cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6139958
title: HeLa "Kyoto" cells under the scope

description: <strong>Name</strong>: HeLa “Kyoto” cells under the scope <strong>Microscope</strong>: Perkin Elmer Operetta microscope with a 20x N.A. 0.8 objective and an Andor Zyla 5.5 camera. <strong>Microscopy data type</strong>: The time-lapse datasets were acquired every 15 minutes, for 60 hours. From the individual plan images (channels, time-points, field of view exported by the PerkinElmer software Harmony) multi-dimension images were generated using the Operetta_Importer-0.1.21 with a downscaling of 4. Channel 1 : Low Contrast DPC (Digital Phase Contrast) Channel 2 : High Contrast DPC Channel 3 : Brightfield Channel 4 : EGFP-α-tubulin Channel 5 : mCherry-H2B <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 540x540 (Pixel size: 0.299 nm), 5c, 1z , 240t <strong>Cell type</strong>: HeLa “Kyoto” cells, expressing EGFP-α-tubulin and mCherry-H2B ( Schmitz <em>et al</em>, 2010 ) <strong>Protocol</strong>: Cells were resuspended in <strong>Imaging media</strong> and were seeded in a microscopy grade 96 wells plate ( CellCarrier Ultra 96, Perkin Elmer). The day after seeding, and for 60 hours, images were acquired in 3 wells, in 25 different fields of view, every 15 minutes. <strong>Imaging media</strong>: DMEM red-phenol-free media (FluoroBrite™ DMEM, Gibco) complemented with Fetal Calf Serum and Glutamax. <strong>NOTE: </strong>This dataset was used to automatically generate label images in the following Zenodo entry: <strong> https://doi.org/10.5281/zenodo.6140064</strong> <strong>NOTE: </strong>This dataset was used to train the cellpose models in the following Zenodo entry:<strong> https://doi.org/10.5281/zenodo.6140111</strong>
DOI: 10.5281/zenodo.6139957
title: HeLa "Kyoto" cells under the scope

description: <strong>Name</strong>: HeLa “Kyoto” cells under the scope <strong>Microscope</strong>: Perkin Elmer Operetta microscope with a 20x N.A. 0.8 objective and an Andor Zyla 5.5 camera. <strong>Microscopy data type</strong>: The time-lapse datasets were acquired every 15 minutes, for 60 hours. From the individual plan images (channels, time-points, field of view exported by the PerkinElmer software Harmony) multi-dimension images were generated using the Operetta_Importer-0.1.21 with a downscaling of 4. Channel 1 : Low Contrast DPC (Digital Phase Contrast) Channel 2 : High Contrast DPC Channel 3 : Brightfield Channel 4 : EGFP-α-tubulin Channel 5 : mCherry-H2B <strong>File format</strong>: .tif (16-bit) <strong>Image size</strong>: 540x540 (Pixel size: 0.299 nm), 5c, 1z , 240t <strong>Cell type</strong>: HeLa “Kyoto” cells, expressing EGFP-α-tubulin and mCherry-H2B ( Schmitz <em>et al</em>, 2010 ) <strong>Protocol</strong>: Cells were resuspended in <strong>Imaging media</strong> and were seeded in a microscopy grade 96 wells plate ( CellCarrier Ultra 96, Perkin Elmer). The day after seeding, and for 60 hours, images were acquired in 3 wells, in 25 different fields of view, every 15 minutes. <strong>Imaging media</strong>: DMEM red-phenol-free media (FluoroBrite™ DMEM, Gibco) complemented with Fetal Calf Serum and Glutamax. <strong>NOTE: </strong>This dataset was used to automatically generate label images in the following Zenodo entry: <strong> https://doi.org/10.5281/zenodo.6140064</strong> <strong>NOTE: </strong>This dataset was used to train the cellpose models in the following Zenodo entry:<strong> https://doi.org/10.5281/zenodo.6140111</strong>
DOI: 10.5281/zenodo.6140111
title: Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images

description: <strong>Name: </strong>Cellpose models for Brightfield and Digital Phase Contrast images <strong>Data type: </strong>Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional <strong>Training Dataset . Includes</strong> corresponding csv files with 'Quality Control' metrics(§) (model.zip). <strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (https://doi.org/10.5281/zenodo.6140064) <strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the Cellpose documentation . Training was done using a Renku environment (renku template). <strong>Command Line Execution for the different trained models</strong> <strong>nuclei_from_bf: </strong> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>cyto_from_bf</strong>: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>nuclei_from_dpc:</strong> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>cyto_from_dpc</strong>: <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>nuclei_from_sqrdpc</strong>: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>cyto_from_sqrdpc</strong>: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <em><strong>NOTE </strong></em>(§): We provide a notebook for Quality Control, which is an adaptation of the "Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic . <em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(https://doi.org/10.5281/zenodo.6140064) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(https://doi.org/10.5281/zenodo.6139958) in order to automatically generate the label images. <strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.
DOI: 10.5281/zenodo.6140110
title: Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images

description: <strong>Name: </strong>Cellpose models for Brightfield and Digital Phase Contrast images <strong>Data type: </strong>Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional <strong>Training Dataset . Includes</strong> corresponding csv files with 'Quality Control' metrics(§) (model.zip). <strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (https://doi.org/10.5281/zenodo.6140064) <strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the Cellpose documentation . Training was done using a Renku environment (renku template). <strong>Command Line Execution for the different trained models</strong> <strong>nuclei_from_bf: </strong> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>cyto_from_bf</strong>: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>nuclei_from_dpc:</strong> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>cyto_from_dpc</strong>: <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>nuclei_from_sqrdpc</strong>: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <strong>cyto_from_sqrdpc</strong>: <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <em><strong>NOTE </strong></em>(§): We provide a notebook for Quality Control, which is an adaptation of the "Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic . <em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(https://doi.org/10.5281/zenodo.6140064) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(https://doi.org/10.5281/zenodo.6139958) in order to automatically generate the label images. <strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.
DOI: 10.5517/ccdc.csd.cc294p8f
title: CCDC 2119664: Experimental Crystal Structure Determination

description: Related Article: Biplab Mondal, Chenna Jagadeesh, Dinabandhu Das, Jaideep Saha|2022|Chem.Commun.|58|2504|doi:10.1039/D1CC06883K
DOI: 10.5517/ccdc.csd.cc294p9g
title: CCDC 2119665: Experimental Crystal Structure Determination

description: Related Article: Biplab Mondal, Chenna Jagadeesh, Dinabandhu Das, Jaideep Saha|2022|Chem.Commun.|58|2504|doi:10.1039/D1CC06883K
DOI: 10.5517/ccdc.csd.cc294pbh
title: CCDC 2119666: Experimental Crystal Structure Determination

description: Related Article: Biplab Mondal, Chenna Jagadeesh, Dinabandhu Das, Jaideep Saha|2022|Chem.Commun.|58|2504|doi:10.1039/D1CC06883K
DOI: 10.5517/ccdc.csd.cc25bzdx
title: CCDC 2006549: Experimental Crystal Structure Determination

description: Related Article: Zoë R. Turner, Jamie T. Wilmore, Nicholas H. Rees, Jean-Charles Buffet|2022|Dalton Trans.|51|3060|doi:10.1039/D1DT04297A
DOI: 10.5517/ccdc.csd.cc295pfm
title: CCDC 2120630: Experimental Crystal Structure Determination

description: Related Article: Wei-Wei Ma, Chao Yang, Qiang Xie, Yun-He Xu|2022|Org.Biomol.Chem.|20|1386|doi:10.1039/D1OB02335G
DOI: 10.5517/ccdc.csd.cc297qv4
title: CCDC 2122596: Experimental Crystal Structure Determination

description: Related Article: Jialin Ma, Yuwei Dong, Zhou Yu, Yan Wu, Zhen Zhao|2022|New J.Chem.|46|3348|doi:10.1039/D1NJ05919J
DOI: 10.5517/ccdc.csd.cc29ctj1
title: CCDC 2126523: Experimental Crystal Structure Determination

description: Related Article: Jack Harrowfield, Youssef Atoini, Pierre Thuéry|2022|CrystEngComm|24|1475|doi:10.1039/D1CE01663F
DOI: 10.5517/ccdc.csd.cc29ctk2
title: CCDC 2126524: Experimental Crystal Structure Determination

description: Related Article: Jack Harrowfield, Youssef Atoini, Pierre Thuéry|2022|CrystEngComm|24|1475|doi:10.1039/D1CE01663F
DOI: 10.5517/ccdc.csd.cc29ctl3
title: CCDC 2126525: Experimental Crystal Structure Determination

description: Related Article: Jack Harrowfield, Youssef Atoini, Pierre Thuéry|2022|CrystEngComm|24|1475|doi:10.1039/D1CE01663F
DOI: 10.5517/ccdc.csd.cc29ctm4
title: CCDC 2126526: Experimental Crystal Structure Determination

description: Related Article: Jack Harrowfield, Youssef Atoini, Pierre Thuéry|2022|CrystEngComm|24|1475|doi:10.1039/D1CE01663F
DOI: 10.5517/ccdc.csd.cc29j8ty
title: CCDC 2130810: Experimental Crystal Structure Determination

description: Related Article: Jing-Yi Wang, Shuming Zhang, Xian-Yang Yu, Yu-Hao Wang, Hong-Lin Wan, Shu Zhang, Wei Tan, Feng Shi|2022|Tetrahedron Chem|1|100007|doi:10.1016/j.tchem.2022.100007
DOI: 10.5281/zenodo.6269326
title: Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits

description: Supporting data associated with the manuscript "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits". These include: <strong>StringTie2.438.GEUVADIS.gtf.gz</strong> - Assembled transcriptome using RNA-seq from 438 samples from the GEUVADIS project by StringTie2 <strong>puQTL_nominal_output.txt.gz</strong> - puQTL results from QTLtools nominal pass <strong>puQTL_conditional_output.txt.gz</strong> - puQTL results from QTLtools conditional pass <strong>eQTL_nominal_output.txt.gz</strong> - eQTL results from QTLtools nominal pass <strong>eQTL_conditional_output.txt.gz</strong> - eQTL results from QTLtools conditional pass See https://qtltools.github.io/qtltools/ for column descriptions of QTL mapping results.
DOI: 10.5281/zenodo.6269325
title: Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits

description: Supporting data associated with the manuscript "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits". These include: <strong>StringTie2.438.GEUVADIS.gtf.gz</strong> - Assembled transcriptome using RNA-seq from 438 samples from the GEUVADIS project by StringTie2 <strong>puQTL_nominal_output.txt.gz</strong> - puQTL results from QTLtools nominal pass <strong>puQTL_conditional_output.txt.gz</strong> - puQTL results from QTLtools conditional pass <strong>eQTL_nominal_output.txt.gz</strong> - eQTL results from QTLtools nominal pass <strong>eQTL_conditional_output.txt.gz</strong> - eQTL results from QTLtools conditional pass See https://qtltools.github.io/qtltools/ for column descriptions of QTL mapping results.
DOI: 10.5281/zenodo.6302289
title: Deep Potential Model for W

description: The DP-HYB model and training datasets for the manuscript: "A Tungsten Deep Potential with High Accuracy and transferability based on a Newly Designed Three-body Embedding Formalism"
DOI: 10.5281/zenodo.6302288
title: Deep Potential Model for W

description: The DP-HYB model and training datasets for the manuscript: "A Tungsten Deep Potential with High Accuracy and transferability based on a Newly Designed Three-body Embedding Formalism"
DOI: 10.6084/m9.figshare.14544676
title: Comparative video annotation and visual literacy: performance analysis of Rina Yerushalmi’s theatre language

description: The growing availability of video recordings of theatre performances, a phenomenon that has increased during Covid19 as theatres worldwide share videos online, affects the field of theatre and performance studies. Video recordings of theatre performances are archival documents and mediated ‘performance texts’ that enable new kinds of performance analysis and studying bodily practices. This paper addresses annotative methodologies as part of the research project, ‘The Art of Adaptation: The Theatre of Rina Yerushalmi and the Itim Ensemble.’ The project studies the video archive of the ensemble, which includes recordings of full productions and rehearsal processes. We discuss three kinds of digital comparative annotative methodologies to show how annotation can be used as a research tool for performance analysis: (1) Accumulative annotation: analysis of multi-layered theatrical sequences, revealing theatrical moments in their multiplicity. (2) Annotation of different scenes in a corpus of works: juxtaposing scenes from different productions enables the articulation of repetitive performative patterns, embodied practices, and visual images that construct a theatre language. (3) Annotation of the same scene at different phases: juxtaposing video recordings of the same scene at different moments of its development reveals nuanced sets of information about directorial choices, acting, movement, duration, and more.
DOI: 10.6084/m9.figshare.14544676.v1
title: Comparative video annotation and visual literacy: performance analysis of Rina Yerushalmi’s theatre language

description: The growing availability of video recordings of theatre performances, a phenomenon that has increased during Covid19 as theatres worldwide share videos online, affects the field of theatre and performance studies. Video recordings of theatre performances are archival documents and mediated ‘performance texts’ that enable new kinds of performance analysis and studying bodily practices. This paper addresses annotative methodologies as part of the research project, ‘The Art of Adaptation: The Theatre of Rina Yerushalmi and the Itim Ensemble.’ The project studies the video archive of the ensemble, which includes recordings of full productions and rehearsal processes. We discuss three kinds of digital comparative annotative methodologies to show how annotation can be used as a research tool for performance analysis: (1) Accumulative annotation: analysis of multi-layered theatrical sequences, revealing theatrical moments in their multiplicity. (2) Annotation of different scenes in a corpus of works: juxtaposing scenes from different productions enables the articulation of repetitive performative patterns, embodied practices, and visual images that construct a theatre language. (3) Annotation of the same scene at different phases: juxtaposing video recordings of the same scene at different moments of its development reveals nuanced sets of information about directorial choices, acting, movement, duration, and more.
DOI: 10.6084/m9.figshare.14544679.v1
title: ‘I gave that cue.’ Integrating dance studies, praxeology, and computational perspectives to model change in the case study of William Forsythe’s <i>Duo</i>

description: Choreographer William Forsythe calls the work <i>Duo</i> a ‘project’—reflecting the piece’s long history of vicissitudes from 1996 to the present. We attempt to visualize continuity and change over several iterations of <i>Duo</i>, spanning a period of 20 years. Our methods involved graphical and statistical approaches to performance video annotation, considering seven videos acquired from Forsythe’s private archive. Collaboration with <i>Duo</i> dancers was critical to develop this choreographic knowledge. The duet <i>Duo</i> was chosen to focus on annotation of partnering, choreographic structure, and interpretation; the case study furthermore enabled review of annotation methods from Forsythe’s <i>Synchronous Objects for One Flat Thing, reproduced</i> (Forsythe et al. 2009) and built upon prior research of entrainment in <i>Duo</i> (Waterhouse, Watts, and Bläsing 2014). Studying a choreography longitudinally, with close regard of the performers’ testimonies and digital traces, the problem required innovative methods. For this article, we focus on how annotation was used within this project. We outline our particular interdisciplinary approach, merging perspectives from dance studies, praxeology and creative coding. We present the language and concepts of annotation chosen, technical tools used for annotating, procedures of annotation analysis, and conclusions of the research. Thereby we present novel visualizations of choreographic process.
DOI: 10.6084/m9.figshare.19246197
title: The electric field on the C-D bond of CXF-D in ethanol using fixed-charge MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on D (MV/cm), average field of Fc and Fd (MV/cm), field drop between Fc and Fd (MV/cm), and the C-D bond length (nm)
DOI: 10.6084/m9.figshare.19246197.v1
title: The electric field on the C-D bond of CXF-D in ethanol using fixed-charge MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on D (MV/cm), average field of Fc and Fd (MV/cm), field drop between Fc and Fd (MV/cm), and the C-D bond length (nm)
DOI: 10.6084/m9.figshare.19247916
title: The electric field on C-D of CXF-D in methanol using fixed-charge MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on D (MV/cm), average field of Fc and Fd (MV/cm), field drop between Fc and Fd (Mv/cm), and the C-D bond length
DOI: 10.6084/m9.figshare.19247916.v1
title: The electric field on C-D of CXF-D in methanol using fixed-charge MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on D (MV/cm), average field of Fc and Fd (MV/cm), field drop between Fc and Fd (Mv/cm), and the C-D bond length
DOI: 10.6084/m9.figshare.19247922
title: The electric field on C-D of CXF-D in water using fixed-charge MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on D (MV/cm), average field of Fc and Fd (MV/cm), field drop between Fc and Fd (MV/cm), and the C-D bond length (nm)
DOI: 10.6084/m9.figshare.19247922.v1
title: The electric field on C-D of CXF-D in water using fixed-charge MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on D (MV/cm), average field of Fc and Fd (MV/cm), field drop between Fc and Fd (MV/cm), and the C-D bond length (nm)
DOI: 10.6084/m9.figshare.19247928
title: The electric field on C=O of CXF-D in ethanol using MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on O (MV/cm), average field of Fc and Fo (MV/cm), field drop between Fc and Fo (MV/cm), and the C=O bond length (nm)
DOI: 10.6084/m9.figshare.19247928.v1
title: The electric field on C=O of CXF-D in ethanol using MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on O (MV/cm), average field of Fc and Fo (MV/cm), field drop between Fc and Fo (MV/cm), and the C=O bond length (nm)
DOI: 10.6084/m9.figshare.19247937.v1
title: The electric field on C=O of CXF-D in methanol using MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on O (MV/cm), average field of Fc and Fo (MV/cm), field drop between Fc and Fo (MV/cm), and the C=O bond length (nm)
DOI: 10.6084/m9.figshare.19247937
title: The electric field on C=O of CXF-D in methanol using MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on O (MV/cm), average field of Fc and Fo (MV/cm), field drop between Fc and Fo (MV/cm), and the C=O bond length (nm)
DOI: 10.6084/m9.figshare.19247940
title: The electric field on C=O of CXF-D in water using MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on O (MV/cm), average field of Fc and Fo (MV/cm), field drop between Fc and Fo (MV/cm), and the C=O bond length (nm)
DOI: 10.6084/m9.figshare.19247940.v1
title: The electric field on C=O of CXF-D in water using MD simulations

description: From left to right are time frame (ps), field on C (MV/cm), field on O (MV/cm), average field of Fc and Fo (MV/cm), field drop between Fc and Fo (MV/cm), and the C=O bond length (nm)
DOI: 10.6084/m9.figshare.19247970
title: The electric field calculations on CXF-D using AMOEBA polarizable simulations

description: AMOEBA polarizable MD simulations of CXF-D in solvents.
DOI: 10.6084/m9.figshare.19247970.v1
title: The electric field calculations on CXF-D using AMOEBA polarizable simulations

description: AMOEBA polarizable MD simulations of CXF-D in solvents.
DOI: 10.6084/m9.figshare.19247976.v1
title: FTIR spectra of CXF-D in solvents

description: FTIR spectra of the C=O and C-D of N-[formyl-d]-Cyclohexylformamide in solvents
DOI: 10.6084/m9.figshare.19247976
title: FTIR spectra of CXF-D in solvents

description: FTIR spectra of the C=O and C-D of N-[formyl-d]-Cyclohexylformamide in solvents