cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57770/cnnftp
title: Replication Data for: Plug-and-Play: Improve Depth Estimation via Sparse Data Propagation

description: We propose a novel plug-and-play (PnP) module for improving depth prediction with taking arbitrary patterns of sparse depths as input. Given any pre-trained depth predic-tion model, our PnP module updates the intermediate feature map such that the model outputs new depths consistent with the given sparse depths. Our method requires no additional training and can be applied to practical applications such as leveraging both RGB and sparse LiDAR points to robustly estimate dense depth map. Our approach achieves consistent improvements on various state-of-the-art methods on indoor (i.e., NYU-v2) and outdoor (i.e., KITTI) datasets. Various types of LiDARs are also synthesized in our experiments to verify the general applicability of our PnP module in practice.
DOI: 10.57770/wbh60v
title: Replication Data for: Summarizing First-Person Videos from Third Persons' Points of Views

description: Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage. However, most existing studies rely on training data of third-person videos, which cannot easily generalize to highlight the first-person ones. With the goal of deriving an effective model to summarize first-person videos, we propose a novel deep neural network architecture for describing and discriminating vital spatiotemporal information across videos with different points of view. Our proposed model is realized in a semi-supervised setting, in which fully annotated third-person videos, unlabeled first-person videos, and a small number of annotated first-person ones are presented during training. In our experiments, qualitative and quantitative evaluations on both benchmarks and our collected first-person video datasets are presented.
DOI: 10.6084/m9.figshare.20055185
title: 国界.shp

description: The data set is mainly the soil bulk density described
DOI: 10.6084/m9.figshare.20055185.v4
title: 国界.shp

description: The data set is mainly the soil bulk density described
DOI: 10.6084/m9.figshare.20055185.v3
title: 国界.shp

description: The data set is mainly the soil bulk density described
DOI: 10.57770/hgfqld
title: Replication Data for: Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

description: While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.
DOI: 10.5439/1326961
title: SACRADV-QVP

description: SACR-ADV-QVP provides a quasi-vertical representation of azimuthally averaged polarimetric variables to reveal important signatures in ice and mixed-phase clouds. SACR-ADV-QVP is produced using a sequence of plan-position-indicator (PPI) scans, which typically covers 3 different elevation angles and is repeated every 30 minutes. The scientific basis of the algorithm is described in Ryzhkov et al. (2016). The SACR2CORR VAP generates the main inputs of this VAP. They include corrected reflectivity, differential reflectivity, spectrum width, differential phase, linear depolarization ratio, and correlation coefficient. This VAP outputs a daily NetCDF file containing quasi-vertical profiles of all aforementioned polarimetric variables every time the PPI scan strategy is performed (this could result an irregular time stamp).
DOI: 10.5439/1327820
title: aossp2rbc1m.c1

description: AOS SP2 refractory black carbon (BC) concentrations
DOI: 10.5281/zenodo.6640388
title: Top of Atmospheric Reflectance (ToA) Landsat 8 OLI

description: The data set presented here are the Top of Atmopsheric reflectance (ToA) images calculated from the Landsat 8 OLI level 2. covering path : 147 and row:47 for the cloud free images captured during year 2016. The ToA is converted from the digital numbers using Semi-Automatic Classification plugin of QGIS. The data can be readily used for the classification and feature extraction as per the use of the research. The dataset includes all the visible and near infrared bands of the Landsat (B1 to B7 Bands)..
DOI: 10.5281/zenodo.6640387
title: Top of Atmospheric Reflectance (ToA) Landsat 8 OLI

description: The data set presented here are the Top of Atmopsheric reflectance (ToA) images calculated from the Landsat 8 OLI level 2. covering path : 147 and row:47 for the cloud free images captured during year 2016. The ToA is converted from the digital numbers using Semi-Automatic Classification plugin of QGIS. The data can be readily used for the classification and feature extraction as per the use of the research. The dataset includes all the visible and near infrared bands of the Landsat (B1 to B7 Bands)..
DOI: 10.15121/1872101
title: Utah FORGE Seismicity Associated with the 2019 Well 58-32 Stimulation

description: This catalog describes the seismicity associated with the 2019 stimulation at Utah FORGE. Containing both matched-filter detections (Dzubay et al., 2022) and Schlumberger-recorded events (detected with a 12-level geophone string), the final combined catalog contains a total of 534 microseismic events spanning -2.0 Mw to -0.1 Mw. Users may differentiate between SLB and MF events using the fact that SLB event magnitudes are recorded to a higher level of precision (MF mags determined using relative amplitude ratios). Users should be wary of locations and depths (measured from sea level) for MF events, as all detections were assigned the same locations as their template events.
DOI: 10.5281/zenodo.6640520
title: Discovery of Ultra-depleted Melt Inclusion in Late Cretaceous Intraplate Basaltic Andesites in South China

description: 1. Suporting informations, including methods, figures, and one table. 2. Six Datasets, including one dataset for basaltic rocks from different settings and five datasets for chemical and isotopic compositions of whole-rocks and melt inclusions.
DOI: 10.6084/m9.figshare.19817509
title: Combining NDVI and bacterial blight score to predict grain yield in field pea

description: <br> Field pea is the most commonly grown temperate pulse crop, with close to 15 million tonnes produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial high throughput phenotyping (Normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low to moderate broad-sense (0.29- 0.55) and narrow-sense heritability (0.12 - 0.52) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0 - S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6% to 50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.
DOI: 10.6084/m9.figshare.19817509.v1
title: Combining NDVI and bacterial blight score to predict grain yield in field pea

description: <br> Field pea is the most commonly grown temperate pulse crop, with close to 15 million tonnes produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines advancement stages of the breeding program (S0, S1, S2, and S3) were assessed with grain yield, aerial high throughput phenotyping (Normalized difference vegetation index, NDVI), and bacterial blight disease scores (BBSC). Low to moderate broad-sense (0.29- 0.55) and narrow-sense heritability (0.12 - 0.52) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0 - S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6% to 50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models.
DOI: 10.5281/zenodo.6640428
title: Sutra to DNA : An interdisciplinary approach to cultural heritage preservation

description: Materials pertaining to the project Sutra2DNA. We have chosen the Diamond Sūtra as the first Buddhist text to be encoded in DNA. The DNA is encased in metal capsule, which in turn are housed in specially designed, 3D printed miniature stūpas. A QR code with the DOI of this archive will be printed on the miniature stūpas. Interested finders who have arrived here because of the QR code should first read the final report of the project. The upload contains the following files: * Sutra2DNA_finalReport.odt<br> Explains the project and outcome. * diamondSutraT235_textOnly.txt<br> The Chinese text of the Diamond Sūtra as provided to the encoder. * Twist-Decoding Instructions-DS14.pdf<br> Contains some of the decoding options available in 2022. * twist_bioscience_codec_v2_external.tar.gz<br> Docker file with the decoder for the DNA sequence as provided by TwistBioscience. * miniatureStupa_STLfiles/*.stl<br> Three files to 3D print the three parts of the miniature stūpa that houses the capsule with the DNA. * dna-fountain-master.zip<br> Justin Brody's porting of Erlich's fountain code algorithm (2017) to Python 3. (2022-06 code from https://github.com/jdbrody/dna-fountain)
DOI: 10.5281/zenodo.6640427
title: Sutra to DNA : An interdisciplinary approach to cultural heritage preservation

description: Materials pertaining to the project Sutra2DNA. We have chosen the Diamond Sūtra as the first Buddhist text to be encoded in DNA. The DNA is encased in metal capsule, which in turn are housed in specially designed, 3D printed miniature stūpas. A QR code with the DOI of this archive will be printed on the miniature stūpas. Interested finders who have arrived here because of the QR code should first read the final report of the project. The upload contains the following files: * Sutra2DNA_finalReport.odt<br> Explains the project and outcome. * diamondSutraT235_textOnly.txt<br> The Chinese text of the Diamond Sūtra as provided to the encoder. * Twist-Decoding Instructions-DS14.pdf<br> Contains some of the decoding options available in 2022. * twist_bioscience_codec_v2_external.tar.gz<br> Docker file with the decoder for the DNA sequence as provided by TwistBioscience. * miniatureStupa_STLfiles/*.stl<br> Three files to 3D print the three parts of the miniature stūpa that houses the capsule with the DNA. * dna-fountain-master.zip<br> Justin Brody's porting of Erlich's fountain code algorithm (2017) to Python 3. (2022-06 code from https://github.com/jdbrody/dna-fountain)
DOI: 10.5281/zenodo.6586456
title: DCASE 2022 Challenge Task 2 Evaluation Dataset

description: <strong>Description</strong> This dataset is the "evaluation dataset" for the <strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>". <strong>Condition of use</strong> This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. <strong>Citation</strong> If you use this dataset, please cite all the following three papers. Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [URL] Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [URL] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL] <strong>Contact</strong> If there is any problem, please contact us: Kota Dohi, kota.dohi.gr@hitachi.com Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com Keisuke Imoto, keisuke.imoto@ieee.org
DOI: 10.5281/zenodo.6586455
title: DCASE 2022 Challenge Task 2 Evaluation Dataset

description: <strong>Description</strong> This dataset is the "evaluation dataset" for the <strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>". <strong>Condition of use</strong> This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. <strong>Citation</strong> If you use this dataset, please cite all the following three papers. Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [URL] Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [URL] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL] <strong>Contact</strong> If there is any problem, please contact us: Kota Dohi, kota.dohi.gr@hitachi.com Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com Keisuke Imoto, keisuke.imoto@ieee.org
DOI: 10.5281/zenodo.6462969
title: DCASE 2022 Challenge Task 2 Additional Training Dataset

description: <strong>Description</strong> This dataset is the "additional training dataset" for the <strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>". <strong>Condition of use</strong> This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. <strong>Citation</strong> If you use this dataset, please cite all the following three papers. Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [URL] Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [URL] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL] <strong>Contact</strong> If there is any problem, please contact us: Kota Dohi, kota.dohi.gr@hitachi.com Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com Keisuke Imoto, keisuke.imoto@ieee.org
DOI: 10.5281/zenodo.6462968
title: DCASE 2022 Challenge Task 2 Additional Training Dataset

description: <strong>Description</strong> This dataset is the "additional training dataset" for the <strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>". <strong>Condition of use</strong> This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. <strong>Citation</strong> If you use this dataset, please cite all the following three papers. Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [URL] Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [URL] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL] <strong>Contact</strong> If there is any problem, please contact us: Kota Dohi, kota.dohi.gr@hitachi.com Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com Keisuke Imoto, keisuke.imoto@ieee.org
DOI: 10.5281/zenodo.6640544
title: Discovery of Ultra-depleted Melt Inclusion in Late Cretaceous Intraplate Basaltic Andesites in South China

description: 1. Suporting informations, including methods, figures, and one table. 2. Six Datasets, including one dataset for basaltic rocks from different settings and five datasets for chemical and isotopic compositions of whole-rocks and melt inclusions.
DOI: 10.5281/zenodo.6640519
title: Discovery of Ultra-depleted Melt Inclusion in Late Cretaceous Intraplate Basaltic Andesites in South China

description: 1. Suporting informations, including methods, figures, and one table. 2. Six Datasets, including one dataset for basaltic rocks from different settings and five datasets for chemical and isotopic compositions of whole-rocks and melt inclusions.
DOI: 10.34740/kaggle/dsv/3797902
title: BBC News
DOI: 10.25612/837.prwz8w3q5dwj
title: Electric Vehicle Charging Stations - Texas

description: Electric Vehicle Charging Stations in the state of Texas as of April 14, 2022.
DOI: 10.57770/vow6ci
title: Replication Data for: STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

description: We propose a novel superpixel-based multi-view convolutional neural network for semantic image segmentation. The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene. Particularly in indoor videos such as captured by robotic platforms or handheld and bodyworn RGBD cameras, nearby video frames provide diverse viewpoints and additional context of objects and scenes. To leverage such information, we first compute region correspondences by optical flow and image boundary-based superpixels. Given these region correspondences, we propose a novel spatio-temporal pooling layer to aggregate information over space and time. We evaluate our approach on the NYU--Depth--V2 and the SUN3D datasets and compare it to various state-of-the-art single-view and multi-view approaches. Besides a general improvement over the state-of-the-art, we also show the benefits of making use of unlabeled frames during training for multi-view as well as single-view prediction.
DOI: 10.21227/8qr9-6449
title: Dataset for Motion Artifact Removal for Ambulatory Electrocardiography 

description: The dataset is for Paper " Motion Artifact Removal for Ambulatory Electrocardiography ". There are figures and corresponding original data, which is gathered by ADI5940EVA board, and processed by OariginPro or Matlab.
DOI: 10.6084/m9.figshare.20063780
title: Table S1. Summary of the main morphotectonic events across the NE Tibetan Plateau

description: Summary of the main morphotectonic events across the NE Tibetan Plateau