Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

About this dataset

Context

The dataset tabulates the Credit River township household income by gender. The dataset can be utilized to understand the gender-based income distribution of Credit River township income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Credit River Township, Minnesota annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Credit River Township, Minnesota annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Credit River township income distribution by gender. You can refer the same here

The G.19 Statistical Release, Consumer Credit, reports outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real estate. Total consumer credit comprises two major types: revolving and nonrevolving. Revolving credit plans may be unsecured or secured by collateral and allow a consumer to borrow up to a prearranged limit and repay the debt in one or more installments. Credit card loans comprise most of revolving consumer credit measured in the G.19, but other types, such as prearranged overdraft plans, are also included. Nonrevolving credit is closed-end credit extended to consumers that is repaid on a prearranged repayment schedule and may be secured or unsecured. To borrow additional funds, the consumer must enter into an additional contract with the lender. Consumer motor vehicle and education loans comprise the majority of nonrevolving credit, but other loan types, such as boat loans, recreational vehicle loans, and personal loans, are also included. This statistical release is designated by OMB as a Principal Federal Economic Indicator (PFEI).

Costa Rica CR: Internet Users: Individuals: % of Population data was reported at 82.598 % in 2022. This records a decrease from the previous number of 82.749 % for 2021. Costa Rica CR: Internet Users: Individuals: % of Population data is updated yearly, averaging 25.100 % from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 82.749 % in 2021 and a record low of 0.000 % in 1991. Costa Rica CR: Internet Users: Individuals: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Telecommunication. Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.;International Telecommunication Union (ITU) World Telecommunication/ICT Indicators Database;Weighted average;Please cite the International Telecommunication Union for third-party use of these data.

This dataset contains 1,000 financial records with five key features and one target variable, Loan Default Risk. It is designed for credit risk analysis, helping to predict whether a customer is likely to default on a loan based on financial attributes.

Income: The individual's annual income. Credit Score: A credit rating score ranging from 300 to 850, where higher values indicate better creditworthiness. Spending Score: A normalized score between 0 and 100, representing the individual's spending habits. Transaction Count: The number of transactions made by the individual in a given period. Savings Ratio: The ratio of savings to income, ranging from 0 to 1. Loan Default Risk (Target): 0: Low risk (likely to repay the loan). 1: High risk (likely to default on the loan).

Feel free to use this dataset for research, projects, or educational purposes. If you use it in a publication, kindly provide attribution.

This dataset was synthetically generated. The features were adjusted to resemble real-world financial data, but they do not represent actual individuals or real financial records.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

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 Credit River township. The dataset can be utilized to gain insights into gender-based income distribution within the Credit River township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Credit River township, among individuals aged 15 years and older with income, there were 2,019 men and 1,741 women in the workforce. Among them, 1,380 men were engaged in full-time, year-round employment, while 861 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 1.30% fell within the income range of under $24,999, while 2.67% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 44.35% of men in full-time roles earned incomes exceeding $100,000, while 33.22% 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)

https://i.neilsberg.com/ch/credit-river-township-mn-income-distribution-by-gender-and-employment-type.jpeg" alt="Credit River Township, Minnesota gender and employment-based income distribution analysis (Ages 15+)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Credit River township median household income by gender. You can refer the same here

The dataset comprises a broad range of variables to understand the full picture of consumers’ financial health: family socio-demographics, total income, total expenses, employment information, as well as all credit details. The features considered for the analyses were: socio-demographic characterization (marital status, level of education completed, number of people in the household), the perceived causes for over-indebtedness (from a predetermined pool of causes), and data concerning their economic situation, including the total income and expenses of the household as well as data concerning their credits and debts (amount of the monthly installments for credit cards, housing credit, car credit, personal credit and other types of credit or debts; total monthly installment concerning all credits). Each household is represented by one record (one observation) of the dataset with many features to describe their characteristics and behavior

Recommended citation

Gütschow, J.; Busch, D.; Pflüger, M. (2024): The PRIMAP-hist national historical emissions time series v2.6.1 (1750-2023). zenodo. doi:10.5281/zenodo.15016289.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

Use of the dataset and full description

Abstract

Support

Sources

Files included in the dataset

Notes

Data format description (columns)

References

Changelog

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2023, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC.

For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "National Inventory Submissions". Until 2023 data was submitted in the "Common Reporting Format" (CRF). Since 2024 the new "Common Reporting Tables" (CRT) are used. For developing countries, non-AnnexI in terms of the UNFCCC, we use the "Biannial Transparency Reports" (BTR) which mostly come with data also using the "Common Reporting Tables". We also use older data available through the UNFCCC DI portal (di.unfccc.int) and additional country submissions from "Biannial Update Reports" (BUR), "National Communications" (NC), and "National Inventory Reports" (NIR) read from pdf and where available xls(x) or csv files. For a list of these submissions please see below. For South Korea the 2023 official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. We have mostly replaced the official data that has not been submitted to the UNFCCC used in v2.6 as countries have now submitted their data in CRT format, but had to make some exceptions as the CRT data was not usable for all countries.

Gaps in the country reported data are filled using third party data such as CDIAC, EI (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR 2024 (all sectors for CO2, CH4, N2O, HFCs, PFCs, SF6, NF3, except energy CO2). Lower priority data are harmonized to higher priority data in the gap-filling process.

For the third party priority time series gaps in the third party data are filled from country reported data sources.

Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions.

The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.6.1 includes data for 2023. For energy CO2 growth rates from the EI Statistical Review of World Energy are used to extend the country reported (CR) or CDIAC (TP) data to 2023. For CO2 from cement production Andrew cement data are used. For other gases and sectors we use EDGAR 2024 data. In a few cases we have to rely on numerical methods to estimate emissions for 2023.

Version 2.6.1 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.

The PRIMAP-hist v2.6.1 dataset is an updated version of

Gütschow, J.; Pflüger, M.; Busch, D. (2024): The PRIMAP-hist national historical emissions time series v2.6 (1750-2023). zenodo. doi:10.5281/zenodo.13752654.

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset. Detailed per country information is available from the detailed changelog which is available on the primap.org website and on zenodo.

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (johannes.guetschow@climate-resource.com) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation.

We have also included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag "v2.6.1" in any issue you create regarding this dataset.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@climate-resource.com.

Climate Resource makes this data available CC BY 4.0 licence. Free support is limited to simple questions and non-commercial users. We also provide additional data, and data support services to clients wanting more frequent updates, additional metadata or to integrate these datasets into their workflows. Get in touch at contact@climate-resource.com if you are interested.

Sources

Global CO2 emissions from cement production v250226 data, paper: Andrew(2025), Andrew (2019)

EI Statistical Review of World Energy website: Energy Institute (2024)

CDIAC data: Hefner and Marland (2023), data: Hefner (2024), paper: Gilfillan and Marland (2021)

CEDS: data: Hoesly et al. (2020), paper: Hoesly et al. (2018)

EDGAR 2024: data/website: European Commission, European Commision, JRC (2024), report: European Commission. Joint Research Centre & IEA. (2024)

EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)

FAOSTAT database data: Food and Agriculture Organization of the United Nations (2024)

RCP historical data data, paper: Meinshausen et al. (2011)

UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal website, data: UNFCCC (2024e), Pflüger and Gütschow (2024), github

UNFCCC Bnnial Update Reports, National Communications, and National Inventory Reports for developing countries website-BURs, website-NCs, data: UNFCCC (2024d), UNFCCC (2024b).

Notes:

Not all BUR and NC submissions are included as reading the data is time consuming and not all submission contain sufficient data to be used in PRIMAP-hist.

Not all submissions included in PRIMAP-hist are available in the github repository as we do not (yet) have code that we can publish for all submissions.

No submissions have been added for PRIMAP-hist v2.6.1

UNFCCC First Biannial Transparency Reports website, [data] UNFCCC (2025)

Notes:

For a list of added submissions see section "Data source updates (v2.6.1)" in the changelog in the pdf data description.

UNFCCC Common Reporting Format (CRF) website, paper, data (24-01-08): UNFCCC (2024c) (processed as described in Jeffery et al. (2018))

Official country repositories (non-UNFCCC)

Belarus: Greenhouse gas statistics (1990-2022) website: National Statistical Committee of theRepublic of Belarus (2024)

EU, Iceland, Norway, Switzerland: National emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanism, April 2024 website: European Environment Agency(2024)

South Korea: 2023 Inventory website, data: Republic of Korea (2023)

Taiwan / Republic of China: 2023 Inventory website, data: Republic of China - EnvironmentalProtection Administration (2023)

For the pre-1990 LULUCF time-series we use the following additional data sources:

Houghton land use CO2 website: Houghton (2008)

HYDE land cover data website: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)

SAGE Global Potential Vegetation Dataset website: Ramankutty and Foley (1999)

FAO Country Boundaries website: Food and Agriculture Organization of the United Nations(2015)

Files included in the dataset

For each dataset we have three files:

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NLUCat

Dataset Description

Dataset Summary

NLUCat is a dataset of NLU in Catalan. It consists of nearly 12,000 instructions annotated with the most relevant intents and spans. Each instruction is accompanied, in addition, by the instructions received by the annotator who wrote it.

The intents taken into account are the habitual ones of a virtual home assistant (activity calendar, IOT, list management, leisure, etc.), but specific ones have also been added to take into account social and healthcare needs for vulnerable people (information on administrative procedures, menu and medication reminders, etc.).

The spans have been annotated with a tag describing the type of information they contain. They are fine-grained, but can be easily grouped to use them in robust systems.

The examples are not only written in Catalan, but they also take into account the geographical and cultural reality of the speakers of this language (geographic points, cultural references, etc.)

This dataset can be used to train models for intent classification, spans identification and examples generation.

This is the complete version of the dataset. A version prepared to train and evaluate intent classifiers has been published in HuggingFace.

In this repository you'll find the following items:

• NLUCat_annotation_guidelines.docx: the guidelines provided to the annotation team
• NLUCat_dataset.json: the completed NLUCat dataset
• NLUCat_stats.tsv: statistics about de NLUCat dataset
• dataset: folder with the dataset as published in HuggingFace, splited and prepared for training and evaluating intent classifiers
• reports: folder with the reports done as feedback to the annotators during the annotation process

This dataset can be used for any purpose, whether academic or commercial, under the terms of the CC BY 4.0. Give appropriate credit , provide a link to the license, and indicate if changes were made.

Supported Tasks and Leaderboards

Intent classification, spans identification and examples generation.

Languages

The dataset is in Catalan (ca-ES).

Dataset Structure

Data Instances

Three JSON files, one for each split.

Data Fields

• example: `str`. Example
• annotation: `dict`. Annotation of the example
• intent: `str`. Intent tag
• slots: `list`. List of slots
• Tag:`str`. tag to the slot
• Text:`str`. Text of the slot
• Start_char: `int`. First character of the span
• End_char: `int`. Last character of the span

Example

An example looks as follows:

{
"example": "Demana una ambulància; la meva dona està de part.",
"annotation": {
"intent": "call_emergency",
"slots": [
{
"Tag": "service",
"Text": "ambulància",
"Start_char": 11,
"End_char": 21
},
{
"Tag": "situation",
"Text": "la meva dona està de part",
"Start_char": 23,
"End_char": 48
}
]
}
},

Data Splits

• NLUCat.train: 9128 examples
• NLUCat.dev: 1441 examples
• NLUCat.test: 1441 examples

Dataset Creation

Curation Rationale

We created this dataset to contribute to the development of language models in Catalan, a low-resource language.

When creating this dataset, we took into account not only the language but the entire socio-cultural reality of the Catalan-speaking population. Special consideration was also given to the needs of the vulnerable population.

Source Data

Initial Data Collection and Normalization

We commissioned a company to create fictitious examples for the creation of this dataset.

Who are the source language producers?

We commissioned the writing of the examples to the company m47 labs.

Annotations

Annotation process

The elaboration of this dataset has been done in three steps, taking as a model the process followed by the NLU-Evaluation-Data dataset, as explained in the paper.
* First step: translation or elaboration of the instructions given to the annotators to write the examples.
* Second step: writing the examples. This step also includes the grammatical correction and normalization of the texts.
* Third step: recording the attempts and the slots of each example. In this step, some modifications were made to the annotation guides to adjust them to the real situations.

Who are the annotators?

The drafting of the examples and their annotation was entrusted to the company m47 labs through a public tender process.

Personal and Sensitive Information

No personal or sensitive information included.

The examples used for the preparation of this dataset are fictitious and, therefore, the information shown is not real.

Considerations for Using the Data

Social Impact of Dataset

We hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account, and that it will especially help to improve the quality of life of people with special needs.

Discussion of Biases

When writing the examples, the annotators were asked to take into account the socio-cultural reality (geographic points, artists and cultural references, etc.) of the Catalan-speaking population.
Likewise, they were asked to be careful to avoid examples that reinforce the stereotypes that exist in this society. For example: be careful with the gender or origin of personal names that are associated with certain activities.

Other Known Limitations

[N/A]

Additional Information

Dataset Curators

Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es)

This work has been promoted and financed by the Generalitat de Catalunya through the Aina project.

Licensing Information

This dataset can be used for any purpose, whether academic or commercial, under the terms of the CC BY 4.0.
Give appropriate credit, provide a link to the license, and indicate if changes were made.

Citation Information

DOI

Contributions

The drafting of the examples and their annotation was entrusted to the company m47 labs through a public tender process.

Data for a Kaggle competition

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit.

Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years.

The goal of this competition is to build a model that borrowers can use to help make the best financial decisions.

Historical data are provided on 250,000 borrowers and the prize pool is $5,000 ($3,000 for first, $1,500 for second and $500 for third).

Costa Rica CR: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 18.700 % in 2022. This records a decrease from the previous number of 19.600 % for 2021. Costa Rica CR: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 20.000 % from Dec 1981 (Median) to 2022, with 36 observations. The data reached an all-time high of 25.000 % in 1981 and a record low of 18.300 % in 2010. Costa Rica CR: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

• Analyze 11/1/2016 in relation to 2/1/2017
• Study the influence of 4/1/2017 on 1/1/2017
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Some studies have found that constituents do not evaluate legislators more favorably for claiming credit for delivering large grants than for claiming credit for delivering tiny ones. It remains unclear, however, whether the lack of sensitivity to the amount of money claimed reflects innumeracy or the difficulty that many people have understanding the size of a government expenditure in the abstract. We perform a survey experiment in which we give respondents information about both the absolute and relative size of projects. We find that subjects evaluate legislators significantly more favorably for claiming credit for relatively large projects. Our results suggest that subjects are responsive to the magnitudes in claims of accomplishment, but only when provided a benchmark. We also find evidence of an asymmetric effect; subjects are more inclined to punish legislators for delivering grants of below average size than to reward them for delivering grants of above average size

We propose Safe Human dataset consisting of 17 different objects referred to as SH17 dataset. We scrapped images from the Pexels website, which offers clear usage rights for all its images, showcasing a range of human activities across diverse industrial operations.

To extract relevant images, we used multiple queries such as manufacturing worker, industrial worker, human worker, labor, etc. The tags associated with Pexels images proved reasonably accurate. After removing duplicate samples, we obtained a dataset of 8,099 images. The dataset exhibits significant diversity, representing manufacturing environments globally, thus minimizing potential regional or racial biases. Samples of the dataset are shown below.

Key features

Collected from diverse industrial environments globally

High quality images (max resolution 8192x5462, min 1920x1002)

Average of 9.38 instances per image

Includes small objects like ears and earmuffs (39,764 annotations < 1% image area, 59,025 annotations < 5% area)

Classes

Person

Head

Face

Glasses

Face-mask-medical

Face-guard

Ear

Earmuffs

Hands

Gloves

Foot

Shoes

Safety-vest

Tools

Helmet

Medical-suit

Safety-suit

The data consists of three folders,

images contains all images

labels contains labels in YOLO format for all images

voc_labels contains labels in VOC format for all images

train_files.txt contains list of all images we used for training

val_files.txt contains list of all images we used for validation

Disclaimer and Responsible Use:

This dataset, scrapped through the Pexels website, is intended for educational, research, and analysis purposes only. You may be able to use the data for training of the Machine learning models only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations.

Users should adhere to Copyright Notice of Pexels when utilizing this dataset.

Legal Simplicity: All photos and videos on Pexels can be downloaded and used for free.

Allowed 👌

All photos and videos on Pexels are free to use.

Attribution is not required. Giving credit to the photographer or Pexels is not necessary but always appreciated.

You can modify the photos and videos from Pexels. Be creative and edit them as you like.

Not allowed 👎

Identifiable people may not appear in a bad light or in a way that is offensive.

Don't sell unaltered copies of a photo or video, e.g. as a poster, print or on a physical product without modifying it first.

Don't imply endorsement of your product by people or brands on the imagery.

Don't redistribute or sell the photos and videos on other stock photo or wallpaper platforms.

Don't use the photos or videos as part of your trade-mark, design-mark, trade-name, business name or service mark.

No Warranty Disclaimer:

The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others.

Ethical Use:

Users are encouraged to consider the ethical implications of their analyses and the potential impact on broader community.

GitHub Page:

https://github.com/ahmadmughees/SH17dataset

The Fish predator/prey database (stomach database) contains data covering 11 countries. A total of 217 399 stomachs are reported in the North Sea data. Eight predator species were analysed and 854 NODC prey codes have been reported AccConstrDescription=This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials. AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution 4.0 International License. AccConstrEN=Attribution (CC BY) AccessConstraint=Attribution (CC BY) AccessConstraints=ICES Data Policy: https://www.ices.dk/data/guidelines-and-policy/Pages/ICES-data-policy.aspx Acronym=STOMACH BrackishFlag=0 CDate=2009-10-19 cdm_data_type=Other CheckedFlag=0 Citation=ICES Fish stomach contents dataset (STOMACH). The International Council for the Exploration of the Sea, Copenhagen. 2010. Online source: http://ecosystemdata.ices.dk Comments=None ContactEmail=None Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=2144 DasOrigin=Data collection DasType=Data DasTypeID=1 DescrCompFlag=1 DescrTransFlag=0 Easternmost_Easting=12.5 EmbargoDate=None EngAbstract=The Fish predator/prey database (stomach database) contains data covering 11 countries. A total of 217 399 stomachs are reported in the North Sea data. Eight predator species were analysed and 854 NODC prey codes have been reported EngDescr=Year of the Stomach content data The first year of the stomach ran in 1981 and covered a handful of species in the North Sea. Follow up data collection was performed in 1985-1986, and a decade later in 1991 the 2nd year of the stomach ran. The data were collected as part of an ICES initiative and the results and analysis were presented in two cooperative reports (CRR 164 and CRR 219).

Geographical Coverage There are 2 datasets related to the year of the stomach, North Sea and Baltic respectively. The map to the right shows the distribution of the North Sea records. Currently, only the North Sea dataset is available online.

About this dataset While ICES has made every effort to ensure that the dataset presented here is the most complete set of information, it should be noted that there are a number of versions of this dataset held by institutions and individuals which may differ from this set. FreshFlag=0 geospatial_lat_max=63.25 geospatial_lat_min=51.25 geospatial_lat_units=degrees_north geospatial_lon_max=12.5 geospatial_lon_min=-5.5 geospatial_lon_units=degrees_east infoUrl=None InputNotes=None institution=ICES License=https://creativecommons.org/licenses/by/4.0/ Lineage=None MarineFlag=1 Northernmost_Northing=63.25 OrigAbstract=None OrigDescr=None OrigDescrLang=English OrigDescrLangNL=Engels OrigLangCode=en OrigTitle=None OrigTitleLang=None OrigTitleLangCode=None OrigTitleLangNL=None Progress=Completed PublicFlag=1 ReleaseDate=None ReleaseDate0=None RevisionDate=None SizeReference=1,149,608 Records sourceUrl=(local files) Southernmost_Northing=51.25 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=ICES Fish stomach contents dataset subsetVariables=ScientificName,BasisOfRecord,YearCollected,MonthCollected,DayCollected,aphia_id TerrestrialFlag=0 time_coverage_start=1980-01-01T01:00:00Z UDate=2022-06-16 VersionDate=None VersionDay=None VersionMonth=None VersionName=None VersionYear=None VlizCoreFlag=1 Westernmost_Easting=-5.5