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Found 3 translations.

NameCurrent message text
 h English (en)'''Cube:'''
Although our sample is very small physically (only 350x300x60 microns), in the scale at which we are working the dataset is very large. This area has been divided into many smaller spaces which are also called [[The_Eyewire_Lexicon#V|volumes]]. The size of one of these volumes is 4.1 x4.1 x5.6 microns or 4100x4100x5600 nm. (As you look at your computer screen's resolution, one voxel, the smallest unit, represents an area approximately equal to 16.5x16.5x23 nm. Thus each cube consists of about 256^3  = 1,6777,216 voxels.)  Each of these volumes contain sections of dendrites from hundreds of neurons. In order to manage this complexity, players trace one cell at a time as it passes through these spaces. Out of one volume many cubes with individual identification numbers are created, one for each of the different neurons passing through that volume or space in the dataset. As we play Eyewire, our tasks are to follow the traces of each dendrite through each relevant space. These [[The_Eyewire_Lexicon#T|task]]s are called cubes. Our cubes overlap each other at their edges so a trace that is close enough to an edge will have an iteration (a trace that is the same) in the next cube. If a trace is near a place where two edges meet it may have as many as 4 iterations, and those near the intersection of three edges of the cube may have as many as eight iterations.  This is important to know when correcting a merger or adding a missing part of the trace.
 h Spanish (es)''' ''Cube'' | Cubo:'''
Pese a que el tamaño de nuestra muestra es pequeño (apenas 350x300x60 μm), la escala en la que manejamos el conjunto de datos es muy grande. Dicha muestra se ha dividido en pequeños fragmentos llamados [[The_Eyewire_Lexicon/es#V|volúmenes]]. Cada volumen mide 4.1x4.1x5.6 μm (4100x4100x5600 nm). (Del mismo modo en que ves la resolución de tu pantalla, un voxel, la menor unidad de medida, equivale a 16.5x16.5x23 nm. De esta forma, cada cubo se compone de 256³ = 16777216 voxeles.) Cada uno de éstos contiene segmentos de dentritras de cientos de neuronas. Para manejar tal complejidad, los jugadores trazan célula por célula en la medida que avanzan por estas zonas. En cada volumen, se crearon números de identificación para la mayoría de los cubos, uno para cada clase de neurona contenido en ese volumen o espacio del conjunto de datos. Jugando en EyeWire, nuestra tarea es rastrear cada dendrita de cada espacio relevante. Estas [[The_Eyewire_Lexicon/es#T|tarea]]s se llaman cubos. Nuestros cubos solapan sus bordes, por lo que un trazo suficientemente cerca del borde tendrá una iteración (un trazo idéntico) en el cubo siguiente. Si un trazo está suficientemente cerca del contacto entre dos bordes, puede tener hasta cuadro iteraciones y para los que están cerca de la convergencia de tres bordes, pueden tener hasta ocho iteraciones. Esto es importante para corregir fusiones o añadir o quitar segmemtos.
 h Korean (ko)'''큐브 : '''
Although our sample is very small physically (only 350x300x60 microns), in the scale at which we are working the dataset is very large. This area has been divided into many smaller spaces which are also called [[The_Eyewire_Lexicon/ko#V|volumes]]. The size of one of these volumes is 4.1 x4.1 x5.6 microns or 4100x4100x5600 nm. (As you look at your computer screen's resolution, one voxel, the smallest unit, represents an area approximately equal to 16.5x16.5x23 nm. Thus each cube consists of about 256^3  = 1,6777,216 voxels.)  Each of these volumes contain sections of dendrites from hundreds of neurons. In order to manage this complexity, players trace one cell at a time as it passes through these spaces. Out of one volume many cubes with individual identification numbers are created, one for each of the different neurons passing through that volume or space in the dataset. As we play Eyewire, our tasks are to follow the traces of each dendrite through each relevant space. These [[The_Eyewire_Lexicon/ko#T|task]]s are called cubes. Our cubes overlap each other at their edges so a trace that is close enough to an edge will have an iteration (a trace that is the same) in the next cube. If a trace is near a place where two edges meet it may have as many as 4 iterations, and those near the intersection of three edges of the cube may have as many as eight iterations.  This is important to know when correcting a merger or adding a missing part of the trace.