cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/vv1w-k355
title: A Machine Learning Benchmark for Facies Classification

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply deep learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach.To address these issues, we open-source an accurate 3D geological model of the Netherlands F3 Block. This geological model is based on both well log data and 3D seismic data and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on deconvolution networks and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work can help advance research in this area and create an objective benchmark for comparing the results of different machine learning approaches for facies classification for researchers to use in the future.
DOI: 10.21227/8z1w-rv93
title: A Machine Learning Benchmark for Facies Classification

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply deep learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach.To address these issues, we open-source an accurate 3D geological model of the Netherlands F3 Block. This geological model is based on both well log data and 3D seismic data and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on deconvolution networks and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work can help advance research in this area and create an objective benchmark for comparing the results of different machine learning approaches for facies classification for researchers to use in the future.
DOI: 10.21227/bqqp-nc32
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/cy5p-0z26
title: Ten Dynamic Hand Gesture Tracking

description: This data consists of ten gestures. The format is CSV, arranged in thirty features that end with a label. Each movement is repeated five times and the coordinates are obtained. The sensor used is LeapMotion. This data can be used as a means of machine learning exercises. can be used for students learning machine learning subjects. articles that have used this data can be seen at the link: https://doi.org/10.1109/CENIM.2018.8711397andhttp://www.engineeringletters.com/issues_v26/issue_3/EL_26_3_09.pdf
DOI: 10.21227/f5jz-qr73
title: A Machine Learning Benchmark for Facies Classification

description: The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits. In addition, it is common for papers that apply deep learning for facies classification to not contain quantitative results, and rather rely solely on visual inspection of the results. All of these practices have lead to subjective results and have greatly hindered the ability to compare different machine learning models against each other and understand the advantages and disadvantages of each approach.To address these issues, we open-source an accurate 3D geological model of the Netherlands F3 Block. This geological model is based on both well log data and 3D seismic data and is grounded on the careful study of the geology of the region. Furthermore, we propose two baseline models for facies classification based on deconvolution networks and make their codes publicly available. Finally, we propose a scheme for evaluating different models on this dataset, and we share the results of our baseline models. In addition to making the dataset and the code publicly available, this work can help advance research in this area and create an objective benchmark for comparing the results of different machine learning approaches for facies classification for researchers to use in the future.
DOI: 10.21227/mb66-ge25
title: High Resolution Photoluminescence Microscopy Images And Spectra Of Air-Suspended Single-Wall Carbon Nanotubes

description: The dataset contains high-resolution microscopy images and confocal spectra of semiconducting single-wall carbon nanotubes. Carbon nanotubes allow down-scaling of electronic components to the nano-scale. There is initial evidence from Monte Carlo simulations that microscopy images with high digital resolution show energy information in the Bessel wave pattern that is visible in these images. In this dataset, images from Silicon and InGaAs cameras, as well as spectra, give valuable insights into the spectroscopic properties of these single-photon emitters. The source data is connected to a GitLab pipeline running on docker containers for data processing to various formats that are valid input to machine learning algorithms to further prove that there is information about the excitonic emission wavelength contained in the microscopic images.
DOI: 10.21227/my6z-8n66
title: Ten Dynamic Hand Gesture Tracking

description: This data consists of ten gestures. The format is CSV, arranged in thirty features that end with a label. Each movement is repeated five times and the coordinates are obtained. The sensor used is LeapMotion. This data can be used as a means of machine learning exercises. can be used for students learning machine learning subjects. articles that have used this data can be seen at the link: https://doi.org/10.1109/CENIM.2018.8711397andhttp://www.engineeringletters.com/issues_v26/issue_3/EL_26_3_09.pdf
DOI: 10.21227/wqe7-hn10
title: Plantar Thermogram Database for the Study of Diabetic Foot Complications

description: The presented database contains thermal images (thermograms) of the plantar region. The database was obtained from 122 subjects with a diabetes diagnosis (DM group) and 45 non-diabetic subjects (control group). The relevance of this database consists in to study how the temperature is distributed in the plantar region of both groups and how their differences can be measured. Previous reports in the literature have established that an increase in the plantar temperature is associated with a higher ulceration risk. Therefore, it is important to study how the thermal changes in the plantar region occur and how they can be measured in order to develop new diagnostic support tools that help the medical expert to take preventive actions. The thermogram database was obtained from 122 diabetic and 45 non-diabetic subjects. Extra information about the analysis of the thermograms for diabetic foot complications can be founded in the scientific reports of the corresponding author link.
DOI: 10.21227/z48k-f394
title: UNS Hoverflies Classification Dataset

description: Dataset was created as part of joint efforts of two research groups from the University of Novi Sad, which were aimed towards development of vision based systems for automatic identification of insect species (in particular hoverflies) based on characteristic venation patterns in the images of the insects' wings.The set of wing images consists of high-resolution microscopic wing images of several hoverfly species. There is a total of 868 wing images of eleven selected hoverfly species from two different genera, Chrysotoxum and Melanostoma. Besides images of the whole wings "UNS_Hoverflies" dataset also consists of small image patches (64x64 pixels) corresponding to 18 predetermined landmark points in each wing, which were systematically collected from the preprocessed wing images and organized inside the second root folder named "Training - test set".Each wing specimen was uniquely numbered and associated with the corresponding taxonomy group.
DOI: 10.21227/zc4r-2666
title: Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation

description: Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation Dataset.An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device. In order to quantify the number of berries per image, a network based on Mask R-CNN for object detection and instance segmentation was proposed. Several backbones such as ResNet101, ResNet50 and MobileNetV1 were tested. The performance of the algorithm was evaluated using the Intersection over Union Error (IoU) and the competitive mean Average Precision (mAP) per image and per batch. The best detection result was obtained with the ResNet50 backbone achieving a mIoU score of 0.595 and mAP scores of 0.759 and 0.724 respectively (for IoU thresholds 0.5 and 0.7). For instance segmentation, the best results obtained were 0.726 for the mIoU metric and 0.909 and 0.774 for the mAP metric using thresholds of 0.5 and 0.7 respectively.
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n.1
title: C38H28Br2N4O4
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: This dataset contains experimental data from a sample (InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) infrared absorption spectroscopy (IR) mass spectrometry (MS) mass spectrometry (MS)
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: dataset for mass spectrometry (MS) MS (FAB, 3-NBA): m/z (%) = 762.0 (6) [M]+, 763.0 (10) [M / M + H], 764.0 (17) [M / M + H], 765.0 (19) [M + H]+, 766.0 (14) [M + H]+, 767.0 (11), 768.0 (6) [M + H]+, 663 (100).
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n/chmo0000470.1
title: mass spectrometry (MS) ()
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: dataset for mass spectrometry (MS) HRMS (C38H29O4N479Br81Br): ber.: 765.0530, gef.: 765.0528.
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = –2.77 (s, 2H, NH), 1.57 (t, J = 7.1 Hz, 6H, CH3), 4.60 (q, J = 7.1 Hz, 4H, CH2), 8.23 (d, J = 8.1 Hz, 4H, HPh), 8.47 (d, J = 8.1 Hz, 4H, HPh), 8.78 (d, J = 4.8 Hz, 4H, HPyr), 9.62 (d, J = 4.9 Hz, 4H, HPyr).
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3, ppm) δ = 14.7 (+, CH3), 61.6 (–, CH2), 104.3 (Cq), 120.4 (Cq), 128.2 (+), 130.5 (Cq), 134.6 (+), 146.1 (Cq), 166.8 (Cq, COO), (residual 29 Cs distributed on the listed signals).
DOI: 10.14272/djgwmtkkmozbmf-yvbdpahpsa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C38H28Br2N4O4/c1-3-47-37(45)23-9-5-21(6-10-23)33-25-13-17-29(41-25)35(39)31-19-15-27(43-31)34(22-7-11-24(12-8-22)38(46)48-4-2)28-16-20-32(44-28)36(40)30-18-14-26(33)42-30/h5-20,41,44H,3-4H2,1-2H3/b33-25-,33-26-,34-27-,34-28-,35-29+,35-31+,36-30+,36-32+

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3314 (vw), 2921 (w), 1709 (m), 1605 (w), 1557 (w), 1465 (w), 1397 (w), 1366 (w), 1336 (w), 1302 (w), 1272 (m), 1193 (w), 1175 (w), 1122 (w), 1106 (m), 1018 (w), 997 (w), 979 (w), 961 (m), 869 (w), 848 (w), 795 (m), 785 (m), 755 (m), 729 (m), 706 (m), 630 (w), 555 (vw), 523 (w), 500 (w), 458 (w), 394 (vw) cm–1.
DOI: 10.21227/3zw2-3652
title: Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation

description: Deepblueberry: Quantification of Blueberries in the Wild Using Instance Segmentation Dataset.An accurate and reliable image-based quantification system for blueberries may be useful for the automation of harvest management. It may also serve as the basis for controlling robotic harvesting systems. Quantification of blueberries from images is a challenging task due to occlusions, differences in size, illumination conditions and the irregular amount of blueberries that can be present in an image. This paper proposes the quantification per image and per batch of blueberries in the wild, using high definition images captured using a mobile device. In order to quantify the number of berries per image, a network based on Mask R-CNN for object detection and instance segmentation was proposed. Several backbones such as ResNet101, ResNet50 and MobileNetV1 were tested. The performance of the algorithm was evaluated using the Intersection over Union Error (IoU) and the competitive mean Average Precision (mAP) per image and per batch. The best detection result was obtained with the ResNet50 backbone achieving a mIoU score of 0.595 and mAP scores of 0.759 and 0.724 respectively (for IoU thresholds 0.5 and 0.7). For instance segmentation, the best results obtained were 0.726 for the mIoU metric and 0.909 and 0.774 for the mAP metric using thresholds of 0.5 and 0.7 respectively.
DOI: 10.7910/dvn/brnub5/cevzjh
title: SVS13C.C18O21.robust1.fits
DOI: 10.7910/dvn/brnub5/gmn3gp
title: SVS13C.N2DP.robust1.fits
DOI: 10.7910/dvn/brnub5/kdebgw
title: SVS13C.13CO21.robust1.fits
DOI: 10.7910/dvn/brnub5/keu8aw
title: SVS13C.12CO21.robust1.fits
DOI: 10.17632/5453nzyzcy.4
title: Data for: Comparisons of sea ice motion and deformation, and their responses to ice conditions and cyclonic activity in the western Arctic Ocean between two summers

description: The data are the position data measured by the buoys deployed in summer 2014 and 2016
DOI: 10.7910/dvn/brnub5/0vwoqf
title: SVS13C.N2DP.robust-1.fits
DOI: 10.7910/dvn/brnub5/4h67ab
title: SVS13C.13CO21.robust-1.fits
DOI: 10.7910/dvn/brnub5/glhbnn
title: SVS13C.cont1.3mm.robust1.fits
DOI: 10.7910/dvn/brnub5/hatgqy
title: SVS13C.12CO21.robust-1.fits
DOI: 10.7910/dvn/brnub5/lgk1cz
title: SVS13C.12CO32.robust1.fits
DOI: 10.7910/dvn/brnub5/oqqxio
title: SVS13C.C18O21.robust-1.fits
DOI: 10.7910/dvn/brnub5/pka10w
title: SVS13C.cont1.3mm.robust-1.fits
DOI: 10.7910/dvn/brnub5/tnlgst
title: SVS13C.cont850um.robust-1.fits
DOI: 10.7910/dvn/brnub5/w2yhqq
title: SVS13C.H13COP43.robust1.fits
DOI: 10.7910/dvn/brnub5/xlichy
title: SVS13C.HCOP43.robust1.fits
DOI: 10.17632/26fsjgm4ym.1
title: circular plates

description: Microrplate.
DOI: 10.17632/tt7btzn6nd.1
title: The Effects of Extra Virgin Olive Oil and Canola Oil on Inflammatory Markers and Gastrointestinal Symptoms in Patients with Ulcerative Colitis

description: we have reported the data of a cross over clinical trial regarding to the compare the effect of extra virgin olive oil and canola oil on gastrointestinal symptoms and inflammatory markers in patients with ulcerative colitis.
DOI: 10.7910/dvn/brnub5/2tlp1j
title: SVS13C.HCOP43.robust-1.fits
DOI: 10.7910/dvn/brnub5/78uuwy
title: SVS13C.12CO21.robust-1.pbcor.fits
DOI: 10.7910/dvn/brnub5/k1wnch
title: SVS13C.C18O21.robust-1.pbcor.fits
DOI: 10.7910/dvn/brnub5/kf7mxc
title: SVS13C.C18O21.robust1.pbcor.fits
DOI: 10.7910/dvn/brnub5/lhg89y
title: SVS13C.12CO32.robust-1.fits
DOI: 10.7910/dvn/brnub5/opmqpd
title: SVS13C.cont850um.robust1.fits
DOI: 10.7910/dvn/brnub5/s86tag
title: SVS13C.H13COP43.robust-1.fits
DOI: 10.7910/dvn/brnub5/u2yvrl
title: SVS13C.N2DP.robust-1.pbcor.fits
DOI: 10.7910/dvn/brnub5/ugdysw
title: SVS13C.12CO21.robust1.pbcor.fits
DOI: 10.17632/4n2z3h8px4.1
title: Effect of gluten on yeast nitrogen metabolism

description: In order to preliminarily clarify the influence of nitrogen source concentration on the nitrogen metabolism pathway of yeast and the regulation mechanism. In this study, the traditional rice wine brewing process was used, adding glutenin to the fermentation experiment when dropping the tank. The content of higher alcohol and acetate in yeast nitrogen metabolism was monitored during fermentation, and the expression of related genes in yeast metabolic pathway was analyzed. The results showed that glutenin promoted the growth of yeast and changed the amino acid assimilation mode of yeast, promoted the absorption of amino acids in group B by yeast, and increased the accumulation of higher alcohol in branched chain. When the concentration of the nitrogen source is too high, the contents of higher alcohols and acetates tend to decrease, and reached the lowest value when the amount of glutenin added was 15.2 g, which was 3553.46 μg/L and 1230.77 μg/L, respectively. Compared with the control, it decreased by 14.0% and 27.0%, respectively. In addition, excessive nitrogen concentration inhibited gene expression.
DOI: 10.21227/7dht-c698
title: HisarMod: A new challenging modulated signals dataset

description: In order to increase the diversity in signal datasets, we create a new dataset called HisarMod, which includes 26 classes and 5 different modulation families passing through 5 different wireless communication channel. During the generation of the dataset, MATLAB 2017a is employed for creating random bit sequences, symbols, and wireless fading channels. The dataset includes 26 modulation types from 5 different modulation families which are analog, FSK, PAM, PSK, and QAM. In the dataset, there are 1500 signals, which have the length of 1024 I/Q samples, for each modulation type. To make HisarMod similar to RadioML2016.10a for a fair comparison, there are 20 different SNR levels in between -20dB and 18dB. As a result, the dataset covers totally 780000 signals. When generating signals, oversampling rate is chosen as 2 and raised cosine pulse shaping filter is employed with roll-off factor of 0.35.
DOI: 10.5281/zenodo.3539513
title: High-resolution ptychotomography dataset of nanoporous glass

description: This repository contains nanoporous glass tomography dataset, acquired at the cSAXS beamline at the Swiss Light Source at the Paul Scherrer Institute in Villigen (Switzerland). Details about the data acquisition can be found in Ref. [1]. <strong>Citation and acknowledgements</strong> For use of the ptychographic X-ray computed tomography dataset on nanoporous glass: <em>[1] M. Holler, A. Diaz, M. Guizar-Sicairos, P. Karvinen, E. Färm, E. Härkönen, M. Ritala, A. Menzel, J. Raabe and O. Bunk. Scientific Reports, 4, 3857 (2014).</em> Alignment and tomographic reconstruction provided in "<em>aligned_phase_sinogram" and "tomogram_delta" </em> were performed by a method described in <em>[2] M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,"Alignment methods for nanotomography with deep sub-pixel accuracy", Opt. Express, (2019).</em> <strong>Dataset description</strong> The provided dataset is saved in Matlab MAT v7.3 format. <em>stack_object</em> - complex valued unaligned projections with dimensions [Npix_vertical, Npix_horizontal, number_of_angles] <em>rotation_angle</em> - vector of corresponding projection angles in degrees <em>lambda</em> - wavelength of the illumination beam [m] <em>pixel_size</em> - size of the reconstruction pixel [m] <em>probe_size</em> - size of the reconstruction probe in pixels <em>aligned_phase_sinogram</em> - aligned and unwrapped phase projections <em>tomogram_delta</em> - reconstructed tomogram of real part of refractive index delta, n = 1-delta-i*beta
description: {"references": ["Holler, Mirko, et al. \"X-ray ptychographic computed tomography at 16 nm isotropic 3D resolution.\" Scientific reports 4 (2014): 3857."]}