cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25493/ycya-x6n
title: Probabilistic cytoarchitectonic map of HC-Subiculum (Hippocampus) (v13.2)

description: This dataset contains the distinct architectonic HC-Subiculum (Hippocampus) in the MNI Colin 27 and MNI ICBM 152 reference spaces. As part of the Julich-Brain 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. 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 HC-Subiculum (Hippocampus). The probability map of HC-Subiculum (Hippocampus) 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 HC-Subiculum (Hippocampus): Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/57EJ-01Z](https://doi.org/10.25493%2F57EJ-01Z) Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/QKJH-F45](https://doi.org/10.25493%2FQKJH-F45) Amunts et al. (2020) [Data set, v13.0] [DOI: 10.25493/1Y3K-MJX](https://doi.org/10.25493%2F1Y3K-MJX) The most probable delineation of HC-Subiculum (Hippocampus) 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: 10.25493/60jm-jb
title: Probabilistic cytoarchitectonic map of VTM (Amygdala) (v8.2)

description: This dataset contains the distinct architectonic VTM (Amygdala) 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 VTM (Amygdala). The probability map of VTM (Amygdala) 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 VTM (Amygdala): Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/99HN-XRE](https://doi.org/10.25493%2F99HN-XRE) Kedo et al. (2020) [Data set, v8.0] [DOI: 10.25493/Q6VJ-4BJ](https://doi.org/10.25493%2FQ6VJ-4BJ) The most probable delineation of VTM (Amygdala) 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/njp1-etn
title: Probabilistic cytoarchitectonic map of Area 7A (SPL) (v9.2)

description: This dataset contains the distinct architectonic Area 7A (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 7A (SPL). The probability map of Area 7A (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 7A (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/MQ4V-YRR](https://doi.org/10.25493%2FMQ4V-YRR) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/7HX2-AJH](https://doi.org/10.25493%2F7HX2-AJH) Scheperjans et al. (2019) [Data set, v9.0] [DOI: 10.25493/9HMG-SHD](https://doi.org/10.25493%2F9HMG-SHD) The most probable delineation of Area 7A (SPL) 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/kcha-t7u
title: Probabilistic cytoarchitectonic map of Area 7PC (SPL) (v9.2)

description: This dataset contains the distinct architectonic Area 7PC (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 7PC (SPL). The probability map of Area 7PC (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 7PC (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/27J7-2YX](https://doi.org/10.25493%2F27J7-2YX) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/Z45N-1T](https://doi.org/10.25493%2FZ45N-1T) Scheperjans et al. (2020) [Data set, v9..0] [DOI: 10.25493/QX5Q-N9D](https://doi.org/10.25493%2FQX5Q-N9D) The most probable delineation of Area 7PC (SPL) 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: 10.25493/6v5t-qd9
title: Probabilistic cytoarchitectonic map of DG (Hippocampus) (v13.2)

description: This dataset contains the distinct architectonic DG (Hippocampus) 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 DG (Hippocampus). The probability map of DG (Hippocampus) 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 DG (Hippocampus): Amunts et al. (2018) [Data set, v11b.0] [DOI: 10.25493/1KV8-B9G](https://doi.org/10.25493%2F1KV8-B9G) Amunts et al. (2019) [Data set, v11.1] [DOI: 10.25493/M8JP-XQT](https://doi.org/10.25493%2FM8JP-XQT) Amunts et al. (2020) [Data set, v13.0] [DOI: 10.25493/FAVB-NDW](https://doi.org/10.25493%2FFAVB-NDW) The most probable delineation of DG (Hippocampus) 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: 10.25493/45nn-q1x
title: Probabilistic cytoarchitectonic map of Area s24 (sACC) (v20.1)

description: This dataset contains the distinct architectonic Area s24 (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 s24 (sACC). The probability map of Area s24 (sACC) 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 s24 (sACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/FQ3R-3JX](https://doi.org/10.25493%2FFQ3R-3JX) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/HXWM-NRX](https://doi.org/10.25493%2FHXWM-NRX) Palomero-Gallagher et al. (2020) [Data set, v18.0] [DOI: 10.25493/QZFS-1MT](https://doi.org/10.25493%2FQZFS-1MT) The most probable delineation of Area s24 (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/cnh6-b5j
title: Probabilistic cytoarchitectonic map of Area PGp (IPL) (v11.2)

description: This dataset contains the distinct architectonic Area PGp (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 PGp (IPL). The probability map of Area PGp (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 PGp (IPL): Caspers et al. (2018) [Data set, v9.2] [DOI: 10.25493/V9NJ-TBQ](https://doi.org/10.25493%2FV9NJ-TBQ) Caspers et al. (2019) [Data set, v9.4] [DOI: 10.25493/FPFJ-ZCD](https://doi.org/10.25493%2FFPFJ-ZCD) Caspers et al. (2020) [Data set, v11.0] [DOI: 10.25493/DY1S-EDW](https://doi.org/10.25493%2FDY1S-EDW) The most probable delineation of Area PGp (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/c3xj-b61
title: Probabilistic cytoarchitectonic map of Area OP4 (POperc) (v12.2)

description: This dataset contains the distinct probabilistic cytoarchitectonic map of Area OP4 (POperc) 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 Area OP4 (POperc). The probability map of Area OP4 (POperc) 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 Area OP4 (POperc): Eickhoff et al. (2018) [Data set, v9.2] [DOI: 10.25493/51S0-K7W](https://doi.org/10.25493%2F51S0-K7W) Eickhoff et al. (2019) [Data set, v9.4] [DOI: 10.25493/BVT0-H3U](https://doi.org/10.25493%2FBVT0-H3U) Eickhoff et al. (2020) [Data set, v11.0] [DOI: 10.25493/99GW-N85](https://doi.org/10.25493%2F99GW-N85) Eickhoff et al. (2020) [Data set, v12.0] [DOI: 10.25493/E3QX-88A](https://doi.org/10.25493%2FE3QX-88A) The most probable delineation of Area OP4 (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) 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: 10.25493/1fhw-7bw
title: Probabilistic cytoarchitectonic map of Area hIP8 (IPS) (v7.3)

description: This dataset contains the distinct architectonic Area hIP8 (IPS) 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 hIP8 (IPS). The probability map of Area hIP8 (IPS) 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 hIP8 (IPS): Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/JBV5-J8J](https://doi.org/10.25493%2FJBV5-J8J) Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/YYT8-FT8](https://doi.org/10.25493%2FYYT8-FT8) The most probable delineation of Area hIP8 (IPS) 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: 10.6084/m9.figshare.15085767
title: stress & empathy (60 subjects).sav

description: This is date including the information of acute stress and empathy.
DOI: 10.6084/m9.figshare.15085767.v1
title: stress & empathy (60 subjects).sav

description: This is date including the information of acute stress and empathy.
DOI: 10.25493/8rx6-4w1
title: Probabilistic cytoarchitectonic map of Area 33 (ACC) (v20.1)

description: This dataset contains the distinct architectonic Area 33 (ACC) 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 33 (ACC). The probability map of Area 33 (ACC) 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 33 (ACC): Palomero-Gallagher et al. (2018) [Data set, v16.0] [DOI: 10.25493/S6PK-Z7E](https://doi.org/10.25493%2FS6PK-Z7E) Palomero-Gallagher et al. (2019) [Data set, v16.1] [DOI: 10.25493/X9QP-C6F](https://doi.org/10.25493%2FX9QP-C6F) Palomero-Gallagher et al. (2020) [Data set, v18.0] [DOI: 10.25493/5VPP-6BY](https://doi.org/10.25493%2F5VPP-6BY) The most probable delineation of Area 33 (ACC) 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/10.25493/8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493/TAKY-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: 10.15468/q3teeh
title: Goodyera malipoensis (Cranichideae, Orchidaceae), a new species from China: Evidence from morphological and molecular analyses

description: This dataset contains the digitized treatments in Plazi based on the original journal article Guan, Qiu-Xiang, Chen, Gui-Zhen, Li, Ming-He, Chen, Shi-Pin (2014): Goodyera malipoensis (Cranichideae, Orchidaceae), a new species from China: Evidence from morphological and molecular analyses. Phytotaxa 186 (1): 51-60, DOI: 10.11646/phytotaxa.186.1.4, URL: http://dx.doi.org/10.11646/phytotaxa.186.1.4