Categories
Free Software Generative Art Glitch Art Projects

Glitcherature In Emacs 2

glitcherature-mode for Emacs has been updated to add new functions for applying multiple commands to words, sentences and paragraphs, randomly or in order. There are also new commands to sort characters, to copy structure from one text another and to render a falling rain effect.

You can get the code and instructions here:

https://gitorious.org/robmyers/glitcherature

Here’s an extreme example of what used to be Sherlock Holmes.

“T oSH anYo HeLMNa ms heINSH iSEYsHEwo, MaNIhaV  eSE-LDOmhEA Rd HImM eNti ONHe rUNDE. rI tWA StNortha
EeElt AYeSeSh  EecliPsE SAn dpr EdOMInaTE SThew holE OF hERSex ons Adt atT HPFRT
ICuNYLMot, iONAK iNt ol,oVEF or IreneADLERALl, emOt iaBLybNlAT He DONEDa HEW AL
aRIYwerE Abho,RRenttOHi  S ColDPreCi sEbuT adMi rv inGmacH NCet Min tewor
SHtaK, eIt the mostper fECt re aSonInGA Nd OBSER LfiNaF, INseHA tiThO NHl dEa
SSeENbuTas Al OVE rhEw, oUl dhaV ep laCe dhIMSEA NDAanEA lTp oSwiREAd, ENa
vERSp, Ok eOftheSoftER PAssiONS saVeWi tHaGib EN Gth Esv eErf rHEYMe. es mom
IREBlEthI NGS , forTHeObSEr VErE xC elL ENTfo rd RAwiS, UCh ntiLsIoo mIn nohiSti
VNSAndacTION sBUtfORThetraInEDrEAS on ERToadmittR oD uCIArUiST nsT ITgfA cOwDE,
liC ATEa NdfinE  LyAD jUst ED t eMpEr aME ntWA StoIntSGr ET dARacsin IV eIT
orRWhiCHMightTh.Rowad ouBTUp onAl  L HIsMe NTal rESUl SwouidI nT bSE noRtDI: sT
NSt iUmEntO  R acRAck i  No NEOfhIsO WnH IGhpoWe RLeNsE, AN DyE lTnoreEm Be tO
NEU RbM ngthA Na STR ongemOtIoNin an AtuREsuC hA ShisofDtbIh, eSA waSquus TI OnW
oeaNtOHI MaNdthaTW o Manwa. stHelate IrenEAdler yMy UaroUagendde rif teA
BLsmEM oRY

Categories
Art Projects

Proof of Existence

Genome Bitcoin Address

I have placed the hash of my genome into the Bitcoin Blockchain:

SHA256:bada4cf5328394f733cd278c33509e79b839cc0b0838658503b116d6ca9ca14b

Address:1KXH7jSTwLi9FLo6MpNUPnHGvEETfuaKhz

This proves my existence.

Categories
Art History Art Open Data Projects

Exploring Tate Art Open Data 0

Why visualise the Tate’s collection dataset?

The Tate is the UK’s largest art institution. The free and open release of Tate’s collection data shows just how far open data has come in the last decade, and makes a major resource available for study. This resource allows us to follow two lines of investigation.

The first is into the history of art, using the Tate’s collection as a model of art in general, particularly of British art. The Tate’s collection data describes the form, content, attribution and dates of a sample of art from the past several hundred years. This is a history of art, and as long as we place it in its historical context it can be a useful one.

The second is institutional critique, to analyse the Tate’s collection and contrast it with other collections and with other models of the history of art (verbal, data-based or otherwise). Rather than allowing or controlling for the historical context of the data this makes recovering and examining that context the focus.

It’s possible to succeed or fail at each, and neither requires taking the claims of Museums to represent history or of data to represent reality at face value or in a vacuum. Data visualisation and statistical analysis are ways of dealing with datasets that would take a human reader many years to examine. They are forms of rhetoric, but they are also useful tools.

With suitable modesty of aims and suitable reflection on the historical and political contexts which have given rise to our tools and materials, let us begin…

Categories
Art Art Computing Free Software Generative Art Glitch Art Projects

Glitcherature in Emacs

Glitcherature is glitch literature, glitch aesthetics applied to text. “Kathy Acker uploaded by Bryce Lynch“, as I said of Orphan Drift’s novel “Cyberpositive“.

I’ve converted my earlier glitcherature Python code experiments to a mode for the powerful and easily hackable Emacs text editor. You can get the code here:

https://gitorious.org/robmyers/glitcherature/

Installing and enabling glitcherature-mode in Emacs allows you to glitch text by introducing OCR scanning errors, inserting random characters around words or lines, converting text to binary or 1337 and more. Additional functions are planned, and here’s an example of what you can do already:

Uly_R_

STATELY, plump Buck Mulligan came from the STAIRHEAD, BEARING A bowl of LATHER on which A MIRROR AND a RAZOR LAY CROSSED. A YELLOW DRESSINGGOWN, ungirdled, was SUSTAINED GENTLY BEHIND him ON THE MILD morning AIR. He HELD THE bowl ALOFT and INTONED:

—Introibo AD ALTARE DEI.

HALTED, HE PEERED down THE DARK winding stairs AND CALLED out coarsely:

—Come up, Kinch! Come up, you fearful jesuit!

Solemwly he came fo_ard awd mouw_d the rouwd guwre_t. He f__ _ut awd ble___ gravely thri_ the tower. the _urrouwdiwg lawd awd the _aki wg mouwtaiw _. Thew. ca_hiwg _ight of S_phew Dedalu_. he bewt toward_ him awd made r_id cro__e_ iw the _ r. gurgl iwg iw hi_ thr_t awd _hakiwg hi_ hed. Stephew _lu_. di_J

Buck Mulligan peeped an instant under the mirror and then covered the bowl smartly.

—Back to barracks! he said sternly.

He added in a preacher’s tone:
___*_****)**%)*%*)***_**__%_*_))_*_%_%%_)*_*)%))_%)_)*_%*%%_%%)_**__)__*%_%%%)%_
—For this, O dearly beloved, is the genuine Christine: body and soul and blood and ouns. Slow music, please. Shut your eyes, gents. One moment. A little trouble about those white corpuscles. Silence, all.

He peered sideways up and gave a long slow whistle of call, then paused awhile in rapt attention, his even white teeth glistening here and there with gold points. 010000110110100001110010011110010111001101101111011100110111010001101111011011010110111101110011. Two strong shrill whistles answered through the calm.

—Thanks, old chap, he cried briskly. That will do nicely. Switch off the current, will you?
%*))__)*%)**_**%*))%%)*)**%_)__)**%%%%_*%__)_**)___)))**%__%*))_)%*_%)*)_*%%*)%*
HE SKIPPED OFF the gunrest and looked GRAVELY at his watcher, gathering ABOUT HIS LEGS THE LOOSE FOLDS OF his gown. THE PLUMP shadowed face and SULLEN oval JOWL recalled A PRELATE, patron OF ARTS in the MIDDLE AGES. A PLEASANT SMILE broke quietly OVER his lips.

—The mockery of it! he said gaily. Your absurd name, an ancient C_ek
!

H£ p[]1nt£d h1s f1ng£r 1n fr1£ndly j£st /\nd w£nt []v£r t[] th£ p/\r/\p£t, l/\|_|gh1ng t[] h1ms£lf. St£ph£n D£d/\l|_|s st£pp£d |_|p, f[]ll[]w£d h1m w£/\r1ly h/\lfw/\y /\nd s/\t d[]wn []n th£ £dg£ []f th£ g|_|nr£st, w/\tch1ng h1m st1ll /\s h£ pr[]pp£d h1s m1rr[]r []n th£ p/\r/\p£t, d1pp£d th£ br|_|sh 1n th£ b[]wl /\nd l/\th£r£d ch££ks /\nd n£ck.

Bμck Mμll|g/-\n’s g/-\y v0|c€ w€nt 0n.
?::?: —My– ==name– ==is– ==absurd– ==too:– ==Malachi– ==Mulligan,– ==two– ==dactyls.– ==But– ==it– ==has– ==a– ==Hellenic– ==ring,– ==hasn’t– ==it?– ==Tripping– ==and– ==sunny– ==like– ==the– ==buck– ==himself.– ==We– ==must– ==go– ==to– ==Athens.– ==Will– ==you– ==come– ==if– ==I– ==can– ==get– ==the– ==aunt– ==to– ==fork– ==out– ==twenty– ==quid?
<<<“???

He laid the brush aside and, laughing with delight, cried:

—Will he come? The jejune jesuit!

CeaSInG, hE BeGAn TO SHaVe wItH cARE.

—Tell me, Mulligan, Stephen said quietly.

—Yes, my 1o\\//3?

—How long is Haines going to stay in this tower?

010000100111010101100011011010110010000001001101011101010110110001101100011010010110011101100001011011100010000001110011011010000110111101110111011001010110010000100000011000010010000001110011011010000110000101110110011001010110111000100000011000110110100001100101011001010110101100100000011011110111011001100101011100100010000001101000011010010111001100100000011100100110100101100111011010000111010000100000011100110110100001101111011101010110110001100100011001010111001000101110

—God, `|sn’t he dreadful? he said )=ra~kly. A pon∂erous Saxon. He thinks you’re not a gentleman. God, these bloØdy E~glis)-(! Bursting with money and indigestion. Because he comes from Oxford. You know, Dedalus, you have the real Oxford manner. He can’t make you out. O, my name for you is the best: Kinch, the knife-blade.

He shaved warily over his chin.

—He WAS raving all night about A black PANTHER, Stephen SAID. Where IS his GUNCASE?

—A woful lunatic! Mulligan said. Were you in a funk?|=-=_|||-_—_-=_=||_=_===_-|_-_-|_|=||-__-|–_=|-||_=_=-=-==|-|=|-=|-||_-=-__=-

–=_||_-__-_-=|-=_=_=__||__—-_|_-_—=_==–|–=_-=–|=__-_|__|_-_-_||_==—_=-—I was, St3ph3n s@!d w!th 3n3rgy @nd gr()w!ng f3@r. Out here in the dark with a man I don’t know raving and moaning to himself about shooting a black panther. You saved men from drowning. I’m not a hero, however. If he stays on here I am off.

Buck.Mulligan..frowned.at…the.lather..on…his..razorblade…He…..hopped.down..from…..his.perch…and..began….to…..search….his….trouser.pockets..hastily.

—Scutter! he cried thickly.

H3 c@m3 ()v3r t() th3 g\/nr3st @nd, thr\/st!ng @ h@nd !nt() St3ph3n’s \/pp3r p()ck3t, s@!d:

—Lend us a loan of your noserag to wipe my razor.

Stephen—-suffered—-him—-to—-pull–out–and—hold–up-on——show—–by—its–corner—a—-dirty——crumpled——handkerchief.–Buck—-Mulligan–wiped—–the–razorblade—–neatly.-Then,—gazing——over—the–handkerchief,-he–said:

000101000101010001101000011001010010000001100010011000010111001001100100001001110111001100100000011011100110111101110011011001010111001001100001011001110010000100100000010000010010000001101110011001010111011100100000011000010111001001110100001000000110001101101111011011000110111101110101011100100010000001100110011011110111001000100000011011110111010101110010001000000100100101110010011010010111001101101000001000000111000001101111011001010111010001110011001110100010000001110011011011100110111101110100011001110111001001100101011001010110111000101110001000000101100101101111011101010010000001100011011000010110111000100000011000010110110001101101011011110111001101110100001000000111010001100001011100110111010001100101001000000110100101110100001011000010000001100011011000010110111000100111011101000010000001111001011011110111010100111111

^_#_!#!^&*)_)&@_((^%^()^_*$#(#*^^@)#^(@&^@_^$)*&^#@*$$*&!((&)&%_*&^^*^)^($)&)(#_

He mounted to the parapet again and gazed out over Dublin bay, his fair oakpale hair stirring slightly.

)^*%#%*#@$**&&)%%&!#^*&!(^_%)!)(!^)&#)!(*&^*(*^#)@^&*)@*#_^^*#(&&!^&(#&@#%^##)@#

—God! he said quietly. I$|\|’7 7|-|3 $3@ vv|-|@7 A16`/ (@11$ !7: @ 6®3`/ $vv337 44()7|-|3®? T|-|3 $|\|()76®33|\| $3@. T|-|3 $(®()7\/447!6|-|73|\|!|\|6 $3@. E|*! ()!|\|()|*@ |*()|\|7()|\|. A|-|, D3|)@1\/$, 7|-|3 G®33|<$! I 44\/$7 73@(|-| `/()\/. Y()\/ 44\/$7 ®3@|) 7|-|344 !|\| 7|-|3 ()®!6!|\|@1. T|-|@1@77@! T|-|@1@77@! S|-|3 !$ ()\/® 6®3@7 $vv337 44()7|-|3®. C()443 @|\||) 1()()|<.

Categories
Art Computing Art History Art Open Data Projects

Exploring Tate Art Open Data 2

This is the second in a series of posts examining Tate’s excellent collection dataset. You can read the first part here.The R and R Markdown code for this series is available at https://gitorious.org/robmyers/tate-data-r/ .

As before, let’s get started by loading the data.

source("../r/load_tate_data.r")

Movement Artwork Counts

Next let’s load some code to visualize the number of artworks in the collection categorized as being produced by a particular movement each year.

source("../r/movement_artwork_counts.r")

You can see the code in the Git repository above. It loads the Tate collection data files and declares some functions that we can use to plot movement artwork counts.

We can plot the number of artworks from a given movement, for example the Young British Artists (YBAs):

plotMovementFrequency("Young British Artists (YBA)")

YBAs
Or we can plot the combined counts for multiple movements, for example those since 1800:

plotArtworkCountsByYear()

Movements Since 1800
These figures are available as PDFs in the Git repository.

Movement Durations

When did a movement start and end, and how long did it last? We can plot this for movements as defined by the date of production of the artworks labelled as being part of that movement in the Tate collection.

source("../r/movement_durations.r")

First by movement name:

plotMovementDurations(movement.durations.alpha, movement.order.alpha)

Movements By Name

And then by movement start date:

plotMovementDurations(movement.durations.from, movement.order.from)

Movements By Start Date

These figures are also available as PDFs in the Git repository.

Movement Influences

We can use artists who are in two or more movements as links between movements, constructing a network graph of social connections between movements.
Like the Wikipedia data-based update of Alfred Barr’s handmade diagram for the MoMA Cubism & Abstract Art exhibition of 1936 Collectivizing The Barr Model we can extract a family tree (or Rhizome) of influence between art movements and otherwise use network analysis methods to study the social network of art movements:

plotMovementArtistLinks()

Movements Connected By ArtistsAgain, this figure is also available as PDFs in the Git repository.

Conclusions

As you can see some of these graphics work better as posters or large-scale PDFs than as bitmaps. There’s much that could be done with curve fitting and comparison of movement artwork counts. And all the techniques of social network analysis can be applied to the graph of artists and movements.

Next we’ll look at artwork genres, which are not explicitly labelled in the collection dataset.

Categories
Generative Art Magick Projects

Crypto Sigils

sigil

A cryptographic hash function is a piece of computer code that take a piece of data and produce a (hopefully) unique short string representing it. This string will in no way resemble the input data, and you will not be able to guess the input data from it. The function always outputs the same string for the same data, and changing the data will change the output string.

For example I can use the SHA256 function on the UNIX command line to make a unique representation of my name:

# echo rob | sha256sum 
30d71981944699f23038164f4eb8189950b4dcf9b39ea2c1ecbda13aea8b7d4a  -

And if I do this again I get the same result:

# echo rob | sha256sum 
30d71981944699f23038164f4eb8189950b4dcf9b39ea2c1ecbda13aea8b7d4a  -

But if I add just one extra character I get different result:

# echo robM | sha256sum 
731a1886a0005b3504805845eeecfac3a0839a651d383f242242d0df2f568ec8  -

And importantly the amount of difference in the input has no effect on the amount of difference between the output strings:

# echo robN | sha256sum 
58bf3ee9cae6247705d1262c048cc71d28924f2cff04ada514f8240ce3555bec  -

So the outputs of cryptographic hash functions produce identities for data that can be used to uniquely refer to the data but do not disclose the content of the data.

Hash functions achieve this by feeding the data through a complex mathematical transformation. This is a mapping through mathematical space that maintains identity and difference while occulting content.

Much like a sigil.

It’s true that if one knows the word or words abstracted to make a sigil one can recognize their traces in the sigil. But these traces are a means to an end, they are a way of producing a striking and unique new identity to focus on and invest in.

More cryptographic hash strings are created every hour than sigils have been made in the entirity of human history. Billions of mappings through mathematical platonic space to establish, conceal and communicate identity. Their consensual reality and status as exports from the platonic realm of mathematical objects make them ideal magickal material.

A full 32-byte SHA256 hash is a lot to memorize, although doing so is a feat that could be ritually powerful. It may be enough to abstract it to its first few digits, as Git commits do. We don’t need to use the hexadecimal (base-16: 0123456789ABCDEF rather than base-10: 0123456789) digit strings that are the usual human readable output of hash functions. An HTML-style colour can be represented with three or six hexidecimal digits, for example blue is 0000FF or OOF. We can choose a unique colour using the first six digits of the hash.

For example:

echo this is my intent | sha256sum 
1b0fd74346abfe6858b12b8e3036649a63c09f2a049634dfe3c835f32422f58e  -

As an HTML colour this is #1b0fd7:

Hex Digest as Colour

We can also use pairs of digits as positions on a 16×16 grid, or more digits for a larger grid, or three groups of digits to produce a three dimensional path for 3D printing or importing into virtual reality.

Here’s a simple Python example:

import hashlib

digest = hashlib.sha256()
digest.update(" the spammish repetition")
digest_string = digest.hexdigest()
digest_numbers = [int(char, 16) for char in digest_string]
coords = [digest_numbers[i:i+2]
           for i in range(0, len(digest_numbers), 2)]
print "%!ps\nnewpath"
print "%i %i moveto" % tuple(coords[0])
for coord in coords[1:]:
    print "%s %s lineto" % tuple(coord)
print "0.25 setlinewidth\nstroke"

You can see the output of this program rendered at the top of this article. We can combine this with colour (or render the colours of the hash as a grid of coloured squares).

Another way of generating visual forms from hashes is using shape grammars, as used by libvisualid. Here’s “this is my intent” rendered by libvisualid:

this is my intent

Hashes can be attached to emails or tweets to place and circulate them in the world. Or they can be placed into the Bitcoin blockchain using a system such as https://github.com/vog/bitcoinproof, to be rehashed constantly as the Bitcoin blockchain is updated. Here’s the hash for a spell in the blockchain:

1Eui1Wje41JEJ4W1QYWbSAYG4h7JBaoPXQ

We can use a system similar to Bitcoin’s proof-of-work system to find auspicious hashes for data, those that start with a run of leading zeroes or some other number (or target string or bitmap encoded as a number).

More Python:

import hashlib
import binascii

target = "0000"
complement = -1
digest_numbers = ""

while not digest_numbers.startswith(target):
    complement = complement + 1
    digest = hashlib.sha256()
    digest.update("this is my intent")
    digest.update(str(complement))
    digest_numbers = digest.hexdigest()
    print "%d %s" % (complement, digest_numbers)

print binascii.b2a_uu(str(complement))
print binascii.b2a_base64(str(complement))

And its output, which is the key to creating an auspicious hash of the input string:

0 eae2ffcee00aa95306e706dd4bc67ab6b9fd2ffe61b32dfe4177b76c0afd682d
1 84ba18490876919df8bbff194eeb861c6c44a27e9bfbd8db485ecf704e41fcbd
2 f53226b118fa492dc21cd4336d67b4c8ce4148e49e8e4b094baf3e5ecff688ba
[...]
74962 38d5f823e881857f031def1822a28546d29b40903959b1c9bf1f5a1bebd42d9e
74963 b906fd259413ac714de31b9acaf6f0e5268560221d07f557f0f491a081a2cd09
74964 00006dd9f148ca454d331179bd7c87b42d7ab734df7738e1ae90e25013f02a1d
%-S0Y-C0 

NzQ5NjQ=

%-S0Y-C0 and NzQ5NjQ= are different representations of the number 74964. They can be used to create sigils, or the number could be represented verbally using a mnemonic generator.

There’s more that can be done with cryptographic hashes and with cryptographic signing, which I haven’t covered in this article. But hopefully these examples can inspire further experimentation.

(All code licensed CC0.)

Categories
Art History Art Open Data Free Culture Projects

Exploring Tate Art Open Data 1

This is the first in a series of posts examining Tate's excellent collection dataset available at http://www.tate.org.uk/about/our-work/digital/collection-data .

I've processed that dataset using code for Mongo DB and Node.js available at https://gitorious.org/robmyers/tate-data/ .

The R and R Markdown code for this series is available at https://gitorious.org/robmyers/tate-data-r/ .

This document has been produced using Knitr. Text in light grey boxes is R code or the output of that code.

Let's get started by loading the data.

source("../r/load_tate_data.r")

That file reads the comma separated value (csv) files containing information about the Tate's collection and generates some useful extra tables of information. Now we have everything in memory we can start examining the collection data.

Artists

What can we find out about artists in general?

summary(artist[c("name", "gender", "dates", "yearOfBirth", "yearOfDeath", "placeOfBirth", 
    "placeOfDeath")])
              name         gender                 dates     
 Bateman, James :   2         : 112   dates not known:  59  
 Doyle, John    :   2   Female: 521   born 1967      :  42  
 Hone, Nathaniel:   2   Male  :2894   born 1936      :  38  
 Peri, Peter    :   2                 born 1930      :  36  
 Stokes, Adrian :   2                 born 1938      :  36  
 Wilson, Richard:   2                 born 1941      :  34  
 (Other)        :3515                 (Other)        :3282  
  yearOfBirth    yearOfDeath                      placeOfBirth 
 Min.   :1497   Min.   :1543                            : 491  
 1st Qu.:1855   1st Qu.:1874   London, United Kingdom   : 446  
 Median :1910   Median :1944   Paris, France            :  57  
 Mean   :1887   Mean   :1920   Edinburgh, United Kingdom:  47  
 3rd Qu.:1941   3rd Qu.:1982   New York, United States  :  43  
 Max.   :2004   Max.   :2013   Glasgow, United Kingdom  :  35  
 NA's   :57     NA's   :1309   (Other)                  :2408  
                    placeOfDeath 
                          :2079  
 London, United Kingdom   : 442  
 Paris, France            :  82  
 New York, United States  :  45  
 Roma, Italia             :  22  
 Edinburgh, United Kingdom:  18  
 (Other)                  : 839  

There are more male than female artists, and the yBA and Pop generations lead the births.

Depending on whether we treat place of birth or place of death as more representative, London and Paris are ahead of New York or Edinburgh.

We can smooth out the birth and death dates by grouping them by decade or century.

summary(artist.birth.decade)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1500    1860    1910    1890    1940    2000      57 
summary(artist.death.decade)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1540    1870    1940    1920    1980    2010    1309 
sort(table(artist.birth.decade), decreasing = TRUE)
artist.birth.decade
1940 1930 1960 1920 1970 1900 1950 1910 1880 1890 1860 1870 1840 1780 1800 
 363  285  256  255  222  217  197  186  153  151  136  123   77   72   69 
1850 1820 1830 1980 1790 1810 1760 1770 1740 1750 1730 1700 1720 1710 1630 
  69   67   65   58   57   49   45   44   42   38   31   27   15   13   12 
1680 1640 1660 1600 1580 1590 1610 1650 1690 1620 1990 2000 1500 1530 1540 
  10    9    8    6    5    4    4    4    4    3    3    3    2    2    2 
1550 1560 1670 1570 
   2    2    2    1 

summary(artist.birth.century)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1500    1900    1900    1890    1900    2000      57 
summary(artist.death.century)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1500    1900    1900    1920    2000    2000    1309 
sort(table(artist.death.decade), decreasing = TRUE)
artist.death.decade
2000 1980 1960 1990 1970 1940 2010 1920 1930 1950 1900 1910 1840 1860 1880 
 224  191  172  157  140  131  112  102   92   89   80   69   59   59   54 
1850 1870 1890 1820 1830 1800 1810 1780 1790 1700 1760 1770 1750 1720 1730 
  53   49   49   46   44   42   40   24   23   15   14   12   10    7    7 
1740 1680 1710 1640 1690 1620 1650 1660 1570 1670 1600 1630 1540 
   7    6    6    5    5    4    4    4    3    3    2    2    1 

That's quite a different result from that suggested by the yearly results. Decade-wise, birth percentiles are clustered around the turn of the 20th century, deaths around the second world war. But the largest number of births are in the 1930s/1940s with the 1960s coming in second. The deaths look like they reflect the distribution of births, although it would be useful to confirm this statistically.

The maximim birth being in the 2000s doesn't mean that the Tate is collecting child artists, the birth data also includes the years that artist groups were started.

How well is gender represented in the collection?

table(artist.birth.decade, artist$gender)

artist.birth.decade     Female Male
               1500   1      0    1
               1530   1      0    1
               1540   0      0    2
               1550   0      0    2
               1560   0      0    2
               1570   0      0    1
               1580   0      0    5
               1590   1      0    3
               1600   3      0    3
               1610   1      0    3
               1620   0      0    3
               1630   0      1   11
               1640   0      0    9
               1650   0      0    4
               1660   0      0    8
               1670   1      0    1
               1680   0      0   10
               1690   0      0    4
               1700   4      1   22
               1710   0      0   13
               1720   0      1   14
               1730   0      0   31
               1740   1      1   40
               1750   0      3   35
               1760   0      1   44
               1770   0      1   43
               1780   1      5   66
               1790   1      0   56
               1800  10      0   59
               1810   0      2   47
               1820   0      1   66
               1830   1      6   58
               1840   0      5   72
               1850   0      2   67
               1860   1     10  125
               1870   0     15  108
               1880   4     23  126
               1890   4     18  129
               1900   8     38  171
               1910   3     37  146
               1920   2     33  220
               1930   4     38  243
               1940  12     62  289
               1950   2     40  155
               1960   6     77  173
               1970   8     70  144
               1980   3     21   34
               1990   2      0    1
               2000   2      0    1

table(artist.birth.century, artist$gender)

artist.birth.century      Female Male
                1500    2      0    5
                1600    5      1   44
                1700    6      4  157
                1800   13     24  576
                1900   39    293 1667
                2000   22    190  422

The first, unlabelled, column is for artists whose gender is not currently recorded in the data.

As we saw in the summary, there are more male artists than female artists in the Tate's collection. There is no decade or century in which this trend is reversed. The story is slightly different when we look at artistic movements.

Movements

The data for artists includes information on


Error in movements$movement.name : 
  $ operator is invalid for atomic vectors

artists movements. If we looked at the artwork data there might be more, but we'll stick with the artists for now.

summary(artist.movements[c("artist.fc", "artist.gender", "movement.era.name", 
    "movement.name")])
                       artist.fc   artist.gender
 Ben Nicholson OM           :  6         :  5   
 Dame Barbara Hepworth      :  5   Female: 27   
 Gilbert Soest              :  5   Male  :324   
 Joseph Beuys               :  5                
 Sir Peter Lely             :  5                
 British School 17th century:  4                
 (Other)                    :326                
              movement.era.name
 16th and 17th century : 47    
 18th century          : 27    
 19th century          : 63    
 20th century 1900-1945: 95    
 20th century post-1945:124    


                                 movement.name
 Performance Art                        : 14  
 Conceptual Art                         : 10  
 Netherlands-trained, working in Britain: 10  
 Constructivism                         :  9  
 Body Art                               :  8  
 British Surrealism                     :  8  
 (Other)                                :297  
summary(artist.movements$movement.era.name)
 16th and 17th century           18th century           19th century 
                    47                     27                     63 
20th century 1900-1945 20th century post-1945 
                    95                    124 
summary(artist.movements$movement.name)
                         Performance Art 
                                      14 
                          Conceptual Art 
                                      10 
 Netherlands-trained, working in Britain 
                                      10 
                          Constructivism 
                                       9 
                                Body Art 
                                       8 
                      British Surrealism 
                                       8 
                          St Ives School 
                                       8 
                         Victorian/Genre 
                                       8 
                    Abstraction-Création 
                                       7 
                         British War Art 
                                       7 
                                   Court 
                                       7 
                       Environmental Art 
                                       7 
                            Later Stuart 
                                       7 
                             Picturesque 
                                       7 
                              Surrealism 
                                       7 
                               Symbolism 
                                       7 
                              Abject art 
                                       6 
                                 Baroque 
                                       6 
                  British Constructivism 
                                       6 
                   British Impressionism 
                                       6 
                               Decadence 
                                       6 
                          Pre-Raphaelite 
                                       6 
                                Unit One 
                                       6 
                            Grand Manner 
                                       5 
                             Kinetic Art 
                                       5 
                                Land Art 
                                       5 
                              Minimalism 
                                       5 
                         Neo-Romanticism 
                                       5 
                                Tachisme 
                                       5 
                               Vorticism 
                                       5 
                      Aesthetic Movement 
                                       4 
                       Camden Town Group 
                                       4 
                      Conversation Piece 
                                       4 
                                  Cubism 
                                       4 
                            Feminist Art 
                                       4 
                        Geometry of Fear 
                                       4 
                       Neo-Expressionism 
                                       4 
                             Restoration 
                                       4 
                         Return to Order 
                                       4 
                          Seven and Five 
                                       4 
                                 Sublime 
                                       4 
                             British Pop 
                                       3 
              Civil War and Commonwealth 
                                       3 
                                    Dada 
                                       3 
                           Fancy Picture 
                                       3 
                           Fin de Siècle 
                                       3 
                           Impressionism 
                                       3 
                            London Group 
                                       3 
                    New English Art Club 
                                       3 
                      Post-Impressionism 
                                       3 
                                   Tudor 
                                       3 
             Young British Artists (YBA) 
                                       3 
                            Art Informel 
                                       2 
                             Art Nouveau 
                                       2 
                    Auto-Destructive art 
                                       2 
                          Direct Carving 
                                       2 
                      Euston Road School 
                                       2 
                          Neo-Classicism 
                                       2 
                          Neo-Plasticism 
                                       2 
                           Newlyn School 
                                       2 
                           New Sculpture 
                                       2 
                             Optical Art 
                                       2 
                                 Pop Art 
                                       2 
              Post Painterly Abstraction 
                                       2 
                                Regional 
                                       2 
                               Situation 
                                       2 
              Situationist International 
                                       2 
                  Abstract Expressionism 
                                       1 
                               Actionism 
                                       1 
                           Arte Nucleare 
                                       1 
                  Artist Placement Group 
                                       1 
       Artists International Association 
                                       1 
                                 Bauhaus 
                                       1 
                                   Cobra 
                                       1 
                        Der Blaue Reiter 
                                       1 
                                De Stijl 
                                       1 
                            Early Stuart 
                                       1 
English-born, working in the Netherlands 
                                       1 
                           Expressionism 
                                       1 
                                 Fauvism 
                                       1 
                                  Fluxus 
                                       1 
      French-trained, working in Britain 
                                       1 
                                Futurism 
                                       1 
                    German Expressionism 
                                       1 
                              Grand Tour 
                                       1 
                       Independent Group 
                                       1 
     Italian-trained, working in Britain 
                                       1 
                                    Merz 
                                       1 
                        Metaphysical Art 
                                       1 
                    Modern Moral Subject 
                                       1 
                          Modern Realism 
                                       1 
                       Neo-Impressionism 
                                       1 
                             Neue Wilden 
                                       1 
                   New British Sculpture 
                                       1 
                          Norwich School 
                                       1 
                        Nouveau Réalisme 
                                       1 
                             Orientalist 
                                       1 
                           Origine group 
                                       1 
                        Post-Reformation 
                                       1 
                                 (Other) 
                                       9 

The artists included in the most movements are some of the grand elders of British 20th Century art. Being in an art movement doesn't improve gender representation.

The most movements are post-1945. Performance art is more popular than Conceptual art, which is interesting given public discussion of state art funding in the UK. “Netherlands-trained, working in Britain” clearly isn't a movement, as with the birth dates the movement name field doesn't always describe a movement per se.

Let's break down gender by movement.

table(artist.movements$movement.era.name, artist.movements$artist.gender)

                             Female Male
  16th and 17th century    5      0   42
  18th century             0      0   27
  19th century             0      0   63
  20th century 1900-1945   0      9   86
  20th century post-1945   0     18  106
movement.gender <- table(artist.movements$movement.name, artist.movements$artist.gender)
movement.gender <- movement.gender[order(movement.gender[, 2], decreasing = TRUE), 
    ]
movement.gender[1:20, ]

                                Female Male
  Performance Art             0      5    9
  Feminist Art                0      4    0
  Abject art                  0      3    3
  Abstraction-Création        0      2    5
  Constructivism              0      2    7
  St Ives School              0      2    6
  Body Art                    0      1    7
  Camden Town Group           0      1    3
  Kinetic Art                 0      1    4
  Minimalism                  0      1    4
  Rayonism                    0      1    0
  Seven and Five              0      1    3
  Surrealism                  0      1    6
  Unit One                    0      1    5
  Young British Artists (YBA) 0      1    2
  Abstract Expressionism      0      0    1
  Actionism                   0      0    1
  Aesthetic Movement          0      0    4
  Arte Nucleare               0      0    1
  Art Informel                0      0    2

Representation improves slightly over time. Unsurprisingly, feminist art has more female than male artists represented. Abject art is a tie, and there are more than half as many female performance artists as male ones.

Artworks

There are


Error in eval(expr, envir, enclos) : object 'artwork.title' not found

artworks in the dataset.

summary(artwork[c("artist", "title", "dateText")])
                            artist                    title      
 Turner, Joseph Mallord William:39389   [title not known]: 3659  
 Jones, George                 : 1046   [blank]          : 3520  
 Moore, Henry, OM, CH          :  623   Blank            : 1995  
 Daniell, William              :  612   [no title]       : 1883  
 Beuys, Joseph                 :  578   Untitled         :  627  
 British (?) School            :  388   Mountains        :  540  
 (Other)                       :26493   (Other)          :56905  
           dateText    
 date not known: 5993  
 1819          : 2908  
 1801          : 1331  
 c.1830–41     : 1194  
 1833          : 1171  
 1831          : 1170  
 (Other)       :55362  
summary(artwork$year)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   1540    1820    1830    1870    1950    2010    5397 

JMW Turner has tens of thousands more works in the collection than the next nearest artist. Is this a glitch? No, it's due to the fact that the Tate holds the Turner Bequest of around 30,000 works on paper.

What are artworks titled? Usually Untitled, or simply no title. “Mountains” appears to be the most popular actual title, although if we stemmed or otherwise abstracted and clustered the titles other popular ones might emerge.

The most popular years for artworks are in the early 1800s. This, and possibly the titles, are again attributable to Turner. It would probably be productive to remove Turner's works on paper (or more simply just remove all Turner's works) from the dataset and try again, as his presence is clearly skewing the analysis.

Both artists and artworks have movements. Let's look at how artwork movements differ from artists.

summary(artwork.movements)
   artwork.id    
 Min.   :    22  
 1st Qu.:  6050  
 Median : 11496  
 Mean   : 21962  
 3rd Qu.: 21954  
 Max.   :114918  

                                                  artwork.title 
 [no title]                                              : 674  
 [title not known]                                       : 169  
 Untitled                                                : 116  
 Insertions into Ideological Circuits 2: Banknote Project:  54  
 Walking the Dog                                         :  39  
 Exquisite Corpse                                        :  37  
 (Other)                                                 :5894  
      year                   artwork.medium movement.era.id
 Min.   :1545   Screenprint on paper:1301   Min.   :  8    
 1st Qu.:1920   Oil paint on canvas :1113   1st Qu.:290    
 Median :1963   Lithograph on paper : 527   Median :415    
 Mean   :1936   Etching on paper    : 393   Mean   :327    
 3rd Qu.:1973   Graphite on paper   : 205   3rd Qu.:415    
 Max.   :2009   Bronze              : 113   Max.   :415    
 NA's   :303    (Other)             :3331                  
              movement.era.name  movement.id             movement.name 
 16th and 17th century : 177    Min.   :  293   British Pop     : 846  
 18th century          : 469    1st Qu.:  363   Conceptual Art  : 445  
 19th century          :1004    Median :  433   Pre-Raphaelite  : 405  
 20th century 1900-1945:1156    Mean   : 2421   St Ives School  : 400  
 20th century post-1945:4177    3rd Qu.: 1683   School of London: 373  
                                Max.   :18626   Neo-Classicism  : 310  
                                                (Other)         :4204  
summary(artwork.movements$movement.name)[1:20]
                British Pop              Conceptual Art 
                        846                         445 
             Pre-Raphaelite              St Ives School 
                        405                         400 
           School of London              Neo-Classicism 
                        373                         310 
                    Pop Art Young British Artists (YBA) 
                        246                         226 
          Independent Group              Constructivism 
                        178                         147 
            British War Art                  Minimalism 
                        141                         138 
            Victorian/Genre           Neo-Expressionism 
                        125                         111 
            Neo-Romanticism      Abstract Expressionism 
                        107                         102 
                 Surrealism            Geometry of Fear 
                         96                          84 
            Performance Art          British Surrealism 
                         81                          75 
summary(artwork.movements$movement.era.name)
 16th and 17th century           18th century           19th century 
                   177                    469                   1004 
20th century 1900-1945 20th century post-1945 
                  1156                   4177 

Pop and Pre-Raphaelitism gain in popularity, but Conceptualism and Surrealism are still popular.

Subjects

Each artwork is tagged with descriptions of the subjects that it depicts. Subjects have levels, from general to specific, which I've named the category, subcategory and subject. We can group the subjects of artworks by artists and movements to find out what their characteristic subjects were.

summary(artwork.subjects[c("artwork.title", "artwork.dateText", "category.name", 
    "subcategory.name", "subject.name")])
           artwork.title          artwork.dateText       category.name  
 [title not known]: 13992   date not known: 29732   nature      :76796  
 [no title]       :  8146   1819          : 12948   places      :60314  
 Untitled         :  2148   1833          :  5865   architecture:57507  
 Mountains        :   899   1801          :  5023   people      :52820  
 Shipping         :   462   1831          :  4817   objects     :22990  
 Walking the Dog  :   412   1840          :  4498   society     :20032  
 (Other)          :316957   (Other)       :280133   (Other)     :52557  
                      subcategory.name              subject.name   
 landscape                    : 32722   hill              :  9737  
 adults                       : 22048   wooded            :  8223  
 townscapes, man-made features: 21272   man               :  8164  
 seascapes and coasts         : 12202   figure            :  8118  
 water: inland                : 11839   townscape, distant:  7916  
 countries and continents     : 11704   England           :  7661  
 (Other)                      :231229   (Other)           :293197  
summary(artwork.subjects$category.name)[1:20]
                 abstraction                 architecture 
                       13304                        57507 
emotions, concepts and ideas                      history 
                       11583                         1948 
                   interiors         leisure and pastimes 
                        2467                         3446 
      literature and fiction                       nature 
                        2977                        76796 
                     objects                       people 
                       22990                        52820 
                      places          religion and belief 
                       60314                         4376 
                     society   symbols & personifications 
                       20032                         6242 
        work and occupations                          
                        6214                           NA 
                                                  
                          NA                           NA 
                                                  
                          NA                           NA 
summary(artwork.subjects$subcategory.name)[1:16]
                       landscape                           adults 
                           32722                            22048 
   townscapes, man-made features             seascapes and coasts 
                           21272                            12202 
                   water: inland         countries and continents 
                           11839                            11704 
        UK countries and regions cities, towns, villages (non-UK) 
                           10800                            10160 
            non-representational                 transport: water 
                            9583                             9537 
   actions: postures and motions                      UK counties 
                            9055                             8867 
                        features                         military 
                            8694                             7091 
                formal qualities    UK cities, towns and villages 
                            6934                             6695 
summary(artwork.subjects$subject.name)[1:20]
              hill             wooded                man 
              9737               8223               8164 
            figure townscape, distant            England 
              8118               7916               7661 
             river              woman           mountain 
              7549               7303               5932 
            castle             bridge              rocky 
              5298               3769               3759 
             group              coast              Italy 
              3694               3545               3509 
     boat, sailing          townscape             colour 
              3381               3157               2859 
               sea              tower 
              2810               2803 

The summary looks like Turner is skewing the results again. The subjects are mostly English landscape of the early 19th Century. But the categories are led by more non-representional subjects, before the subcategories and subjects return to landscape. People (“adults”, “man”, “woman”) emerge as popular subjects as well, indeed they are the second largest subcategory.

summary(artist.subjects[c("artist.name", "category.name", "subcategory.name", 
    "subject.name")])
                                          artist.name        category.name
 David Lucas                                    :1653   nature      :991  
 Jacques Lipchitz                               : 301   places      :551  
 Colin Lanceley                                 : 181   people      :471  
 Bernard Leach                                  : 104   architecture:345  
 Langlands & Bell (Ben Langlands and Nikki Bell):  78   abstraction :275  
 Linder                                         :  65   objects     :256  
 (Other)                                        :1091   (Other)     :584  
                 subcategory.name    subject.name 
 landscape               : 287    figure   : 157  
 adults                  : 250    England  : 144  
 weather                 : 198    wooded   : 138  
 non-representational    : 186    cloud    :  99  
 UK countries and regions: 151    man      :  84  
 animals: mammals        : 145    geometric:  74  
 (Other)                 :2256    (Other)  :2777  
summary(artist.subjects$category.name)[1:20]
                 abstraction                 architecture 
                         275                          345 
emotions, concepts and ideas                      history 
                         132                           16 
                   interiors         leisure and pastimes 
                          18                           36 
      literature and fiction                       nature 
                          36                          991 
                     objects                       people 
                         256                          471 
                      places          religion and belief 
                         551                           70 
                     society   symbols & personifications 
                         153                           51 
        work and occupations                          
                          72                           NA 
                                                  
                          NA                           NA 
                                                  
                          NA                           NA 
summary(artist.subjects$subcategory.name)[1:16]
                    landscape                        adults 
                          287                           250 
                      weather          non-representational 
                          198                           186 
     UK countries and regions              animals: mammals 
                          151                           145 
                  UK counties townscapes, man-made features 
                          129                           118 
UK cities, towns and villages                 water: inland 
                          118                            99 
             formal qualities     from recognisable sources 
                           92                            89 
         seascapes and coasts actions: postures and motions 
                           66                            62 
             transport: water                   residential 
                           57                            49 
summary(artist.subjects$subject.name)[1:20]
       figure       England        wooded         cloud           man 
          157           144           138            99            84 
    geometric         woman       Suffolk          hill        colour 
           74            62            58            52            47 
          cow         river         horse          rain      ceramics 
           40            38            37            35            32 
monochromatic   River Stour         Essex       sunbeam      farmland 
           29            28            27            27            26 

The results from artist subjects don't differ appreciably from the artwork ones. We wouldn't expect any difference, but some artworks have more than one artist or have none, so this introduces variations.

summary(movement.subjects[c("movement.name", "era.name", "artwork.title ", "category.name", 
    "subcategory.name", "subject.name")])
Error: undefined columns selected
summary(movement.subjects$category.name)[1:20]
                 abstraction                 architecture 
                        4977                         2831 
emotions, concepts and ideas                      history 
                        4486                          578 
                   interiors         leisure and pastimes 
                         619                          821 
      literature and fiction                       nature 
                         858                         5634 
                     objects                       people 
                        7516                        11828 
                      places          religion and belief 
                        2635                         1296 
                     society   symbols & personifications 
                        4097                         1843 
        work and occupations                          
                        1568                           NA 
                                                  
                          NA                           NA 
                                                  
                          NA                           NA 
summary(movement.subjects$subcategory.name)[1:16]
                          adults             non-representational 
                            4077                             3651 
                formal qualities    actions: postures and motions 
                            2500                             2138 
   clothing and personal effects                     inscriptions 
                            1602                             1402 
       from recognisable sources                             body 
                            1326                             1170 
                       landscape               universal concepts 
                            1161                             1049 
    emotions and human qualities                   social comment 
                             937                              913 
   townscapes, man-made features                      furnishings 
                             898                              868 
reading, writing, printed matter                         features 
                             820                              735 
summary(movement.subjects$subject.name)[1:20]
          woman             man          figure       geometric 
           1854            1649            1197            1191 
         colour    photographic irregular forms     head / face 
           1111             920             563             531 
       standing         England         sitting          female 
            519             503             497             476 
   printed text            text           group        gestural 
            443             428             411             389 
         wooded       landscape        man-made             sea 
            333             305             276             243 

“Insertions into Ideological Circuits 2: Banknote Project” has multiple json records with multiple movements and topics in each, so it's over-represented here. The subjects are still similar, although with more photography.

Conclusions

What can we conclude from this? The collection is dominated by male British pop artists, more from England than from Scotland or the rest of the UK. The subjects of artworks are what one would expect: landscape, human figures, abstracts. The Turner Bequest skews some of the data, and this should be accounted for or addressed in analysis. A few other artworks also skew some results.

Next we'll look more closely at artistic movements with some data visualizations.

Categories
Art Art Computing Virtual Reality

Gloves

Making music with datagloves in the 1980s:

http://www.youtube.com/watch?v=P3oy98HUs8E

Making music with datagloves in the 2010s:

http://www.youtube.com/watch?v=6btFObRRD9k

http://www.youtube.com/watch?v=p9Ah8TKopks

I’m interested in applications of VR gloves to visual art making.
There’s examples of this for 3D modelling, e.g.:

http://scratchpad.wikia.com/wiki/P5_Glove:Art

but I’m more interested in 2D image production. If anyone knows of any
examples I’d be very interested.

Heap’s use of wrist microphones could be replaced with palm cameras for
capturing image samples or video samples rather than sound samples,
making literal the eye-in-hand motif popular in logos a while back (e.g.
http://secondlife.com/525/_img/page/index/secondlife-logo.png ). These
samples could be manipulated using a glove interface providing the kind
of spatialization of sample properties that Heap demonstrates, either in
image composition or VJ applications.

The role of and constraints on bodily performance are different for
image and music making, maybe this would be more suitable for
livecoding, but I still think that a defamiliarising, flexible and
expressive interface is a useful affordance for art.

Categories
Art Art Computing Howto Projects

Bluetooth Throwies

LED throwies are light grafitti Improvised Aesthetic Devices:

http://www.instructables.com/id/E9D2ZJ3FG0EP286JEJ/

They cost around a dollar each to make. Bluetooth 4 beacons cost a lot
more and don’t transmit much information. But good-old-fashioned
Bluetooth devices transmit at least a human readable name. You can get
Arduino-compatible ones for about five dollars:

http://www.ebay.com/sch/i.html?_sacat=0&_from=R40&_nkw=bluetooth+serial+module&rt=nc&LH_BIN=1

And an arduino-compatible chip for three:

http://www.instructables.com/id/Simplest-and-Cheapest-Arduino/

Add a battery and either a magnet or (if that doesn’t mix with the
electronics…) an adhesive pad and we can make a Bluetooth Throwie for
less than ten dollars. Call it five pounds. That’s still at least five
times too much to make them comparable to LED throwies, but maybe in bulk…

So yeah, Bluetooth Throwies. Electromagnetic grafitti Improvised
Aesthetic Devices…