cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6582573
title: Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

description: Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org). Details on saved models coming soon.
description: {"references": ["J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359\u2013366, 1998.", "Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49\u201370, 2000.", "https://www.textures.com/", "Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.", "Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018."]}
DOI: 10.5281/zenodo.6583851
title: Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

description: Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org). Details on saved models coming soon.
description: {"references": ["J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359\u2013366, 1998.", "Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49\u201370, 2000.", "https://www.textures.com/", "Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.", "Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018."]}
DOI: 10.5281/zenodo.6583892
title: Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

description: Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org). Details on saved models coming soon.
description: {"references": ["J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359\u2013366, 1998.", "Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49\u201370, 2000.", "https://www.textures.com/", "Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.", "Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018."]}
DOI: 10.5281/zenodo.6584407
title: Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

description: Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org). Details on saved models coming soon.
description: {"references": ["J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359\u2013366, 1998.", "Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49\u201370, 2000.", "https://www.textures.com/", "Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.", "Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018."]}
DOI: 10.5281/zenodo.6584667
title: Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

description: Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org). Details on saved models coming soon.
description: {"references": ["J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359\u2013366, 1998.", "Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49\u201370, 2000.", "https://www.textures.com/", "Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.", "Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018."]}
DOI: 10.5281/zenodo.6576019
title: Training and test data, plus saved models for the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to the SVRHM 2022 Workshop @ NeurIPS

description: Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:

'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.
'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.
'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.
'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.
Each .zip file contains a saved model. Details on these are coming soon.
For more details, see the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" published at the SVRHM 2022 Workshop @ NeurIPS (link).
DOI: 10.5281/zenodo.6576020
title: Training and test data for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

description: Each file is in the pickle format and was generated with Python 3.8.5. Each file contains a Python dictionary with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (to be published soon on arXiv.org).
description: {"references": ["J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359\u2013366, 1998.", "Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49\u201370, 2000.", "https://www.textures.com/", "Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.", "Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018."]}
DOI: 10.5281/zenodo.6584838
title: Training and test data, plus saved models for the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022

description: Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields: 'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. 'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each. 'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family. Each .zip file contains a saved model. Details on these are coming soon. For more details, see the paper "Top-down inference in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to NeurIPS 2022 (preprint).
description: {"references": ["J Hans Van Hateren and Arjen van der Schaaf. Independent component filters of natural images compared445 with simple cells in primary visual cortex. Proceedings of the Royal Society of London. Series B: Biological446 Sciences, 265(1394):359\u2013366, 1998.", "Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex430 wavelet coefficients. International journal of computer vision, 40(1):49\u201370, 2000.", "https://www.textures.com/", "Phil Brodatz. Textures: a photographic album for artists and designers. Dover publications, 1966.", "Gabriel Barello, Adam S Charles, and Jonathan W Pillow. Sparse-coding variational auto-encoders.355 bioRxiv, page 399246, 2018."]}
DOI: 10.5281/zenodo.6644895
title: Training and test data, plus saved models for the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to the SVRHM 2022 Workshop @ NeurIPS

description: Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:

'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.
'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.
'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.
'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.
Each .zip file contains a saved model. Details on these are coming soon.
For more details, see the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" published at the SVRHM 2022 Workshop @ NeurIPS (link).
DOI: 10.17026/dans-z6v-zxsc
title: Supplemental Table 2 PRISMA DTA for Abstracts

description: The abstract is in line with the PRISMA DTA for Abstracts
DOI: 10.17026/dans-zxq-c63v
title: Data underlying the paper 'Effect of the use of a client-accessible youth health record on experienced autonomy among parents and adolescents in preventive child healthcare and youth care; a mixed methods intervention study.'

description: Intervention study, investigating the effect of use of a client-accessible electronic patient record for preventive child health and youth care (EPR-Youth) on experienced client autonomy and the role of professional autonomy-supportive behaviour in strengthening this effect.To evaluate effect of EPR-Use on client autonomy and the role of professional attitude in strengthening that autonomy, A mixed methods design combined baseline and follow-up questionnaires with focus group interviews, over a period of two years. Different samples of clients completed a questionnaire about experienced autonomy and portal use at start (1202 parents, 202 adolescents) and 12 months after implementation (914 parents, 89 adolescents). Professionals completed questionnaires about autonomy-supportive behaviour at baseline (N=100), after 5 months (N=57) and 24 months (N=110). After 14 months, focus group interviews were held with separate groups of clients (eight parents, four adolescents) and professionals (N=12).
DOI: 10.5517/ccdc.csd.cc2h79xz
title: CCDC 2300941: Experimental Crystal Structure Determination

description: Related Article: Xiongfei Zheng, Agamemnon E. Crumpton, Mathias A. Ellwanger, Simon Aldridge|2023|Dalton Trans.|52|16591|doi:10.1039/D3DT03416J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h79y0
title: CCDC 2300942: Experimental Crystal Structure Determination

description: Related Article: Xiongfei Zheng, Agamemnon E. Crumpton, Mathias A. Ellwanger, Simon Aldridge|2023|Dalton Trans.|52|16591|doi:10.1039/D3DT03416J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h79z1
title: CCDC 2300943: Experimental Crystal Structure Determination

description: Related Article: Xiongfei Zheng, Agamemnon E. Crumpton, Mathias A. Ellwanger, Simon Aldridge|2023|Dalton Trans.|52|16591|doi:10.1039/D3DT03416J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h7qxd
title: CCDC 2301344: Experimental Crystal Structure Determination

description: Related Article: Yali Ma, Haitang Wang, Hailong Wang, Jiani Wang, Shuaiyu Jiang, Qiang Zheng, Songyan Jia, Xue Li, Tianyi Ma|2023|CrystEngComm|25|6489|doi:10.1039/D3CE01022H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17026/dans-zyb-9pcv
title: tRNAome regulating hepatitis E virus-triggered inflammasome response

description: These are original data, processed data and figures from a study entitled "Hepatitis E virus infection remodels the mature tRNAome in macrophages to orchestrate NLRP3 inflammasome response”. This study is under consideration by PNAS for publication. These data can be easily re-used, once this paper will be published (according to the description in the paper).
DOI: 10.5517/ccdc.csd.cc1my9gb
title: CCDC 1517712: Experimental Crystal Structure Determination

description: Related Article: Subhadip Roy, Susital Mal, Rupak Banik, Subrata Das, Ľubor Dlháň, Ján Titiš, Roman Boča, Alexander M. Kirillov, Alexander S. Novikov, Paul Hazendonk, Ray J. Butcher, Antonio Bauza, Antonio Frontera|2023|CrystEngComm|25|6503|doi:10.1039/D3CE00871A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2hblk0
title: CCDC 2304092: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, R. Macias|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17026/dans-22t-su39
title: Data underlying the publication "Communities of nematodes, bacteria and fungi differ among soils of different wild cabbage populations"

description: This data set underlies the study that aimed to describe bacterial, fungal and nematode diversity in soil associated with wild cabbage plants (Brassica oleracea) from five different population growing naturally along the Dorset coast in the UK. Samples were taken from bulk soil, rhizosphere soil and the roots of cabbage plants in 2011 and 2012. Nematode composition and abundance was determined in bulk soil, rhizosphere and roots. Presence/absence of fungal and bacterial OTUs was determined only in rhizosphere soil in 2011.
DOI: 10.5517/ccdc.csd.cc2hbll1
title: CCDC 2304093: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, J. Bould, C.A. Kilner, C.M. Pask|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.