cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23641/asha.14880360.v1
title: Nonword repetition in Arabic-speaking children (Taha et al., 2021)

description: <b>Purpose: </b>This study evaluates the effectiveness of a nonword repetition (NWR) task in discriminating between Palestinian Arabic–speaking children with developmental language disorder (DLD) and age-matched typically developing (TD) children.<b>Method: </b>Participants were 30 children with DLD aged between 4;0 and 6;10 (years;months) and 60 TD children aged between 4;0 and 6;8 matched on chronological age. The Arabic version of a Quasi-Universal NWR task was administered. The task comprises 30 nonwords that vary in length, presence of consonant clusters (CCs) and wordlikeness ratings. Responses were scored using an item-level scoring method to assess the diagnostic accuracy of the task. Receiver operating characteristic curve analysis was conducted to determine the best cutoff point with the highest sensitivity and specificity values, and likelihood ratios were calculated.<b>Results: </b>Children with DLD scored significantly lower on the NWR task than their age-matched TD peers. Only the DLD group was influenced by the phonological complexity of the nonwords, with nonwords with two CC being more difficult than nonwords with no or only one CC. For both groups, three-syllable nonwords were repeated less accurately than two- and one-syllable nonwords. Also, high word-like nonwords were repeated more accurately than nonwords with low wordlikeness ratings. The best cutoff score had sensitivity and specificity of 93% and highly informative likelihood ratios.<b>Conclusions: </b>NWR was an area of difficulty for Palestinian Arabic–speaking children with DLD. NWR showed excellent discriminatory power in differentiating Arabic-speaking children diagnosed with DLD from their age-matched TD peers. NWR appears to hold promise for clinical use as it is a useful indicator of DLD in Arabic. These results need to be further validated using population-based studies.<br><b>Supplemental Material S1. </b>Individual raw scores for the TD and DLD groups on the background measures and the Arabic version of the QU-LITMUS-NWR.<br><b>Supplemental Material S2.</b> Summary of model fitting.<br>Taha, J., Stojanovik, V., & Pagnamenta, E. (2021). Nonword repetition performance of Arabic-speaking children with and without developmental language disorder: A study on diagnostic accuracy.<i> Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2021_JSLHR-20-00556
DOI: 10.23641/asha.14880285
title: Communication-related parameters in TD children (Schölderle et al., 2021)

description: <b>Purpose:</b> This article describes the developmental trajectories of four communication-related parameters (i.e., intelligibility, articulation rate, fluency, and communicative efficiency) in a cross-sectional study of typically developing children between 3 and 9 years. The four target parameters were related to auditory-perceptual parameters of speech function.<b>Method: </b>One hundred forty-four typically developing children (ages 3;0–9;11 [years;months]; 72 girls and 72 boys) participated. Speech samples were collected using the materials of the Bogenhausen Dysarthria Scales for Childhood Dysarthria, a German assessment tool for childhood dysarthria, and analyzed following established auditory-perceptual criteria on relevant speech functions. To assess intelligibility, naïve listeners transcribed sentences repeated by the children. Articulation rate and fluency were measured by acoustic analyses; communicative efficiency was determined by multiplying the proportion of correctly transcribed syllables by speech rate.<b>Results:</b> Intelligibility showed a steep developmental trajectory, with the majority of children obtaining a proportion of intelligible syllables close to 1.0 at the age of 5 years. Articulation rate demonstrated a flatter trajectory, with high variability still within the older children. Disfluencies, on the contrary, occurred only in the youngest children. By definition, communicative efficiency shared the characteristics of intelligibility and rate curves. A principal component analysis revealed, among other findings, strong connections between intelligibility and articulation, as well as between communicative efficiency, articulation, and rate measures.<b>Conclusions: </b>While children speak intelligibly, in terms of the applied assessment, at a comparably young age, other communication-relevant parameters show a slower developmental progress. Knowledge on the typical development of communication-related parameters and on their complex relationships with functional speech variables is crucial for the clinical assessment of childhood dysarthria.<br><b>Supplemental Material S1. </b>Proposal for clinical use of the age norms. <br>Schölderle, T., Haas, E., Baumeister, S., & Ziegler, W. (2021). Intelligibility, articulation rate, fluency, and communicative efficiency in typically developing children. <i>Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2021_JSLHR-20-00640
DOI: 10.23641/asha.14880285.v1
title: Communication-related parameters in TD children (Schölderle et al., 2021)

description: <b>Purpose:</b> This article describes the developmental trajectories of four communication-related parameters (i.e., intelligibility, articulation rate, fluency, and communicative efficiency) in a cross-sectional study of typically developing children between 3 and 9 years. The four target parameters were related to auditory-perceptual parameters of speech function.<b>Method: </b>One hundred forty-four typically developing children (ages 3;0–9;11 [years;months]; 72 girls and 72 boys) participated. Speech samples were collected using the materials of the Bogenhausen Dysarthria Scales for Childhood Dysarthria, a German assessment tool for childhood dysarthria, and analyzed following established auditory-perceptual criteria on relevant speech functions. To assess intelligibility, naïve listeners transcribed sentences repeated by the children. Articulation rate and fluency were measured by acoustic analyses; communicative efficiency was determined by multiplying the proportion of correctly transcribed syllables by speech rate.<b>Results:</b> Intelligibility showed a steep developmental trajectory, with the majority of children obtaining a proportion of intelligible syllables close to 1.0 at the age of 5 years. Articulation rate demonstrated a flatter trajectory, with high variability still within the older children. Disfluencies, on the contrary, occurred only in the youngest children. By definition, communicative efficiency shared the characteristics of intelligibility and rate curves. A principal component analysis revealed, among other findings, strong connections between intelligibility and articulation, as well as between communicative efficiency, articulation, and rate measures.<b>Conclusions: </b>While children speak intelligibly, in terms of the applied assessment, at a comparably young age, other communication-relevant parameters show a slower developmental progress. Knowledge on the typical development of communication-related parameters and on their complex relationships with functional speech variables is crucial for the clinical assessment of childhood dysarthria.<br><b>Supplemental Material S1. </b>Proposal for clinical use of the age norms. <br>Schölderle, T., Haas, E., Baumeister, S., & Ziegler, W. (2021). Intelligibility, articulation rate, fluency, and communicative efficiency in typically developing children. <i>Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2021_JSLHR-20-00640
DOI: 10.6084/m9.figshare.14924190
title: Mass Spectrometry Raw Data McCaul et al

description: This mass spectrometry raw data accompanies the publication by McCaul et al., 2021, JBC<br>180323_28_29matthias-4.raw: MEF wt; 6P-ATP #1180323_34_29matthias-5.raw: MEF wt; 6P-ATP #2<br>180324_01_29matthias-6.raw: MEF KO; 6P-ATP #2180324_07_29matthias-7.raw: MEF-KO; 6P-ATP 180324_13_29matthias-8.raw: NoCells; 6P-ATP #1
DOI: 10.6084/m9.figshare.14924190.v1
title: Mass Spectrometry Raw Data McCaul et al

description: This mass spectrometry raw data accompanies the publication by McCaul et al., 2021, JBC
DOI: 10.18419/darus-1181/3
title: Spectroelectrochemistry.zip

description: Raw data and plot of electrochemically induced absorption spectral changes
DOI: 10.18419/darus-1181/1
title: NMR-data.zip

description: NMR spectral data for reported complexes CyRu(L)
DOI: 10.18419/darus-1181/4
title: Kinetics.zip

description: Raw kinetics data for the addition of vinylferrocene and ethynylferrocene to the titular ruthenium compound
DOI: 10.18419/darus-1181/2
title: Electrochemistry.zip

description: Cyclic Voltammograms and raw data
DOI: 10.5517/ccdc.csd.cc253fg6
title: CCDC 1999297: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253flb
title: CCDC 1999301: Experimental Crystal Structure Determination

description: Related Article: Harald Hillebrecht|2020|CSD Communication|||
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DOI: 10.5517/ccdc.csd.cc253gwn
title: CCDC 1999341: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc13stwl
title: CCDC 1036760: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253sy1
title: CCDC 1999653: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253sbf
title: CCDC 1999634: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253scg
title: CCDC 1999635: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253sdh
title: CCDC 1999636: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253sfj
title: CCDC 1999637: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253sgk
title: CCDC 1999638: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253w5c
title: CCDC 1999722: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253w6d
title: CCDC 1999723: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253w7f
title: CCDC 1999724: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253y19
title: CCDC 1999780: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc253y2b
title: CCDC 1999781: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc2546tc
title: CCDC 2000052: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc25473p
title: CCDC 2000061: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc2549tg
title: CCDC 2000145: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc254n44
title: CCDC 2000465: Experimental Crystal Structure Determination

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title: CCDC 2000592: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc25575s
title: CCDC 2001024: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc2557cz
title: CCDC 2001030: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc2557d0
title: CCDC 2001031: Experimental Crystal Structure Determination

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title: CCDC 2001032: Experimental Crystal Structure Determination

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DOI: 10.6084/m9.figshare.13132892.v1
title: MalesvFemales_IndivXCMS.zip

description: These data are raw peak areas for urinary VOCs in the maned wolf (Chrysocyon brachyurus).
DOI: 10.6084/m9.figshare.13132892
title: Maned wolf (Chrysocyon brachyurus) urinary volatile organic compounds

description: These data are raw peak areas for urinary VOCs in the urine samples of 13 maned wolves (Chrysocyon brachyurus) collected weekly for 6 months over one breeding season. Information about each individual wolf is provided in the table below:<br> CallName SB# Age Sex Institution Housed with Pairing Status Breeding Information Bonita 3231 3 F Beardsley SB#3232 Unpaired NA Same sex sibling pair Chiquita 3232 3 F Beardsley SB#3231 Unpaired NA Same sex sibling pair Yellow 2845 8 F Fossil Rim * Unpaired None seen Mac 3206 4 M Fossil Rim SB#3207 (sister) Unpaired NA Female spayed Cheech 3192 3 M Philadelphia SB#3195 <br> Unpaired NA Same sex sibling pair Chong 3195 3 M Philadelphia SB#3192 <br> Unpaired NA Same sex sibling pair Flint 2810 8 M SCBI Single Unpaired NA Single Blue 2844 8 M SCBI SB#3184 Paired None seen Uno 3184 4 F SCBI SB#2844 Paired None seen Zonka 2660 10 M WOCC SB#2945 Paired Estimated breeding date = Oct 8, 2014 Kim 2945 8 F WOCC SB#2660 Paired Estimated breeding date = Oct 8, 2014 Elisa 2536 11 F Buffalo SB#3014 N/A N/A Male neutered Kev 3014 7 NeutM Buffalo SB#2536 N/A<br> N/A Male neutered Raw data from GC-MS in MassHunter’s .D format were converted into .mzXML format for data processing steps using ProteoWizard’s MSConvert tool. Files from replicates of each sample were processed together through the xcms R package using R ver 3.6.1. The xcms package contains functions to identify ion peaks, group the same ion peaks across replicates, correct retention time drift using the Obiwarp algorithm, and then use the corrected retention times to regroup ion peaks across replicates. Next, missing ion peaks were filled in. Then a deconvolution algorithm in the camera R package assigned ion peaks into compounds. Ions with the same peak shapes at the same retention times were attributed to the same group to create a compound made up of component ions. This open-source data analysis pipeline is robust, free of cost, and easily accessible, allowing analysis of hundreds or thousands of samples containing millions of ion peaks on a personal computer. Thus, this same data analysis pipeline could easily extend to other species or areas of VOC research. Ion peak areas were then averaged across the three replicates of each sample, creating one mean value per urine sample for each ion. Within each compound group, only the peak area for the quantitative ion (ion with the largest average area) was retained. This value is representative of the abundance of the VOC in that sample. These quantitative ion peak areas are the data that are shared.
DOI: 10.6084/m9.figshare.13132892.v3
title: Maned wolf (Chrysocyon brachyurus) urinary volatile organic compounds

description: These data are raw peak areas for urinary VOCs in the urine samples of 13 maned wolves (Chrysocyon brachyurus) collected weekly for 6 months over one breeding season. Information about each individual wolf is provided in the table below:<br> CallName SB# Age Sex Institution Housed with Pairing Status Breeding Information Bonita 3231 3 F Beardsley SB#3232 Unpaired NA Same sex sibling pair Chiquita 3232 3 F Beardsley SB#3231 Unpaired NA Same sex sibling pair Yellow 2845 8 F Fossil Rim * Unpaired None seen Mac 3206 4 M Fossil Rim SB#3207 (sister) Unpaired NA Female spayed Cheech 3192 3 M Philadelphia SB#3195 <br> Unpaired NA Same sex sibling pair Chong 3195 3 M Philadelphia SB#3192 <br> Unpaired NA Same sex sibling pair Flint 2810 8 M SCBI Single Unpaired NA Single Blue 2844 8 M SCBI SB#3184 Paired None seen Uno 3184 4 F SCBI SB#2844 Paired None seen Zonka 2660 10 M WOCC SB#2945 Paired Estimated breeding date = Oct 8, 2014 Kim 2945 8 F WOCC SB#2660 Paired Estimated breeding date = Oct 8, 2014 Elisa 2536 11 F Buffalo SB#3014 N/A N/A Male neutered Kev 3014 7 NeutM Buffalo SB#2536 N/A<br> N/A Male neutered Raw data from GC-MS in MassHunter’s .D format were converted into .mzXML format for data processing steps using ProteoWizard’s MSConvert tool. Files from replicates of each sample were processed together through the xcms R package using R ver 3.6.1. The xcms package contains functions to identify ion peaks, group the same ion peaks across replicates, correct retention time drift using the Obiwarp algorithm, and then use the corrected retention times to regroup ion peaks across replicates. Next, missing ion peaks were filled in. Then a deconvolution algorithm in the camera R package assigned ion peaks into compounds. Ions with the same peak shapes at the same retention times were attributed to the same group to create a compound made up of component ions. This open-source data analysis pipeline is robust, free of cost, and easily accessible, allowing analysis of hundreds or thousands of samples containing millions of ion peaks on a personal computer. Thus, this same data analysis pipeline could easily extend to other species or areas of VOC research. Ion peak areas were then averaged across the three replicates of each sample, creating one mean value per urine sample for each ion. Within each compound group, only the peak area for the quantitative ion (ion with the largest average area) was retained. This value is representative of the abundance of the VOC in that sample. These quantitative ion peak areas are the data that are shared.