OpenLLM-France/wikimedia huggingface dataset:
# Scouting/BSA/Water Sports Merit Badge
## Requirement 1

Show that you know first aid for injuries or illnesses that could occur while participating in water sports, including hypothermia, heat exhaustion, heatstroke, dehydration, sunburn, minor cuts and blisters.

## Requirement 2

Do the following:
    A. Identify the conditions that must exist before performing CPR on a person. Explain how such conditions are recognized.
    B. Demonstrate proper technique for performing CPR using a training device approved by your counselor.

## Requirement 3

Before doing the following requirements, successfully complete the BSA swimmer test.

## Requirement 4

Do the following:
    A. Discuss the BSA Safety Afloat policy. Tell how it applies to Water Sports.
    B. Name the different types of life jackets, and explain when each type should be used. Show how to choose and properly fit a life jacket.
    C. Know the Sports Safety Code. Promise that you will live up to it and follow it in all water work for this badge. Know the safety precautions that must be used by the boat operator in pulling water-skiers and wakeboarders.

## Requirement 5

Show the following skier signals to the safety observer in the boat: skier safe, faster, slower, turns, back to dock, cut motor, skier in water.

## Requirement 6

Showing reasonable control while using two skis, one ski, or a wakeboard, do EACH of the following:
    A. Show how to enter the water from a boat and make a deepwater start without help.
    B. Show you can cross both wakes four times and return to the center of the wake each time without falling.
    C. Show you can fall properly to avoid an obstacle. Also, show that you can drop handle and coast to a stop without loss of balance.

## Requirement 7

While on shore, show that you now how to properly adjust the bindings on your ski(s) or wakeboard to fit yourself. Then, in deep water, show that you can adjust bindings to fit. Recover and put on your ski(s) or wakeboard that has come off during a fall.

## External links

* Water Sports Merit Badge with Workbook PDF, current requirements, and resources.
* How to Chose a PFD from REI
* Waterski Magazine
* Wakeboarding Magazine
* Earning Merit Badges in the Boy Scouts of America
* Sports and Outdoor Hobbies
* Archery | Athletics | Backpacking | Canoeing | Camping | Climbing | Cycling | Fishing | Gardening | Geocaching | Golf | Hiking | Horsemanship | Kayaking | Motorboating | Orienteering | Pioneering | Rifle Shooting | Rowing | Shotgun Shooting | Skating | Small-Boat Sailing | Snow Sports | Sports | Swimming | Water Sports | Whitewater | Wilderness Survival
# R Programming/Multinomial Ordered Models

R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index
# R Programming/Manage your workspace

R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index
This page explains how to manage your workspace.

## Basic functions

* ls lists the objects in your workspace.
* list.files lists the files located in the folder's workspace
* rm removes objects from your workspace; rm(list = ls) removes them all.
Each object can be saved to the disk using the save function. They can then be loaded into memory using load.
* save.image saves your workspace.

## Informations about the session

* sessionInfo gives information about your session, i.e., loaded packages, R version, etc.
* R.version provides information about the R version.

## Memory usage

Note: According to R version 3.5.1 on Linux and Mac, memory.size and memory.limit are Windows-specific.
memory.size gives the total amount of memory currently used by R.
memory.limit without any argument gives the limit of memory used by R. This can also be used to increase the limit. The maximum amount is limited by the memory of the computer.
object.size returns the size of an R object. You can print the results and choose the unit (byte,kilobytes,megabytes,etc).
memory.profile returns more details.
* gc initiates the garbage collector which causes R to free memory from objects no longer used.

## External links

* Dumping functions from the global environment into an R script file
# R Programming/Multiple Imputation

R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index
Multiple imputation of missing data generally includes two steps. First, an imputation step which results in multiple complete datasets. Second, combining the results obtained by applying the chosen technique on each separate dataset. The packages needed for these two steps are not necessary the same, but can be.
* "mi" package Andrew Gelman Blog Entry on the "mi" package
* "mice" package.
* Amelia
* mitools by Thomas Lumley
* survey and Zelig have support for multiple imputated datasets.
# R Programming/Method of Moments

R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index
First, it is possible to estimate a simple linear model or a simple linear model with instrumental variables using the gmm function. The GMM method is often used to estimate heteroskedastic instrumental variable models.
# R Programming/Network Analysis

R Programming/analysis
R Programming
* R Basics
** Introduction
** Sample Session
** Manage your workspace
** Settings and Installation
** Documentation
** Control Structures
** Working with functions
** Debugging
** Using C or Fortran
** Utilities
** Estimation utilities
** Packages
* Data Management
** Data types
** Working with data frames
** Importing and exporting data
** Text Processing
** Times and Dates
* Graphics
** Grammar of graphics
* Publication quality output
* Descriptive Statistics
* Mathematics
** Optimization
** Probability Distributions
** Random Number Generation
* Statistical Core Methods
** Maximum Likelihood
** Method of Moments
** Bayesian Methods
** Bootstrap
** Multiple Imputation
** Nonparametric Methods
* Regression Models
** Linear Models
** Quantile Regression
** Binomial Models
** Multinomial Models
** Tobit And Selection Models
** Count Data Models
** Duration Analysis
* Time Series
* Factor Analysis
* Classification
** Ordination
** Clustering
* Network Analysis
* High Performance Computing
** Profiling R code
** Parallel computing with R
* Appendix
** Sources
** Index

## Introduction

We mainly use the following packages to demonstrate network analysis in R: statnet, sna, igraph. They are however not representing a complete list. See Task view of gR, graphical models in R for a complete list.

## Creating graphs from data

First load the igraph package
then you can choose your preferred format. Below are examples of data provided as edge list and as adjacency matrix.

### Creating graph from an edge list

An edge list is formed by a two-column matrix, with each row defining one edge. An edge is drawn from each element in the first column to the corresponding element in the second one. Use the graph.edgelist function to import your data.

### Creating graph from an adjacency matrix

An adjacency matrix is a n × n matrix containing n vertices and where each entry a$_{ij}$ represents the number of edges from vertex i to vertex j. To import your adjacency matrix, use the graph.adjacency function.

## References

* Statnet website includes all the documentation on network analysis using R.
* Julien Barnier's introduction (in French)
* Journal of Statistical Software #24 Special Issue on Networks in R
# Roblox Studio Tutorials/New spawns

You will need new spawns for each level in your obby, but when you put multiple spawns, if you test your game a died, you will spawn at random positions. To prevent that, we needed teams. We can do that by click on the advanced section, then click on services, then select teams. We can add a new team by click on the plus sign by teams, then insert the object "team". Change the team color to whatever color you want and change the spawn you want to have that color in properties and change neutral to false and change TeamColorChangeOnTouch to true. Keep the lobby spawn neutral though, because the player is not going to spawn on the lobby spawn if you don't do so.
# Roblox Studio Tutorials/Ways to die in Roblox Natural Disaster Survival

* Fall damage
* Drowned
* Burnt
* Hit by a piece of lava
* Fell off the edge
* Hit by a piece of thing in a sandstorm
* Staying outside in a blizzard
* Hit by a meteor
* Attempting to cross over a tsunami when not high enough
* Struck by lightning
* Falling into the volcano
* Volcano spawned on you
* Staying on the edge in an earthquake
* Staying outside in an acid rain
* Walking into the tornado
* Falling off second floor of lobby
* Staying on green or black stuff in an acid rain
* Staying on white stuff in a blizzard
* Attempting to reach the volcano when you have orange or red health bar
* Falling off the play area
* Reset your character
# Roblox Studio Tutorials/Effects and lighting

This tutorial will be about how to make a light that goes off in the day and goes on at night. First, make the part that shines. Insert a script and a PointLight inside it. And let's say it is named "Light". Script:
# Programming Languages
## Table of Chapters

* Introduction
* Specifying the Syntax of Programming Languages
* Specifying the Semantics of Programming Languages
* Functional Languages
* Imperative Languages
* Object-oriented Languages
* Scripting Languages
* Logical Languages
* Aspect-oriented Languages
* Domain-specific Languages
* Procedural Languages
* Concurrent Languages
# Programming Languages/Aspect-oriented Languages
## Aspect-Oriented Languages

[TODO: talk about AspectJ]

### More on "Aspect-Oriented programming"

    in wikibooks and in wikipedia.
# Proteomics/Proteolytic processing/Autoprocessing
## Autoprocessing

This is the process in which a protein modifies itself to convert a proprotein into an active protein. Classic example of this is the autoprocessing of furin family of endoprotease. Furin the most widely used proteolytic processing enzyme and functions mainly for cleavage of proproteins needed to maintain cellular physiology.Furin family of protease is also called the PACE family. Furin is transmembrane protein which is synthesized as a 100KDa protein. This protein undergoes catalytic cleavage in the Endoplasmic reticulum to become acxtive furin which is 94 KDa. The proprotein however remains bound to the protein and acts as the furin inhibitor. The polypeptide release then occurs in the trans golgi network where furin becomes active.
Next section: Lethal effects of proteolytic processing
# Proteomics/Proteolytic processing/Proprotein
## ProProtein

Protein in its inactive state is called proprotein. Proproteins are converted into active protein by many post translational modification steps among them the most important being proteolytic processing.
Next section: Proteomics/Proteolytic processing/Specificity of proteolytic processing
# Proteomics/Proteolytic processing
## Introduction

Proteolytic processing in the broad sense means a protein or a protein complexing processing another protein or protein complex to modify its activity. This processing may lead of activation, inhibition or modulation of the protein activity. Much of the cellular processes are triggered by proteolytic processing. Proteolytic processing is the major part of post translational modification of a protein to make a functional protein complex.
Next section: Proteomics/Proteolytic processing/Proprotein
# Proteomics/Proteolytic processing/Examples of proteolytic processing in various organs of our body
## Examples of proteolytic processing in various organs of our body

* Renal proteins
In concern with the proteolytic processing with respect to kidney the human prorenin is considered. The proteolytic processing of human prorenin in renal tissues is discussed here. The mouse proprotein convertase also known as PC1 accurately cleaves the prorenin and generates active rennin. The human Proprotein Convertase also known as PC3 can also process the prorenin to generate rennin but not as efficiently as mPC1 does. This is because of the differences in the sequences of the carboxy terminus from PC1 to hybrid hPC1/mPC1. The cleavage of prorenin bay PC1 in humans takes place at a pair of basic amino acids by removal of 43 amino acid prosegment from the amino terminus of the it along with identification of a functionally important site in the hPC1. Also PC1 generates active rennin in other tissues like adrenal medulla and certain adrenal tumors. If human prorenin has either in Lys or Arg residues at the cleavage site then the hPC1 cannot generate active rennin.
* Blood protein
The Kell Blood Group Protein belongs to family of metalloendopeptidases. It’s a membrane glycoprotein which shares a pentametric zinc-binding consensus sequence. It conserves all the amino acids required for the endopeptidase activity. Kell Blood Group proteins have a homology with M13 family in which a disulphide bond is covalently linked to a protein named XK, this spans the membrane 10 times, whereas kell proteins is linked to XK present is fifth extracellular loop. Activation of endothelins and processing of bioactive peptides can be done by Kell proteins.
* Human Brain Spectrin
Fodrin, Human Brain Alpha Spectrin plays an important role in the nervous system yet its mechanism is not completely understood. Its isoforms are present within the cells and in some neurotransmitter neurons. It is proteolytically processed by the calcium dependent proteases. This processing of fodrin is a central molecular mechanism does the development of long term memory. Foldrin has a proteolytically hypersensitive site in its centre for cleavage. Calcium-dependent binding of calmodulin takes place at this site.
Next section: Proteomics/Proteolytic processing/Proteolytic processing of the Human Immunodeficiency Virus type 1
# Proteomics/Proteolytic processing/Proteolytic processing of the Human Immunodeficiency Virus type 1
## Proteolytic processing of the Human Immunodeficiency Virus type 1

Polyprotein precursors translates the proteins that forms the retroviral particle. One of these precursors has viral protease that processes the precursors itself. This viral protease cleaves the retroviral precursor proteins during the virus assembly. Fully infectious viral particles are produced by precise Protease-mediated precursor processing and if the order of the cleavage changes, it results in the less infectious viral particle. To become active the protease has to be localized along with the requirement of enzyme dimerization. Enzymes come from PR(Protease) domains of GagPol precursors. The processing events are first carried out by the embedded PR, prior to the release from the GagPol precursor.The activated protease domain on one pair of GagPol molecules cleaves initial sites on those same molecules. Further substitutions at the first amino acid in the protease, a proline, lift this constraint and free the protease domain within GagPol to cleave additional native processing sites in the precursor not cleaved by the wild-type embedded protease.
Next section: Proteomics/Proteolytic processing/Proteolytic processing- role in therapeutics
# Proteomics/Proteolytic processing/Proteolytic processing in Secretory Pathway
## Proteolytic processing in Secretory Pathway

Proteolysis of Precursor proteins regulates many cellular processes like gene expression, embryogenesis, cell cycle, programmed cell death, intracellular protein targeting and endocrine/neural functions. All these processes needs proteolytic cleavage of the precursor protein and in this context this can be done by serine proteases in the secretory pathway. These proteases are calcium dependent serine endoproteases and are related to yeast and subtilisin proteases and therefore called Subtilisin-like Proprotein Convertases (SPCs) or PCs. Till now they identified & characterized seven members of this family in mammals and they have conserved signal peptides, pro-regions, catalytic and P-domains but they differ in C-terminal domains. These proteases get activated by autocatalytic cleavage of an N-terminal propeptide just like Subtilisin which is required for folding and activity. Studies predict that an eight-stranded beta barrel is formed due to folding of the P domain which interacts through a hydrophobic patch to a catalytic domain. Folding and activity requires a downstream domain of 150 amino acids named P- or Homo B-domain which plays a regulatory role, influencing both the calcium dependence and pH optima. Here the C-terminal regions play a minor in subcellular routing. Furin, one of the seven members of the SPCs, serves as a model for other PCs. The autoactivation mechanism involves intramolecular cleavage of prodomain that allows furin to exit the endoplasmic reticulum (ER). Until the cleaved inactive proenzyme reaches the trans Golgi Network (TGN) where dissociation of prodomain is facilitated by calcium-enriched environment and acidic pH conditions, the prodomain remains attached noncovalently to it. For full activation, further inhibitory reactions takes place within the prodomain by a second cleavage. Proinsulin, Proglucagon and POMC are the well studied peptide hormone precursors. PCs are responsible for the cleavage at specific sites in each of these precursors. The most frequent sites for cleavage are KR and RR. Proglucagon is processed by PC2 in the alpha cells and by PC1/PC3 in the intestinal L cells and releases active forms of glucagons-like peptides namely GLP-1 and GLP-2. The convertases PC2 and PC1/PC3 are majorly expressed in brain and neuroendocrine system to act on neuropeptide precursors in the secretory pathway. Glucose, the second messengers in the neuroendocrine cells regulates the transcription and translation of PC2 and PC1/PC3. Other convertases like PC4 expressed in testis and an isoform of PC6 that lacks a TM domain belong to PC family. Recently discovered convertases are PC7, furin, PACE4 and PC6B. These are expressed in tissues like liver, gut, brain, neuroendocrine system. There are still many unidentified enzymes that participate in cleavages at single basic residues and unusual sites, other than the subtilisin-like Proprotein Convertases.
Next section: Proteomics/Proteolytic processing/Examples of proteolytic processing in various organs of our body
# Proteomics/Proteolytic processing/Specificity of proteolytic processing
## Specificity of proteolytic processing

Proteolytic processing is highly specific process. Mechanism of proteolytic processing varies according to the protein being processed, location of the protein, and protein processing it. There is no general rule of the thumb for proteolytic processing. This section therefore gives examples of different proteolytic processing approaches to get the basic idea
Next section: Proteomics/Proteolytic processing/Proteolytic processing in Secretory Pathway
# Proteomics/Proteolytic processing/Lethal effects of proteolytic processing
## Lethal effects of proteolytic processing

Proteolytic processing not only helps in carrying out normal cellular processes but it also can cause processing of proteins to give lethal proteins. A classic example of proteolytic processing which develops lethal protein is development of Netherton syndrome . Netherton syndrome is a autosomal recessive congenital syndrome which causes ichthyosiform erythroderma caused by a mutation in the human SPINK5. Biochemical analysis of Netherton syndrome have shown an increase in the proteolytic processing of profilggrin into its constituent filaggrin monomers. Defects in filaggrin processing cause congenital disorders like icthyosis. SPINK5 undergoes extreme proteolytic processing furin is assumed to be involved in the proteolytic processing of SPINX5 however the exact extent of processing is yet to be determined.In the absence of serine protease inhibitor SPINK5 there is abnormal increase in the processing of profilaggrin resulting in the increased production of filaggarin monomers causing severely compromised epidermal barrier function.