OpenLLM-France/wikimedia huggingface dataset:
# Illustrated Companion to the Latin Dictionary/Musivum
## Illustrated Companion to the Latin Dictionary, and Greek Lexicon (Rich, 1849)

MUSI'VUM (μουσεῖον). The original from which our term mosaic is derived; but the ancients employed the word in a somewhat more restricted sense than we attach to our term. Amongst them musivum means a mosaic formed with small pieces of coloured glass or composition in enamel, as opposed to lithostrotum, which was made of natural stones or different coloured marbles. Mosaics of this description were not originally used for pavements, but only in ceilings (Plin. H. N. xxxvi. 4. Inscript. ap. Furnaletti de Musiv. cap. 1. p. 2.), because at first it was feared that the material was not of sufficient durability to bear the wear and tear of footsteps; but when this was discovered to be a groundless alarm, the same materials were employed in making ornamental pavements (Augustin. Civ. D. xvi. 8.), either alone, or with the admixture of real stones, which enabled the artist to make his work more perfect, and his tints more varied and more true; in short, to imitate a picture with considerable precision in all its colours, forms, and varieties; whence this style of the art obtained the name of mosaic painting — pictura de musivo — and became the most perfect amongst the different processes employed for works of this nature, each of which had a characteristic name of its own, which will be found in the list of the Classed Index. Plin. H. N. xxxvi. 64. Spart. Pesc. 6. Visconti. Mus. Pio-Clem. vii. p. 236.
# Illustrated Companion to the Latin Dictionary/Lithostrotum
## Illustrated Companion to the Latin Dictionary, and Greek Lexicon (Rich, 1849)

LITHOSTRO'TUM (λιθόστρωτον). Literally, paved with stones; whence the pavement of a Roman road, which was laid with polygonal blocks of volcanic formation (silex); or of any flat open square, like an area, or a forum, which were paved with broad square flags; or the floor of a building, like that of the Pantheon at Rome, which is formed with slabs of porphyry and jaune antique; were all lithostrata in a generic sense. But the word is mostly applied, in the passages which remain, to the various kinds of ornamental pavements which go by the common name of mosaic with us; more especially to those which were composed of small pieces of stone or marbles of natural colours, as contradistinguished from those which were made of glass or composition, artificially stained to imitate different tints. Varro, R. R. iii. 1. 10. Plin. H. N. xxxvi. 60. Capitol. Gord. 32. and the various names enumerated in the Classed Index.
# MDLD/homothety

Start=homothety Target=Endomorphism/Homothety/Definition
# MDLD/coordinate tuple

Start=coordinate tuple Target=Basis/Unique representation/Coordinates/Remark
# MDLD/direct (sum)

Start=direct (sum) Target=Vector space/Linear subspaces/Finite/Direct sum/Definition
# MDLD/sum (linear subspaces)

Start=sum (linear subspaces) Target=Linear subspaces/Sum/Definition
# MDLD/diagonalizable (ev)

Start=diagonalizable (ev) Target=Linear mapping/Diagonalizable/Eigenvectors/Definition
# MDLD/field of rational functions

Start=field of rational functions Target=Rational function/Field/Definition
# MDLD/invariant (linear mapping)

Start=invariant (linear mapping) Target=Endomorphism/Invariant subspace/Definition
# MDLD/linear factors (1K)

Start=linear factors (1K) See=linear factor (1K) MDLD/linear factor (1K) Target=[[MDLD/linear factor (1K)]]
# MDLD/linear factor (1K)

Start=linear factor (1K) Target=Polynomial ring/Field/Zero/Linear factor/Fact
# MDLD/permutation matrix

Start=permutation matrix Target=Permutation matrix/Definition
# Permutation matrix/Cycle/Characteristic polynomial/Fact

characteristic polynomial of a permutation matrix $M_{\rho }$ for a cycle $\rho \in S_{n}$ of
order $k$ is
    $\chi _{M}=(X-1)^{n-k}(X^{k}-1)\,.$
# MDLD/order (cycle)

Start=order (cycle) Target=Permutation/Cycle/Definition
# MDLD/root of unity

Start=root of unity Target=Roots of unity/Field/Definition
# Permutation matrix/Cycle/C/Eigenvalues/Fact/Proof

We have
    ${\begin{aligned}M_{\rho }(v_{\zeta })&=M_{\rho }(\zeta ^{k-1}e_{i_{1}}+\zeta ^{k-2}e_{i_{2}}+\cdots +\zeta e_{i_{k-1}}+e_{i_{k}})\\&=\zeta ^{k-1}M_{\rho }(e_{i_{1}})+\zeta ^{k-2}M_{\rho }(e_{i_{2}})+\cdots +\zeta M_{\rho }(e_{i_{k-1}})+M_{\rho }(e_{i_{k}})\\&=\zeta ^{k-1}e_{i_{2}}+\zeta ^{k-2}e_{i_{3}}+\cdots +\zeta e_{i_{k}}+e_{i_{1}}\\&=e_{i_{1}}+\zeta ^{k-1}e_{i_{2}}+\zeta ^{k-2}e_{i_{3}}+\cdots +\zeta e_{i_{k}}\\&=\zeta (\zeta ^{k-1}e_{i_{1}}+\zeta ^{k-2}e_{i_{2}}+\cdots +\zeta e_{i_{k-1}}+e_{i_{k}})\\&=\zeta v_{\zeta }.\end{aligned}}$
Since there are $k$ different $k$-th roots of unity in $\mathbb {C}$, these vectors are linearly independent due to fact, and they generate a $k$-dimensional linear subspace $U$ of $K^{n}$. In fact, we have
    $U=\langle e_{i_{j}},\,j=1,\ldots ,k\rangle \,.$
Since the vectors $e_{i}$, $i\neq {i_{j}}$, are fixed vectors, the $v_{\zeta }$ together with the $e_{i}$, $i\neq {i_{j}}$, form a basis consisting of eigenvectors of $M_{\rho }$. Hence, $M_{\rho }$ is diagonalizable.
# MDLD/fixed vectors

Start=fixed vectors See=fixed vector MDLD/fixed vector Target=[[MDLD/fixed vector]]
# MDLD/similar (matrix)

Start=similar (matrix) Target=Matrix/Similarity/Definition
# MDLD/trigonalizable endomorphism

Start=trigonalizable endomorphism Target=Linear mapping/Trigonalizable/Via upper triangular form/Definition
# Endomorphism/Trigonalizable/Introduction/Section
## Definition

Let $K$ denote a field, and let $V$ denote a finite-dimensional vector space. A linear mapping $\varphi \colon V\rightarrow V$
is called trigonalizable, if there exists a basis such that the describing matrix of $\varphi$ with respect to this basis is an
Diagonalizable linear mappings are in particular trigonalizable. The inverse statement is not true, as example shows. We will see in fact that a linear mapping is trigonalizable if and only if its characteristic polynomial factors into linear factors. A square matrix $M$ is called trigonalizable if the corresponding linear mapping $K^{n}\rightarrow K^{n}$ is trigonalizable. This means that there exists a basis such that the mapping is described by an upper triangular matrix with respect to this basis, or, that there exists an invertible matrix $B$ (the base change matrix) such that
    $BMB^{-1}$
is an upper triangular matrix. Therefore, a matrix is trigonalizable if and only if it is similar to an upper triangular matrix. The process of finding such a basis and to perform this base change is called trigonalization.

## Example

We claim that the matrix
    $M={\begin{pmatrix}3&1\\-1&1\end{pmatrix}}\,$
is trigonalizable. The matrix
    $B={\begin{pmatrix}3&2\\1&1\end{pmatrix}}\,$
is invertible with the inverse matrix
    $B^{-1}={\begin{pmatrix}1&-2\\-1&3\end{pmatrix}}\,.$
A direct computation shows
    $BMB^{-1}={\begin{pmatrix}3&2\\1&1\end{pmatrix}}{\begin{pmatrix}3&1\\-1&1\end{pmatrix}}{\begin{pmatrix}1&-2\\-1&3\end{pmatrix}}={\begin{pmatrix}3&2\\1&1\end{pmatrix}}{\begin{pmatrix}2&-3\\-2&5\end{pmatrix}}={\begin{pmatrix}2&1\\0&2\end{pmatrix}}\,.$
In this verification of trigonalizability, the transformation matrix $B$ arises just like that. We get a more reasonable proof with the help of the characteristic polynomial and fact. The characteristic polynomial is
    $\chi _{M}=\det {\begin{pmatrix}X-3&-1\\1&X-1\end{pmatrix}}=(X-3)(X-1)+1=X^{2}-4X+4=(X-2)^{2}\,,$
and this factors into linear factors.

## Lemma

Let $V_{1},\ldots ,V_{n}$ denote finite-dimensional vector spaces over the field $K$, let
    $\varphi _{i}\colon V_{i}\longrightarrow V_{i}$
denote linear mappings, and let
    $\varphi =\varphi _{1}\times \cdots \times \varphi _{n}\colon V_{1}\times \cdots \times V_{n}\longrightarrow V_{1}\times \cdots \times V_{n}$
denote the product mapping. Then $\varphi$ is trigonalizable
$\varphi _{i}$

### Proof

$\Box$
In particular, the preceding statement holds when
    $V=\bigoplus _{i\in I}V_{i}\,$
is a direct sum of $\varphi$-invariant linear subspaces.
# Linear mapping/Diagonalizable/Direct sum of eigenspaces/Fact

Let $K$ denote a field, and let $V$ denote a $K$-vector space of finite dimension. Let
    $\varphi \colon V\longrightarrow V$
be a linear mapping.
$\varphi$
diagonalizable if and only if $V$ is the direct sum of the
# MDLD/direct sum (vs)

Start=direct sum (vs) Target=Linear subspaces/Sum/Definition
# MDLD/invariant linear subspaces

Start=invariant linear subspaces See=invariant linear subspace MDLD/invariant linear subspace Target=Endomorphism/Invariant subspace/Definition
# MDLD/invariant (linear subspace)

Start=invariant (linear subspace) Target=Endomorphism/Invariant subspace/Definition
# MDLD/flag

Start=flag Target=Vector space/Flag/Definition
# MDLD/invariant flag

Start=invariant flag Target=Linear mapping/Invariant flag/Definition
# MDLD/invariant (linear)

Start=invariant (linear) Target=Endomorphism/Invariant subspace/Definition
# MDLD/minimal polynomial (linear)

Start=minimal polynomial (linear) Target=Endomorphism/Finite-dimensional/Minimal polynomial/Definition
# MDLD/polynomials (1)

Start=polynomials (1) See=polynomial (1) MDLD/polynomial (1) Target=Polynomial/Field/1/Definition
# MDLD/common divisor (polynomial ring 1)

Start=common divisor (polynomial ring 1) Target=Polynomial ring/K/Common divisor/Definition
# MDLD/divides (polynomial ring 1)

Start=divides (polynomial ring 1) See=divide (polynomial ring 1) MDLD/divide (polynomial ring 1) Target=[[MDLD/divide (polynomial ring 1)]]
# MDLD/divide (polynomial ring 1)

Start=divide (polynomial ring 1) Target=Polynomial ring/K/Division/Definition
# MDLD/Polynomials (1)

Start=Polynomials (1) See=polynomial (1) MDLD/polynomial (1) Target=Polynomial/Field/1/Definition
# MDLD/greatest common divisor (polynomial ring)

Start=greatest common divisor (polynomial ring) Target=Polynomial ring/K/Greatest common divisor/Definition
# MDLD/coprime polynomials

Start=coprime polynomials Target=Polynomial ring/K/Relatively prime/Definition
# MDLD/coprime (polynomial)

Start=coprime (polynomial) Target=Polynomial ring/K/Relatively prime/Definition
# Polynomial/Bezout/X^2+X+1 and X-1/Example

We want to determine the greatest common divisor for the polynomials $X^{2}+X+1$ and $X-1$ in $\mathbb {Q} [X]$. For this, we perform the division with remainder, and we obtain
    $X^{2}+X+1={\left(X+2\right)}{\left(X-1\right)}+3\,.$
Thus the two polynomials are coprime. A representation of the $1$ is
    $1={\frac {1}{3}}{\left(X^{2}+X+1\right)}-{\frac {1}{3}}{\left(X+2\right)}{\left(X-1\right)}\,.$
# MDLD/greatest common divisor (polynomial)

Start=greatest common divisor (polynomial) Target=Polynomial ring/K/Greatest common divisor/Definition
# MDLD/invariance (endomorphism)

Start=invariance (endomorphism) Target=Endomorphism/Invariant subspace/Definition
# MDLD/generalized eigenspace

Start=generalized eigenspace Target=Vector space/Linear mapping/Generalized eigenspace/Definition
# MDLD/nilpotent (matrix)

Start=nilpotent (matrix) Target=Matrix/Nilpotent/Definition
# MDLD/nilpotent (linear)

Start=nilpotent (linear) Target=Endomorphism/Nilpotent/Definition
# MDLD/nilpotent (endomorphism)

Start=nilpotent (endomorphism) Target=Endomorphism/Nilpotent/Definition
# MDLD/nilpotent endomorphism

Start=nilpotent endomorphism Target=Endomorphism/Nilpotent/Definition
# MDLD/generalized eigenspaces

Start=generalized eigenspaces See=generalized eigenspace MDLD/generalized eigenspace Target=Vector space/Linear mapping/Generalized eigenspace/Definition
# MDLD/nilpotent mapping

Start=nilpotent mapping Target=Endomorphism/Nilpotent/Definition
# MDLD/Jordan matrix

Start=Jordan matrix Target=Jordan matrix/Above/Definition
# MDLD/Jordan normal form

Start=Jordan normal form Target=Upper triangular matrix/Jordan normal matrix/Definition
# MDLD/trigonalizable mapping

Start=trigonalizable mapping Target=Linear mapping/Trigonalizable/Via upper triangular form/Definition
# MDLD/permutation matrices

Start=permutation matrices See=permutation matrix MDLD/permutation matrix Target=Permutation matrix/Definition
# MDLD/Jordan blocks

Start=Jordan blocks See=Jordan block MDLD/Jordan block Target=[[MDLD/Jordan block]]
# MDLD/Jordan block

Start=Jordan block Target=Endomorphism/Jordan normal form/Introduction/Section
# MDLD/order (group element)

Start=order (group element) Target=Group theory/Order of an element/Definition
# Real positive number/Root/Unique existence/Fact

For every nonnegative real number $c\in \mathbb {R} _{\geq 0}$ and every $k\in \mathbb {N} _{+}$
$x$
    $x^{k}=c\,.$
# Finite field/Smooth projective curve/Vector bundle/Strongly semistable degree nonnegative/Finite annihilation/Fact

Let $K$ denote a finite field (or the algebraic closure of a finite field) and let $X$ be a smooth projective curve over $K$. Let ${\mathcal {S}}$ be a strongly semistable locally free sheaf over $X$ of nonnegative degree and let $c\in H^{1}(X,{\mathcal {S}})$ denote a cohomology class.
    $\varphi \colon Y\longrightarrow X$
$\varphi ^{*}(c)$
# Composition/Polynomial/Example

The composition of
    $F\colon \mathbb {R} \longrightarrow \mathbb {R} ,t\longmapsto t^{3},$
and
    $G\colon \mathbb {R} \longrightarrow \mathbb {R} ,x\longmapsto x^{2}-x,$
is given by
    $(G\circ F)(t)=(t^{3})^{2}-t^{3}=t^{6}-t^{3}\,.$
However,
    $(F\circ G)(x)=(x^{2}-x)^{3}=x^{6}-3x^{5}+3x^{4}-x^{3}\,.$
Hence, the composition of two mappings depends on the ordering.
# Binary operations/Introduction/Section
## Definition

An operation (or binary operation) $\circ$ on a set $M$ is a mapping
    $\circ \colon M\times M\longrightarrow M,(x,y)\longmapsto \circ (x,y)=x\circ y.$
A binary operation assigns to a pair
    $(x,y)\in M\times M\,$
another element
    $x\circ y\in M\,.$
Many mathematical constructions are captured by this concept: addition, difference, multiplication, division of numbers, the composition of mappings, the intersection or the union of sets, etc. Basically, any symbol can be used to denote a binary operation, like $\circ ,\cdot ,+,-,\oplus ,\clubsuit ,\heartsuit$. Depending on the symbol, we call the binary operation also multiplication or addition, but this does not mean that we are referring to any natural multiplication. Important structural properties of a binary operation are listed in the following definitions.

## Definition

A binary operation
    $\circ \colon M\times M\longrightarrow M,(x,y)\longmapsto x\circ y,$
on a set $M$ is called commutative, if for all $x,y\in M$ the equality
    $x\circ y=y\circ x\,$

## Definition

A binary operation
    $\circ \colon M\times M\longrightarrow M,(x,y)\longmapsto x\circ y,$
on a set $M$ is called associative, if for all $x,y,z\in M$ the equality
    $(x\circ y)\circ z=y\circ (x\circ z)\,$

## Definition

Let a set $M$ and a binary operation
    $\circ \colon M\times M\longrightarrow M,(x,y)\longmapsto x\circ y,$
be given. An element $e\in M$ is called neutral element of the operation, if for all $x\in M$ the equalities $x\circ e=x=e\circ x$
In the commutative case, it is enough to check only one property of the neutral element.

## Definition

Let a set $M$ with a binary operation
    $\circ \colon M\times M\longrightarrow M,(x,y)\longmapsto x\circ y,$
and a neutral element $e\in M$ be given. For an element $x\in M$, an element $y\in M$ is called inverse element (for $x$), if the equalities
    $x\circ y=e=y\circ x\,$
# Ring/Commutative/Definition

A ring is called commutative if its multiplication is commutative.
# Linear mapping/Matrix/Commutative diagram/Fact/Proof/Exercise

Proof fact.
# Linear mapping/Image and preimage/linear subspaces/Fact/Proof/Exercise

Let $K$ be a field, let $V$ and $W$ denote $K$-vector spaces and let
    $\varphi \colon V\longrightarrow W$
be a $K$-linear mapping. Prove the following statements.
* For a linear subspace $S\subseteq V$, the image $\varphi (S)$ is a linear subspace of $W$.
* In particular, the image $\operatorname {Im} \varphi =\varphi (V)$ of the mapping is a linear subspace of $W$.
* For a linear subspace $T\subseteq W$, the preimage $\varphi ^{-1}(T)$ is a linear subspace of $V$.
* In particular, $\varphi ^{-1}(0)$ is a linear subspace of $V$.
# Linear mapping/Image and preimage/Linear subspaces/Fact

Let $K$ be a field, let $V$ and $W$ denote $K$-vector spaces and let
    $\varphi \colon V\longrightarrow W$
be a $K$-linear mapping. Then the following hold.
* For a linear subspace $S\subseteq V$, the image $\varphi (S)$ is a linear subspace of $W$.
* In particular, the image $\operatorname {Im} \varphi =\varphi (V)$ of the mapping is a linear subspace of $W$.
* For a linear subspace $T\subseteq W$, the preimage $\varphi ^{-1}(T)$ is a linear subspace of $V$.
* In particular, $\varphi ^{-1}(0)$ is a linear subspace of $V$.
# Gravitation/Four potential gravitation
##### Four potential gravitation

Four potential gravitation may be defined by a gravitational potential being a Lorentz four vector as for example set out in Gravitational four-potential from user:Fedosin.
A different study is drafted here /Poth.
# Gravitation/Scalar theories
## Scalar theories of gravitation

This resource is a place to use to support the study of the Wikipedia article and its topic. Additional content from the article may be brought here to support the study, though it may be better to create subpages for specific section studies.

## The Wikipedia lede

See permanent link as of 06:28, 20 April 2015].
Scalar theories of gravitation are field theories of gravitation in which the gravitational field is described using a scalar field, which is required to satisfy some field equation.
Note: This article focuses on relativistic classical field theories of gravitation. The best known relativistic classical field theory of gravitation, w:general relativity, is a tensor theory, in which the gravitational interaction is described using a tensor field
    Isn't this contradictory? The article is "Scalar theories of gravitation, but then it purports to focus on "relativistic classical field theories", of which the "best known" is not a scalar theory, it is a tensor theory. Something is off! --Abd (discuss • contribs) 00:51, 14 May 2015 (UTC)

## Newtonian gravity

permanent link to section06:28, 20 April 2015
The prototypical scalar theory of gravitation is Newtonian gravitation. [...]

## Nordström's theories of gravitation

permanent link to section, 06:28, 20 April 2015
The first attempts to present a relativistic (classical) field theory of gravitation were also scalar theories. Gunnar Nordström created two such theories. [...]

## Einstein's scalar theory

permanent link to section, 06:28, 20 April 2015
In 1913, Einstein [,...] [i]nspired by Nordström's work, ... proposed his own scalar theory. [...]
Einstein dropped this theory in late 1914. [...] [T]he theory he finally arrived at in 1915, general relativity, is a tensor theory, not a scalar theory. [...]

## Additional variations

permanent link to section 06:28, 20 April 2015
* Kaluza–Klein theory
* Brans–Dicke theory
* Zee
* Watt–Misner theory

## Essays and studies

Essays and studies are attributed to individual Wikiversity users, who are responsible for them. They may represent fringe science or error. If work is supported by what would be considered independent reliable source on Wikipedia, or enjoys user consensus here, it may be presented in mainspace resources without these cautions. The resource may be discussed within the resource or on the attached Talk page.
# Hinduism/Books
## Ramayana

Ramayana is an Epic Hindu Text.
If the Mahabharata seems to be about normal flawed human beings with their insecurities and jealousies, the Ramayana is about utopian ideals and lofty principles. Ramayana creates the perfect: the virtuous elder brother, the obedient, ever-devoted younger brothers and the self-sacrificing, ever-loving wife. All those who appear to be flawed in some way – like Dhashratha, the father of lord Rama, Keikeyi, the step-mother, and above all Ravana the villain – seem to have been put in the story only as examples of how-not-to-be and to enhance the goodness of the principle characters.

## Mahabharata

The Mahabharata, originally called Jaya - (victory)
The Mahabharata, perhaps with some justification, is said to be the longest poem in the world. 100,000 stanzas strong, the epic has a story which is as relevant to the world today as it was then. If the Greek Epics - Iliad and the Odyssey - are considered to be long, then it wouldn’t be amiss to mention that the Mahabharata is eight times longer than Homer’s two epics – combined!

## Ramayana and Mahabharata

Tradition has it that the Ramayana (see Ramayana) was written at the date much earlier than the Mahabharata. History, however, tells another tale. Polyandry was an early Aryan concept. Further, the Ramayana talks about the southern peninsula, which had not been penetrated into in the time of the Mahabharata.
# Hydroculture/Horticultural aquaponics/Instructional hints and tweaks

Here are additional hints and instructions for building aquaponics related projects.

### Common tools

* drill bit
** A hand socket screwdriver and a drill bit will work as a hand-drill
* hole saw or gardening clippers
** with guiding drill works well for smaller hole saw sizes with a hex-bit hand screwdriver; gardening clippers are as capable of making as neat of holes as a hole saw; for an electrical drill a short guiding drill may slip easily
* For advanced projects
** pipe cutter, pvc primer and cement

#### Safety precautions

Some tools can be dangerous if misused or if extra care is not taken. Razor blades are more dangerous than kitchen knives, and should always point away from the body including fingers when used. It is important to read handtool safety and General Hand and Power Tool safety (OSHA) before continuing.

### Common materials

All component materials should be nonhazardous/noncancerous.
* basic timer with output plugin (enough to turn on for at least a few minutes 3 times a day)
* substrates that have a high air to water ratio, such as: expanded clay, parboiled rice husks (I'm uncertain if growstones being of glass material are safe for the fish)
* net-pots
* led (or other type) high lumen output light bulb (lamp or candelabra type may be practical)

### Watertight seals

To make a watertight seal: put one o-ring on the male connection, place through hole in plastic material. Then, on the other side of the plastic material, add another o-ring, and screw on the female connector to the male connector. Corresponding components must be of the same size. Connection must be snug. Check for drips.
Threaded fittings with o-rings can reduce or eliminate the need for pipe cement. If the desired threaded fitting is not available in PVC form, galvanized or brass fittings will connect to PVC fittings. Any metallic connector should be non-lead. PVC primer and glue can also be used.

## Tweaks

Add a second tote solely for the fish. (leave the lid off of this tote). Obviously both containers are to be at the same level. Run a tube with required o-rings and connectors between totes. Place halfway between bottom and water level, in case of a failure, not all of the water will drain out. Make sure this setup is watertight and ready before adding fish. Water will transfer passively. A second air pump and a aeration stone is required in this setup.
To make this system even fancier, have a pipe with a diameter enough for fish to travel between both totes.
Alternatively like in typical aquaponic systems, use a pump to periodically transfer water between totes.
Other tweaks include: having separate timers for the air-lift pumps, water pumps, and lighting. Running DC made components on solar power is another tweak.
# Hydroculture/Horticultural aquaponics/Instructional hints and tweaks/Tool safety

Some tools can be dangerous if misused or if extra care is not taken. Razorblades are very dangerous, and should always point away from the body including fingers when used. It is important to read handtool safety and General Hand and Power Tool safety (OSHA).