size and value "anomalies" in stock returns using an economically motivated two-beta model. We break the beta of a stock with the market portfolio into two components, one reflecting news about the market's future cash flows and one reflecting news about the market's discount rates. Intertemporal asset pricing theory suggests that the former should have a higher price of risk; thus beta, like cholesterol, comes in "bad" and "good" varieties. Empirically, we find that value stocks and small stocks have considerably higher cash-flow betas than growth stocks and large stocks, and this can explain their higher average returns. The poor performance of the capital asset pricing model (CAPM) since 1963 is explained by the fact that growth stocks and high-past-beta stocks have predominantly good betas with low risk prices.

ibragim-bad/random-data-0 dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

中文

  README for bad_data.json Dataset





  Updated on 2024.8.22: Important: For security reasons, the current dataset is an abridged version. See Bad_Data.





  bad_data.json





  Overview

The bad_data.json dataset is a collection of text data specifically curated for training and evaluating language models on challenging and sensitive content. The dataset covers a wide range of topics, including ethical dilemmas, illegal activities, pornographic content, and… See the full description on the dataset page: https://huggingface.co/datasets/ystemsrx/Bad_Data_Alpaca.

Bad Climate

## Overview

Bad Climate is a dataset for object detection tasks - it contains Car annotations for 279 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

United States CCI: Present Situation: sa: Business Conditions: Bad data was reported at 16.100 % in Apr 2025. This records a decrease from the previous number of 16.500 % for Mar 2025. United States CCI: Present Situation: sa: Business Conditions: Bad data is updated monthly, averaging 19.600 % from Feb 1967 (Median) to Apr 2025, with 637 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.H049: Consumer Confidence Index. [COVID-19-IMPACT]

This collection contains the restricted-access data for the article "A Few Bad Apples? Racial Bias in Policing" published in the American Economic Review and supplements the main replication package found in OpenICPSR. These data are a Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped for release, but not checked or processed.

No description is available. Visit https://dataone.org/datasets/sha256%3Aef652914e0f4c44ad056acc63844d0563b25717c5abb8746b94e9b82e19c3629 for complete metadata about this dataset.

This dataset provides information about the number of properties, residents, and average property values for Bad Land Road cross streets in Platteville, WI.

Can a government agency mitigate the negative effect of “bad news” on public trust? To answer this question, we carried out a baseline survey to measure public trust five days before a major press release involving bad news about an error committed by an independent regulatory agency in the Netherlands. Two days after the agency’s press release, we carried out a survey experiment to test the effects on public trust of the press release itself as well as related newspaper articles. Results show that the press release had no negative effect on trustworthiness, which may be because the press release “steals thunder” (i.e. breaks the bad news before the news media discovered it) and focuses on a “rebuilding strategy” (i.e. offering apologies and focusing on future improvements). In contrast, the news articles mainly focused on what went wrong, which affected the competence dimension of trust but not the other dimensions (benevolence and integrity). We conclude that strategic communication by an agency can break negative news to people without necessarily breaking trust in that agency. And although effects of negative news coverage on trustworthiness were observed, the magnitude of these effects should not be overstated.

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.

Sure! Here's a concise data description within 3000 characters for a project titled "Good and Bad Classification of Apples":

Project Title: Good and Bad Classification of Apples

Data Description:

The dataset used in this project is centered around the classification of apples into two categories: good (fit for sale/consumption) and bad (damaged, rotten, or otherwise unfit). The dataset comprises images of apples collected under controlled as well as natural conditions, and optionally, corresponding annotations or metadata.

1. Data Types:

Image Data: The primary data consists of RGB images of individual apples.

Labels: Each image is labeled as either “good” or “bad”.

Optional Metadata (if available):

Time of capture

Lighting condition

Apple variety

Temperature or humidity readings at the time of image capture

1. Image Characteristics:

Resolution: Images range from 224x224 to 512x512 pixels.

Background: Mixture of plain (controlled lab settings) and complex (orchard or market environments).

Lighting: Includes both natural and artificial lighting.

Angle and Orientation: Varies to simulate real-world usage scenarios in sorting systems.

1. Good Apples:

Visually appealing

No visible bruises, rot, or mold

Uniform shape and color

Examples might show apples with minimal surface blemishes or minor imperfections

1. Bad Apples:

Presence of:

Mold

Bruising

Cuts or cracks

Discoloration or rot

Some may be partially decomposed

Often irregular in shape or visibly damaged

1. Sources:

Agricultural research datasets

Custom image captures from farms or marketplaces

Open-source image repositories with suitable licensing (e.g., Creative Commons)

1. Data Split:

Training set: 70%

Validation set: 15%

Test set: 15%

Stratified to ensure balanced class representation across splits

1. Preprocessing:

Image resizing and normalization

Data augmentation (flipping, rotation, brightness/contrast adjustments) to increase model robustness

Optional noise filtering and background removal to improve focus on the apple surface

1. Use Cases:

Automated sorting systems in agriculture

Quality control for fruit suppliers and supermarkets

Educational tools for machine learning in agricultural contexts

Let me know if you’d like to include technical details about models or preprocessing pipelines as well.

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

We present results from six randomized control trials of an integrated approach to improve livelihoods among the very poor. The approach combines the transfer of a productive asset with consumption support, training, and coaching plus savings encouragement and health education and/or services. Results from the implementation of the same basic program, adapted to a wide variety of geographic and institutional contexts and with multiple implementing partners, show statistically significant cost-effective impacts on consumption (fueled mostly by increases in self-employment income) and psychosocial status of the targeted households. The impact on the poor households lasted at least a year after all implementation ended. It is possible to make sustainable improvements in the economic status of the poor with a relatively short-term intervention.

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