cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23641/asha.6170495.v1
title: Children with Suspected Childhood Apraxia of Speech (Murray et al., 2015)

description: <b>Purpose </b>The gold standard for diagnosing childhood apraxia of speech (CAS) is expert judgment of perceptual features. The aim of this study was to identify a set of objective measures that differentiate CAS from other speech disorders. <br><b>Method </b>Seventy-two children (4–12 years of age) diagnosed with suspected CAS by community speech-language pathologists were screened. Forty-seven participants underwent diagnostic assessment including presence or absence of perceptual CAS features. Twenty-eight children met two sets of diagnostic criteria for CAS (American Speech-Language-Hearing Association, 2007b; Shriberg, Potter, & Strand, 2009); another 4 met the CAS criteria with comorbidity. Fifteen were categorized as non-CAS with phonological impairment, submucous cleft, or dysarthria. Following this, 24 different measures from the diagnostic assessment were rated by blinded raters. Multivariate discriminant function analysis was used to identify the combination of measures that best predicted expert diagnoses.<br><b>Results </b>The discriminant function analysis model, including syllable segregation, lexical stress matches, percentage phonemes correct from a polysyllabic picture-naming task, and articulatory accuracy on repetition of /pətəkə/, reached 91% diagnostic accuracy against expert diagnosis. <br><b>Conclusions </b>Polysyllabic production accuracy and an oral motor examination that includes diadochokinesis may be sufficient to reliably identify CAS and rule out structural abnormality or dysarthria. Testing with a larger unselected sample is required.
DOI: 10.7267/dz010x02m
title: Assessing the impact of multicomponent diffusion in direct numerical simulations of premixed, high-Karlovitz, turbulent flames: hydrogen data

description: This data contains results from direct numerical simulations of premixed, two-dimensional unsteady and three-dimensional, turbulent hydrogen/air flames, using the multicomponent and mixture-averaged diffusion models. In particular, this dataset contains information about diffusion flux angles and magnitudes. We performed the simulations using the finite-difference code NGA; simulation details are described completely in the associated article.
DOI: 10.23641/asha.6170306
title: Item Response Theory of Philadelphia Naming Test (Fergadiotis et al., 2015)

description: <b>Purpose </b>In this study, we investigated the fit of the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) to an item-response-theory measurement model, estimated the precision of the resulting scores and item parameters, and provided a theoretical rationale for the interpretation of PNT overall scores by relating explanatory variables to item difficulty. This article describes the statistical model underlying the computer adaptive PNT presented in a companion article (Hula, Kellough, & Fergadiotis, 2015). <br><b>Method </b>Using archival data, we evaluated the fit of the PNT to 1- and 2-parameter logistic models and examined the precision of the resulting parameter estimates. We regressed the item difficulty estimates on three predictor variables: word length, age of acquisition, and contextual diversity. <br><b>Results </b>The 2-parameter logistic model demonstrated marginally better fit, but the fit of the 1-parameter logistic model was adequate. Precision was excellent for both person ability and item difficulty estimates. Word length, age of acquisition, and contextual diversity all independently contributed to variance in item difficulty. <br><b>Conclusions </b>Item-response-theory methods can be productively used to analyze and quantify anomia severity in aphasia. Regression of item difficulty on lexical variables supported the validity of the PNT and interpretation of anomia severity scores in the context of current word-finding models.
DOI: 10.23641/asha.6170306.v1
title: Item Response Theory of Philadelphia Naming Test (Fergadiotis et al., 2015)

description: <b>Purpose </b>In this study, we investigated the fit of the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) to an item-response-theory measurement model, estimated the precision of the resulting scores and item parameters, and provided a theoretical rationale for the interpretation of PNT overall scores by relating explanatory variables to item difficulty. This article describes the statistical model underlying the computer adaptive PNT presented in a companion article (Hula, Kellough, & Fergadiotis, 2015). <br><b>Method </b>Using archival data, we evaluated the fit of the PNT to 1- and 2-parameter logistic models and examined the precision of the resulting parameter estimates. We regressed the item difficulty estimates on three predictor variables: word length, age of acquisition, and contextual diversity. <br><b>Results </b>The 2-parameter logistic model demonstrated marginally better fit, but the fit of the 1-parameter logistic model was adequate. Precision was excellent for both person ability and item difficulty estimates. Word length, age of acquisition, and contextual diversity all independently contributed to variance in item difficulty. <br><b>Conclusions </b>Item-response-theory methods can be productively used to analyze and quantify anomia severity in aphasia. Regression of item difficulty on lexical variables supported the validity of the PNT and interpretation of anomia severity scores in the context of current word-finding models.
DOI: 10.6084/m9.figshare.15085647
title: Data on pollination and seed set of common bean creole cultivars

description: <b>30 individuals of each cultivar. Each individual, two inflorescences with buds were randomly selected: one was bagged with organza bags and the other was marked with plastic tags. The number of flower buds present in bagged and marked inflorescences were counted.</b><b><br></b><b>Data about number of pollen per flower and viability.</b><b><br></b><b>Data on petals size, nectar volume and nectar sugar concentration.<br></b><b><br></b><b><br></b>
DOI: 10.6084/m9.figshare.15085647.v1
title: Data on pollination and seed set of common bean creole cultivars

description: <b>30 individuals of each cultivar. Each individual, two inflorescences with buds were randomly selected: one was bagged with organza bags and the other was marked with plastic tags. The number of flower buds present in bagged and marked inflorescences were counted.</b><b><br></b><b>Data about number of pollen per flower and viability.</b><b><br></b><b>Data on petals size, nectar volume and nectar sugar concentration.<br></b><b><br></b><b><br></b>
DOI: 10.25493/4rsw-2a1
title: Probabilistic cytoarchitectonic map of Area hIP3 (IPS) (v9.2)

description: This dataset contains the distinct architectonic Area hIP3 (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 hIP3 (IPS). The probability map of Area hIP3 (IPS) 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 hIP3 (IPS): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/J9T6-TX9](https://doi.org/10.25493%2FJ9T6-TX9) Scheperjans et al. (2018) [Data set, v8.4] [DOI: 10.25493/P8X0-V1G](https://doi.org/10.25493%2FP8X0-V1G) Scheperjans et al. (2021) [Data set, v9.0] [DOI: 10.25493/AEQ9-D0K](https://doi.org/10.25493%2FAEQ9-D0K) The most probable delineation of Area hIP3 (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, v1.13] [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/qwq3-pd4
title: Probabilistic cytoarchitectonic map of Fastigial Nucleus (Cerebellum) (v6.3)

description: This dataset contains the distinct architectonic Fastigial Nucleus (Cerebellum) 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 Fastigial Nucleus (Cerebellum). The probability map of Fastigial Nucleus (Cerebellum) 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 Fastigial Nucleus (Cerebellum): Tellmann et al. (2018) [Data set, v6.0] [DOI: 10.25493/JNN1-V3Q](https://doi.org/10.25493%2FJNN1-V3Q) Tellmann et al. (2019) [Data set, v2.3] [DOI: 10.25493/3YJ9-S6G](https://doi.org/10.25493%2F3YJ9-S6G) The most probable delineation of Fastigial Nucleus (Cerebellum) 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: 10.25493/7zyc-dbv
title: Probabilistic cytoarchitectonic map of Area hIP7 (IPS) (v7.3)

description: This dataset contains the distinct architectonic Area hIP7 (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 hIP7 (IPS). The probability map of Area hIP7 (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 hIP7 (IPS): Richter et al. (2019) [Data set, v7.0] [DOI: 10.25493/QN1F-WAF](https://doi.org/10.25493%2FQN1F-WAF) Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/WRCY-8Z1](https://doi.org/10.25493%2FWRCY-8Z1) The most probable delineation of Area hIP7 (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: 10.25493/s843-bte
title: Probabilistic cytoarchitectonic map of SF (Amygdala) (v8.2)

description: This dataset contains the distinct architectonic SF (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 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 both reference spaces, where each voxel was assigned the probability to belong to SF (Amygdala). The probability map of SF (Amygdala) 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 SF (Amygdala): Kedo et al. (2018) [Data set, v6.1] [DOI: 10.25493/BRNZ-SXW](https://doi.org/10.25493%2FBRNZ-SXW) Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/WD31-SEA](https://doi.org/10.25493%2FWD31-SEA) Kedo et al. (2020) [Data set, v8.0] [DOI: 10.25493/ZVHS-83B](https://doi.org/10.25493%2FZVHS-83B) The most probable delineation of SF (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, v1.13] [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, v2.6] [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/nsrx-dfy
title: Probabilistic cytoarchitectonic map of Area 5Ci (SPL) (v9.2)

description: This dataset contains the distinct architectonic Area 5Ci (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 5Ci (SPL). The probability map of Area 5Ci (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 5Ci (SPL): Scheperjans et al. (2018) [Data set, v8.2] [DOI: 10.25493/BQ0H-ZNC](https://doi.org/10.25493%2FBQ0H-ZNC) Scheperjans et al. (2019) [Data set, v8.4] [DOI: 10.25493/SQVP-GK1](https://doi.org/10.25493%2FSQVP-GK1) The most probable delineation of Area 5Ci (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/j0a1-mc9
title: Probabilistic cytoarchitectonic map of Area 3a (PostCG) (v12.1)

description: This dataset contains the distinct architectonic Area 3a (PostCG) 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 3a (PostCG). The probability map of Area 3a (PostCG) 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 3a (PostCG): Geyer et al. (2018) [Data set, v8.2] [DOI: 10.25493/R19N-B8E](https://doi.org/10.25493%2FR19N-B8E) Geyer et al. (2019) [Data set, v8.4] [DOI: 10.25493/C5QQ-EFB](https://doi.org/10.25493%2FC5QQ-EFB) Geyer et al. (2020) [Data set, v10.0] [DOI: 10.25493/6V9E-6ZF](https://doi.org/10.25493%2F6V9E-6ZF) The most probable delineation of Area 3a (PostCG) 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/behp-n67
title: Probabilistic cytoarchitectonic map of HC-Transsubiculum (Hippocampus) (v13.2)

description: This dataset contains the distinct architectonic HC-Transsubiculum (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 HC-Transsubiculum (Hippocampus). The probability map of HC-Transsubiculum (Hippocampus) 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 HC-Transsubiculum (Hippocampus): Palomero-Gallagher et al. (2020) [Data set, v13.0] [DOI: 10.25493/5WPD-DCT](https://doi.org/10.25493%2F5WPD-DCT) 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. (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)