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 New Canada town. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In New Canada town, the median income for all workers aged 15 years and older, regardless of work hours, was $52,250 for males and $27,500 for females.

These income figures highlight a substantial gender-based income gap in New Canada town. Women, regardless of work hours, earn 53 cents for each dollar earned by men. This significant gender pay gap, approximately 47%, underscores concerning gender-based income inequality in the town of New Canada town.

- Full-time workers, aged 15 years and older: In New Canada town, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,125, while females earned $51,375, leading to a 19% gender pay gap among full-time workers. This illustrates that women earn 81 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

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

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 New Canada town 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 Little Canada. 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 Little Canada, the median income for all workers aged 15 years and older, regardless of work hours, was $49,764 for males and $40,129 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 19% between the median incomes of males and females in Little Canada. With women, regardless of work hours, earning 81 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Little Canada.

- Full-time workers, aged 15 years and older: In Little Canada, among full-time, year-round workers aged 15 years and older, males earned a median income of $69,643, while females earned $59,934, resulting in a 14% gender pay gap among full-time workers. This illustrates that women earn 86 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Little Canada.

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

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 Little Canada median household income by race. You can refer the same here

Indexes of real expenditure per capita in the United States relative to those in Canada for categories of gross domestic income (GDI), Canada=100, on an International Comparison Project Classification (ICP) basis.

The Gross Domestic Product (GDP) in Canada was worth 2142.47 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Canada represents 2.03 percent of the world economy. This dataset provides - Canada GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Salient Features of Dentists Email Addresses

So make sure that you don’t find excuses for failing at global marketing campaigns and in reaching targeted medical practitioners and healthcare specialists. With our Dentists Email Leads, you will seldom have a reason not to succeed! So make haste and take action today!

1. 1.2 million phone calls per month as a part of a data verification
2. 85% telephone and email verified Dentist Mailing Lists
3. Quarterly SMTP and NCOA verified to keep data fresh and active
4. 15 million verification messages sent every month to validate email addresses
5. Connect with top Dentists across the US, Canada, UK, Europe, EMEA, Australia, APAC and many more countries.
6. egularly updated and cleansed databases to keep it free of duplicate and inaccurate data

How Can Our Dentists Data Help You to Market to Dentists?

We provide a variety of methods for marketing your dental appliances or products to the top-rated dentists in the United States. Take a glance at some of the available channels:

• Email blast • Marketing viability • Test campaigns • Direct mail • Sales leads • Drift campaigns • ABM campaigns • Product launches • B2B marketing

Data Sources

The contact details of your targeted healthcare professionals are compiled from highly credible resources like: • Websites • Medical seminars • Medical records • Trade shows • Medical conferences

What’s in for you? Over choosing us, here are a few advantages we authenticate- • Locate, target, and prospect leads from 170+ countries • Design and execute ABM and multi-channel campaigns • Seamless and smooth pre-and post-sale customer service • Connect with old leads and build a fruitful customer relationship • Analyze the market for product development and sales campaigns • Boost sales and ROI with increased customer acquisition and retention

Our security compliance

We use of globally recognized data laws like –

GDPR, CCPA, ACMA, EDPS, CAN-SPAM and ANTI CAN-SPAM to ensure the privacy and security of our database. We engage certified auditors to validate our security and privacy by providing us with certificates to represent our security compliance.

Our USPs- what makes us your ideal choice?

At DataCaptive™, we strive consistently to improve our services and cater to the needs of businesses around the world while keeping up with industry trends.

• Elaborate data mining from credible sources • 7-tier verification, including manual quality check • Strict adherence to global and local data policies • Guaranteed 95% accuracy or cash-back • Free sample database available on request

Guaranteed benefits of our Dentists email database!

85% email deliverability and 95% accuracy on other data fields

We understand the importance of data accuracy and employ every avenue to keep our database fresh and updated. We execute a multi-step QC process backed by our Patented AI and Machine learning tools to prevent anomalies in consistency and data precision. This cycle repeats every 45 days. Although maintaining 100% accuracy is quite impractical, since data such as email, physical addresses, and phone numbers are subjected to change, we guarantee 85% email deliverability and 95% accuracy on other data points.

100% replacement in case of hard bounces

Every data point is meticulously verified and then re-verified to ensure you get the best. Data Accuracy is paramount in successfully penetrating a new market or working within a familiar one. We are committed to precision. However, in an unlikely event where hard bounces or inaccuracies exceed the guaranteed percentage, we offer replacement with immediate effect. If need be, we even offer credits and/or refunds for inaccurate contacts.

Other promised benefits

• Contacts are for the perpetual usage • The database comprises consent-based opt-in contacts only • The list is free of duplicate contacts and generic emails • Round-the-clock customer service assistance • 360-degree database solutions

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

What’s Included

Welcome to the English Language General Conversation Speech Dataset, a comprehensive and diverse collection of voice data specifically curated to advance the development of English language speech recognition models, with a particular focus on Canadian accents and dialects.

With high-quality audio recordings, detailed metadata, and accurate transcriptions, it empowers researchers and developers to enhance natural language processing, conversational AI, and Generative Voice AI algorithms. Moreover, it facilitates the creation of sophisticated voice assistants and voice bots tailored to the unique linguistic nuances found in the English language spoken in Canada.

Speech Data:

This training dataset comprises 30 hours of audio recordings covering a wide range of topics and scenarios, ensuring robustness and accuracy in speech technology applications. To achieve this, we collaborated with a diverse network of 40 native English speakers from different states/provinces of Canada. This collaborative effort guarantees a balanced representation of Canadian accents, dialects, and demographics, reducing biases and promoting inclusivity.

Each audio recording captures the essence of spontaneous, unscripted conversations between two individuals, with an average duration ranging from 15 to 60 minutes. The speech data is available in WAV format, with stereo channel files having a bit depth of 16 bits and a sample rate of 8 kHz. The recording environment is generally quiet, without background noise and echo.

Metadata:

In addition to the audio recordings, our dataset provides comprehensive metadata for each participant. This metadata includes the participant's age, gender, country, state, and dialect. Furthermore, additional metadata such as recording device detail, topic of recording, bit depth, and sample rate will be provided.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English language speech recognition models.

Transcription:

This dataset provides a manual verbatim transcription of each audio file to enhance your workflow efficiency. The transcriptions are available in JSON format. The transcriptions capture speaker-wise transcription with time-coded segmentation along with non-speech labels and tags.

Our goal is to expedite the deployment of English language conversational AI and NLP models by offering ready-to-use transcriptions, ultimately saving valuable time and resources in the development process.

Updates and Customization:

We understand the importance of collecting data in various environments to build robust ASR models. Therefore, our voice dataset is regularly updated with new audio data captured in diverse real-world conditions.

If you require a custom training dataset with specific environmental conditions such as in-car, busy street, restaurant, or any other scenario, we can accommodate your request. We can provide voice data with customized sample rates ranging from 8kHz to 48kHz, allowing you to fine-tune your models for different audio recording setups. Additionally, we can also customize the transcription following your specific guidelines and requirements, to further support your ASR development process.

License:

This audio dataset, created by FutureBeeAI, is now available for commercial use.

Conclusion:

Whether you are training or fine-tuning speech recognition models, advancing NLP algorithms, exploring generative voice AI, or building cutting-edge voice assistants and bots, our dataset serves as a reliable and valuable resource.

Data on annual expenditure by educational institutions per student, in Canadian and American dollars, reference year 2020/2021. At the primary/secondary level, the amount spent on educational core services and ancillary services is also presented.

We present a new, publicly available database of real-time data and forecasts from the Bank of Canada's staff economic projections, which will be updated on an annual basis. We describe the data construct, its variables, coverage, and frequency. We then provide a forecast evaluation for gross domestic product (GDP) growth, consumer price index (CPI) inflation and the policy rate since 1982: We compare the staff's forecasts with those from commonly used time series models estimated with the real-time data, and with forecasts from other professional forecasters, and provide standard bias tests. Finally, we study changes in predictability of the Canadian economy following the announcement of the inflation-targeting regime in 1991. Our data set is unprecedented outside the USA, and our evidence is particularly interesting, as it covers over 30 years of staff forecasts, two severe recessions, and different monetary policy regimes.

Replication data for peer-reviewed article published in Journal of Applied Econometrics. Paper published online December 11, 2019.

Displays a representation of where all the surveyed businesses across York Region are located. This data is collected through the Region’s annual comprehensive employment survey and each record contains employment and business contact information about each business with the exception of home and farm-based businesses. Home-based businesses are not included as they are distributed throughout residential communities within the Region and are difficult to survey. Employment data for farm-based businesses are collected through the Census of Agriculture conducted by Statistics Canada, and are not included in the York Region Employment Survey dataset.Update Frequency: Not PlannedDate Created: 17/03/2023Date Modified: 17/03/2023Metadata Date: 17/03/2023Citation Contacts: York Region, Long Range PlanningAttribute DefinitionsBUSINESSID: Unique key to identify a business.NAME: The common business name used in everyday transactions. FULL_ADDRESS: Full street address of the physical address. (This field concatenates the following fields: Street Number, Street Name, Street Type, Street Direction)STREET_NUM: Street number of the physical addressSTREET_NAME: Street name of the physical addressSTREET_TYPE: Street type of the physical addressSTREET_DIR: Street direction of the physical addressUNIT_NUM: Unit number of the physical addressCOMMUNITY: Community name where the business is physically locatedMUNICIPALITY: Municipality where the business is physically locatedPOST_CODE: Postal code corresponding to the physical street addressEMPLOYEE_RANGE: The numerical range of employees working in a given firm. PRIM_NAICS, PRIM_NAICS_DESC: The Primary 5-digit NAIC code defines the main business activity that occurs at that particular physical business location.SEC_NAICS, SEC_NAICS_DESC: If there is more than one business activity occurring at a particular business location (that is substantially different from the primary business activity), then a secondary NAIC is assigned.PRIM_BUS_CLUSTER, SEC_BUS_CLUSTER: A business cluster is defined as a geographic concentration of interconnected businesses and institutions in a common industry that both compete and cooperate. As defined by York Region, this field indicates the primary business cluster that this business belongs to.BUS_ACTIVITY_DESC: This is a comment box with a detailed text description of the business activity.TRAFFIC_ZONE: Specifies the traffic zone in which the business is located. MANUFACTURER: Indicates whether or not the business manufactures at the physical business location. CAN_HEADOFFICE: The business at this location is considered the Canadian head office.HEADOFFICEPROVSTATE: Indicates which state or province the head office is located if the head office is located in Canada (outside of Ontario) or in the United StatesHEADOFFICECOUNTRY: Indicates which country the head office is locatedYR_CURRENTLOC: Indicates the year that the business moved into its current address.MAIL_FULL_ADDRESS: The mailing address is the address through which a business receives postal service. This may or may not be the same as the physical street address.MAIL_STREET_NUM, MAIL_STREET_NAME, MAIL_STREET_TYPE, MAIL_STREET_DIR, MAIL_UNIT_NUM, MAIL_COMMUNITY, MAIL_MUNICIPALITY, MAIL_PROVINCE, MAIL_COUNTRY, MAIL_POST_CODE, MAIL_POBOX: Mailing address fields are similar to street address fields and in most cases will be the same as the Street Address. Some examples where the two addresses might not be the same include, multiple location businesses, home-based businesses, or when a business receives mail through a P.O. Box.WEBSITE: The General/Main business website.GEN_BUS_EMAIL: The general/main business e-mail address for that location.PHONE_NO: The general/main phone number for the business location.PHONE_EXT: The extension (if any) for the general/main business phone number.LAST_SURVEYED: The date the record was last surveyedLAST_UPDATED: The date the record was last updatedUPDATEMETHOD: Displays how the business was last updated, based on a predetermined list.X_COORD, Y_COORD: The x,y coordinates of the surveyed business locationFrequently Asked Questions How many businesses are included in the 2022 York Region Business Directory? The 2022 York Region Business Directory contains just over 34,000 business listings. In the past, businesses were annually surveyed, either in person or by telephone to improve the accuracy of the directory. Due to the COVID-19 Pandemic, a survey was not complete in 2020 and 2021. The Region may return to annual surveying in future years, however the next employment survey will be in 2024. This listing also includes home-based businesses that participated in the 2022 employment survey. What is a NAIC code? The North American Industrial Classification (NAIC) coding system is a hierarchical classification system developed in Canada, Mexico and the United States. It was developed to allow for the comparison of business and employment information across a variety of industry categories. The NAICS has a hierarchical structure, designed as follows: Two-digits = sector (e.g., 31-33 contain the Manufacturing sectors) Three-digits = subsector (e.g., 336 = Transportation Equipment Manufacturing) Four-digits = industry group (e.g., 3361 = Motor Vehicle Manufacturing) Five-digits = industry (e.g., 33611 = Automobile and Light Duty Motor Vehicle Manufacturing) For more information on the NAIC coding system click here How do I add or update my business information in the York Region Business Directory? To add or update your business information, please select one of the following methods: • Email: Please email businessdirectory@york.ca to request to be added to the Business Directory. • Online: Go to www.york.ca/employmentsurvey and participate in the employment survey - note, this will only be active in 2024 when the Region performs its next employment survey There is no charge for obtaining a basic listing of your business in the York Region Business Directory. How up-to-date is the information? This directory is based on the 2022 York Region Employment Survey, a survey of businesses which attempts to gather information from all businesses across York Region. In instances where we were unable to gather information, the most recent data was used. Farm-based businesses have not been included in the survey and home-based businesses that participated in the 2022 survey are included in the dataset. The date that the business listing was last updated is located in the LastUpdate column in the attached spreadsheet. Are different versions of the York Region Business Directory available? Yes, the directory is available in two online formats: • An interactive, map-based directory searchable by company name, street address, municipality and industry sector. • The entire dataset in downloadable Microsoft Excel format via York Region's Open Data Portal. This version of the York Region Business Directory 2022 is offered free of charge. The Directory allows for the detailed analysis of business and employment trends, as well as the construction of targeted contact lists. To view the map-based directory and dataset, go to: 2022 Business Directory - Map Is there any analysis of business and employment trends in York Region? Yes. The "2022 Employment and Industry Report" contains information on employment trends in York Region and is based on results from the employment survey. please visit www.york.ca/york-region/plans-reports-and-strategies/employment-and-industry-report to view the report. What other resources are available for York Region businesses? York Region offers an export advisory service and a number of other business development programs and seminars for interested individuals. For details, consult the York Region Economic Strategy Branch. Who do I contact to obtain more information about the Directory? For more information on the York Region Business Directory, contact the Planning and Economic Development Branch at: businessdirectory@york.ca.

Building a comprehensive data inventory as required by section 6.3 of the Directive on Open Government:

“Establishing and maintaining comprehensive inventories of data and information resources of business value held by the department to determine their eligibility and priority, and to plan for their effective release.”

Creating a data inventory is among the first steps in identifying federal data that is eligible for release. Departmental data inventories has been published on the Open Government portal, Open.Canada.ca, so that Canadians can see what federal data is collected and have the opportunity to indicate what data is of most interest to them, helping departments to prioritize data releases based on both external demand and internal capacity.

The objective of the inventory is to provide a landscape of all federal data. While it is recognized that not all data is eligible for release due to the nature of the content, departments are responsible for identifying and including all datasets of business values as part of the inventory exercise with the exception of datasets whose title contains information that should not be released to be released to the public due to security or privacy concerns. These titles have been excluded from the inventory.

Departments were provided with an open data inventory template with standardized elements to populate, and upload in the metadata catalogue, the Open Government Registry. These elements are described in the data dictionary file.

Departments are responsible for maintaining up-to-date data inventories that reflect significant additions to their data holdings.

For purposes of this open data inventory exercise, a dataset is defined as: “An organized collection of data used to carry out the business of a department or agency, that can be understood alone or in conjunction with other datasets”.

The dataset contains large ensembles of bias adjusted daily climate model outputs of minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation, and incoming longwave radiation on a 0.5-degree grid over North America. Intended uses include hydrological/land surface impact modelling and related event attribution studies. The CanLEADv1 dataset is based on archived climate model simulations in the Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) https://open.canada.ca/data/en/dataset/83aa1b18-6616-405e-9bce-af7ef8c2031c and Canadian Earth System Model Large Ensembles (CanESM2 LE) https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c datasets. Specifically, CanLEADv1 provides bias adjusted daily climate variables over North America derived from 50 member initial condition ensembles of CanESM2 (ALL and NAT radiative forcings) and CanESM2-driven CanRCM4 (ALL radiative forcings) simulations (Scinocca et al., 2016; Fyfe et al., 2017). Raw CanESM2 LE and CanRCM4 LE outputs are bias adjusted (Cannon, 2018; Cannon et al., 2015) so that they are statistically consistent with two observationally-constrained historical meteorological forcing datasets (S14FD, Iizumi et al., 2017; EWEMBI, Lange, 2018). File names, formats, and metadata headers follow the recommended Data Reference Syntax for bias-adjusted Coordinated Regional Downscaling Experiment (CORDEX) simulations (Nikulin and Legutke, 2016). Multiple initial condition simulations can be used to investigate the externally forced response, internal variability, and the relative role of external forcing and internal variability on the climate system (e.g., Fyfe et al., 2017). Large ensembles of ALL and NAT simulations can be compared in event attribution studies (e.g., Kirchmeier-Young et al., 2017). Availability of bias adjusted outputs from the CanESM2-CanRCM4 modelling system can be used to investigate the added value of dynamical downscaling (Scinocca et al., 2016). Multiple observational datasets are used for bias adjustment to partly account for observational uncertainty (Iizumi et al., 2017). For CanESM2 LE, there are two sets of radiative forcing scenarios (ALL, which consists of historical and RCP8.5 forcings for the periods 1950-2005 and 2006-2100, respectively, and NAT, which consists of historicalNat forcings for the period 1950-2020), two observationally-constrained target datasets for bias adjustment (S14FD and EWEMBI), and 50 ensemble members, which gives a total of 2 × 2 × 50 = 200 sets of outputs. For CanRCM4 LE, historicalNat simulations were not run; hence, there are 2 × 50 = 100 sets of outputs. In both cases, CanLEADv1 provides variables on the CORDEX NAM-44i 0.5-degree grid. CanESM2 outputs (~2.8-degree grid) and CanRCM4 outputs (0.44-degree grid), are bilinearly interpolated onto the NAM-44i grid before bias adjustment. A multivariate version of quantile mapping (Cannon, 2018) is used to adjust the distribution of each simulated variable, as well as the statistical dependence between variables, so that these properties match those of the target observational dataset. Bias adjustment is performed on a grid cell by grid cell basis. Outside of the historical calibration period, the climate change signal simulated by the climate model is preserved (Cannon et al., 2015). References: Cannon, A. J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2), 31-49. Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938-6959. Fyfe, J. C., Derksen, C., Mudryk, L., Flato, G. M., Santer, B. D., Swart, N. C., Molotch, N. P., Zhang, X., Wan, H., Arora, V. K., Scinocca, J., & Jiao, Y. (2017). Large near-term projected snowpack loss over the western United States. Nature Communications, 8, 14996. Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N., & Nishimori, M. (2017). Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. Journal of Geophysical Research: Atmospheres, 122(15), 7800-7819. Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., & Cannon, A. J. (2017). Attributing extreme fire risk in Western Canada to human emissions. Climatic Change, 144(2), 365-379. Lange, S. (2018). Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth System Dynamics, 9(2), 627-645. Nikulin, G., & Legutke, S. (2016). Data Reference Syntax (DRS) for bias-adjusted CORDEX simulations. https://is-enes-data.github.io/CORDEX_adjust_drs.pdf Scinocca, J. F., Kharin, V. V., Jiao, Y., Qian, M. W., Lazare, M., Solheim, L., Flato, G. M., Biner, S., Desgagne, & Dugas, B. (2016). Coordinated global and regional climate modeling. Journal of Climate, 29(1), 17-35.

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Big Data as a Service Market Size 2024-2028

The big data as a service market size is forecast to increase by USD 41.20 billion at a CAGR of 28.45% between 2023 and 2028.

The market is experiencing significant growth due to the increasing volume of data and the rising demand for advanced data insights. Machine learning algorithms and artificial intelligence are driving product quality and innovation in this sector. Hybrid cloud solutions are gaining popularity, offering the benefits of both private and public cloud platforms for optimal data storage and scalability. Industry standards for data privacy and security are increasingly important, as large amounts of data pose unique risks. The BDaaS market is expected to continue its expansion, providing valuable data insights to businesses across various industries.

What will be the Big Data as a Service Market Size During the Forecast Period?

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Big Data as a Service (BDaaS) has emerged as a game-changer in the business world, enabling organizations to harness the power of big data without the need for extensive infrastructure and expertise. This service model offers various components such as data management, analytics, and visualization tools, enabling businesses to derive valuable insights from their data. BDaaS encompasses several key components that drive market growth. These include Business Intelligence (BI), Data Science, Data Quality, and Data Security. BI provides organizations with the ability to analyze data and gain insights to make informed decisions.



Data Science, on the other hand, focuses on extracting meaningful patterns and trends from large datasets using advanced algorithms. Data Quality is a critical component of BDaaS, ensuring that the data being analyzed is accurate, complete, and consistent. Data Security is another essential aspect, safeguarding sensitive data from cybersecurity threats and data breaches. Moreover, BDaaS offers various data pipelines, enabling seamless data integration and data lifecycle management. Network Analysis, Real-time Analytics, and Predictive Analytics are other essential components, providing businesses with actionable insights in real-time and enabling them to anticipate future trends. Data Mining, Machine Learning Algorithms, and Data Visualization Tools are other essential components of BDaaS.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Data analytics-as-a-Service
  Hadoop-as-a-service
  Data-as-a-service


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    Canada
    US


  APAC

    China


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

The data analytics-as-a-service segment is estimated to witness significant growth during the forecast period.

Big Data as a Service (BDaaS) is a significant market segment, highlighted by the availability of Hadoop-as-a-Service solutions. These offerings enable businesses to access essential datasets on-demand without the burden of expensive infrastructure. DAaaS solutions facilitate real-time data analysis, empowering organizations to make informed decisions. The DAaaS landscape is expanding rapidly as companies acknowledge its value in enhancing internal data. Integrating DAaaS with big data systems amplifies analytics capabilities, creating a vibrant market landscape. Organizations can leverage diverse datasets to gain a competitive edge, driving the growth of the global BDaaS market. In the context of digital transformation, cloud computing, IoT, and 5G technologies, BDaaS solutions offer optimal resource utilization.

However, regulatory scrutiny poses challenges, necessitating stringent data security measures. Retail and other industries stand to benefit significantly from BDaaS, particularly with distributed computing solutions. DAaaS adoption is a strategic investment for businesses seeking to capitalize on the power of external data for valuable insights.

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The Data analytics-as-a-Service segment was valued at USD 2.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 35% 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 during the forecast period.

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Big Data as a Service Market analysis, North America is experiencing signif

Description: This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021. Methods: This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans. The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone. Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes. While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016). Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditions Data Sources: Model output Uncertainties: The historical climatologies were evaluated using observational climatologies generated from stations with a long time series of data over the time period including CTDs, nutrient profiles, lighthouse, satellite SST and buoy data. The model is able to represent the historical conditions with an acceptable bias. The resolution of this model is insufficient to represent the narrow straits and channels of this region so the dataset includes a cautionary mask to exclude these regions. These climate projections are downscaled from a single global climate model (CanESM2/CanRCM4) because the cost of ensembles is presently prohibitive. Our experimental design uses climatological forcing for each time period so the differences between them are almost entirely due to anthropogenic forcing with little effect of natural variability. We caution that our experimental design does not permit analysis of the effects of natural climate variability. We recommend using both of the scenarios (RCP4.5 and RCP8.4) to estimate the scenario uncertainty in these projections.

Report Volume Data

The report counts in this data set are broken down by activity sector, report type, the year and month of receipt, and reporting entity location.

The reporting entity location is represented by the forward sortation area (FSA) component of the Canadian postal code (i.e. the first three characters of the Canadian postal code, e.g. “K1P”) that designates the postal district where the reporting entity is located.

Protecting the Identity of Reporting Entities

By law, FINTRAC must protect the identity of the persons and entities that are required to submit financial transaction reports to the Centre under the Proceeds of Crime (Money Laundering) and Terrorist Financing Act. In keeping with this responsibility to protect information, FINTRAC cannot provide more specific geographic data than is contained in this data set.

Whenever possible, the data set includes the full FSA to identify the location of reporting entities submitting reports to FINTRAC. However, in any case where a certain location contains fewer than five reporting entities, only partial characters of the FSA are shown. This means that certain FSAs may contain only one or two characters (e.g. K or K1) instead of the standard three characters (e.g. K1P). In rare cases, it was not possible to provide a reporting entity location without risking revealing the identity of reporting entities in a given activity sector and so the data is provided at a national level only.

All FSA levels are hierarchically inclusive. This means, for example, that the total number of report counts for K1 includes all reports submitted within all FSAs that start with K1 (i.e. K1A, K1B, K1C), including those that may not be visible because they include fewer than five reporting entities in a given activity sector.

E&OE

Rhodium price data, historical values, forecasts, and news provided by Money Metals Exchange. Rhodium prices and trends updated regularly to provide accurate market insights.

The number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

About this dataset

Context

The dataset presents median household incomes for various household sizes in La Cañada Flintridge, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, La Cañada Flintridge did not include 6-person households. Across the different household sizes in La Cañada Flintridge the mean income is $213,497, and the standard deviation is $75,043. The coefficient of variation (CV) is 35.15%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of La Cañada Flintridge, there were 2 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $76,227. It then further increased to $270,229 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/la-canada-flintridge-ca-median-household-income-by-household-size.jpeg" alt="La Cañada Flintridge, CA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 La Cañada Flintridge median household income. You can refer the same here

The Employment Equity Act prescribes that this report cover the portions of the public service of Canada set out in Schedules I and IV of the Financial Administration Act. Seventy-three departments, agencies and commissions comprise the core public administration (CPA), for which the Treasury Board is the employer (see Table 1). The statistics in this report include only employees working for these organizations, which numbered 181,356 on March 31, 2014.

This report includes information on indeterminate employees, term employees of three months or more, and seasonal employees, with the exception of those seasonal employees who are on leave without pay at the end of March for each fiscal year. No information is reported on students, casual workers or employees on leave without pay, such as those on care and nurturing leave and educational leave.

Statistics in this document also exclude Governor in Council appointees, ministerial staff, federal judges and deputy ministers, who are also on the public service payroll. As required under the Employment Equity Act, annual reports to Parliament present information for the fiscal year beginning April 1 and ending March 31.

The statistics of separate employers, covered under Schedule V of the Financial Administration Act, are not included in this report. Under the Employment Equity Act, separate employers that have more than 100 employees (e.g., the Canada Revenue Agency and the Canadian Food Inspection Agency) are required to provide their reports to the Office of the Chief Human Resources Officer (OCHRO) of the Treasury Board of Canada Secretariat only for the purposes of tabling in Parliament at the same time as this report. To view their employment equity reports, readers should visit those organizations’ websites, or contact them directly.

Reports on employment equity in the Canadian Forces and with respect to members of the Royal Canadian Mounted Police are prepared by those organizations and are also tabled in Parliament at the same time as those of separate employers.

To assure the consistency of data presented in this report, OCHRO uses the incumbent file, which contains information on all employees for whom the Treasury Board is the employer, in accordance with Schedules I and IV of the Financial Administration Act. Since 2011–12, the executive category includes data on the LC Group (Law Management Occupational Group) in its total workforce, which will need to be taken into consideration when comparing data historically.

All tabulations, other than those for women, contain data obtained through self-identification. This data is provided voluntarily by employees and maintained separately and confidentially in the Employment Equity Data Bank by OCHRO, and where applicable, through the self-declaration of individuals applying to the public service through the Public Service Commission of Canada’s (PSC’s) Public Service Resourcing System. A reconciliation process is carried out each year by OCHRO, the PSC and the departments of the CPA to ensure that information derived from the Public Service Resourcing System, the Employment Equity Data Bank, and incumbent file harmonizes with the information from departmental sources.

The completeness and accuracy of employment equity data for the CPA depend on the willingness of employees to self-identify and on departments providing opportunities for them to do so. Employees, including those engaged as students or casual workers, are given an opportunity to provide this information when they are hired and during departmental self-identification surveys or other campaigns. Furthermore, they may complete a self-identification form, available from their departmental employment equity coordinator, at any time.

This is the full Global Wheat Head Dataset 2021. Labels are included in csv.

Tutorials available here: https://www.aicrowd.com/challenges/global-wheat-challenge-2021

🕵️ Introduction

Wheat is the basis of the diet of a large part of humanity. Therefore, this cereal is widely studied by scientists to ensure food security. A tedious, yet important part of this research is the measurement of different characteristics of the plants, also known as Plant Phenotyping. Monitoring plant architectural characteristics allow the breeders to grow better varieties and the farmers to make better decisions, but this critical step is still done manually. The emergence of UAV, camera and smartphone makes in-field RGB images more available and could be a solution to manual measurement. For instance, the counting of the wheat head can be done with Deep Learning. However, this task can be visually challenging. There is often an overlap of dense wheat plants, and the wind can blur the photographs, making identify single heads difficult. Additionally, appearances vary due to maturity, colour, genotype, and head orientation. Finally, because wheat is grown worldwide, different varieties, planting densities, patterns, and field conditions must be considered. To end manual counting, a robust algorithm must be created to address all these issues.

💾 Dataset

The dataset is composed of more than 6000 images of 1024x1024 pixels containing 300k+ unique wheat heads, with the corresponding bounding boxes. The images come from 11 countries and covers 44 unique measurement sessions. A measurement session is a set of images acquired at the same location, during a coherent timestamp (usually a few hours), with a specific sensor. In comparison to the 2020 competition on Kaggle, it represents 4 new countries, 22 new measurements sessions, 1200 new images and 120k new wheat heads. This amount of new situations will help to reinforce the quality of the test dataset. The 2020 dataset was labelled by researchers and students from 9 institutions across 7 countries. The additional data have been labelled by Human in the Loop, an ethical AI labelling company. We hope these changes will help in finding the most robust algorithms possible!

The task is to localize the wheat head contained in each image. The goal is to obtain a model which is robust to variation in shape, illumination, sensor and locations. A set of boxes coordinates is provided for each image.

The training dataset will be the images acquired in Europe and Canada, which cover approximately 4000 images and the test dataset will be composed of the images from North America (except Canada), Asia, Oceania and Africa and covers approximately 2000 images. It represents 7 new measurements sessions available for training but 17 new measurements sessions for the test!

📁 Files

Following files are available in the resources section:

• images: the folder contains all images

• competition_train.csv , competition_val.csv, competition_test.csv : contains the splits used for the 2021 Global Wheat Challenge

  • Val contains the "public test", which is the test set of Global Wheat Head 2020

  • Test contains the "private test".

• Metadata.csv : contains additional metadatas for each domain

💻 Labels

• All boxes are contained in a csv with three columns image_name, BoxesString and domain
• image_name is the name of the image, without the suffix. All images have a .png extension
• BoxesString is a string containing all predicted boxes with the format [x_min,y_min, x_max,y_max]. To concatenate a list of boxes into a PredString, please concatenate all list of coordinates with one space (" ") and all boxes with one semi-column ";". If there is no box, BoxesString is equal to "no_box".
• domain give the domain for each image

If you use the dataset for your research, please do not forget to quote:

@article{david2020global,
 title={Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods},
 author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul A and others},
 journal={Plant Phenomics},
 volume={2020},
 year={2020},
 publisher={Science Partner Journal}
}

@misc{david2021global,
title={Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods},
author={Etienne David and Mario Serouart and Daniel Smith and Simon Madec and Kaaviya Velumani and Shouyang Liu and Xu Wang and Francisco Pinto Espinosa and Shahameh Shafiee and Izzat S. A. Tahir and Hisashi Tsujimoto and Shuhei Nasuda and Bangyou Zheng and Norbert Kichgessner and Helge Aasen and Andreas Hund and Pouria Sadhegi-Tehran and Koichi Nagasawa and Goro Ishikawa and Sébastien Dandrifosse and Alexis Carlier and Benoit Mercatoris and Ken Kuroki and Haozhou Wang and Masanori Ishii and Minhajul A. Badhon and Curtis Pozniak and David Shaner LeBauer and Morten Lilimo and Jesse Poland and Scott Chapman and Benoit de Solan and Frédéric Baret and Ian Stavness and Wei Guo},
year={2021},
eprint={2105.07660},
archivePrefix={arXiv},
primaryClass={cs.CV}
}