cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3936068
title: Datos Autores USTA (Boletín bibliométrico 2020)

description: Consolidado de los datos de los autores USTA (Colombia) con perfil de autor en Google Scholar. Parte del servicio de vigilancia tecnológica del Observatorio de Cienciometría de la Universidad Santo Tomás. Ficha técnica Fuente Expresión de búsqueda Google académico Consultas combinadas en Harzing’s Publish or Peris por nombre en profile name. Seleccionado las perfiles con vinculación Universidad Santo Tomás Periodo de análisis: no aplica Herramientas: Microsoft Excel, Harzing’s Publish or Peris versión 7.12.257.7189 Fecha de consulta: 3/03/2020
description: https://observatoriocienciometria.usta.edu.co
description: {"references": ["Corchuelo-Rodriguez, C. A., Barreto Montenegro, A. E., L\u00f3pez B\u00e1ez, J. D., & Ostos Ortiz, O. L. (2019). Bolet\u00edn bibliom\u00e9trico USTA - No. 1 (2019). N. 1, 72. https://doi.org/10.15332/dt.inv.2019.00138"]}
DOI: 10.21227/wwq3-7q68
title: Using deep learning edge detection to improve non-destructive radiographic tests of engine

description: This paper aims to improve the existing techniques on X-ray image inspection of aerial engine by using artificial intelligence (AI) based object detection model. This technique seeks to augment and improve existing automated non-destructive testing (NDT) diagnosis of metal structure of engine parts. Traditional jet-engine maintenance and overhaul processes are resorted to NDT to find defects in internal welds. An application of deep learning for NDT technology can effectively identify presence and location of up to eight types of defects, leading to enhanced work quality and efficiency. The finer object detection of image feature maps will lead to more accurate identification of weld flaws than can be accomplished by the standard visual examination. The proposed approach adopts a region-based convolutional neural network and a deep learning neural network for object detection to render an efficient X-ray image diagnosis system. The approach may benefit the inspection work in the aviation industry via increased accuracy and efficiency.
DOI: 10.21227/80v1-hp35
title: Using deep learning edge detection to improve non-destructive radiographic tests of engine

description: This paper aims to improve the existing techniques on X-ray image inspection of aerial engine by using artificial intelligence (AI) based object detection model. This technique seeks to augment and improve existing automated non-destructive testing (NDT) diagnosis of metal structure of engine parts. Traditional jet-engine maintenance and overhaul processes are resorted to NDT to find defects in internal welds. An application of deep learning for NDT technology can effectively identify presence and location of up to eight types of defects, leading to enhanced work quality and efficiency. The finer object detection of image feature maps will lead to more accurate identification of weld flaws than can be accomplished by the standard visual examination. The proposed approach adopts a region-based convolutional neural network and a deep learning neural network for object detection to render an efficient X-ray image diagnosis system. The approach may benefit the inspection work in the aviation industry via increased accuracy and efficiency.
DOI: 10.5066/p95yj3cn
title: A comprehensive inventory of maximum glacial extent in Glacier National Park during the peak of the Little Ice Age

description: This data release consists of digitized glacier margins that represent the maximum extent of glaciers in Glacier National Park (GNP) and two glaciers on U.S. Forest Service's Flathead National Forest land during the peak of the Little Ice Age. Glacier margins are based on moraine deposits that result from active glaciation, and do not depict perennial snow and ice. Moraines are continuous arcuate rock deposits with distinct crests that form at the ice edge of active glaciers. The criteria used for identifying the former size and location of glaciers were a) the presence of a terminal moraine, b) the presence of one or both lateral moraines, and c) whether the identified glacier area was 0.020 km2 (five acres) or more. Previous work also included hummocky morainal debris immediately in front of well-defined moraines. This release includes a comprehensive inventory of past and present glaciers in GNP as well as a subset of the 37 named glaciers. Final digitized glacier margins, based on moraines, were subjected to independent review by other scientists.
DOI: 10.17632/nph7xtjnmg.5
title: Data for: Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes

description: The RAPS algoritihim and associated files. The 7 new genome scale models for algae
DOI: 10.18430/m36xgs
title: X-ray diffraction data for the crystal Structure of Dihydrodipicolinate synthase (DHDPS) from Brucella suis 1330 (6XGS)
DOI: 10.5281/zenodo.3934403
title: The V2S Dataset: A Set of Android Screen Recordings, Training Images, and Models

description: This is the dataset and models used in the paper entitled "Translating Video Recordings of Mobile App Usages into Replayable Scenarios" published at the 42nd International Conference on Software Engineering (ICSE'20) Link to V2S Paper: https://arxiv.org/abs/2005.09057
DOI: 10.5281/zenodo.3934402
title: The V2S Dataset: A Set of Android Screen Recordings, Training Images, and Models

description: This is the dataset and models used in the paper entitled "Translating Video Recordings of Mobile App Usages into Replayable Scenarios" published at the 42nd International Conference on Software Engineering (ICSE'20) Link to V2S Paper: https://arxiv.org/abs/2005.09057
DOI: 10.6078/m77s7kwt
title: Visualizing Antiquarian Excavations: Digital Reunification of the Mecklenburg Collection
DOI: 10.5281/zenodo.3922909
title: Performance of 6 Different GNSS Receivers at Low Latitude under Moderate and Strong Scintillation

description: These data sets contain ionospheric scintillation (GPS L1) observations (S<sub>4</sub> and \(\mathbf{\sigma_\phi}\) indexes) from 5 different GNSS receivers. The data are from two nights, 20/21 February and 27/28 November of 2013, and stored in different files. The files are in the HDF5 format using zlib compression and were generated by a Python script using the Pandas library. Each file contain the following folders: /csm_maker_calc /csm_own_calc /csm_raw /gpstation6_maker_calc /gpstation6_own_calc /gpstation6_raw /gsv_4004b_lisn_calc /gsv_4004b_maker_calc /septentrio_maker_calc /septentrio_own_calc /septentrio_raw /su_iono_sdr_own_calc /su_iono_sdr_raw The suffix <strong>maker_calc</strong> means that the indices were calculated in real time or post-processed using receiver manufacturer's tools. The suffix <strong>own_calc</strong> means that the indices were calculated using our own methodology. The suffix <strong>raw</strong> corresponds to the raw data (power and phase cycles). And finally the suffix <strong>lisn_calc</strong> corresponds to the S<sub>4</sub> index obtained from the LISN network website.
DOI: 10.5281/zenodo.3922908
title: Performance of 6 Different GNSS Receivers at Low Latitude under Moderate and Strong Scintillation

description: These data sets contain ionospheric scintillation (GPS L1) observations (S<sub>4</sub> and \(\mathbf{\sigma_\phi}\) indexes) from 5 different GNSS receivers. The data are from two nights, 20/21 February and 27/28 November of 2013, and stored in different files. The files are in the HDF5 format using zlib compression and were generated by a Python script using the Pandas library. Each file contain the following folders: /csm_maker_calc /csm_own_calc /csm_raw /gpstation6_maker_calc /gpstation6_own_calc /gpstation6_raw /gsv_4004b_lisn_calc /gsv_4004b_maker_calc /septentrio_maker_calc /septentrio_own_calc /septentrio_raw /su_iono_sdr_own_calc /su_iono_sdr_raw The suffix <strong>maker_calc</strong> means that the indices were calculated in real time or post-processed using receiver manufacturer's tools. The suffix <strong>own_calc</strong> means that the indices were calculated using our own methodology. The suffix <strong>raw</strong> corresponds to the raw data (power and phase cycles). And finally the suffix <strong>lisn_calc</strong> corresponds to the S<sub>4</sub> index obtained from the LISN network website.
DOI: 10.5281/zenodo.3910757
title: TCGA@Focus Dataset

description: <strong>Content</strong> A dataset of 1000 whole slide images from 52 types of organs was gathered from The Cancer Genome Atlas (TCGA) repository provided by the National Cancer Institute (NCI)/ National Institute of Health (NIH). Two different categories of focus were annotated on every region of interest in these WSIs; they were labelled "in-focus" and "out-focus" and given respective binary ground truth scores of "1" and "0". From these regions of interest, a dataset of 14,371 image patches was created. Of these patches, 11,328 are labelled in-focus, and 3,043 are labelled out-focus. The organ types were selected to be diverse in order to include a wide spectrum of tissue textures and colour information to enrich the dataset. <strong>More Information</strong> For more information, please refer to the following paper. <strong>Please cite this paper when using the dataset.</strong> <pre><code>@InProceedings{wang2020focuslitenn, title={FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology}, author={Wang, Zhongling and Hosseini, Mahdi and Miles, Adyn and Plataniotis, Konstantinos and Wang, Zhou}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020}, year={2020}, publisher="Springer International Publishing" }</code></pre> For the full code released on GitHub, please visit the repository at: https://github.com/icbcbicc/FocusLiteNN/ <strong>Contact</strong> For questions, please contact: Mahdi Hosseini mahdi.hosseini@utoronto.ca http://orcid.org/0000-0002-9147-0731
DOI: 10.5281/zenodo.3910756
title: TCGA@Focus Dataset

description: <strong>Content</strong> A dataset of 1000 whole slide images from 52 types of organs was gathered from The Cancer Genome Atlas (TCGA) repository provided by the National Cancer Institute (NCI)/ National Institute of Health (NIH). Two different categories of focus were annotated on every region of interest in these WSIs; they were labelled "in-focus" and "out-focus" and given respective binary ground truth scores of "1" and "0". From these regions of interest, a dataset of 14,371 image patches was created. Of these patches, 11,328 are labelled in-focus, and 3,043 are labelled out-focus. The organ types were selected to be diverse in order to include a wide spectrum of tissue textures and colour information to enrich the dataset. <strong>More Information</strong> For more information, please refer to the following paper. <strong>Please cite this paper when using the dataset.</strong> <pre><code>@InProceedings{wang2020focuslitenn, title={FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology}, author={Wang, Zhongling and Hosseini, Mahdi and Miles, Adyn and Plataniotis, Konstantinos and Wang, Zhou}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020}, year={2020}, publisher="Springer International Publishing" }</code></pre> For the full code released on GitHub, please visit the repository at: https://github.com/icbcbicc/FocusLiteNN/ <strong>Contact</strong> For questions, please contact: Mahdi Hosseini mahdi.hosseini@utoronto.ca http://orcid.org/0000-0002-9147-0731
DOI: 10.17632/t2vps86ht8.2
title: Data for: Identification of Pathways and Genes Associated with Cold and Hot Properties of Chinese Materia Medica Based on Bioinformatics Analysis

description: Identification of Pathways and Genes Associated with Cold and Hot Properties of Chinese Materia Medica Based on Bioinformatics Analysis
DOI: 10.17632/dz6fyzrj5p.1
title: Data for: Do bats seek clean water? A perspective on biodiversity from the Namib Desert

description: Data and R script associated with the article "Do bats seek clean water? A perspective on biodiversity from the Namib Desert." https://doi.org/10.1016/j.biocon.2020.108686 File WaterSampling_Cleaned_Feb2020.csv and waterClass.csv contain data associated with water quality. File WaterSampling_BatData_NoNoise.csv provides bat species identifications from the acoustic dataset. File Laverty_Do bats seek clean water.R includes the R script that produced the results provided in our publication. Please send questions/comments to theresalaverty@gmail.com
DOI: 10.21227/jkzv-1g27
title: Measurement and Evaluation of Human Sitting and Standing Movement Biomechanics

description: Although regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for level-ground walking. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration during activities more representative of aging and rehabilitation populations. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming an 80-kg person and previously published regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules from level-ground walking. Given that these regeneration performance calculations are based on healthy young adults, future research should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. Under Review.
DOI: 10.21227/nx96-sy24
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Although regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for level-ground walking. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration during activities more representative of aging and rehabilitation populations. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming an 80-kg person and previously published regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules from level-ground walking. Given that these regeneration performance calculations are based on healthy young adults, future research should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. Under Review.
DOI: 10.21227/asq2-2m83
title: Beamforming with deep learning from single plane wave RF data

description: Deep learning approaches for improving ultrasound (US) image reconstruction have proven successful in both experimental and clinical settings. In this paper, we present an autoencoder-based deep learning framework for ultrasound beamforming from the radio-frequency (RF) data of a single plane wave. Motivated by U-Net, the network consists of an encoder and a decoder. The network is trained and evaluated with simulated and \textit{in vivo} datasets. When tested on simulated data, the beamformed images from the proposed network achieved an SNR of 25.02, contrast of -40.61dB, and gCNR of 1.0, a mean PSNR of 18.64dB compared to ground truth images and mean axial and lateral FWHM of 0.42 and 0.61 respectively. Each of these metrics outperformed the standard delay and sum (DAS) beamforming algorithm. In addition, the network was evaluated on an \textit{in vivo} breast mass, achieving improved SNR of 3.81. These results demonstrate that the proposed network is capable of generating high quality US images from only one plane wave, which could be applied to multiple ultrasound-based clinical tasks.
DOI: 10.5281/zenodo.3936246
title: Acoustic emission characterization of coral sands during drained triaxial shearing

description: These dataset was derived from saturated coral sands subjected to drained triaxial shearing, combing with a high-performance AE measurement system.
DOI: 10.17632/xhdwd26fdc.1
title: Mapping hidden residual structure within the Myc bHLH-LZ domain using chemical denaturant titration. Panova et al

description: CDT-NMR data and structural ensemble
DOI: 10.17632/j69gfx9pxr.1
title: Heterogeneous kimberlite metasomatism revealed from a combined He-Os isotope study of Siberian megacrystalline dunite xenoliths

description: All data tables associated with the article: 'Heterogeneous kimberlite metasomatism revealed from a combined He-Os isotope study of Siberian megacrystalline dunite xenoliths' published in Geochemical Cosmochim Acta For analytical methods and intertations see published article.
DOI: 10.17632/wxm9p2fhjt.1
title: Predicting Rice Phenotypes with Meta and Multi-Target Learning

description: Rice genotype and phenotype data. The genotype data has ~12,000 features and the phenotype data has twelve traits.
DOI: 10.17632/wskyz3rhsg.1
title: Reverbα regulates liver and adipose tissue response to systemic metabolic challenge
DOI: 10.17632/zwwrzptd36.1
title: Terrorist Violence and Newborn Health. Replication data

description: This contains the do files for replication of the manuscript analysis.
DOI: 10.17632/zwwrzptd36.2
title: Terrorist Violence and Newborn Health. Replication data

description: This contains the do files for replication of the manuscript analysis.
DOI: 10.17632/vn5mtr38x2.1
title: BoC Workbooks and Handbook

description: A set of four excel-based workbooks to assist service planners, providers and commissioners to undertake a BoC study independently plus an accompanying handbook
DOI: 10.17632/fz763jmspp.2
title: Supplementary files
DOI: 10.17632/bjhc5t86vw.1
title: Operational Study of a Monoethylene Glycol (MEG) Desalination Pilot Plant. Part I: Development of a New Method for the Estimation of MEG Content in the Presence of NaCl Solid Particles: data

description: Data as presented in accompanying paper.
DOI: 10.5281/zenodo.3926181
title: FocusPath Full Dataset

description: <strong>Content</strong> This is the full FocusPath dataset, containing 8640 patches of 1024 x 1024 images. These images are extracted from nine different stained slides. The original Whole Slide Images are scanned by Huron TissueScope LE1.2. It uses a 40X optics lens at 0.25µm/pixel resolution. There are 14 absolute z-level scores assigned to these patches which correspond to the ground-truth class for the focus level. <strong>More Information</strong> For more information, please refer to the following paper. <strong>Please cite this paper when using the dataset.</strong> <pre><code>@article{8672094, author={M. S. {Hosseini} and Y. {Zhang} and K. N. {Plataniotis}}, journal={IEEE Transactions on Image Processing}, title={Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment}, year={2019}, volume={28}, number={9}, pages={4510-4525}, doi={10.1109/TIP.2019.2906582}, ISSN={1057-7149}, month={Sep.}, }</code></pre> For information on how FocusPath can be used in a deep learning pipeline for training CNN models, please visit the repository at: https://github.com/icbcbicc/FocusLiteNN/ <strong>Contact</strong> For questions, please contact: Mahdi Hosseini mahdi.hosseini@utoronto.ca http://orcid.org/0000-0002-9147-0731
DOI: 10.5281/zenodo.3926180
title: FocusPath Full Dataset

description: <strong>Content</strong> This is the full FocusPath dataset, containing 8640 patches of 1024 x 1024 images. These images are extracted from nine different stained slides. The original Whole Slide Images are scanned by Huron TissueScope LE1.2. It uses a 40X optics lens at 0.25µm/pixel resolution. There are 14 absolute z-level scores assigned to these patches which correspond to the ground-truth class for the focus level. <strong>More Information</strong> For more information, please refer to the following paper. <strong>Please cite this paper when using the dataset.</strong> <pre><code>@article{8672094, author={M. S. {Hosseini} and Y. {Zhang} and K. N. {Plataniotis}}, journal={IEEE Transactions on Image Processing}, title={Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment}, year={2019}, volume={28}, number={9}, pages={4510-4525}, doi={10.1109/TIP.2019.2906582}, ISSN={1057-7149}, month={Sep.}, }</code></pre> For information on how FocusPath can be used in a deep learning pipeline for training CNN models, please visit the repository at: https://github.com/icbcbicc/FocusLiteNN/ <strong>Contact</strong> For questions, please contact: Mahdi Hosseini mahdi.hosseini@utoronto.ca http://orcid.org/0000-0002-9147-0731
DOI: 10.14457/cmu.the.1999.307
title: ปัจจัยที่สัมพันธ์กับการใช้บริการการนวดไทย ในคลินิกการแพทย์แผนไทย อำเภอดอยสะเก็ด จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.1999.308
title: การพัฒนาแผนการสอนกลุ่มการงานพื้นฐานอาชีพ สำหรับนักเรียนชั้นประถมศึกษาปีที่ 5-6 ตามความต้องการของชุมชนในอำเภอดอยเต่า จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.1999.309
title: วิวัฒนาการของระบบดาวคู่แบบใกล้ติดกัน จีอาร์ ทอรี
DOI: 10.14457/cmu.the.1999.310
title: การเสริมสร้างพลังอำนาจในตนเองของพยาบาลวิชาชีพที่ให้การดูแลผู้ป่วยจิตเวช โรงพยาบาลสวนปรุง
DOI: 10.14457/cmu.the.1999.311
title: การประเมินโดยใช้แฟ้มสะสมงานในรายวิชาบัญชีบริการ ระดับชั้นมัธยมศึกษาปีที่ 3
DOI: 10.14457/cmu.the.1999.312
title: ความสัมพันธ์ระหว่างการทำหน้าที่ของครอบครัวกับการกระทำผิดของเด็กและเยาวชน ในสถานพินิจและคุ้มครองเด็กและเยาวชน จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.1999.313
title: ปทัสถานพฤติกรรมขององค์กรธุรกิจการเงิน
DOI: 10.14457/cmu.the.1999.314
title: การวิเคราะห์การสั่นสะเทือนของแบริ่งแบบลูกกลิ้งในปั๊มไฮดรอลิคแบบเฟือง
DOI: 10.14457/cmu.the.1999.315
title: การอบรมเลี้ยงดูเด็กปฐมวัยของครอบครัวที่มีผู้ปกครองที่ไม่ใช้บิดา มารดา
DOI: 10.14457/cmu.the.1999.316
title: ผลสัมฤทธิ์ทางการเรียนคณิตศาสตร์ของนักเรียนชั้นประถมศึกษาปีที่ 1 ที่เรียนโดยใช้กลวิธีการนับ