This dataset is about companies. It has 17 rows and is filtered where the company is Meta. It features 5 columns: employees, CEO, CEO gender, and CEO approval.

About this dataset

Context

The dataset tabulates the Meta population by age. The dataset can be utilized to understand the age distribution and demographics of Meta.

Content

The dataset constitues the following three datasets

• Meta, MO Age Group Population Dataset: A complete breakdown of Meta age demographics from 0 to 85 years, distributed across 18 age groups
• Meta, MO Age Cohorts Dataset: Children, Working Adults, and Seniors in Meta - Population and Percentage Analysis
• Meta, MO Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

Describe your research hypothesis, what your data shows, any notable findings and how the data can be interpreted. Please add sufficient description to enable others to understand what the data is, how it was gathered and how to interpret and use it.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Meta. The dataset can be utilized to gain insights into gender-based income distribution within the Meta population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Meta, among individuals aged 15 years and older with income, there were 56 men and 49 women in the workforce. Among them, 28 men were engaged in full-time, year-round employment, while 19 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 17.86% fell within the income range of under $24,999, while 5.26% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: none of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Meta 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 Meta. 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 Meta, the median income for all workers aged 15 years and older, regardless of work hours, was $40,000 for males and $25,893 for females.

These income figures highlight a substantial gender-based income gap in Meta. 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 city of Meta.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.08 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 Meta median household income by race. You can refer the same here

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

This dataset considers some socioeconomic variables of professional individuals working in the Meta department in Colombia. They have been randomly simulated for use as a teaching resource in the learning of concepts and methodologies in the Descriptive Statistics course. The data does not contain any sensitive information and can be used for practical learning activities.

About this dataset

Context

The dataset tabulates the population of Meta by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Meta. The dataset can be utilized to understand the population distribution of Meta by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Meta. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Meta.

Key observations

Largest age group (population): Male # 50-54 years (12) | Female # 70-74 years (9). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Meta population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Meta is shown in the following column.
• Population (Female): The female population in the Meta is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Meta for each age group.

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 Meta Population by Gender. You can refer the same here

One of the newest types of multimedia involves body-connected interfaces, usually termed haptics. Haptics may use stylus-based tactile interfaces, glove-based systems, handheld controllers, balance boards, or other custom-designed body-computer interfaces. How well do these interfaces help students learn Science, Technology, Engineering, and Mathematics (STEM)? We conducted an updated review of learning STEM with haptics, applying meta-analytic techniques to 21 published articles reporting on 53 effects for factual, inferential, procedural, and transfer STEM learning. This deposit includes the data extracted from those articles and comprises the raw data used in the meta-analytic analyses.

TDMentions: A Dataset of Technical Debt Mentions in Online Posts (version 1.0)

TDMentions is a dataset that contains mentions of technical debt from Reddit, Hacker News, and Stack Exchange. It also contains a list of blog posts on Medium that were tagged as technical debt. The dataset currently contains approximately 35,000 items.

Data collection and processing

The dataset is mainly collected from existing datasets. We used data from:

• the archive of Reddit posts by Jason Baumgartner (available at https://pushshift.io,
• the archive of Hacker News available at Google's BigQuery (available at https://console.cloud.google.com/marketplace/details/y-combinator/hacker-news), and the Stack Exchange data dump (available at https://archive.org/details/stackexchange).
• the GHTorrent project
• the GH Archive

The data set currently contains data from the start of each source/service until 2018-12-31. For GitHub, we currently only include data from 2015-01-01.

We use the regular expression tech(nical)?[\s\-_]*?debt to find mentions in all sources except for Medium. We decided to limit our matches to variations of technical debt and tech debt. Other shorter forms, such as TD, can result in too many false positives. For Medium, we used the tag technical-debt.

Data Format

The dataset is stored as a compressed (bzip2) JSON file with one JSON object per line. Each mention is represented as a JSON object with the following keys.

• id: the id used in the original source. We use the URL path to identify Medium posts.
• body: the text that contains the mention. This is either the comment or the title of the post. For Medium posts this is the title and subtitle (which might not mention technical debt, since posts are identified by the tag).
• created_utc: the time the item was posted in seconds since epoch in UTC.
• author: the author of the item. We use the username or userid from the source.
• source: where the item was posted. Valid sources are:
  • HackerNews Comment
  • HackerNews Job
  • HackerNews Submission
  • Reddit Comment
  • Reddit Submission
  • StackExchange Answer
  • StackExchange Comment
  • StackExchange Question
  • Medium Post
• meta: Additional information about the item specific to the source. This includes, e.g., the subreddit a Reddit submission or comment was posted to, the score, etc. We try to use the same names, e.g., score and num_comments for keys that have the same meaning/information across multiple sources.

This is a sample item from Reddit:

{
 "id": "ab8auf",
 "body": "Technical Debt Explained (x-post r/Eve)",
 "created_utc": 1546271789,
 "author": "totally_100_human",
 "source": "Reddit Submission",
 "meta": {
  "title": "Technical Debt Explained (x-post r/Eve)",
  "score": 1,
  "num_comments": 0,
  "url": "http://jestertrek.com/eve/technical-debt-2.png",
  "subreddit": "RCBRedditBot"
 }
}

Sample Analyses

We decided to use JSON to store the data, since it is easy to work with from multiple programming languages. In the following examples, we use jq to process the JSON.

How many items are there for each source?

lbzip2 -cd postscomments.json.bz2 | jq '.source' | sort | uniq -c

How many submissions that mentioned technical debt were posted each month?

lbzip2 -cd postscomments.json.bz2 | jq 'select(.source == "Reddit Submission") | .created_utc | strftime("%Y-%m")' | sort | uniq -c

What are the titles of items that link (meta.url) to PDF documents?

lbzip2 -cd postscomments.json.bz2 | jq '. as $r | select(.meta.url?) | .meta.url | select(endswith(".pdf")) | $r.body'

Please, I want CSV!

lbzip2 -cd postscomments.json.bz2 | jq -r '[.id, .body, .author] | @csv'

Note that you need to specify the keys you want to include for the CSV, so it is easier to either ignore the meta information or process each source.

Please see https://github.com/sse-lnu/tdmentions for more analyses

Limitations and Future updates

The current version of the dataset lacks GitHub data and Medium comments. GitHub data will be added in the next update. Medium comments (responses) will be added in a future update if we find a good way to represent these.

The Worldwide Soundscapes project is a global, open inventory of spatio-temporally replicated soundscape datasets. This Zenodo entry comprises the data tables that constitute its (meta-)database, as well as their description.

The overview of all sampling sites can be found on the corresponding project on ecoSound-web, as well as a demonstration collection containing selected recordings. More information on the project can be found here and on ResearchGate.

The audio recording criteria justifying inclusion into the meta-database are:

• Stationary (no transects, towed sensors or microphones mounted on cars)
• Passive (unattended, no human disturbance by the recordist)
• Ambient (no spatial or temporal focus on a particular species or direction)
• Spatially and/or temporally replicated (multiple sites sampled at least at one common daytime or multiple days sampled at least in one common site)

The individual columns of the provided data tables are described in the following. Data tables are linked through primary keys; joining them will result in a database.

datasets

• dataset_id: incremental integer, primary key
• name: name of the dataset. if it is repeated, incremental integers should be used in the "subset" column to differentiate them.
• subset: incremental integer that can be used to distinguish datasets with identical names
• collaborators: full names of people deemed responsible for the dataset, separated by commas
• contributors: full names of people who are not the main collaborators but who have significantly contributed to the dataset, and who could be contacted for in-depth analyses, separated by commas.
• date_added: when the datased was added (DD/MM/YYYY)
• URL_open_recordings: if recordings (even only some) from this dataset are openly available, indicate the internet link where they can be found.
• URL_project: internet link for further information about the corresponding project
• DOI_publication: DOI of corresponding publications, separated by comma
• core_realm_IUCN: The core realm of the dataset. Datasets may have multiple realms, but the main one should be listed. Datasets may contain sampling sites from different realms in the "sites" sheet. IUCN Global Ecosystem Typology (v2.0): https://global-ecosystems.org/
• medium: the physical medium the microphone is situated in
• protected_area: Whether the sampling sites were situated in protected areas or not, or only some.
• GADM0: For datasets on land or in territorial waters, Global Administrative Database level0
  https://gadm.org/
• GADM1: For datasets on land or in territorial waters, Global Administrative Database level1
  https://gadm.org/
• GADM2: For datasets on land or in territorial waters, Global Administrative Database level2
  https://gadm.org/
• IHO: For marine locations, the sea area that encompassess all the sampling locations according to the International Hydrographic Organisation. Map here: https://www.arcgis.com/home/item.html?id=44e04407fbaf4d93afcb63018fbca9e2
• locality: optional free text about the locality
• latitude_numeric_region: study region approximate centroid latitude in WGS84 decimal degrees
• longitude_numeric_region: study region approximate centroid longitude in WGS84 decimal degrees
• sites_number: number of sites sampled
• year_start: starting year of the sampling
• year_end: ending year of the sampling
• deployment_schedule: description of the sampling schedule, provisional
• temporal_recording_selection: list environmental exclusion criteria that were used to determine which recording days or times to discard
• high_pass_filter_Hz: frequency of the high-pass filter of the recorder, in Hz
• variable_sampling_frequency: Does the sampling frequency vary? If it does, write "NA" in the sampling_frequency_kHz column and indicate it in the sampling_frequency_kHz column inside the deployments sheet
• sampling_frequency_kHz: frequency the microphone was sampled at (sounds of half that frequency will be recorded)
• variable_recorder:
• recorder: recorder model used
• microphone: microphone used
• freshwater_recordist_position: position of the recordist relative to the microphone during sampling (only for freshwater)
• collaborator_comments: free-text field for comments by the collaborators
• validated: This cell is checked if the contents of all sheets are complete and have been found to be coherent and consistent with our requirements.
• validator_name: name of person doing the validation
• validation_comments: validators: please insert the date when someone was contacted
• cross-check: this cell is checked if the collaborators confirm the spatial and temporal data after checking the corresponding site maps, deployment and operation time graphs found at https://drive.google.com/drive/folders/1qfwXH_7dpFCqyls-c6b8RZ_fbcn9kXbp?usp=share_link

datasets-sites

• dataset_ID: primary key of datasets table
• dataset_name: lookup field
• site_ID: primary key of sites table
• site_name: lookup field

sites

• site_ID: unique site IDs, larger than 1000 for compatibility with ecoSound-web
• site_name: name or code of sampling site as used in respective projects
• latitude_numeric: exact numeric degrees coordinates of latitude
• longitude_numeric: exact numeric degrees coordinates of longitude
• topography_m: for sites on land: elevation. For marine sites: depth (negative). in meters
• freshwater_depth_m
• realm: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• biome: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• functional_group: Ecosystem type according to IUCN GET https://global-ecosystems.org/
• comments

deployments

• dataset_ID: primary key of datasets table
• dataset_name: lookup field
• deployment: use identical subscript letters to denote rows that belong to the same deployment. For instance, you may use different operation times and schedules for different target taxa within one deployment.
• start_date_min: earliest date of deployment start, double-click cell to get date-picker
• start_date_max: latest date of deployment start, if applicable (only used when recorders were deployed over several days), double-click cell to get date-picker
• start_time_mixed: deployment start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording start time for continuous recording deployments. If multiple start times were used, you should mention the latest start time (corresponds to the earliest daytime from which all recorders are active). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• permanent: is the deployment permanent (in which case it would be ongoing and the end date or duration would be unknown)?
• variable_duration_days: is the duration of the deployment variable? in days
• duration_days: deployment duration per recorder (use the minimum if variable)
• end_date_min: earliest date of deployment end, only needed if duration is variable, double-click cell to get date-picker
• end_date_max: latest date of deployment end, only needed if duration is variable, double-click cell to get date-picker
• end_time_mixed: deployment end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). Corresponds to the recording end time for continuous recording deployments.
• recording_time: does the recording last from the deployment start time to the end time (continuous) or at scheduled daily intervals (scheduled)? Note: we consider recordings with duty cycles to be continuous.
• operation_start_time_mixed: scheduled recording start local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• operation_duration_minutes: duration of operation in minutes, if constant
• operation_end_time_mixed: scheduled recording end local time, either in HH:MM format or a choice of solar daytimes (sunrise, sunset, noon, midnight). If applicable, positive or negative offsets from solar times can be mentioned (For example: if data are collected one hour before sunrise, this will be "sunrise-60")
• duty_cycle_minutes: duty cycle of the recording (i.e. the fraction of minutes when it is recording), written as "recording(minutes)/period(minutes)". For example: "1/6" if the recorder is active for 1 minute and standing by for 5 minutes.
• sampling_frequency_kHz: only indicate the sampling frequency if it is variable within a particular dataset so that we need to code different frequencies for different deployments
• recorder
• subset_sites: If the deployment was not done in all the sites of the

Meta Open Materials 2024 (OMat24) Dataset

  Overview

Several datasets were utilized in this work. We provide open access to all datasets used to help accelerate research in the community. This includes the OMat24 dataset as well as our modified sAlex dataset. Details on the different datasets are provided below. The OMat24 datasets can be used with the FAIRChem package. See section on "How to read the data" below for a minimal example.

  Datasets







  OMat24… See the full description on the dataset page: https://huggingface.co/datasets/facebook/OMAT24.

The most vulnerable group of traffic participants are pedestrians using mobility aids. While there has been significant progress in the robustness and reliability of camera based general pedestrian detection systems, pedestrians reliant on mobility aids are highly underrepresented in common datasets for object detection and classification.

To bridge this gap and enable research towards robust and reliable detection systems which may be employed in traffic monitoring, scheduling, and planning, we present this dataset of a pedestrian crossing scenario taken from an elevated traffic monitoring perspective together with ground truth annotations (Yolo format [1]). Classes present in the dataset are pedestrian (without mobility aids), as well as pedestrians using wheelchairs, rollators/wheeled walkers, crutches, and walking canes. The dataset comes with official training, validation, and test splits.

An in-depth description of the dataset can be found in [2]. If you make use of this dataset in your work, research or publication, please cite this work as:

@inproceedings{mohr2023mau,
author = {Mohr, Ludwig and Kirillova, Nadezda and Possegger, Horst and Bischof, Horst},
title = {{A Comprehensive Crossroad Camera Dataset of Mobility Aid Users}},
booktitle = {Proceedings of the 34th British Machine Vision Conference ({BMVC}2023)},
year = {2023}
}

Archive mobility.zip contains the full detection dataset in Yolo format with images, ground truth labels and meta data, archive mobility_class_hierarchy.zip contains labels and meta files (Yolo format) for training with class hierarchy using e.g. the modified version of Yolo v5/v8 available under [3].
To use this dataset with Yolo, you will need to download and extract the zip archive and change the path entry in dataset.yaml to the directory where you extracted the archive to.

[1] https://github.com/ultralytics/ultralytics
[2] coming soon
[3] coming soon

[Real or Fake] : Fake Job Description Prediction This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.

This file is a ZIP archive which contains ALL publicly released ISA-TAB-Nano datasets developed within the NanoPUZZLES EU project [http://www.nanopuzzles.eu]. The (meta)data in these datasets were extracted from literature references.

These datasets are also available via FigShare (see below). ****Any necessary updates, e.g. to correct errors not spotted during the review of the datasets within the NanoPUZZLES project prior to their being released, will be uploaded to FigShare and the changes documented in the FigShare dataset descriptions. This Zenodo entry corresponds to the original publicly released versions of these datasets.****

*****Before working with these datasets, you are strongly advised to read the following text - especially the "Disclaimers".*****

ISA-TAB-Nano [1,2,3] has been proposed as a nanomaterial data exchange standard. As is explained in the README file contained within each dataset, as well as the "Investigation Description" field of the Investigation file regarding dataset specific deviations, the manner in which certain data and metadata were recorded within these datasets deviates from the expectations of the generic ISA-TAB-Nano specification. Marchese Robinson et al. [3], distributed within each dataset, discusses this in more detail. However, some additional new business rules, going beyond those described in Marchese Robinson et al. [3], may also have been applied to each dataset - as documented in the README file.

Each dataset was developed using Excel-based templates developed in the NanoPUZZLES project [4]. (N.B. The latest version of the templates, at the time of writing, was version 4 as opposed to version 3 which was described in Marchese Robinson et al. [3]. This latest version of the templates should be contained within the README file of each dataset.) Since these templates were iteratively updated, not all datasets may be perfectly consistent with the latest version - although efforts were made to minimise inconsistencies.

The three copies of each dataset contained within each individual [DATASET ID]_all_copies.zip are as follows:
(a) [DATASET ID].zip: the original dataset prepared within Excel
(b) [DATASET ID]-txt_opt-N.zip: a tab-delimited text version of each dataset prepared using version 2.0 of the cited Python program [5], with the -N flag selected (designed to minimise inconsistencies with the latest version of the NanoPUZZLES templates)
(c) [DATASET ID]-txt_opt-a_opt-c_opt-N.zip: a tab-delimited text version of each dataset prepared using version 2.0 of the cited Python program [5], with the -N, -a (truncate ontology IDs) and -c (remove Investigation file comments) flags selected, as required for submission to the nanoDMS online database system [3,6].

The original datasets prepared in Excel were prepared via manual curation. In some cases, it was necessary to extract data from graphs. In some cases, the GSYS software program was employed to facilitate estimation of the values of numerical data points reported in graphs [7,8].

Disclaimers:

(1) this work has not undergone peer review
(2) no endorsement by third parties should be inferred
(3) *You are strongly advised to read the README file and the "Investigation Description" field of the Investigation file before working with anyone of these datasets. The latter field may document dataset specific caveats such as possible problems or uncertainties associated with curation from the original reference(s). *Other such comments may be found in Study, Material or Assay file "Comment" fields.

Cited references:
[1] Thomas, D.G. et al. BMC Biotechnol. 2013, 13, 2. doi:10.1186/1472-6750-13-2
[2] https://wiki.nci.nih.gov/display/ICR/ISA-TAB-Nano (accessed 18th of December 2015)
[3] Marchese Robinson, R.L. et al. Beilstein J. Nanotechnol. 2015, 6, 1978–1999. doi:10.3762/bjnano.6.202
[4] http://www.myexperiment.org/files/1356.html (accessed 18th of December 2015)
[5] https://github.com/RichardLMR/xls2txtISA.NANO.archive (accessed 18th of December 2015)
[6] http://biocenitc-deq.urv.cat/nanodms (accessed 18th of December 2015)

[7] http://www.jcprg.org/gsys/2.4/ (last accessed 11th of April 2016)

[8] R. Suzuki, "Introduction, Design and Implementation of Digitization Software GSYS", IAEA Report INDC(NDS)-0629, p. 19, IAEA, Vienna, Austria (2013)

FigShare versions:

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Cytotoxicity_and_some_physicochemical_data_reported_by_Wang_et_al_2014_DOI_10_3109_17435390_2013_796534_/2056140

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Zebrafish_mortality_and_basic_nanomaterial_composition_data_extracted_from_Kovriznych_et_al_2013_doi_10_2478_intox_2013_0012_/2056137

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Physicochemical_and_in_vitro_cytotoxicity_data_LDH_membrane_damage_extracted_from_Sayes_and_Ivanov_2010_DOI_10_1111_j_1539_6924_2010_01438_x_/2056134

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Cytotoxicity_and_physicochemical_data_for_nanomaterials_extracted_from_Murdock_et_al_2008_DOI_10_1093_toxsci_kfm240_/2056131

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Data_reported_in_Shaw_et_al_2008_DOI_10_1073_pnas_0802878105_/2056128

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Data_extracted_from_Puzyn_et_al_2011_DOI_10_1038_NNANO_2011_10_/2056125

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Toxicity_and_physicochemical_data_extracted_from_Zhang_et_al_2012_DOI_10_1021_nn3010087_/2056122

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Curation_of_carbon_nanotubes_experimental_data_reported_by_Zhou_et_al_2008_DOI_10_1021_nl0730155_supplemented_with_carbon_nanotubes_structure_files_3D_SDF_created_according_to_the_approach_described_by_Shao_et_al_/2056110

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_C60_fullerene_nanoparticle_Ames_test_and_in_vivo_micronucleus_data_extracted_from_Shinohara_et_al_2009_DOI_10_1016_j_toxlet_2009_09_012_/2056104

https://figshare.com/articles/NanoPUZZLES_ISA_TAB_Nano_dataset_Data_extracted_from_NanoCare_project_final_scientific_report/2056095

Funding:

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/ 2007-2013) under grant agreement no. 309837 (NanoPUZZLES project).

As we all well aware Internet is dominated by English and finding resources for other languages (especially one's from the developing world) is hard to near impossible, so this is my small effort to bring some of the well known works from the world of Hindi to Kaggle, so people can experiment and work with the same. I am starting out with Premchand, will try to add more authors over time.

This corpus contain all the work of Munshi Premchand who is beloved figure in the world Hindi Literature, I have aggregated this dataset from multiple websites which host work of Munshi Premchand. The file is TSV, where each row is individual work, and some meta data associates with the work, i.e. Title, Work Type (Story/Novel)

First thing that comes to mind is text generation, one can start out with very naïve methods and work your way up to more complex methods. Also textual style transfer is one of the thing that can be experimented, as Premchand was very much know for his writing style as much as for the stories themselves.

1. There is a growing need for scientific synthesis in ecology and evolution. In many cases, meta-analytic techniques can be used to complement such synthesis. However, missing data is a serious problem for any synthetic efforts and can compromise the integrity of meta-analyses in these and other disciplines. Currently, the prevalence of missing data in meta-analytic datasets in ecology and the efficacy of different remedies for this problem have not been adequately quantified. 2. We generated meta-analytic datasets based on literature reviews of experimental and observational data and found that missing data were prevalent in meta-analytic ecological datasets. We then tested the performance of complete case removal (a widely used method when data are missing) and multiple imputation (an alternative method for data recovery) and assessed model bias, precision, and multi-model rankings under a variety of simulated conditions using published meta-regression datasets. 3. We found that complete case removal led to biased and imprecise coefficient estimates and yielded poorly specified models. In contrast, multiple imputation provided unbiased parameter estimates with only a small loss in precision. The performance of multiple imputation, however, was dependent on the type of data missing. It performed best when missing values were weighting variables, but performance was mixed when missing values were predictor variables. Multiple imputation performed poorly when imputing raw data which was then used to calculate effect size and the weighting variable. 4. We conclude that complete case removal should not be used in meta-regression, and that multiple imputation has the potential to be an indispensable tool for meta-regression in ecology and evolution. However, we recommend that users assess the performance of multiple imputation by simulating missing data on a subset of their data before implementing it to recover actual missing data.

This file is a ZIP archive which contains three different copies of an ISA-TAB-Nano dataset developed within the NanoPUZZLES EU project [http://www.nanopuzzles.eu]. The (meta)data in this dataset were primarily extracted from the following reference, with additional references consulted as indicated in the Investigation file: Cytotoxicity and some physicochemical data reported by Wang et al. 2014 (DOI:10.3109/17435390.2013.796534)*****Before working with this dataset, you are strongly advised to read the following text - especially the "Disclaimers".*****ISA-TAB-Nano [1,2,3] has been proposed as a nanomaterial data exchange standard. As is explained in the README file contained within this dataset, as well as the "Investigation Description" field of the Investigation file regarding dataset specific deviations, the manner in which certain data and metadata were recorded within these datasets deviates from the expectations of the generic ISA-TAB-Nano specification. Marchese Robinson et al. [3], distributed within this dataset, discusses this in more detail. However, some additional new business rules, going beyond those described in Marchese Robinson et al. [3], may also have been applied to this dataset - as documented in the README file.This dataset was developed using Excel-based templates developed in the NanoPUZZLES project [4]. (N.B. The latest version of the templates, at the time of writing, was version 4 as opposed to version 3 which was described in Marchese Robinson et al. [3]. This latest version of the templates should be contained within the README file of this dataset.) Since these templates were iteratively updated, not all datasets may be perfectly consistent with the latest version - although efforts were made to minimise inconsistencies.The three copies of this dataset are as follows:(a) 10.3109_FS_17435390.2013.796534.zip: the original dataset prepared within Excel(b) 10.3109_FS_17435390.2013.796534-txt_opt-N.zip: a tab-delimited text version of this dataset prepared using version 2.0 of the cited Python program [5], with the -N flag selected (designed to minimise inconsistencies with the latest version of the NanoPUZZLES templates)(c) 10.3109_FS_17435390.2013.796534-txt_opt-a_opt-c_opt-N.zip: a tab-delimited text version of this dataset prepared using version 2.0 of the cited Python program [5], with the -N, -a (truncate ontology IDs) and -c (remove Investigation file comments) flags selected, as required for submission to the nanoDMS online database system [3,6].Disclaimers:(1) this work has not undergone peer review(2) no endorsement by third parties should be inferred(3) *You are strongly advised to read the README file and the "Investigation Description" field of the Investigation file before working with this dataset. The latter field may document dataset specific caveats such as possible problems or uncertainties associated with curation from the original reference(s). Cited references:[1] Thomas, D.G. et al. BMC Biotechnol. 2013, 13, 2. doi:10.1186/1472-6750-13-2[2] https://wiki.nci.nih.gov/display/ICR/ISA-TAB-Nano (accessed 18th of December 2015)[3] Marchese Robinson, R.L. et al. Beilstein J. Nanotechnol. 2015, 6, 1978–1999. doi:10.3762/bjnano.6.202[4] http://www.myexperiment.org/files/1356.html (accessed 18th of December 2015)[5] https://github.com/RichardLMR/xls2txtISA.NANO.archive (accessed 18th of December 2015)[6] http://biocenitc-deq.urv.cat/nanodms (accessed 18th of December 2015)Funding:The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/ 2007-2013) under grant agreement no. 309837 (NanoPUZZLES project).

About this dataset

Context

The dataset tabulates the population of Meta by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Meta across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.78% of total population being female. 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.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Meta is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Meta 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 Meta Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Meta 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 Meta. The dataset can be utilized to understand the population distribution of Meta by age. For example, using this dataset, we can identify the largest age group in Meta.

Key observations

The largest age group in Meta, MO was for the group of age 65 to 69 years years with a population of 18 (14.06%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Meta, MO was the 20 to 24 years years with a population of 0 (0%). 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 Meta is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Meta 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 Meta Population by Age. You can refer the same here