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Additional file 1. Analysis of the movement and force strategies applied to solve the task. We analyzed the strategies used by the subjects for accomplishing the tasks, to verify if they can provide further explanations of the results presented in the manuscript. In Experiment 1 we found that the loading conditions influenced the kinematic strategy during the position matching task. In Experiment 2 the strategy adopted for bimanual force exertion was not influenced by symmetric/asymmetric arm configurations, but by handedness or hand preference effects. Figure S1. Example of speed and force profile.

This dataset contains cytoarchitectonic maps of Area ifj1 (IFS/PreCS) in the BigBrain. The mappings were created using cytoarchitectonic criteria applied on digitized histological sections of 1 ��m resolution, cut in coronal plane. Areal borders have been detected by an oberserver-independent border definition (Schleicher 2000). Mappings are available on sections of the BigBrain and have been transformed to the 3D reconstructed BigBrain space using the transformations used in Amunts et al. 2013. From these delineations, a preliminary 3D map of Area ifj1 (IFS/PreCS) has been created by simple interpolation of the coronal contours in the 3D anatomical space of the Big Brain. This map gives a first impression of the location of this area in the Big Brain, and can be viewed in the atlas viewer using the URL below.

This dataset contains the distinct architectonic Area 7P (SPL) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to Area 7P (SPL). The probability map of Area 7P (SPL) are provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area 7P (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/AHQS-ZR8](https://doi.org/10.25493%2FAHQS-ZR8) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/C3HS-8R7](https://doi.org/10.25493%2FC3HS-8R7) The most probable delineation of Area 7P (SPL) derived from the calculation of a maximum probability map of all currently released JuBrain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53)

The dataset contains location metadata defining the spatial registration of the 6th edition of “The Rat Brain in Stereotaxic Coordinates” (Paxinos & Watson, 2007) to the MRI/DTI image volume of the Waxholm Space atlas of the rat brain (Version 1.01; RRID:SCR017124). The stereotaxic atlas of Paxinos & Watson contains revised delineations of 161 coronal, 27 horizontal and 19 sagittal diagrams from young adult Wistar rats. The schematic diagram images were spatially registered to the Waxholm Space atlas of the rat brain. The dataset consists of an .xml file with spatial metadata that can be opened in the QuickNII tool (RRID:SCR016854) to view overlay images of the two atlases. The atlas images are not shared due to copyright restrictions, but the user can use own images together with the .xml file provided here, following the procedure provided in the accompanying data descriptor file. The dataset can be used for direct comparison of anatomical landmarks and spatial correspondences of brain region delineated in the two atlases.

This dataset contains the distinct architectonic Area OP2 (POperc) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. Subsequently, the results of the cytoarchitectonic analysis are mapped to the MNI Colin 27 and MNI ICBM 152 reference spaces where each voxel is assigned with the probability to belong to Area OP2 (POperc). The probability map of Area OP2 (POperc) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of Area OP2 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/F8W5-HNB](https://doi.org/10.25493%2FF8W5-HNB) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/SDW0-YEZ](https://doi.org/10.25493%2FSDW0-YEZ) The most probable delineation of Area OP2 (POperc) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)

High-resolution bright-field microscopy images of coronal brain sections showing dopamine 1 receptor positive neurons across the late adolescent (49 days) mouse brain. The dataset consists of nine image series covering the rostral part of the brain from Drd1a-EGFP mice. For each brain, every fourth section was processed by diaminobenzidine (DAB) immunohistochemistry using a polyclonal anti-GFP (RRID:AB_300798) antibody. The publication related to the dataset furthermore includes stereological counts of positive neurons in the prelimbic, infralimbic and insula cortex, as well as in dorsal and ventral striatum.

The database includes clinical and connectome data from a sample of ALS patients carrying the C9orf72 mutation (ALSC9+), non-mutation-carriers ALS patients (ALSC9-), and ALS mimics (ALSmimics). The reported data consist of: demographic data (i.e., Age and Sex assigned at birth), clinical data (i.e., Bulbar/spinal onset, ALSFRS, Survival, disease duration, and King's Staging System scores), and connectome results.

This dataset contains the distinct probabilistic cytoarchitectonic map of Ch 123 (Basal Forebrain) in the individual, single subject template of the MNI Colin 27 reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Ch 123 (Basal Forebrain). The probability map of Ch 123 (Basal Forebrain) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Ch 123 (Basal Forebrain): Zaborszky et al. (2019) [Data set, v4.2] [DOI: 10.25493/7SEP-P2V](https://doi.org/10.25493%2F7SEP-P2V) The most probable delineation of Ch 123 (Basal Forebrain) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)

Electrophysiological evidence suggested primarily the involvement of area MT in depth cue integration in macaques, as opposed to human imaging data pinpointing area V3B/KO. To clarify this conundrum, we decoded monkey fMRI responses evoked by stimuli signaling near or far depths defined by binocular disparity, relative motion and their combination, and we compared results with those from an identical experiment previously performed in humans.Responses in macaque area MT are more discriminable when two cues concurrently signal depth, and information provided by one cue is diagnostic of depth indicated by the other. This suggests that monkey area MT computes fusion of disparity and motion depth signals, exactly as shown for human area V3B/KO. Hence, these data reconcile previously reported discrepancies between depth processing in human and monkey by showing the involvement of the dorsal stream in depth cue integration using the same technique, despite the engagement of different regions. data describing fig 1-8 and sfig 1-12data.zip