OpenLLM-France/wikimedia huggingface dataset:
# Pokémon/Pokédex/Shelgon
## Basic Statistics

* National Index: 372
* Johto Index: 372
* Hoenn Index: 188
* Stage: Stage 1
* Height: 1.1m/3'7"
* Weight: 110.5kg/244.0lb
* Gender Distribution: 50% Male
* Ability: Rock Head
* Type: Dragon
* Species: Endurance
* Egg Group: Dragon
* Hatch Steps: 10240
* Catch Rate: 45
* Base Experience: 144
* Max Experience: 1250000
* Hold Item in Advance: Dragon Scale: 5%
| | HP | Attack | Defense | Speed | Special Attack | Special Defense |
| Base Stats (RB/Y) | - | - | - | - | - | - |
| Base Stats (GS/C) | - | - | - | - | - | - |
| Base Stats (RS/FL/E) | 65 | 95 | 100 | 50 | 60 | 50 |
| Base Stats (DP/PT/HGSS) | – | – | – | – | – | – |
| Base Stats (BW/B2W2) | – | – | – | – | – | – |
| Base Stats (XY/ORAS) | – | – | – | – | – | – |
| Base Stats (SM/USUM) | – | – | – | – | – | – |
| Base Stats (Sword/Shield/BDSP) | – | – | – | – | – | – |
| Max Stats (RB/Y) | - | - | - | - | - | - |
| Max Stats (GS/C) | - | - | - | - | - | - |
| Max Stats (RS/FL/E) | 334 | 317 | 328 | 218 | 240 | 218 |
| Max Stats (DP/PT/HGSS) | – | – | – | – | – | – |
| Max Stats (B/W/B2W2) | – | – | – | – | – | – |
| Max Stats (X/Y/ORAS) | – | – | – | – | – | – |
| Max Stats (SM/USUM) | – | – | – | – | – | – |
| Max Stats (Sword/Shield/BDSP) | – | – | – | – | – | – |
| Effort Points (Sword/Shield/BDSP) | – | – | – | – | – | – |

## Type Matching

Move types which are good on Shelgon: Dragon, Ice Move types which are bad on Shelgon: Electric, Fire, Grass, Water
All other move types are normal effectiveness on Shelgon.

## How to Evolve

Shelgon evolves into Salamence at Level 50.

## Moveset

| Level | Ruby / Sapphire Emerald XD / Colosseum | Fire Red / Leaf Green |
| Start | Rage Bite Leer Headbutt | Rage Bite Leer Headbutt |
| 5 | Bite | Bite |
| 9 | Leer | Leer |
| 17 | Headbutt | Headbutt |
| 21 | Focus Energy | Focus Energy |
| 25 | Ember | Ember |
| 30 | Protect | Protect |
| 38 | Dragonbreath | Dragonbreath |
| 47 | Scary Face | Scary Face |
| 56 | Crunch | Crunch |
| 69 | Dragon Claw | Dragon Claw |
| 78 | Double-Edge | Double-Edge |

### TM/HM

* RS/FL: Aerial Ace, Attract, Brick Break, Cut, Double Team, Dragon Claw, Facade, Fire Blast, Flamethrower, Frustration, Hidden Power, Protect, Rain Dance, Rest, Return, Roar, Rock Smash, Rock Tomb, Secret Power, Strength, Sunny Day, Toxic

### Evolution

* RS/FL: Dragon Dance, Dragon Rage, Hydro Pump, Thrash, Twister

## Trading Cards

* EX Dragon #20 - Shelgon
* EX Dragon #41 - Shelgon
* EX Dragon #42 - Shelgon
| Basic: Bagon | Stage 1: Shelgon | Stage 2: Salamence |
# Pokémon/Pokédex/Seedot
## Basic Statistics

* National Index: 273
* Johto Index: 273
* Hoenn Index: 22
* Stage: Basic
* Height: 0.5m/1'8"
* Weight: 4kg/9.0lb
* Gender Distribution: 50% Male
* Ability: Chlorophyll/Early Bird
* Type: Grass
* Species: Acorn
* Egg Group: Ground/Plant
* Hatch Steps: 3840
* Catch Rate: 255
* Base Experience: 74
* Max Experience: 1059860
| | HP | Attack | Defense | Speed | Special Attack | Special Defense |
| Base Stats (RB/Y) | - | - | - | - | - | - |
| Base Stats (GS/C) | - | - | - | - | - | - |
| Base Stats (RS/FL/E) | 40 | 40 | 50 | 30 | 30 | 30 |
| Base Stats (DP/PT/HGSS) | – | – | – | – | – | – |
| Base Stats (BW/B2W2) | – | – | – | – | – | – |
| Base Stats (XY/ORAS) | – | – | – | – | – | – |
| Base Stats (SM/USUM) | – | – | – | – | – | – |
| Base Stats (Sword/Shield/BDSP) | – | – | – | – | – | – |
| Max Stats (RB/Y) | - | - | - | - | - | - |
| Max Stats (GS/C) | - | - | - | - | - | - |
| Max Stats (RS/FL/E) | 284 | 196 | 218 | 174 | 174 | 174 |
| Max Stats (DP/PT/HGSS) | – | – | – | – | – | – |
| Max Stats (B/W/B2W2) | – | – | – | – | – | – |
| Max Stats (X/Y/ORAS) | – | – | – | – | – | – |
| Max Stats (SM/USUM) | – | – | – | – | – | – |
| Max Stats (Sword/Shield/BDSP) | – | – | – | – | – | – |
| Effort Points (Sword/Shield/BDSP) | – | – | – | – | – | – |

## Type Matching

Move types which are good on Seedot: Bug, Fire, Flying, Ice, Poison Move types which are bad on Seedot: Electric, Grass, Ground, Water All other move types are normal effectiveness on Seedot.

## Evolving

Seedot evolves at level 14.

## Location

Seedot is found in the grass between Oldale Town and Petalburg City (Route 102), in the grass between Mauville City and Verdanturf Town (Route 117) and in the grass on Route 120. Not found in Pokémon Sapphire.

## Moveset

| Level | Ruby / Sapphire | Fire Red / Leaf Green |
| Start | Bide | Bide |
| 3 | Harden | Harden |
| 7 | Growth | Growth |
| 13 | Nature Power | Nature Power |
| 21 | Synthesis | Synthesis |
| 31 | Sunny Day | Sunny Day |
| 43 | Explosion | Explosion |

### TM/HM

* RS/FL: Attract, Bullet Seed, Dig, Double Team, Facade, Flash, Frustration, Giga Drain, Hidden Power, Protect, Rest, Return, Rock Smash, Secret Power, Shadow Ball, Solarbeam, Sunny Day, Toxic

### Breeding

* RS/FL: Amnesia, False Swipe, Leech Seed, Quick Attack, Razor Wind, Take Down

## Trading Cards

* EX Sandstorm #76 - Seedot
* EX Sandstorm #77 - Seedot
* EX Hidden Legends #71 - Seedot
* EX Deoxys #71 - Seedot
| Basic: Seedot | Stage 1: Nuzleaf | Stage 2: Shiftry |
# Pokémon/Pokédex/Shedinja
## Basic Statistics

* National Index: 292
* Johto Index: 292
* Hoenn Index: 44
* Stage: Stage 1
* Height: 0.8m/2'7"
* Weight: 1.2kg/3.0lb
* Gender Distribution: Genderless
* Ability: Wonder Guard
* Type: Bug/Ghost
* Species: Shed
* Egg Group: Mineral
* Hatch Steps: 3840
* Catch Rate: 45
* Base Experience: 95
* Max Experience: 600000
| | HP | Attack | Defense | Speed | Special Attack | Special Defense |
| Base Stats (RB/Y) | - | - | - | - | - | - |
| Base Stats (GS/C) | - | - | - | - | - | - |
| Base Stats (RS/FL/E) | 1 | 90 | 45 | 40 | 30 | 30 |
| Base Stats (DP/PT/HGSS) | – | – | – | – | – | – |
| Base Stats (BW/B2W2) | – | – | – | – | – | – |
| Base Stats (XY/ORAS) | – | – | – | – | – | – |
| Base Stats (SM/USUM) | – | – | – | – | – | – |
| Base Stats (Sword/Shield/BDSP) | – | – | – | – | – | – |
| Max Stats (RB/Y) | - | - | - | - | - | - |
| Max Stats (GS/C) | - | - | - | - | - | - |
| Max Stats (RS/FL/E) | 1 | 306 | 207 | 196 | 174 | 174 |
| Max Stats (DP/PT/HGSS) | – | – | – | – | – | – |
| Max Stats (B/W/B2W2) | – | – | – | – | – | – |
| Max Stats (X/Y/ORAS) | – | – | – | – | – | – |
| Max Stats (SM/USUM) | – | – | – | – | – | – |
| Max Stats (Sword/Shield/BDSP) | – | – | – | – | – | – |
| Effort Points (Sword/Shield/BDSP) | – | – | – | – | – | – |

## Type Matching

Move types which are good on Shedinja: Dark, Fire, Flying, Ghost, Rock Moves which effect Shedinja normally: Status Effect Attacks
All other moves don't effect Shedinja, due to its Wonder Guard ability.

## Moveset

| Level | Ruby / Sapphire | Fire Red / Leaf Green |
| Start | Scratch Harden | Scratch Harden |
| 5 | Leech Life | Leech Life |
| 9 | Sand-Attack | Sand-Attack |
| 14 | Fury Swipes | Fury Swipes |
| 19 | Mind-Reader | Mind-Reader |
| 25 | Spite | Spite |
| 31 | Confuse Ray | Confuse Ray |
| 38 | Shadow Ball | Shadow Ball |
| 45 | Grudge | Grudge |

### TM/HM

* RS/FL: Aerial Ace, Cut, Dig, Double Team, Facade, Flash, Frustration, Giga Drain, Hidden Power, Hyper Beam, Protect, Rest, Return, Sandstorm, Secret Power, Shadow Ball, Solarbeam, Sunny Day, Thief, Toxic, Sky Uppercut, Fire Punch, Fire Blast

### Breeding

* RS/FL: Endure, Faint Attack, Gust, Silver Wind

## Trading Cards

* EX Dragon #11 - Shedinja
* EX Deoxys #14 - Shedinja
| Basic: Nincada | Stage 1: Ninjask | (No Stage 2) |
| Basic: Nincada | Stage 1: Shedinja | (No Stage 2) |
# Pokémon/Pokédex/Sheer Cold

Sheer Cold is an Ice-type move that occurs only in versions following Ruby and Sapphire. It causes the foe to faint.

## Type Matching

Irrelevant.

## Move effects

Base Damage: 0
Accuracy: 30%, but increases as the difference between the level of the attacker and that of the foe increase
Special Effects: Causes the foe to faint automatically, unless its level is higher than that of the attacker. Thwarted by Sturdy.

## Pokemon who learn the move

| Pokemon | Level | Version |
| Dewgong | 34 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |
| Lapras | 55 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |
| Articuno | 85 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |
| Glalie | 61 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |
| Spheal | 49 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |
| Sealeo | 55 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |
| Walrein | 61 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |
| Kyogre | 60 | Ruby, Sapphire, Fire Red, Leaf Green, Emerald, Colosseum, XD |

## Pokemon Contests

Appeal type: Beauty
Appeal level: 2
Jam: 1. Jam will be equal to half of the appeal made by the preceding Pokémon, rounded down to the nearest one.
# Python Programming/Progress Bars

Progress bars in Python are abundant. There are several well-known packages for progress bars out there.

## External Progress Bars

Some of the top bars are from the progress and the tqdm library.

### Progress

The progress module provides 7 different progress bars to use. They are:
* Bar
* ChargingBar
* FillingSquaresBar
* FillingCirclesBar
* IncrementalBar
* PixelBar
* ShadyBar
The module also provides 6 spinners. These do not have a finish. They are:
* Spinner
* PieSpinner
* MoonSpinner
* LineSpinner
* PixelSpinner
In order to access a bar/spinner, you run progress.a.b, where a is the type of indicator you want (bar or spinner) and b is the name of the bar/spinner. Furthermore, you have to import the module as progress.a or else it cannot see the bar/spinner.

#### Example code

We can also make it an iterator so we can put it into a for loop.

### Tqdm

The tqdm module relies off of one main class, the tqdm class. It has a GUI version, and a jupyter notebook-compatible version, and one for pandas. It will run as an iterator, a callable bar, and an action in a with statement.
# Python Programming/PyPy

PyPy is a Python interpreter containing a just-in-time compiler. Python programs can usually be run by PyPy without modification but for availability of 3rd party modules since a module made for CPython, the normal Python interpreter, does not automatically work with PyPy. Furthermore, some Python programs can run into trouble because of PyPy's different strategy of when to free up allocated objects including file handles.
The speed-up brought by PyPy compared to CPython depends on the nature of the task. For some computationally heavy tasks, the speed-up factor can reach as high as 50. PyPy speed center reports the geometric average speed-up factor as 7.6, calculated from a set of benchmarks.
PyPy is available both for Python 2 and Python 3, but the version for Python 3 is slower; the above speed statements pertain to Python 2.
Interactive use of PyPy is possible: you can type "pypy" into a command line, and start interacting with it just like with CPython.
The outputs from PyPy are not guaranteed to be exactly the same as from CPython. For instance, PyPy can yield items from a set in an order different from that of CPython since the item order in a set is arbitrary and not guaranteed to the same between different Python implementations; for verification, you can compare the results of {1,2}.pop. Dictionaries have an arbitrary key order as well.
Floating point results may be slightly different between PyPy and CPython in some setups as long as PyPy is built with SSE2 instruction set enabled and CPython is not.
See also Performance.

## External links

* PyPy, pypy.org
* PyPy's Speed Center, speed.pypy.org
* PyPy, en.wikipedia.org
# Python Programming/Python 2 vs. Python 3

Python 3 was created incompatible with Python 2. Support for Python 2.7 ended in 2020.
One noticeable difference is that in Python 3, print is not a statement but rather a function, and therefore, invoking it requires placing brackets around its arguments. Differences with deeper impact include making all strings Unicode and introducing a bytes type, making all integers big integers, letting slash (/) denote a true division rather than per default integer division, etc.; for a compact overview, see Python wiki.
Python 2 code can be made ready for a switch to Python 3 by importing features from __future__ module. For instance, from __future__ import print_function makes Python 2 behave as if it had Python 3 print function.
Python 3 was first released in 2008.
A list of Python packages ready for Python 3 is available from py3readiness.org.
A survey conducted in 2018 by JetBrains and Python Software Foundation suggests significant adoption of Python 3 among Python users.

## External links

* W:History of Python#Version 3, en.wikipedia.org
* What’s New In Python 3.0, docs.python.org
* 28.11. __future__ — Future statement definitions, docs.python.org
* Porting Python 2 Code to Python 3, docs.python.org
* Cheat Sheet: Writing Python 2-3 compatible code, python-future.org
* Should I use Python 2 or Python 3 for my development activity?, wiki.python.org
* Python3.0, wiki.python.org
* Python 3 Readiness, pyreadiness.org
* PEP 373 -- Python 2.7 Release Schedule, legacy.python.org
* Python Developers Survey 2018 Results, jetbrains.com (conducted with Python Software Foundation)
* How we rolled out one of the largest Python 3 migrations ever, 2018, blogs.dropbox.com
# Python Programming/Popularity

The popularity of a programming language is one factor that people consider when choosing a language for a task or a project. Beware that even a good programming language can be unpopular.
The popularity of Python can be determined with the help of various indices, which are not identical to the real popularity in the real world. While the statistics are linked from the external links section below, it is advisable that you use them with a grain of salt.
Python is the main scripting language used by Google, per Google Python Style Guide.
Python is embedded in various software packages to support their extensibility and automation, including Gimp and Inkscape; see also Wikipedia's W:Python (programming_language)#Uses and W:List of Python software#Embedded as a scripting language.

## External links

* Tiobe index, tiobe.com
* PYPL PopularitY of Programming Languages, pypl.github.io
* RedMonk rankings, redmonk.com, combines Stack Overflow data with GitHub data
* The 2015 Top Ten Programming Languages, spectrum.ieee.org, 2015
* W:Measuring programming language popularity
* W:Programming languages used in most popular websites
* W:Python (programming_language)
* W:List of Python software
* Google Python Style Guide, google.github.io
* Python Success Stories, python.org
* Organizations using Python, wiki.python.org
# Python Programming/Performance

Since Python is an interpreted language in its most commonly used CPython implementation, it is many times slower in a variety of tasks than the most commonly used compiled non-managed languages such as C and C++; for some tasks, it is more than 100 times slower. CPython seems to be on par with Perl, another interpreted language, slower on some tasks, faster on other tasks.
Performance can be measured using benchmarks. Benchmarks are often far from representative of the real-world usage and have to be taken with a grain of salt. Some benchmarks are outright wrong in that non-idiomatic code is used for a language, yielding avoidably low performance for the language.
PyPy is a just-in-time (JIT) compiler that often runs faster than CPython. Another compiler that can lead to greater speeds is Numba, which works for a subset of Python. Yet another compiler is Cython, not to be confused with CPython.

## External links

* Python 3 versus C gcc fastest programs, benchmarksgame-team.pages.debian.net
* Python 3 versus Java fastest programs, benchmarksgame-team.pages.debian.net
* Python 3 versus Go fastest programs, benchmarksgame-team.pages.debian.net
* Perl vs Python 3 - Which programs are fastest?, benchmarksgame-team.pages.debian.net
* Python speed center, speed.python.org
* pypy speed center, speed.pypy.org
* Python Interpreters Benchmarks, pybenchmarks.org
* Performance of Python runtimes on a non-numeric scientific code by Riccardo Murri, arxiv.org
* How To Make Python Run As Fast As Julia by Jean Francois Puget, ibm.com
# Python Programming/Package management

pip is the standard Python package manager, making it easy to download and install packages from the PyPI repository. pip seems to be part of Python distribution since Python 2.7.9 and 3.5.4. If you do not have pip, you can install it by downloading get-pip.py from bootstrap.pypa.io and running python get-pip.py. Other package managers are not covered in this chapter.
Examples of pip use:
* pip install xlrd
** Installs xlrd package from the PyPI repository, or from another repository if customized to search in additional repositories.
* pip install --upgrade xlrd
** Upgrades a package to the latest version.
* pip install mypackage.whl
** Installs the package from wheel file mypackage.whl. This is useful when for whatever reason installation from PyPI fails and you need to download the wheel file (.whl) of the package manually.
* pip freeze
** Lists installed packages and their versions.
* pip show xlrd
** Outputs information about an installed package (here xlrd), including version, author and license.
* python -m pip install xlrd
** Calls pip via python and -m option. Useful e.g. for installing packages for PyPy (a just-in-time compiler for Python), in which case you use pypy -m pip install xlrd.
* pip --version
** Outputs the pip version.
* pip install --upgrade pip
** Upgrades pip itself.
PyPI is an online repository of Python packages, many of which are published under a rather permissive license such as MIT license or one of the BSD licenses. PyPI hosts both pure-Python packages and Python packages taking advantage of the C language. Installing pure-Python packages such as xlrd is usually seamless. As for C language packages, many of them have precompiled binaries for multiple operating systems, making the installation seamless as well. However, for a C language package that has only sources published, pip needs a working and properly set up compiler to successfully install the package.
A wheel file is a package distribution. It can contain pure-Python code but also precompiled executable binaries if required. A single package can offer multiple wheels per different Python versions and operating systems. An example wheel file containing precompiled binaries is numpy-1.16.2-cp27-cp27m-win32.whl, for numpy package, available from pypi Download files section for the package. If you are using pip with no problems, you do not need to worry about wheel files.

## requirements.txt

This file lists the application dependencies. It's equivalent to composer.json in PHP or package.json in JavaScript.
File generation:
Dependencies installation:

## External links

* pip - The Python Package Installer, pip.pypa.io
* Installation in pip documentation, pip.pypa.io
* How do I install pip on Windows?, stackoverflow.com
* Installing Packages, packaging.python.org
* Packaging Python Projects, packaging.python.org
* The Python Package Index (PyPI), pypi.org
* Over 10% of Python Packages on PyPI are Distributed Without Any License, 2018, snyk.io
# Python Programming/Password checker

The basic premise of a password checker is to check passwords...
sounds simple enough, right? Just basic string analysis. But recently I have been issued a challenge to make sure the string doesn't contain any consecutive letters on a qwerty keyboard (see bottom of page). I have just completed the basic premise and come up with this code:
Hope this helps everyone else on a now advanced password checker!
just FYI, qwerty keyboards are:
# Proteomics/Post-translational Modification
### Presentation

| Protein Primary Structure | Post-translational Modification | Protein - Protein Interactions | List of Topics | Protein - Protein Interactions |

## Introduction

I. Definition
II. Types of modifications
III. Manipulating in-vivo modifications
IV. In vitro reconstitution strategies
V. Methods of detection
# Proteomics/Introduction to Proteomics/Bioinformatics

* What is Proteomics?
* Principles of Proteomics
* Bioinformatics in Proteomics
* Links

## Bioinformatics in Proteomics

As with other subsets of biology, an increased ability to generate large amounts of data from the use of high throughput methods has led to an increased reliance on computers for data acquisition, storage, and analysis. The internet has also enabled collaboration and sharing of data that would have previously not been possible, leading to the development of large public databases with contributors all over the world. Many databases exist for protein-related information, such as the Protein Data Bank (PDB) which handles structure and sequence information for proteins with a determined crystal structure. Expasy is a popular and well-curated resource for proteomics databases and tools, including resources such as the Prosite protein feature and domain database, protein BLAST (Basic Local Alignment and Search Tool, for similarity searching), and structure prediction. NCBI also provides many resources for many types of data, including proteins, which are all searchable and well integrated.
As with other bioinformatics resources, "in-silico" discovery is not meant as a replacement for lab techniques, but rather as a supplement to work done in a wet lab. For example, if a protein thought to be a transmembrane protein was analyzed with a sequence-based localization tool that agreed with the hypothesis, it would probably still be worth experimentally confirming before drawing a conclusion. However, bioinformatics tools can be extremely useful time savers, and can provide a possible place to start with experimentation, narrow down a problem domain, or provide potential solutions to problems which would be very difficult or impossible to determine experimentally, such as with protein folding. Protein folding has become a benchmark application for many supercomputers and distributed computing systems, such as IBM's BlueGene and Stanford's Folding@Home project. Distributed computing makes use of many independent client nodes that connect to a master server to obtain data to process and send back results, making them well suited to use over LANs and the internet. Projects like Grid.org and Folding@Home can be run by anyone on their own computers, and currently have thousands of participants. Although folding for now is not a replacement for structure determination by crystallography, it can provide a reasonable estimate of structure which can be investigated until actual structure is elucidated.
<Previous page | Next Page>
# Proteomics/Introduction
### Presentation

| Introduction | Topic List | List of Topics | Topic List |
* The focus of proteomics is a biological group called the proteome. The proteome is dynamic, defined as the set of proteins expressed in a specific cell, given a particular set of conditions. Within a given human proteome, the number of proteins can be as large as 2 million.
# Neuroscience/Cognitive Neuroscience/Reading List

In the emerging field of Cognitive Neuroscience there are number of real pioneers. I would propose a bibliography here to start the readers on the right track to exploring the new frontiers of the mind-brain interface science.
# White matter tracts

Transection of the fornix results in a temporal lobe amnesia. A much more severe and dense amnesia than that produced by fornix transection is seen when other subcortical pathways, in addition to the fornix, are cut (Gaffan 2001). In naturally occurring lesions in the human brain these other pathways are damaged only in conjunction with the medial temporal cortex itself. Dense amnesia can be produced in monkeys experimentally by a surgical procedure that destroys the subcortical white matter pathways while leaving intact the medial temporal cortical areas (namely the hippocampus, the entorhinal and perirhinal cortex, and the parahippocampal gyrus (Gaffan 2001)). The crucial pathways that are interrupted in dense amnesia are thought to be the ascending axon projections from the basal forebrain and brainstem that pass through the anterior medial temporal lobe on their way to widespread cortical targets (Easton & Gaffan 2001 and Easton et al 2002 ).