cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/ksy8-h3f
title: Probabilistic cytoarchitectonic map of Area 6d1 (PreCG) (v7.1)

description: This dataset contains the distinct architectonic Area 6d1 (PreG) 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 6d1 (PreG). The probability map of Area 6d1 (PreG) 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 6d1 (PreCG): Sigl et al. (2019) [Data set, v4.0] [DOI: 10.25493/4JDW-STM](https://doi.org/10.25493%2F4JDW-STM) Sigl et al. (2019) [Data set, v4.1] [DOI: 10.25493/4WSQ-8FM](https://doi.org/10.25493%2F4WSQ-8FM) Sigl et al. (2020) [Data set, v5.0] [DOI: 10.25493/167T-527](https://doi.org/10.25493%2F167T-527) The most probable delineation of Area 6d1 (PreCG) 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) 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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/67ta-jrq
title: Probabilistic cytoarchitectonic map of Area STS1 (STS) (v5.3)

description: This dataset contains the distinct architectonic Area STS1 (STS) 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 STS1 (STS). The probability map of Area STS1 (STS) 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 STS1 (STS): Zachlod et al. (2019) [Data set, v3.0] [DOI: 10.25493/2G11-1WA](https://doi.org/10.25493%2F2G11-1WA) Zachlod et al. (2019) [Data set, v3.1] [DOI: 10.25493/F6DF-H8P](https://doi.org/10.25493%2FF6DF-H8P) Zachlod et al. (2020) [Data set, v5.0] [DOI: 10.25493/YFP9-0JZ](https://doi.org/10.25493%2FYFP9-0JZ) The most probable delineation of Area STS1 (STS) 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, v1.18] [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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/rmz5-16j
title: Probabilistic cytoarchitectonic map of Area TE 1.0 (HESCHL) (v6.2)

description: This dataset contains the distinct architectonic Area TE 1.0 (HESCHL) 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 TE 1.0 (HESCHL). The probability map of Area TE 1.0 (HESCHL) 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 TE 1.0 (HESCHL): Morosan et al. (2018) [Data set, v5.0] [DOI: 10.25493/CP2T-FYT](https://doi.org/10.25493%2FCP2T-FYT) Morosan et al. (2019) [Data set, v5.1] [DOI: 10.25493/MV3G-RET](https://doi.org/10.25493%2FMV3G-RET) Morosan et al. (2020) [Data set, v6.0] [DOI: 10.25493/AKAE-ZWP](https://doi.org/10.25493%2FAKAE-ZWP) The most probable delineation of Area TE 1.0 (HESCHL) 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) 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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/bp4p-70u
title: Probabilistic cytoarchitectonic map of Area p32 (pACC) (v20.1)

description: This dataset contains the distinct architectonic Area p32 (pACC) 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 p32 (pACC). The probability map of Area p32 (pACC) 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 p32 (pACC): Palomero-Gallagher et al. (2019) [Data set, v16.0] [DOI: 10.25493/FZXB-M6S](https://doi.org/10.25493%2FFZXB-M6S) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/3JX0-7E5](https://doi.org/10.25493%2F3JX0-7E5) Palomero-Gallagher et al. (2020) [Data set, v18.0] [DOI: 10.25493/Z7HZ-CHD](https://doi.org/10.25493%2FZ7HZ-CHD) The most probable delineation of Area p32 (pACC) 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, v1.18] [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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.17632/fntwxbm64z.1
title: Data for: An integrated modeling approach for mineral and metal transport in acidic rivers at high mountain porphyry Cu systems

description: Here we include the main dataset used in the article "An integrated modeling approach for mineral and metal transport in acidic rivers at high mountain porphyry Cu systems" This dataset includes base information of Yerba Loca basin: 1- Flow discharge data along Yerba Loca creek obtained from WEAP (Water Evaluation and Planning) a semi-distributed hydrological model. 2- Water samples along Yerba Loca. For more water chemistry information see Valenzuela-Diaz, M. J., Navarrete-Calvo, A., McPhee, J., Garcia, A., Correa-Burrows, J. P., Navarro-Valdivia, L., & Caraballo, M. A. (2020). Hydrogeochemical and environmental water quality standards in the overlap between high mountainous natural protected areas and copper mining activities (Mapocho river upper basin, Santiago, Chile). Journal of Hydrology, 125063. https://doi.org/10.1016/j.jhydrol.2020.125063. 3- Qgis project that includes sub-basins and sampling points.
DOI: 10.25493/skca-87x
title: Probabilistic cytoarchitectonic map of Area TE 1.1 (HESCHL) (v6.2)

description: This dataset contains the distinct architectonic Area TE 1.1 (HESCHL) 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 TE 1.1 (HESCHL). The probability map of Area TE 1.1 (HESCHL) 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 TE 1.1 (HESCHL): Morosan et al. (2018) [Data set, v5.0] [DOI: 10.25493/55TY-YS8](https://doi.org/10.25493%2F55TY-YS8) Morosan et al. (2019) [Data set, v5.1] [DOI: 10.25493/4HA3-BBE](https://doi.org/10.25493%2F4HA3-BBE) Morosan et al. (2020) [Data set, v6.0] [DOI: 10.25493/29FC-BCE](https://doi.org/10.25493%2F29FC-BCE) The most probable delineation of Area TE 1.1 (HESCHL) 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) 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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/meec-hc5
title: Probabilistic cytoarchitectonic map of Area STS2 (STS) (v5.3)

description: This dataset contains the distinct architectonic Area STS2 (STS) 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 STS2 (STS). The probability map of Area STS2 (STS) 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 STS2 (STS): Zachlod et al. (2019) [Data set, v3.0] [DOI: 10.25493/Y5QZ-KV](https://doi.org/10.25493%2FY5QZ-KV) Zachlod et al. (2019) [Data set, v3.1] [DOI: 10.25493/KHY9-J3Y](https://doi.org/10.25493%2FKHY9-J3Y) Zachlod et al. (2020) [Data set, v5.0] [10.25493/1E12-161](https://doi.org/10.25493%2F1E12-161) The most probable delineation of Area STS2 (STS) 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, v1.18] [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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/tp56-3t5
title: Probabilistic cytoarchitectonic map of Area 25 (sACC) (v20.1)

description: This dataset contains the distinct architectonic Area 25 (sACC) 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 25 (sACC). The probability map of Area 25 (sACC) 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 25 (sACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/NMPJ-EU](https://doi.org/10.25493%2FNMPJ-EU) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/51AM-WN4](https://doi.org/10.25493%2F51AM-WN4) Palomero-Gallagher et al. (2020) [Data set, v18.0] [DOI: 10.25493/VWVJ-6D2](https://doi.org/10.25493%2FVWVJ-6D2) The most probable delineation of Area 25 (sACC) 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) 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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/hsm6-5cq
title: Probabilistic cytoarchitectonic map of Area PFt (IPL) (v11.2)

description: This dataset contains the distinct architectonic Area PFt (IPL) 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 PFt (IPL). The probability map of Area PFt (IPL) 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 PFt (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/2PNK-M62](https://doi.org/10.25493%2F2PNK-M62) Caspers et al. (2019) [Data set, v9.4] [DOI: 10.25493/JGM9-ZET](https://doi.org/10.25493%2FJGM9-ZET) Caspers et al. (2020) [Data set, v13.0] [DOI: 10.25493/4N6F-VCG](https://doi.org/10.25493%2F4N6F-VCG) The most probable delineation of Area PFt (IPL) 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) 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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)
DOI: 10.25493/az22-fxf
title: Probabilistic cytoarchitectonic map of Entorhinal Cortex (v13.2)

description: This dataset contains the distinct architectonic Entorhinal Cortex 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 Entorhinal Cortex. The probability map of Entorhinal Cortex 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 Entorhinal Cortex: Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/373S-NVK](https://doi.org/10.25493%2F373S-NVK) Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/KNXY-B1Z](https://doi.org/10.25493%2FKNXY-B1Z) Amunts et al. (2020) [Data set, v13.0] [DOI: 10.25493/6Z1P-72J](https://doi.org/10.25493%2F6Z1P-72J) The most probable delineation of Entorhinal Cortex 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) 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) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0) Amunts et al. (2021) [Data set, v2.9] [DOI: 10.25493/VSMK-H94](https://doi.org/10.25493/VSMK-H94)