The Qinghai-Tibet plateau (QTP), called "the Third Pole" of the earth, is the water tower of Asia that not only feeds tens of millions of people, but also maintains fragile ecosystems in arid region of northwestern China. Temporal-spatially complete representations of land surface temperature are required for many purposes in environmental science, especially in the Third pole where the traditional ground measurement is difficult and therefore the data is sparse. The thirteen years cloud-free datasets of daily mean land surface temperature (LST) and mean annual land surface temperature (MAST) during 2004 to 2016 are derived from the quartic daily MODIS (the Moderate Resolution Imaging Spectroradiometer) Terra/Aqua LST products with a resolution of 1 km using a pragmatic data processing algorithm. The comparison between radiance-based LST measurement and the estimated LST shows good agreement in the daily and inter-annual variability, with a correlation of 0.95 and 0.99 and bias of -1.73°C (±3.38°C) and -2.07°C (±1.05°C) for daily-mean-LST and MAST, respectively. The systematic error is mainly source from the defined of daily mean LST, which is represented by the arithmetic average of the daytime and nighttime LSTs. The random error is mainly source from the uncertainty of the original MODIS LST values, especially for the daytime LST products. Trend validation using air temperatures from 94 weather stations indicate that the warming trends derived from time series MAST data is comparable with that derived from CMA data. The dataset is potential useful for various studies, including climatology, hydrology, meteorology, ecology, agriculture, public health, and environmental monitoring in the Third pole and around regions.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W).

The global river discharge data set (Coe and Olejniczak 1999), formerly known as the "Climate, People, and Environment Program (CPEP) Global River Discharge Database," is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.

The purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.

More information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.

LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.

The map shows the latest available data for mortality up to the age of 15. In several countries, the rate has declined to about 0.3%, a mortality rate that is more than 100 times lower than in the past. This was achieved in just a few generations. Progress can be fast.

In the richest parts of the world, child deaths have become very rare, but differences across countries are high. Niger is the country with the highest rate, 15% of newborns die as children.

The fact that several countries show that it is possible for 99.7% of children to survive shows us what the world can aspire to. Global health has improved, and it is on us to make sure that this progress continues to bring the daily tragedy of child deaths to an end.

Our ancestors could have surely not imagined what is reality today. Let’s make it our goal to give children everywhere the chance to live a long and healthy life. The chart above also shows the dramatic progress that was recently achieved. Most children in the world still died at extremely high rates well into the 20th century. Even as recently as 1950 – a time that some readers might well remember – one in four children died globally.

More recently, during our lifetimes, the world has achieved an entirely unprecedented improvement. In a brief episode of human history, the global death rate of children declined from around 50% to 4%.

After millennia of suffering and failure, the progress against child mortality is, for me, one of the greatest achievements of humanity.

This is not an improvement that is only achieved by a few countries. The rate has declined in every single country in the world.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

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Temperature Over Time by State (Starts: 1895)

State and County Temperature Changes

By Environmental Data [source]

About this dataset

Do you want to know how rising temperatures are changing the contiguous United States? The Washington Post has used National Oceanic and Atmospheric Administration's Climate Divisional Database (nClimDiv) and Gridded 5km GHCN-Daily Temperature and Precipitation Dataset (nClimGrid) data sets to help analyze warming temperatures in all of the Lower 48 states from 1895-2019. To provide this analysis, we calculated annual mean temperature trends in each state and county in the Lower 48 states. Our results can be found within several datasets now available on this repository.

We are offering: Annual average temperatures for counties and states, temperature change estimates for each of the Lower 48-states, temperature change estimates for counties in the contiguous U.S., county temperature change data joined to a shapefile in GeoJSON format, gridded temperature change data for the contiguous U.S. in GeoTiff format - all contained with our dataset! We invite those curious about climate change to explore these data sets based on our analysis over multiple stories published by The Washington Post such as Extreme climate change has arrived in America, Fires, floods and free parking: California’s unending fight against climate change, In fast-warming Minnesota, scientists are trying to plant the forests of the future, This giant climate hot spot is robbing West of its water ,and more!

By accessing our dataset containing columns such as fips code, year range from 1895-2019, three season temperatures (Fall/Spring/Summer/Winter), max warming season temps plus temp recorded total yearly - you can become an active citizen scientist! If publishing a story or graphic work based off this data set please credit The Washington Post with a link back to this repository while sending us an email so that we can track its usage as well - 2cdatawashpost.com.

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How to use the dataset

The main files provided by this dataset are climdiv_state_year, climdiv_county_year, model_state, model_county , climdiv_national_year ,and model county .geojson . Each file contains different information capturing climate change across different geographies of the United States over time spans from 1895.

Research Ideas

• Investigating and mapping the temperatures for all US states over the past 120 years, to observe long-term changes in temperature patterns.
• Examining regional biases in warming trends across different US counties and states to help inform resource allocation decisions for climate change mitigation and adaption initiatives.
• Utilizing the ClimDiv National Dataset to understand continental-level average annual temperature changes, allowing comparison of global average temperatures with US averages over a long period of time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: climdiv_state_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: climdiv_county_year.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------| | fips | Federal Information Processing Standard code for each county. (Integer) | | year | Year of the temperature data. (Integer) | | tempc | Temperature change from the previous year. (Float) |

File: model_state.csv | Column name | Description | |:------------------...

Fatal dog attacks in the United States cause the deaths of about 30 to 50 people in the US each year, and the number of deaths from dog attacks appears to be increasing. Around 4.5 million Americans are bitten by dogs every year, resulting in the hospitalization of 6,000 to 13,000 people each year in the United States. Below are the lists of fatal dog attacks in the United States reported by the news media, published in scholarly papers, or mentioned through other sources. In the lists below, the breed is assigned by the sources.

COLUMN DESCRIPTION

• 'date': date of the incident
• 'year': year of the incident
• 'city': name of the city
• 'state': name of the state
• 'vic_name': name of the victim
• 'vic_age': age of the victim
• 'dog_type': type of the dog
• 'desc': description of the circumstance

FILE DESCRIPTION

Name of the file: dog_attacks.csv

The file contains the following columns: - 'date': date of the incident - 'year': year of the incident - 'city': name of the city - 'state': name of the state - 'vic_name': name of the victim - 'vic_age': age of the victim - 'dog_type': type of the dog - 'desc': description of the circumstance

Context

ECMWF ERA-5 NetCDF files for 10S-40N and 50E-120E for JJAS months of the year 1956.

Content

4D data for time, level, lat, lon

Acknowledgements

Thanks to ERA team for providing high resolution gridded dataset.

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Context

This dataset contains several files. Each of those contains meteo informations for one region in one month (one line per day).

Content

This dataset contains the meteo data for these regions : Region list: 'Île-de-France', 'Nouvelle-Aquitaine', 'Auvergne-Rhône-Alpes', 'Bourgogne-Franche-Comté', 'Hauts-de-France', 'Grand Est', 'Guadeloupe', 'Martinique', 'Guyane', 'La Réunion', 'Mayotte', 'Centre-Val de Loire', 'Normandie', 'Pays de la Loire', 'Bretagne', 'Occitanie', "Provence-Alpes-Côte d'Azur", 'Corse'

For the Year 2018 to 2020 The result is stored in a csv file (in the input folder) with that format:

• Index: Row index (concat of Region and day)
• TempMax_Deg: Maximum Temperature of the day in Celcius degree
• TempMin_Deg: Minimum Temperature of the day in Celcius degree
• Wind_kmh: Wind speed (km/h)
• Wet_percent: Wet in (%)
• Visibility_km: Visibility (km)
• CloudCoverage_percent: Cloud coverage (%)
• Dayduration_hour: Day/sun duration (min)
• region: Region name
• day: Day in format YYYY/MM/DD

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

National Science Foundation (NSF)

The National Science Foundation (NSF) is an independent federal agency created by Congress in 1950 "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense..." NSF is vital because we support basic research and people to create knowledge that transforms the future.

With an annual budget of $8.1 billion (FY 2019), we are the funding source for approximately 24 percent of all federally supported basic research conducted by America's colleges and universities. In many fields such as mathematics, computer science and the social sciences, NSF is the major source of federal backing.

Every year they receive about 50,000 research proposal. which is evaluated by diverse mix of faculty and university faculty members, who help NSF to assess the significance and the quality of the proposed research. They have rigorous Merit review process is considered the gold standard the world over for evaluating proposals in a competitive environment and they fund about 12000 new awards annually. And every year 93% of the foundations budget goes back out to support more than 360,000 researchers , teachers, post doctoral fellows. trainees and students at 2,000 institutions. Well over 200 NSF supported scientists have received Nobel prize for their ground breaking discoveries

Process: https://en.wikipedia.org/wiki/National_Science_Foundation#Grants_and_the_merit_review_process The NSF uses four main mechanisms to communicate funding opportunities and generate proposals: dear colleague letters, program descriptions, program announcements, and program solicitations.

Content

Ref# - Website: https://www.nsf.gov/about/ - NSF PAR(Public Access Repository) - https://www.research.gov/research-web/ - Data API - https://www.research.gov/common/webapi/awardapisearch-v1.htm, NSF Award Search Web API (ASWA). This web API provides an interface to the Research Spending and Results (RS&R) functionality available through NSF's Research.gov system. The award search data demonstrates how federal research dollars are being spent, what research is being performed, and how the outcomes of research are benefiting society as a whole.

Goal: Help Merit Review Process. Predict fundsObligatedAmt based on past data about the project proposals.

https://www.nsf.gov/news/mmg/mmg_disp.jsp?med_id=76467