Context

This dataset is based on the original SpaceNet 7 dataset, with a few modifications.

Content

The original dataset consisted of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. The original dataset will comprised over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations.

This dataset builds upon the original dataset, such that each image is segmented into 64 x 64 chips, in order to make it easier to build a model for.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4101651%2F66851650dbfb7017f1c5717af16cea3c%2Fchips.png?generation=1607947381793575&alt=media" alt="">

The images also compare the changes that between each image of each month, such that an image taken in month 1 is compared with the image take in month 2, 3, ... 24. This is done by taking the cartesian product of the differences between each image. For more information on how this is done check out the following notebook.

The differences between the images are captured in the output mask, and the 2 images being compared are stacked. Which means that our input images have dimensions of 64 x 64 x 6, and our output mask has dimensions 64 x 64 x 1. The reason our input images have 6 dimensions is because as mentioned earlier, they are 2 images stacked together. See image below for more details:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4101651%2F9cdcf8481d8d81b6d3fed072cea89586%2Fdifference.png?generation=1607947852597860&alt=media" alt="">

The image above shows the masks for each of the original satellite images and what the difference between the 2 looks like. For more information on how the original data was explored check out this notebook.

Data Structure

The data is structured as follows:
chip_dataset
└── change_detection
└── fname
├── chips
│ └── year1_month1_year2_month2
│ └── global_monthly_year1_month1_year2_month2_chip_x###_y###_fname.tif
└── masks
└── year1_month1_year2_month2
└── global_monthly_year1_month1_year2_month2_chip_x###_y###_fname_blank.tif

The _blank in the mask chips, indicates whether the mask is a blank mask or not.

For more information on how the data was structured and augmented check out the following notebook.

Acknowledgements

All credit goes to the team at SpaceNet for collecting and annotating and formatting the original dataset.

About this dataset

Context

The dataset tabulates the Earth Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Earth, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Earth.

Key observations

Among the Hispanic population in Earth, regardless of the race, the largest group is of Mexican origin, with a population of 588 (94.84% of the total Hispanic population).

Content

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

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Earth
• Population: The population of the specific origin for Hispanic or Latino population in the Earth is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Earth total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Earth Population by Race & Ethnicity. You can refer the same here

This introduction provides an overview and commentary on the papers in a special issue of PARKS, which is devoted to the impact and implications of COVID-19 on the world’s protected and conserved areas. It describes how 11 peerreviewed papers and 14 essays have brought together the knowledge and findings of numerous experts from all parts of the world, supported by several wide-ranging surveys. The resulting global synthesis of experience answers some key questions: why did the pandemic occur? what has it meant for protected and conserved areas, and the people that depend on them? what were the underlying reasons for the disaster we now face? and how can we avoid this happening again? We applaud the international effort to combat the disease but suggest that humanity urgently needs to devote as much effort to addressing the root causes of the pandemic – our fractured relationship to nature. Unless we repair it, humanity will face consequences even worse than this pandemic. Call Number: [EL] Physical Description: 200 p.

The How’s Life? database is the one-stop shop for the 80+ indicators of the OECD Well-being Dashboard, covering social, economic and environmental outcomes that matter most for people, the planet and future generations. It consists of six datasets: current well-being, current well-being vertical inequalities, current well-being by age, educational attainment, sex, and resources for future well-being. To learn more about the database, visit the database's definitions and metadata.

This dataflow covers: Current well-being.


There are 11 dimensions of current well-being in this dataset:

Material conditions
• Income and Wealth
• Housing
• Work and Job Quality

Quality of Life
• Health
• Knowledge and Skills
• Environmental Quality
• Subjective Well-being
• Safety

Community relationships
• Work-Life Balance
• Social Connections
• Civic Engagement

Check out the latest publication using this database How's Life? 2024 - Well-being and resilience in times of crisis and the How's Life? country notes.

Have questions? Reach out to us at wellbeing@oecd.org.

About this dataset

Summary

Medicines play a key component of every healthcare system on the planet. As such, this dataset brings to light information of more than 50k medicines and their corresponding prices. The data was released by the goverment of Brazil on the price of 53437 different medicines, by type and many other variables.

How to use this dataset

• Your kernel can be featured here!
• More datasets

Acknowledgements

If you use this dataset on your research, please credit the authors.

BibTeX

@misc{Agência Nacional de Vigilância Sanitária - ANVISA, title={Preços de Medicamentos - Consumidor}, url={http://www.dados.gov.br/dataset/anvisa-precos-de-medicamentos-consumidor}, journal={PORTAL BRASILEIRO DE DADOS ABERTOS}}

License

Public domain, Open Data

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Photo by Michał Parzuchowski on Unsplash.

Analysis of ‘COVID vaccination vs. mortality ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sinakaraji/covid-vaccination-vs-death on 12 November 2021.

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

Context

The COVID-19 outbreak has brought the whole planet to its knees.More over 4.5 million people have died since the writing of this notebook, and the only acceptable way out of the disaster is to vaccinate all parts of society. Despite the fact that the benefits of vaccination have been proved to the world many times, anti-vaccine groups are springing up all over the world. This data set was generated to investigate the impact of coronavirus vaccinations on coronavirus mortality.

Content

country	iso_code	date	total_vaccinations	people_vaccinated	people_fully_vaccinated	New_deaths	population	ratio
country name	iso code for each country	date that this data belong	number of all doses of COVID vaccine usage in that country	number of people who got at least one shot of COVID vaccine	number of people who got full vaccine shots	number of daily new deaths	2021 country population	% of vaccinations in that country at that date = people_vaccinated/population * 100

Data Collection

This dataset is a combination of the following three datasets:

1.https://www.kaggle.com/gpreda/covid-world-vaccination-progress

2.https://covid19.who.int/WHO-COVID-19-global-data.csv

3.https://www.kaggle.com/rsrishav/world-population

you can find more detail about this dataset by reading this notebook:

https://www.kaggle.com/sinakaraji/simple-linear-regression-covid-vaccination

Countries in this dataset:

Afghanistan	Albania	Algeria	Andorra	Angola
Anguilla	Antigua and Barbuda	Argentina	Armenia	Aruba
Australia	Austria	Azerbaijan	Bahamas	Bahrain
Bangladesh	Barbados	Belarus	Belgium	Belize
Benin	Bermuda	Bhutan	Bolivia (Plurinational State of)	Brazil
Bosnia and Herzegovina	Botswana	Brunei Darussalam	Bulgaria	Burkina Faso
Cambodia	Cameroon	Canada	Cabo Verde	Cayman Islands
Central African Republic	Chad	Chile	China	Colombia
Comoros	Cook Islands	Costa Rica	Croatia	Cuba
Curaçao	Cyprus	Denmark	Djibouti	Dominica
Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea
Estonia	Ethiopia	Falkland Islands (Malvinas)	Fiji	Finland
France	French Polynesia	Gabon	Gambia	Georgia
Germany	Ghana	Gibraltar	Greece	Greenland
Grenada	Guatemala	Guinea	Guinea-Bissau	Guyana
Haiti	Honduras	Hungary	Iceland	India
Indonesia	Iran (Islamic Republic of)	Iraq	Ireland	Isle of Man
Israel	Italy	Jamaica	Japan	Jordan
Kazakhstan	Kenya	Kiribati	Kuwait	Kyrgyzstan
Lao People's Democratic Republic	Latvia	Lebanon	Lesotho	Liberia
Libya	Liechtenstein	Lithuania	Luxembourg	Madagascar
Malawi	Malaysia	Maldives	Mali	Malta
Mauritania	Mauritius	Mexico	Republic of Moldova	Monaco
Mongolia	Montenegro	Montserrat	Morocco	Mozambique
Myanmar	Namibia	Nauru	Nepal	Netherlands
New Caledonia	New Zealand	Nicaragua	Niger	Nigeria
Niue	North Macedonia	Norway	Oman	Pakistan
occupied Palestinian territory, including east Jerusalem
Panama	Papua New Guinea	Paraguay	Peru	Philippines
Poland	Portugal	Qatar	Romania	Russian Federation
Rwanda	Saint Kitts and Nevis	Saint Lucia
Saint Vincent and the Grenadines	Samoa	San Marino	Sao Tome and Principe	Saudi Arabia
Senegal	Serbia	Seychelles	Sierra Leone	Singapore
Slovakia	Slovenia	Solomon Islands	Somalia	South Africa
Republic of Korea	South Sudan	Spain	Sri Lanka	Sudan
Suriname	Sweden	Switzerland	Syrian Arab Republic	Tajikistan
United Republic of Tanzania	Thailand	Togo	Tonga	Trinidad and Tobago
Tunisia	Turkey	Turkmenistan	Turks and Caicos Islands	Tuvalu
Uganda	Ukraine	United Arab Emirates	The United Kingdom	United States of America
Uruguay	Uzbekistan	Vanuatu	Venezuela (Bolivarian Republic of)	Viet Nam
Wallis and Futuna	Yemen	Zambia	Zimbabwe

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

Context

The COVID-19 outbreak has brought the whole planet to its knees.More over 4.5 million people have died since the writing of this notebook, and the only acceptable way out of the disaster is to vaccinate all parts of society. Despite the fact that the benefits of vaccination have been proved to the world many times, anti-vaccine groups are springing up all over the world. This data set was generated to investigate the impact of coronavirus vaccinations on coronavirus mortality.

Content

country	iso_code	date	total_vaccinations	people_vaccinated	people_fully_vaccinated	New_deaths	population	ratio
country name	iso code for each country	date that this data belong	number of all doses of COVID vaccine usage in that country	number of people who got at least one shot of COVID vaccine	number of people who got full vaccine shots	number of daily new deaths	2021 country population	% of vaccinations in that country at that date = people_vaccinated/population * 100

Data Collection

This dataset is a combination of the following three datasets:

1.https://www.kaggle.com/gpreda/covid-world-vaccination-progress

2.https://covid19.who.int/WHO-COVID-19-global-data.csv

3.https://www.kaggle.com/rsrishav/world-population

you can find more detail about this dataset by reading this notebook:

https://www.kaggle.com/sinakaraji/simple-linear-regression-covid-vaccination

Countries in this dataset:

Afghanistan	Albania	Algeria	Andorra	Angola
Anguilla	Antigua and Barbuda	Argentina	Armenia	Aruba
Australia	Austria	Azerbaijan	Bahamas	Bahrain
Bangladesh	Barbados	Belarus	Belgium	Belize
Benin	Bermuda	Bhutan	Bolivia (Plurinational State of)	Brazil
Bosnia and Herzegovina	Botswana	Brunei Darussalam	Bulgaria	Burkina Faso
Cambodia	Cameroon	Canada	Cabo Verde	Cayman Islands
Central African Republic	Chad	Chile	China	Colombia
Comoros	Cook Islands	Costa Rica	Croatia	Cuba
Curaçao	Cyprus	Denmark	Djibouti	Dominica
Dominican Republic	Ecuador	Egypt	El Salvador	Equatorial Guinea
Estonia	Ethiopia	Falkland Islands (Malvinas)	Fiji	Finland
France	French Polynesia	Gabon	Gambia	Georgia
Germany	Ghana	Gibraltar	Greece	Greenland
Grenada	Guatemala	Guinea	Guinea-Bissau	Guyana
Haiti	Honduras	Hungary	Iceland	India
Indonesia	Iran (Islamic Republic of)	Iraq	Ireland	Isle of Man
Israel	Italy	Jamaica	Japan	Jordan
Kazakhstan	Kenya	Kiribati	Kuwait	Kyrgyzstan
Lao People's Democratic Republic	Latvia	Lebanon	Lesotho	Liberia
Libya	Liechtenstein	Lithuania	Luxembourg	Madagascar
Malawi	Malaysia	Maldives	Mali	Malta
Mauritania	Mauritius	Mexico	Republic of Moldova	Monaco
Mongolia	Montenegro	Montserrat	Morocco	Mozambique
Myanmar	Namibia	Nauru	Nepal	Netherlands
New Caledonia	New Zealand	Nicaragua	Niger	Nigeria
Niue	North Macedonia	Norway	Oman	Pakistan
occupied Palestinian territory, including east Jerusalem
Panama	Papua New Guinea	Paraguay	Peru	Philippines
Poland	Portugal	Qatar	Romania	Russian Federation
Rwanda	Saint Kitts and Nevis	Saint Lucia
Saint Vincent and the Grenadines	Samoa	San Marino	Sao Tome and Principe	Saudi Arabia
Senegal	Serbia	Seychelles	Sierra Leone	Singapore
Slovakia	Slovenia	Solomon Islands	Somalia	South Africa
Republic of Korea	South Sudan	Spain	Sri Lanka	Sudan
Suriname	Sweden	Switzerland	Syrian Arab Republic	Tajikistan
United Republic of Tanzania	Thailand	Togo	Tonga	Trinidad and Tobago
Tunisia	Turkey	Turkmenistan	Turks and Caicos Islands	Tuvalu
Uganda	Ukraine	United Arab Emirates	The United Kingdom	United States of America
Uruguay	Uzbekistan	Vanuatu	Venezuela (Bolivarian Republic of)	Viet Nam
Wallis and Futuna	Yemen	Zambia	Zimbabwe

Welcome to Stream's Use Case Backlog, an open dataset designed to provide transparency and foster collaboration within the water sector and beyond. This comprehensive resource serves as a central repository for all the proposed, ongoing, and completed use cases that aim to leverage open data to benefit utilities, people, and the planet.Key Features:Comprehensive Use Case Tracking:The backlog includes a detailed record of all use cases, each with a unique identifier, description, goals, primary and secondary actors, stakeholders, required datasets, and current status.Categorised by Stages:Use cases are categorised into stages, allowing users to easily see which use cases are currently in progress, which are queued for future work, and which are in the conceptual stage.Stakeholder Engagement:Each use case details the primary actors involved, including water companies, environmental groups, government bodies, and other relevant stakeholders, fostering an environment of open collaboration and engagement.Data Requirements and Sources:Clearly outlines the datasets required from water companies and other sources, ensuring transparency and facilitating data sharing and integration.Status Updates and Progress Tracking:Regular updates on the status of each use case, including milestones achieved, challenges encountered, and next steps, keeping all stakeholders informed and engaged.Open Access and Contribution:As an open dataset, the backlog is accessible to all interested parties. Stakeholders are encouraged to contribute by proposing new use cases, providing feedback, or supplying relevant data.Benefits:For the Industry:Promotes innovation and efficiency by sharing knowledge and best practices.Helps water companies and other stakeholders identify potential collaboration opportunities and leverage shared data for mutual benefit.For the Environment:Facilitates projects aimed at improving water quality, managing resources sustainably, and protecting ecosystems.Encourages the use of data-driven approaches to tackle environmental challenges.For Society:Enhances transparency and accountability in the water sector.Empowers communities and individuals to understand and engage with water management and conservation efforts.How to Access:The Use Case Backlog is available on Stream's open data portal. Users can browse the dataset, download detailed reports, and contribute by submitting new use cases or providing additional data and insights.Conclusion:Stream's Use Case Backlog is a dynamic and evolving resource designed to drive collaboration, innovation, and transparency in the water sector. By leveraging open data, we aim to create a positive impact on utilities, people, and the planet, ensuring sustainable and efficient water management for future generations.

Thorsten-Voice (Thorsten-21.02-neutral) is a neutrally spoken voice dataset recorded by Thorsten Müller, audio optimized by Dominik Kreutz and licenced under CC0 to provide it for anybody without any financial or licence struggle.

"I contribute my personal voice as a person believing in a world where all people are equal. No matter of gender, sexual orientation, religion, skin color and geocoordinates of birth location. A global world where everybody is warmly welcome on any place on this planet and open and free knowledge and education is available to everyone." (Thorsten Müller)

Dataset details:

• ljspeech file and directory structure
• 22.668 recorded phrases (wav files)
• more than 23 hours of pure audio
• samplerate 22.050Hz
• mono
• normalized to -24dB
• phrase length (min/avg/max): 2 / 52 / 180 chars
• no silence at beginning/ending
• avg spoken chars per second: 14
• sentences with question mark: 2.780
• sentences with exclamation mark: 1.840

See more details on my Github page or Thorsten-Voice project website.

This data was collected as part of the PhD project “Tending Seeds of Civic Activity” in the winter, spring and summer of 2021. Farmers in the Netherlands who were identified as “proto-regenerative” were selected, and new contacts were gathered through the snowball method. Proto-regenerative means that the selected farmers are, to varying degrees, driven by a mission of building regenerative food systems, which is defined as healthy people, healthy communities, and a healthy planet. Land and food cooperatives in Germany and the Netherlands were also identified and contacted for interview. Cooperative initiatives, to varying degrees, share principles of democratic control by members, solidarity, collective ownership, and value-based organizational decision making. Interviews generally followed the interview guide included in the data set. Questions were geared towards understanding the ways in which citizens take matters into their own hands in attempts to address pressing social and ecological crises, (2) regenerative economic relationships that are built by citizens and communities, and (3) ways that responsive governance could better recognize, facilitate and enable these generative alternatives.

Meeting at a special summit at the United Nations in September 2015, world leaders committed themselves to an ambitious global agenda, “Transforming our world: the 2030 Agenda for Sustainable Development”, with the overarching Goal of eradicating poverty and achieving sustainable development. The Agenda is a plan of action for people, planet, prosperity, peace and partnership. All States and all stakeholders recognized their respective responsibilities for the implementation of the Agenda. In paragraph 72 of the Agenda, Governments also emphasized that a robust, voluntary, effective, participatory, transparent and integrated follow-up and review framework would make a vital contribution to implementation, and in paragraph 73, that it would promote accountability to citizens, support active international cooperation in achieving the Agenda and foster exchange of best practices and mutual learning. The present report explores how to put in place a coherent, efficient and inclusive follow-up and review system at the global level, within the mandates outlined in the Agenda. It does not attempt to describe or prescribe how to implement the 2030 Agenda, the primary responsibility for which lies at the national level; nor does it attempt to describe the wide array of possible multilateral support mechanisms to such implementation efforts.

The GEF and UNCCD Secretariats collaborated on this new book to convey how sustainable land management (SLM) practices are helping shape a sustainable future for people and the planet. The book is illustrated with high quality photos donated by the GoodPlanet Foundation and from other sources, to demonstrate how human ingenuity is largely driving innovations in soil, land, water, and vegetation management. It describes how harnessing natural, social, and cultural capital is addressing fundamental needs for livelihood and well-being—food, water, energy, and wealth—while delivering global environmental benefits.1 copy and also available onlineCall Number: 333.72 GLOPhysical Description: 194 p. : col. ; illus. ; 25 cm