cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/0vtf-5628
title: 5 Reasons Why You Need To Automate Your Dynamic Application Security Testing [2022]

description: You don’t need to be a cybersecurity expert to know that the world of application security is changing at an alarming pace. The tools and techniques that attackers use are becoming more sophisticated, and it’s difficult for even the most well-resourced organizations to keep up with them.The good news is that you can automate dynamic application security testing so you’re not left trying to play catch up with every new technique as soon as they come out. Let us explore 5 reasons why you should automate your dynamic application security testing today!Automation of Dynamic Application Security Testing is a need of the hour. Astra Pentest is a well-developed penetration testing platform that delivers great results using the best penetration testing methodologies. Astra Pentest offers manual and automated DAST, vulnerability scanning, vulnerability management, and much more.
DOI: 10.21227/k7a0-3823
title: Data of A fine-grained flexible graph convolution network for vision inspection of resistance spot welds

description: Code of a fine-grained flexible graph convolution network for vision inspection of resistance spot welds using cross-domain features
DOI: 10.21227/x54e-h281
title: Weather Data 2016

description:    Dataset angin adalah
DOI: 10.21227/ycak-n843
title: Rot corn grains

description: Rot corn grain image
DOI: 10.21227/kejh-7d86
title: Apps Developers India

description: My name is Ethan Smith. I'm a professional Mobile App Developers working with India's top app development companies. We deliver top-notch Mobile application development solutions with exceptional design & We offer the best ecommerce Website Development Services. We at RV Technologies, provide customized Shopify development services in India and 20+ offshore countries including the US and Europe. standard coding. We have the best mobile app developers In India to provide the excellent app development services
DOI: 10.21227/68ac-r744
title: none

description: deleted
DOI: 10.21227/rk94-jj90
title: Transmitted_Measured_Processed_DATA

description: As for the experiment results in the manuscript, we would like to provide the corresponding Transmitted, Measured and Processed data zipped in the folder “Transmitted_Measured_Processed_DATA”, for readers who are interest in the SC MIMO transceiver and want to reproduce the experiment results shown in the manuscript. All the parameters including sampling rate, modulation frequency, etc., are the same as those in Experiment part.
DOI: 10.21227/nty0-tc08
title: DSHBR data - Access-2021-13852

description: This paper describes the design and practical application of a dual-stage H-Bridge (DSHBR) circuit to reduce the rectification losses and to minimise ripples in piezoelectric energy harvesting. The proposed DSHBR circuit integrates both AC-DC and DC-DC conversion processes using bidirectional switches and a step-up DC-DC converter, which applies to both positive and negative half cycles. One additional feature is that it does not require external power to turn on the bidirectional switches (Vth &lt; 0.3 V). Such feature facilitates active rectification at very low AC voltages (Vac &lt; 0.5) generated by the piezoelectric device (PD). To validate the performance of the proposed circuit, a series of experimental tests were conducted. Firstly, the performance of circuit was investigated using a shaker to generate high and low frequency excitations. Next, real-life testing was conducted with human arm motion as the source of excitation. Then, the ability of the entire system to charge solar batteries was investigated. The outcome shows that the DSHBR circuit prominently increased the rectified voltage and the output power while stabilising the DC voltage when compared to the conventional H-Bridge circuit.
DOI: 10.21227/rjke-6040
title: Long-Term Data from the Heat Meters in Residential Buildings Depending on the Outside Temperature and Characteristics of Buildings 

description: More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.
DOI: 10.21227/17fy-tw41
title: Earth on Canvas

description: WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few. With increasing complexity and different sensing techniques at our disposal, it has become our primary interest to design efficient algorithms to retrieve data from multiple data modalities, given the complementary information that is captured by different sensors. This type of problem is referred to as inter-modal data retrieval. In remote sensing (RS), there are primarily two important types of problems, i.e., land-cover classification and object detection. In this work, we focus on the target-based object retrieval part, which falls under the realm of object detection in RS. Object retrieval essentially requires high-resolution imagery for objects to be distinctly visible in the image. The main challenge with the conventional retrieval approach using large-scale databases is that, quite often, we do not have any query image sample of the target class at our disposal. The target of interest solely exists as a perception to the user in the form of an imprecise sketch. In such situations where a photo query is absent, it can be immensely useful if we can promptly make a quick hand-made sketch of the target. Sketches are a highly symbolic and hieroglyphic representation of data. One can exploit the notion of this minimalistic representative of sketch queries for sketch-based image retrieval (SBIR) framework. While dealing with satellite images, it is imperative to collect as many samples of images as possible for each object class for object recognition with a high success rate. However, in general, there exists a considerable number of classes for which we seldom have any training data samples. Therefore, for such classes, we can use the zero-shot learning (ZSL) strategy. The ZSL approach aims to solve a task without receiving any example of that task during the training phase. This makes the network capable of handling an unseen class (new class) sample obtained during the inference phase upon deployment of the network. Hence, we propose the aerial sketch-image dataset, namely Earth on Canvas dataset.
DOI: 10.21227/2r33-6e94
title: K-12 online learning data_Hu

description: 无
DOI: 10.21227/4hhz-ph48
title: K-12 online learning data_Hu

description: 无
DOI: 10.21227/vm8p-fk24
title: Earth on Canvas

description: WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few. With increasing complexity and different sensing techniques at our disposal, it has become our primary interest to design efficient algorithms to retrieve data from multiple data modalities, given the complementary information that is captured by different sensors. This type of problem is referred to as inter-modal data retrieval. In remote sensing (RS), there are primarily two important types of problems, i.e., land-cover classification and object detection. In this work, we focus on the target-based object retrieval part, which falls under the realm of object detection in RS. Object retrieval essentially requires high-resolution imagery for objects to be distinctly visible in the image. The main challenge with the conventional retrieval approach using large-scale databases is that, quite often, we do not have any query image sample of the target class at our disposal. The target of interest solely exists as a perception to the user in the form of an imprecise sketch. In such situations where a photo query is absent, it can be immensely useful if we can promptly make a quick hand-made sketch of the target. Sketches are a highly symbolic and hieroglyphic representation of data. One can exploit the notion of this minimalistic representative of sketch queries for sketch-based image retrieval (SBIR) framework. While dealing with satellite images, it is imperative to collect as many samples of images as possible for each object class for object recognition with a high success rate. However, in general, there exists a considerable number of classes for which we seldom have any training data samples. Therefore, for such classes, we can use the zero-shot learning (ZSL) strategy. The ZSL approach aims to solve a task without receiving any example of that task during the training phase. This makes the network capable of handling an unseen class (new class) sample obtained during the inference phase upon deployment of the network. Hence, we propose the aerial sketch-image dataset, namely Earth on Canvas dataset.
DOI: 10.21227/ecph-6q83
title: Earth on Canvas

description: With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few. With increasing complexity and different sensing techniques at our disposal, it has become our primary interest to design efficient algorithms to retrieve data from multiple data modalities, given the complementary information that is captured by different sensors. This type of problem is referred to as inter-modal data retrieval. In remote sensing (RS), there are primarily two important types of problems, i.e., land-cover classification and object detection. In this work, we focus on the target-based object retrieval part, which falls under the realm of object detection in RS. Object retrieval essentially requires high-resolution imagery for objects to be distinctly visible in the image. The main challenge with the conventional retrieval approach using large-scale databases is that, quite often, we do not have any query image sample of the target class at our disposal. The target of interest solely exists as a perception to the user in the form of an imprecise sketch. In such situations where a photo query is absent, it can be immensely useful if we can promptly make a quick hand-made sketch of the target. Sketches are a highly symbolic and hieroglyphic representation of data. One can exploit the notion of this minimalistic representative of sketch queries for sketch-based image retrieval (SBIR) framework. While dealing with satellite images, it is imperative to collect as many samples of images as possible for each object class for object recognition with a high success rate. However, in general, there exists a considerable number of classes for which we seldom have any training data samples. Therefore, for such classes, we can use the zero-shot learning (ZSL) strategy. The ZSL approach aims to solve a task without receiving any example of that task during the training phase. This makes the network capable of handling an unseen class (new class) sample obtained during the inference phase upon deployment of the network. In this database, we propose an aerial skecth-image database that can be useful for designing frameworks for the above-mentioned tasks.
DOI: 10.21227/k0x5-9s29
title: Earth on Canvas

description: With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few. With increasing complexity and different sensing techniques at our disposal, it has become our primary interest to design efficient algorithms to retrieve data from multiple data modalities, given the complementary information that is captured by different sensors. This type of problem is referred to as inter-modal data retrieval. In remote sensing (RS), there are primarily two important types of problems, i.e., land-cover classification and object detection. In this work, we focus on the target-based object retrieval part, which falls under the realm of object detection in RS. Object retrieval essentially requires high-resolution imagery for objects to be distinctly visible in the image. The main challenge with the conventional retrieval approach using large-scale databases is that, quite often, we do not have any query image sample of the target class at our disposal. The target of interest solely exists as a perception to the user in the form of an imprecise sketch. In such situations where a photo query is absent, it can be immensely useful if we can promptly make a quick hand-made sketch of the target. Sketches are a highly symbolic and hieroglyphic representation of data. One can exploit the notion of this minimalistic representative of sketch queries for sketch-based image retrieval (SBIR) framework. While dealing with satellite images, it is imperative to collect as many samples of images as possible for each object class for object recognition with a high success rate. However, in general, there exists a considerable number of classes for which we seldom have any training data samples. Therefore, for such classes, we can use the zero-shot learning (ZSL) strategy. The ZSL approach aims to solve a task without receiving any example of that task during the training phase. This makes the network capable of handling an unseen class (new class) sample obtained during the inference phase upon deployment of the network. In this database, we propose an aerial skecth-image database that can be useful for designing frameworks for the above-mentioned tasks.
DOI: 10.21227/27vm-hw75
title: Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from CE-4

description: Given the difficulty to handle planetary data we provide downloadable files in PNG format from the missions Chang'E-3 and Chang'E-4. In addition to a set of scripts to do the conversion given a different PDS4 Dataset.
DOI: 10.6084/m9.figshare.14153312
title: Dataset of article: "Associations of novel 24-hour accelerometer-derived metrics with adiposity in children and adolescents"

description: The manuscript has been published in Environmental Health and Preventive Medicine journal.<br>
DOI: 10.6084/m9.figshare.14153312.v1
title: Dataset of article: "Associations of novel 24-hour accelerometer-derived metrics with adiposity in children and adolescents"

description: The manuscript is under consideration in BMJ Open.<br>
DOI: 10.5519/qd.4w91vemf
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (8 records)
DOI: 10.15139/s3/gx3hn0
title: MLE Kenya Baseline Data

description: The MLE Kenya Baseline data includes Longitudinal data files covering five cities - Nairobi, Mombasa, Kisumu, Machakos, and Kakamega - as well as Cross-Sectional data files covering three cities - Nairobi, Mombasa, and Kisumu . Longitudinal womanhouseholdSDP Cross-Sectional manhousehold
DOI: 10.15139/s3/vzgat5
title: MLE Kenya Midterm Data

description: The MLE Kenya Midterm data includes Longitudinal data files covering three cities - Nairobi, Mombasa, and Kisumu - as well as Cross-Sectional data files covering one city - Mombasa. Longitudinal womanhousehold Cross-Sectional manhousehold
DOI: 10.15139/s3/zwovul
title: MLE Kenya Endline Data

description: The MLE Kenya Endline data includes Longitudinal data files covering all five cities - Nairobi, Mombasa, Kisumu, Machakos, and Kakamega. Longitudinal womanhousehold SDP
DOI: 10.15139/s3/yg10ev
title: MLE Nigeria Baseline Data

description: The MLE Nigeria Baseline data includes Longitudinal data files covering all six cities - Abuja, Benin City, Ibadan, Ilorin, Kaduna, and Zaria - as well as Cross-Sectional data files covering four cities - Abuja, Ibadan, Ilorin, and Kaduna. Longitudinal womanhousehold SDP Cross-Sectional manhousehold
DOI: 10.15139/s3/fzpu1v
title: MLE Nigeria Midterm Data

description: The MLE Nigeria Midterm data includes Longitudinal data files covering four cities - Abuja, Ibadan, Ilorin, and Kaduna - as well as Cross-Sectional data files covering two cities - Ibadan and Kaduna. Longitudinal womanhousehold Cross-Sectional manhousehold
DOI: 10.15139/s3/c2agax
title: MLE Nigeria Sustainability Data

description: The MLE Nigeria Sustainability data includes data files covering Ilorin, Kaduna, Jos and Plateau State: womanhousehold SDP
DOI: 10.5517/ccdc.csd.cc248m68
title: CCDC 1974489: Experimental Crystal Structure Determination

description: Related Article: Kai-Kai Wang, Yan-Li Li, Ying-Chao Zhao, Shan-Shan Zhang, Rongxiang Chen, Aili Sun|2021|RSC Advances|11|40193|doi:10.1039/D1RA06860A
DOI: 10.5517/ccdc.csd.cc29f9xz
title: CCDC 2127961: Experimental Crystal Structure Determination

description: Related Article: M.A. Malla, R. Bansal, R.J. Butcher, S.K. Gupta|2022|Acta Crystallogr.,Sect.E:Cryst.Commun.|78|1|doi:10.1107/S2056989021013189
DOI: 10.5517/ccdc.csd.cc29fzqg
title: CCDC 2128606: Experimental Crystal Structure Determination

description: Related Article: C. Krebs, I. Jess, M. Ceglarska, C. Näther|2022|Acta Crystallogr.,Sect.E:Cryst.Commun.|78|1|doi:10.1107/S2056989021013281
DOI: 10.5517/ccdc.csd.cc29gcfl
title: CCDC 2128969: Experimental Crystal Structure Determination

description: Related Article: A.P. Novikov, A.A. Bezdomnikov, M.S. Grigoriev, K.E. German|2022|Acta Crystallogr.,Sect.E:Cryst.Commun.|78|80|doi:10.1107/S2056989021013359
DOI: 10.6084/m9.figshare.17826830
title: Comparison of public discussions of gene editing on social media between the United States and China

description: Explanations of the variables:<br>Twitter:'Time of creation': the date that the tweet was created'user id': ID of users'favorite_count': the number of likes of a tweet'reply_count': the number of replies of a tweet'retweet_count': the number of retweets of a tweet'user.verified': whether Twitter verifies the user'user.followers_count': the number of user’s fans'Topic 1': the first topic generated from LDA'Topic 2': the second topic generated from LDA'Topic 3': the third topic generated from LDA'Topic 4': the fourth topic generated from LDA'Topic 5': the fifth topic generated from LDA'Topic 6': the sixth topic generated from LDA'Topic 7': the seventh topic generated from LDA'Topic 8': the eighth topic generated from LDA'Topic 9': the ninth topic generated from LDA'max_value': the maximum value of nine topic values'max_value_Topic ID': the topic that has the largest topic value<br>Weibo:'Time of creation': the date that the post was created'user id': ID of users'repost_count': the number of retweets of a Weibo post'comment_count': the number of comments of a Weibo post'like_count': the number of likes of a Weibo post'user.verified': whether Weibo verifies the user'user.followers_count': the number of user’s fans'Topic 1': the first topic generated from LDA'Topic 2': the second topic generated from LDA'Topic 3': the third topic generated from LDA'max_value': the maximum value of three topic values'max_value_Topic ID': the topic that has the largest topic value
DOI: 10.6084/m9.figshare.17826830.v1
title: Comparison of public discussions of gene editing on social media between the United States and China

description: Explanations of the variables:<br>Twitter:'Time of creation': the date that the tweet was created'user id': ID of users'favorite_count': the number of likes of a tweet'reply_count': the number of replies of a tweet'retweet_count': the number of retweets of a tweet'user.verified': whether Twitter verifies the user'user.followers_count': the number of user’s fans'Topic 1': the first topic generated from LDA'Topic 2': the second topic generated from LDA'Topic 3': the third topic generated from LDA'Topic 4': the fourth topic generated from LDA'Topic 5': the fifth topic generated from LDA'Topic 6': the sixth topic generated from LDA'Topic 7': the seventh topic generated from LDA'Topic 8': the eighth topic generated from LDA'Topic 9': the ninth topic generated from LDA'max_value': the maximum value of nine topic values'max_value_Topic ID': the topic that has the largest topic value<br>Weibo:'Time of creation': the date that the post was created'user id': ID of users'repost_count': the number of retweets of a Weibo post'comment_count': the number of comments of a Weibo post'like_count': the number of likes of a Weibo post'user.verified': whether Weibo verifies the user'user.followers_count': the number of user’s fans'Topic 1': the first topic generated from LDA'Topic 2': the second topic generated from LDA'Topic 3': the third topic generated from LDA'max_value': the maximum value of three topic values'max_value_Topic ID': the topic that has the largest topic value
DOI: 10.6073/pasta/c354d44ae332d6aea896291b018e7277
title: Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021

description: This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.
DOI: 10.14284/545
title: Detection range assessment Belwind offshore wind farm

description: Data used for a detection range assessment of acoustic receivers, deployed in Belwind offshore wind farm in 2020. These data include transmission and detection data of VR2AR receiver built-in transmitters, deployment metadata and environmental data.
description: The analysis of the data is detailed in the GitHub repository https://github.com/JolienGoossens/RangeTestingTime.
DOI: 10.15468/av4262
title: Notes à la flore de Corse, XXV

description: This dataset contains the digitized treatments in Plazi based on the original journal article Jeanmonod, Daniel (2015): Notes à la flore de Corse, XXV. Candollea 70 (1): 109-140, DOI: 10.15553/c2015v701a10