cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57770/dhpucc
title: Replication Data for: Colorization of Depth Map via Disentanglement

description: Vision perception is one of the most important components for a computer or robot to understand the surrounding scene and achieve autonomous applications. However, most of the vision models are based on the RGB sensors, which in general are vulnerable to the insufficient lighting condition. In contrast, the depth camera, another widely-used visual sensor, is capable of perceiving 3D information and being more robust to the lack of illumination, but unable to obtain appearance details of the surrounding environment compared to RGB cameras. To make RGB-based vision models workable for the low-lighting scenario, prior methods focus on learning the colorization on depth maps captured by depth cameras, such that the vision models can still achieve reasonable performance on colorized depth maps. However, the colorization produced in this manner is usually unrealistic and constrained to the specific vision model, thus being hard to generalize for other tasks to use. In this paper, we propose a depth map colorization method via disentangling appearance and structure factors, so that our model could 1) learn depth-invariant appearance features from an appearance reference and 2) generate colorized images by combining a given depth map and the appearance feature obtained from any reference. We conduct extensive experiments to show that our colorization results are more realistic and diverse in comparison to several image translation baselines.
DOI: 10.17036/researchdata.aston.ac.uk.00000553
title: High-Speed Long-Haul Multi-Channel Nonlinear Optical Communication Systems Influenced by Equalization Enhanced Phase Noise
DOI: 10.5281/zenodo.6637538
title: Trialstreamer data

description: Trialstreamer annotated collection of RCTs. This respository contains baseline files (large), and subsequent updates (daily for PubMed, weekly for ICTRP).
DOI: 10.57770/sjfhu3
title: Replication Data for: Learning Low-Shot Generative Networks for Cross-Domain Data

description: We tackle a novel problem of learning generators for cross-domain data under a specific scenario of low-shot learning. Basically, given a source domain with sufficient amount of training data, we aim to transfer the knowledge of its generative process to another target domain, which not only has few data samples but also contains the domain shift with respect to the source domain. This problem has great potential in practical use and is different from the well-known image translation task, as the target-domain data can be generated without requiring any source-domain ones and the large data consumption for learning target-domain generator can be alleviated. Built upon a cross-domain dataset where (1) each of the low shots in the target domain has its correspondence in the source and (2) these two domains share the similar content information but different appearance, two approaches are proposed: a Latent-Disentanglement-Orientated model (LaDo) and a Generative-Hierarchy-Oriented (GenHo) model. Our LaDo and GenHo approaches address the problem from different perspectives, where the former relies on learning the disentangled representation composed of domain-invariant content features and domain-specific appearance ones; while the later decomposes the generative process of a generator into two parts for synthesizing the content and appearance sequentially. We perform extensive experiments under various settings of cross-domain data and show the efficacy of our models for generating target-domain data with the abundant content variance as in the source domain, which lead to the favourable performance in comparison to several baselines.
DOI: 10.5517/ccdc.csd.cc26bfmm
title: CCDC 2035820: Experimental Crystal Structure Determination

description: Related Article: Rong-Hua Hu, Shu-Zhen Liu, Yu-Yue Xu, Xiu-Guang Yi, Wen-Tong Chen, Wei-Sheng Lin|2022|J.Mol.Struct.|1265|133396|doi:10.1016/j.molstruc.2022.133396
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.cc2866k8
title: CCDC 2091339: Experimental Crystal Structure Determination

description: Related Article: Svetlana E. Korolenko, Elena A. Malinina, Varvara V. Avdeeva, Alexey S. Kubasov, Lyudmila V. Goeva, Elena A. Ugolkova, Vadim V. Minin, Anatolii S. Burlov, Lyudmila N. Divaeva, Nikolay T. Kuznetsov|2022|Inorg.Chim.Acta|539|121038|doi:10.1016/j.ica.2022.121038
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.cc2866l9
title: CCDC 2091340: Experimental Crystal Structure Determination

description: Related Article: Svetlana E. Korolenko, Elena A. Malinina, Varvara V. Avdeeva, Alexey S. Kubasov, Lyudmila V. Goeva, Elena A. Ugolkova, Vadim V. Minin, Anatolii S. Burlov, Lyudmila N. Divaeva, Nikolay T. Kuznetsov|2022|Inorg.Chim.Acta|539|121038|doi:10.1016/j.ica.2022.121038
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: 10.6084/m9.figshare.19780297
title: datasets of "Two millennia of complexity and variability in a peri-alpine socioecological system (Savoie, France): the contribution of palynology and sedaDNA analysis. Frontiers in Ecology and Evolution. "

description: DNA analyses targetting plants and mammals based on metabarcoding approach (raw data and final datasets). XRF core scanner data age-depth model output Loss on ignition data Pollen data <br> each dataset is associated with a "readme" file
DOI: 10.6084/m9.figshare.19780297.v1
title: datasets of "Two millennia of complexity and variability in a peri-alpine socioecological system (Savoie, France): the contribution of palynology and sedaDNA analysis. Frontiers in Ecology and Evolution. "

description: DNA analyses targetting plants and mammals based on metabarcoding approach (raw data and final datasets). XRF core scanner data age-depth model output Loss on ignition data Pollen data <br> each dataset is associated with a "readme" file
DOI: 10.57770/me15hv
title: Replication Data for: LayoutMP3D: Layout Annotation of Matterport3D

description: Inferring the information of 3D layout from a single equirectangular panorama is crucial for numerous applications of virtual reality or robotics (e.g., scene understanding and navigation). To achieve this, several datasets are collected for the task of 360 layout estimation. To facilitate the learning algorithms for autonomous systems in indoor scenarios, we consider the Matterport3D dataset with their originally provided depth map ground truths and further release our annotations for layout ground truths from a subset of Matterport3D. As Matterport3D contains accurate depth ground truths from time-of-flight (ToF) sensors, our dataset provides both the layout and depth information, which enables the opportunity to explore the environment by integrating both cues.
DOI: 10.5258/soton/d2142
title: Stabilisation of transverse mode purity in a radially polarised Ho:YAG laser using machine learning

description: Dataset for the article 'Stabilisation of transverse mode purity in a radially polarised Ho:YAG laser using machine learning' in Applied Physics B. This dataset contains the data used in the generation of Figures 3, 6 and 8 in the manuscript. The figures are as follows: Fig. 3. Beam propagation factor (M2) of the radially polarised Ho:YAG laser at 28 W of incident pump power, indicating a high quality LG01 mode. Inset: emission spectrum. Fig. 6. Variation of the radial polarisation purity as a function of incident pump power when the machine learning stabilisation is, or is not, being used. Fig. 8. Output power curves for the Ho:YAG laser with either no machine learning stabilisation, radial LG01 stabilisation or fundamental HG00 stabilisation.
DOI: 10.57770/ctog0d
title: Replication Data for: Towards Interpretable Deep Networks for Monocular Depth Estimation

description: eep networks for Monocular Depth Estimation (MDE) have achieved promising performance recently and it is of great importance to further understand the interpretability of these networks. Existing methods attempt to provide posthoc explanations by investigating visual cues, which may not explore the internal representations learned by deep networks. In this paper, we find that some hidden units of the network are selective to certain ranges of depth, and thus such behavior can be served as a way to interpret the internal representations. Based on our observations, we quantify the interpretability of a deep MDE network by the depth selectivity of its hidden units. Moreover, we then propose a method to train interpretable MDE deep networks without changing their original architectures, by assigning a depth range for each unit to select. Experimental results demonstrate that our method is able to enhance the interpretability of deep MDE networks by largely improving the depth selectivity of their units, while not harming or even improving the depth estimation accuracy. We further provide a comprehensive analysis to show the reliability of selective units, the applicability of our method on different layers, models, and datasets, and a demonstration on analysis of model error.
DOI: 10.57770/aburxp
title: Replication Data for: LED2-Net: Monocular 360˚ Layout Estimation via Differentiable Depth Rendering

description: Despite that significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360˚ layout estimation as a problem of predicting depth on the horizon line of a panorama. Specifically, we propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable, thus making our proposed model end-to-end trainable while leveraging the 3D geometric information, without the need of providing the ground truth depth. We show that our method achieves state-of-the-art performance on numerous 360˚ layout benchmark datasets. Moreover, our formulation enables a pre-training step on the depth dataset, which further improves the generalizability of our layout estimation model.
DOI: 10.57770/f6s2ib
title: Replication Data for: Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images

description: This paper presents a novel preconditioning strategy for the classic 8-point algorithm (8-PA) for estimating an essential matrix from 360-FoV images (i.e., equirectangular images) in spherical projection. To alleviate the effect of uneven key-feature distributions and outlier correspondences, which can potentially decrease the accuracy of an essential matrix, our method optimizes a non-rigid transformation to deform a spherical camera into a new spatial domain, defining a new constraint and a more robust and accurate solution for an essential matrix. Through several experiments using random synthetic points, 360-FoV, and fish-eye images, we demonstrate that our normalization can increase the camera pose accuracy by about 20% without significantly overhead the computation time. In addition, we present further benefits of our method through both a constant weighted least-square optimization that improves further the well known Gold Standard Method (GSM) (i.e., the non-linear optimization by using epipolar errors); and a relaxation of the number of RANSAC iterations, both showing that our normalization outcomes a more reliable, robust, and accurate solution.
DOI: 10.5281/zenodo.6634408
title: Datos depositados: lista de topónimos en -ain

description: Lista de topónimos acabados en el sufijo -<em>ain</em>.
DOI: 10.5281/zenodo.6511344
title: Datos depositados: lista de topónimos en -ain

description: Lista de topónimos acabados en el sufijo -<em>ain</em>.
DOI: 10.5285/d111d44e-0794-28dc-e053-6c86abc0fc99
title: Sampling of estuarine chemistry and organic matter for 16 estuaries in Great Britain in 2017/8 as part of the LOCATE project.

description: Water measurements were taken quarterly from 16 estuaries across England, Scotland and Wales from April 2017 until April 2018, to provide information on the geographical and seasonal variation in carbon fluxes. The estuaries were chosen such that their catchments reflected different land use types. The parameters measured were: temperature; specific electrical conductivity (SEC); salinity; concentrations of ammonium, nitrate, phosphate, non-particulate organic carbon, total dissolved carbon, total nitrogen and total phosphorous; alkalinity; pH; dissolved organic matter fluorescence (fDOM); absorbance; particulate organic carbon; particulate organic nitrogen; 13C; 15N. Samples were also taken for molecular analysis, and are reported elsewhere. Water samples were collected from six fixed sites on each estuary, within the span of the same 2-3 days in a given quarter, wherever possible. Water was sampled using a single bottle before being split for different analyses. Coordination of the sampling was carried out by Andy Tye of the British Geological Survey. The samples were taken for Land Ocean CArbon TransfEr (LOCATE), which is a multidisciplinary NERC project involving the National Oceanography Centre, the British Geological Survey, the UK Centre for Ecology and Hydrology and the Plymouth Marine Laboratory, with assistance from the University of Lancaster, University of Durham, University of Hull, the University of the Highlands and Islands and the Environment Agency. LOCATE aims to improve understanding of the flow of carbon from land into the ocean, with this dataset concerning the flow through estuaries. There is a companion LOCATE dataset covering the river data (doi:10.5285/08223cdd-5e01-43ad-840d-15ff81e58acf).
DOI: 10.34740/kaggle/dsv/3793123
title: Dutch News Articles
DOI: 10.5258/soton/d2256
title: Dataset for Lasting effects of discontinuous shear thickening in cornstarch suspensions upon flow cessation

description: This dataset supports the publication: 'Lasting effects of discontinuous shear thickening in cornstarch suspensions upon flow cessation' by Jae Hyung Cho, Andrew H. Griese, Ivo Peters and Irmgard Bischofberger, Physical Review Fluids (2022) The data is in .xlsx format accessible in excel. The file contains data to reproduce the following figures: Fig 1. Flow curves Fig 3. Temporal change in stresses and shear rate Fig 4. Characteristic times Fig 5. Transition times, scale parameter T, stress at transition, and residual stress Fig 7. Transition mean normal stress The data in the file is organized by figure, with a separate sheet for each figure containing the data to reproduce the respective figure
DOI: 10.57770/3zpxg0
title: Replication Data for: Time Flies: Animating a Still Image with Time-Lapse Video as Reference

description: Time-lapse videos usually perform eye-catching appearances but are often hard to create. In this paper, we propose a self-supervised end-to-end model to generate the timelapse video from a single image and a reference video. Our key idea is to extract both the style and the features of temporal variation from the reference video, and transfer them onto the input image. To ensure both the temporal consistency and realness of our resultant videos, we introduce several novel designs in our architecture, including classwise NoiseAdaIN, flow loss, and the video discriminator. In comparison to the baselines of state-of-the-art style transfer approaches, our proposed method is not only efficient in computation but also able to create more realistic and temporally smooth time-lapse video of a still image, with its temporal variation consistent to the reference.
DOI: 10.5281/zenodo.6637475
title: Multiple Sequence Alignments of primate proteomes

description: This image dataset contains Multiple Sequence Alignments of protein sequences extracted from the Uniprot reference proteomes and RefSeq databases.
DOI: 10.57770/zk53ai
title: Replication Data for: BiFuse: Monocular 360 Depth Estimation via Bi-projection Fusion

description: Depth estimation from a monocular 360◦ image is an emerging problem that gains popularity due to the availability of consumer-level 360◦ cameras and the complete surrounding sensing capability. While the standard of 360◦ imaging is under rapid development, we propose to predict the depth map of a monocular 360◦ image by mimicking both peripheral and foveal vision of the human eye. To this end, we adopt a two-branch neural network leveraging two common projections: equirectangular and cubemap projections. In particular, equirectangular projection incorporates a complete field-of-view but introduces distortion, whereas cubemap projection avoids distortion but introduces discontinuity at the boundary of the cube. Thus we propose a bi-projection fusion scheme along with learnable masks to balance the feature map from the two projections. Moreover, for the cubemap projection, we propose a spherical padding procedure which mitigates discontinuity at the boundary of each face. We apply our method to four panorama datasets and show favorable results against the existing state-of-the-art methods.
DOI: 10.5281/zenodo.6637474
title: Multiple Sequence Alignments of primate proteomes

description: This image dataset contains Multiple Sequence Alignments of protein sequences extracted from the Uniprot reference proteomes and RefSeq databases.
DOI: 10.57770/65qh83
title: Replication Data for: 360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume

description: Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation of perspective images. However, 360° images captureed under equirectangular projection cannot benefit from directly adopting existing methods due to distortion introduced (i.e., lines in 3D arenot projected into lines in 2D). To tackle this issue, we present a novel architecture specifically designed for spherical disparity using the setting of top-bottom 360° camera pairs. Moreover, we propose to mitigate the distortion issue by: 1) an additional input branch capturing the position and relation of each pixel in the spherical coordinate, and 2) a cost volume built upon a learnable shifting filter. Due to the lack of 360° stereo data, we collect two 360° stereo datasets from Matterport3D and Stanford3D for training and evaluation. Extensive experiments and ablation study are provided to validate our method against existing algorithms. Finally, we show promising results on real-world environments capturing images with two consumer-level cameras
DOI: 10.6084/m9.figshare.19525471.v1
title: Presence_absence_data_Plasmid_gene_matrix

description: Presence absence data for the plasmid gene matrix with each sample as a row and each gene as a column. This links to Figure S5. It shows plasmid interactomes for all bacterial samples across all unique plasmid-derived genes. The axes were sorted alphabetically.
DOI: 10.57770/lgiw4n
title: Replication Data for: Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer

description: Style transfer has been widely applied to give real-world images a new artistic look. However, given a stylized image, the attempts to use typical style transfer methods for de-stylization or transferring it again into another style usually lead to artifacts or undesired results. We realize that these issues are originated from the content inconsistency between the original image and its stylized output. Therefore, in this paper we advance to keep the content information of the input image during the process of style transfer by the power of steganography, with two approaches proposed: a two-stage model and an end-to-end model. We conduct extensive experiments to successfully verify the capacity of our models, in which both of them are able to not only generate stylized images of quality comparable with the ones produced by typical style transfer methods, but also effectively eliminate the artifacts introduced in reconstructing original input from a stylized image as well as performing multiple times of style transfer in series.
DOI: 10.6084/m9.figshare.19328687
title: Geometries and Boundary Conditions

description: The geometries and boundary conditions of the seven mean cases belonging to the following publication: <br> Goubergrits L, Vellguth K, Obermeier L, Schlief A, Tautz L, Bruening J, Lamecker H, Szengel A, Nemchyna O, Knosalla C, Kuehne T and Solowjowa N (2022) CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics. Front. Cardiovasc. Med. 9:901902. doi: 10.3389/fcvm.2022.901902<br> <br> The dataset contains the geometries (as STL) of: the left ventricle (upper and lower parts), left atrium, aorta, both valve planes (inner and outer parts) and the mitral insuffiency plane as well as the boundary conditions (as CSV) of: the ventricular motion (GridDisplacement), the volume curve and flux, pressure at the atrial inlet, mass flow at the aortic outlet and resistance tables at the valves.<br> <br> Data for all seven investigated cases (A0ML, A0MH, A1ML, A1MH, A1T, A1I, A1HK) are included.<br> <br>
DOI: 10.6084/m9.figshare.19328687.v1
title: Geometries and Boundary Conditions

description: The geometries and boundary conditions of the seven mean cases belonging to the following publication: <br> Goubergrits L, Vellguth K, Obermeier L, Schlief A, Tautz L, Bruening J, Lamecker H, Szengel A, Nemchyna O, Knosalla C, Kuehne T and Solowjowa N (2022) CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics. Front. Cardiovasc. Med. 9:901902. doi: 10.3389/fcvm.2022.901902<br> <br> The dataset contains the geometries (as STL) of: the left ventricle (upper and lower parts), left atrium, aorta, both valve planes (inner and outer parts) and the mitral insuffiency plane as well as the boundary conditions (as CSV) of: the ventricular motion (GridDisplacement), the volume curve and flux, pressure at the atrial inlet, mass flow at the aortic outlet and resistance tables at the valves.<br> <br> Data for all seven investigated cases (A0ML, A0MH, A1ML, A1MH, A1T, A1I, A1HK) are included.<br> <br>
DOI: 10.5281/zenodo.6637679
title: Slow practice and tempo management strategies in instrumental music learning: Investigating prevalence and cognitive functions

description: This dataset corresponds to the publication of the same title and the following DOI: https://doi.org/10.1177/03057356211073481 The dataset contains 3 excel files. The file named QuantiativeSurveyDataClean_withKey contains all collected, unprocessed, cleaned data variables from the quantitative questionnaire. This includes all variables used for the principle components analysis. The file named ANOVA_data contains the variables used for the ANOVA analyses, and the file named regression_data contains the variables used for the regressions analyses. In all three files, descriptions of the variables can be found in the sheet titled "key", and the data can be found in the sheet titled "data".