Post processed forecasts based on the latest run of the AROME-Arctic model

The dataset has N=1000 rows and 5 columns. 1000 rows have no missing values on any column.

Table of variables

This table contains variable names, labels, and number of missing values. See the complete codebook for more.

Note

This dataset was automatically described using the codebook R package (version 0.9.2).

The geodatabase on Main Ports - Waste at ports was created in 2018 by CETMAR using the Ports 2013 data available in Eurostat web page (http://ec.europa.eu/eurostat/web/main/home). It is the result of the aggregation and harmonization of datasets provided by several sources from all across the EU and is available for viewing and download on EMODnet - Human Activities web portal (www.emodnet-humanactivities.eu). Following the MARPOL Convention waste at ports have been reported by Ports indistincly in cubic meters(m3)and/or in tonnes and classified as oily waste (Annex I), garbage (Annex V), sewage (Annex IV), Harbor Waste (garbage) and Total Amount*. These datasets are updated on an annual basis and includes annual data from 2000 to 2018 (where available) in the following countries: Estonia, Finland, France, Latvia, Portugal, Italy, Greece, Romania, Sweden, Croatia, Malta, Netherlands and Spain.*Total Amounts only report the sum of available values for each of the given units (m3 or tonnes).Waste at Ports (m3)

The dataset has N=1135 rows and 211 columns. 2 rows have no missing values on any column.

Table of variables

This table contains variable names, labels, and number of missing values. See the complete codebook for more.

[truncated]

Note

This dataset was automatically described using the codebook R package (version 0.9.2).

The dataset has N=205 rows and 9 columns. 205 rows have no missing values on any column.

Table of variables

This table contains variable names, labels, their central tendencies and other attributes.

Note

This dataset was automatically described using the codebook R package (version 0.8.0).

Compiled and harmonized information on the rate of sedimentation on the seafloor

The dataset is modelled on OSPAR's dataset on offshore installations, having the same fields and attributes

The dataset provides information about the location of freshwater finfish farms in the EU and partner countries where data are available.

The dataset is the result of the aggregation and harmonization of datasets provided by national sources across the EU (plus Norway) and by the project Euroshell

The dataset is the result of the aggregation and harmonization of datasets provided by several sources from all across the EU

The dataset is the result of the aggregation and harmonization of datasets provided by several sources from all across the EU

Context

Hello. As a big ski jumping fan, I would like to invite everybody to something like a project called "Ski Jumping Data Center". Primary goal is as below:

Collect as many data about ski-jumping as possible and create as many useful insights based on them as possible

In the mid-September last year (12.09.20) I thought "Hmm, I don't know any statistical analyses of ski jumping". In fact, the only easily found public data analysis about SJ I know is https://rstudio-pubs-static.s3.amazonaws.com/153728_02db88490f314b8db409a2ce25551b82.html

Question is: why? This discipline is in fact overloaded with data, but almost nobody took this topic seriously. Therefore I decided to start collecting data and analyzing them. However, the amount of work needed to capture various data (i.e. jumps and results of competitions) was so big and there is so many ways to use these informations, that make it public was obvious. In fact, I have a plan to expand my database to be as big as possible, but it requires more time and (I wish) more help.

Content

Data below is (in a broad sense) created by merging a lot of (>6000) PDFs with the results of almost 4000 ski jumping competitions organized between (roughly) 2009 and 2021. Creation of this dataset costed me about 150 hours of coding and parsing data and over 4 months of hard work. My current algorithm can parse in a quasi-instant way results of the consecutive events, so this dataset can be easily extended. For details see the Github page: https://github.com/wrotki8778/Ski_jumping_data_center The observations contain standard information about every jump - style points, distance, take-off speed, wind etc. Main advantage of this dataset is the number of jumps - it's quite high (by the time of uploading it's almost 250 000 rows), so we can analyze this data in various ways, although the number of columns is not so insane.

Acknowledgements

Big "thank you" should go to the creators of tika package, because without theirs contribution I probably wouldn't create this dataset at all.

Inspiration

I plan to make at least a few insights from this data: 1) Are the wind/gate factor well adjusted? 2) How strong is the correlation between the distance and the style marks? Is the judgement always fair? 3) (advanced) Can we create a model that predicts the performance/distance of an athlete in a given competition? Maybe some deep learning model? 4) Which characteristics of athletes are important in achieving the best jumps - height/weight etc.?

Daily mean fields from global ocean physics analysis and forecast updated daily

Marine litter material categories percentage per year per beach from research & cleaning operations

Pre-Quaternary -ageThis web map service shows the chronostratigraphic age of geological units of the seafloor originated earlier than 2,588 Ma from now (pre-Quaternary). International Geological Map of Europe and Adjacent Areas (Asch, 2005). The scale varies between 25,000 and 5 000 000.The data were compiled by BGR from the EMODnet geology partner organisations in the EMODnet Geology project phases I, II and III between 2009 and 2019. Pre-Quaternary -lithologyThis web map service shows the rock type (lithology) of geological units of the seafloor originated earlier than 2,588 Ma from now (pre-Quaternary). International Geological Map of Europe and Adjacent Areas (Asch, 2005). The scale varies between 25 000 and 5 000 000.The data were compiled by BGR from the EMODnet geology partner organisations in the EMODnet Geology project phases I, II and III between 2009 and 2019. The scale varies between 25 000 and 5 000 000.

Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). Abundances have been calculated on each beach and year using the following computation: Platic bags abundance=(total number of plastic bags items (normalized at 100m))/(Number of surveys on the year) Percentiles 50, 75 & 95 have been calculated taking into account data from all years.

The dataset has N=15384 rows and 5 columns. 15343 rows have no missing values on any column.

Table of variables

This table contains variable names, labels, and number of missing values. See the complete codebook for more.

Note

This dataset was automatically described using the codebook R package (version 0.9.2).

Shapes about dumping sites show features defined as either polygons and points in Baltic Sea, North Sea, Celtic Seas, Iberian Coast and Bay of Biscay, Macaronesia and Mediterranean Sea. Information was picked form different sources depending on the country.

Daily mean fields from global ocean physics analysis and forecast updated daily

Submarine landslides, detectable on the seabed, outcropping or buried, mapped by various national and regional mapping projects and recovered in the literature. Locally landslides are extended on land to include their origin. Note: blank areas do not necessarily correspond to no occurrence.