Art His­to­ry and Com­put­er Sci­ence are two dis­ci­plines with dif­fer­ent approach­es in doing research. Nev­er­the­less, the dig­i­tal mate­r­i­al on art that is increas­ing­ly pro­vid­ed by muse­ums or oth­er cul­tur­al insti­tu­tions with their online col­lec­tions is inter­est­ing for both sides. While art his­to­ri­ans have new pos­si­bil­i­ties to access, use, ana­lyze and inter­pret their source mate­r­i­al, com­put­er sci­en­tists find datasets of art­works and thus images and meta­da­ta with quite par­tic­u­lar char­ac­ter­is­tics. The chal­lenge for art his­to­ri­ans is to acquire the nec­es­sary tech­no­log­i­cal knowl­edge and to apply suit­able tools in order to draw upon the strengths a dig­i­tal work­flow offers. These are pre­req­ui­sites for being able to ade­quate­ly apply the advan­tages of tech­nol­o­gy to study art his­to­ry in a dig­i­tal envi­ron­ment and, at the same time, to estab­lish new meth­ods for the dis­ci­pline. Com­put­er sci­en­tists are inter­est­ed in cul­tur­al data as a basis to devel­op appli­ca­tions and tools. Or they are curi­ous to test it with exist­ing pro­to­types to see what mod­i­fi­ca­tions are in need that these remain use­ful for that kind of data. This coin­cides with recent advances in com­put­er sci­ence that par­tic­u­lar­ly include top­ics in areas with a focus on visu­al data, like com­put­er vision or machine learn­ing with images, as well as all aspects around infor­ma­tion retrieval and the seman­tic web.

Sev­er­al inter­est­ing resources from cul­tur­al insti­tu­tions exist such as the Rijksmu­se­um, the Tate, but also on Euro­peana that pro­vide images of art­works from their col­lec­tions or at least the cor­re­spond­ing meta­da­ta that can be reused. The Get­ty Vocab­u­lar­ies, e.g., the Unit­ed List of Artist Names (ULAN), are a pow­er­ful resource to enrich the meta­da­ta pro­vid­ed by the muse­ums. Such a record includes infor­ma­tion about spelling vari­ants of the artist’s name across dif­fer­ent lan­guages, her or his birth and death place, fur­ther bio­graph­i­cal facts and rela­tion­ships with oth­er artists. This small overview alone makes clear that tech­no­log­i­cal­ly far more is pos­si­ble than just using the data­bas­es to find images through a search mask and use these to tra­di­tion­al­ly study the art­work, com­pare it with oth­er exam­ples and write a text about it. Instead, by using the pow­er of the seman­tic web, net­works can be cre­at­ed with com­pu­ta­tion­al meth­ods and allow to get an overview about a spe­cif­ic data set, which ide­al­ly allows to get new insights, dif­fer­ent inter­pre­ta­tions and inter­est­ing con­clu­sions. Numer­i­cal val­ues, such as col­or, amounts of dig­i­tal objects or infor­ma­tion etc., can be processed eas­i­ly with com­put­er tech­nol­o­gy. This means that sta­tis­tics become impor­tant, what is con­trary to the usu­al meth­ods in art his­to­ry. To use the data pro­vid­ed in dig­i­tal art col­lec­tions, e.g., in a data visu­al­iza­tion – good exam­ples can be found on Google Arts & Cul­ture Exper­i­ments –, it is nec­es­sary to have the abil­i­ty of how to use these APIs.

There is often a gap between the knowl­edge and the skills of art his­to­ri­ans on the one hand and com­put­er sci­en­tists on the oth­er. Where­as in art his­tor­i­cal projects usu­al­ly a sat­is­fy­ing use of tech­nol­o­gy is miss­ing, inno­v­a­tive appli­ca­tions that use dig­i­tal art col­lec­tions are often inter­est­ing from a tech­no­log­i­cal point of view and allow to pre­dict future devel­op­ments of, e.g., image search meth­ods, but lack of pro­vid­ing in-depth knowl­edge of wider art his­tor­i­cal rela­tions or cul­tur­al con­texts that are relat­ed to the art­works from dif­fer­ent peri­ods. It is impor­tant to take both sides equal­ly into account for sophis­ti­cat­ed tools or appli­ca­tions. There­fore, the idea of Cod­ing Dür­er is to offer art his­to­ri­ans and infor­ma­tion sci­en­tists a chance to col­lab­o­rate in mixed groups to find chal­leng­ing ideas and to ini­ti­ate a fruit­ful dia­log between both dis­ci­plines. The main focus is to enable the col­lab­o­ra­tion in inter­dis­ci­pli­nary groups. This means that the largest part of the time is reserved to work on small, exper­i­men­tal projects with the aim to present a pro­to­type at the end of the week. A few pre­sen­ta­tions by invit­ed speak­ers dur­ing the week offer inputs from oth­er per­spec­tives. It will be also pos­si­ble that par­tic­i­pants give an insight in own projects that are of inter­est for the plenum.