cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/trszfnrfgc.1
title: AL_CMT_PER

description: Authentic Leadership_commitment_performance
DOI: 10.25919/5e0e4c330af19
title: Parkes observations for project P958 semester 2019OCTS_41

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last two year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 31 bursts, five of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.17632/4jtc76xd2r.1
title: ear lobule and young's modulus

description: Hypothesis: Attached ear lobule can bear more load than free lobule due to the larger area of attachment adding to its strength Observation from data: The strain or displacement of lobule after a fixed load is mounted on the tool hanging from the ear hole in a free lobule is more than the attached lobule and hence the young's modulus of the free lobule is less than that of the attached variety explaining the disparity in the strength. This helps us t understand that since the attached lobule has more young's modulus compared to the free variety , the subjects of attached lobule face lesser ear lobule deformities like split ear hole or enlarged ear hole for the same load or weight (weight of the jewellery worn in the ear lobule) as compared to that of the free variety. This study was done using a self designed tool to measure the displacement of the ear lobule when subjected to a load.
DOI: 10.17632/bhxzc6pxyy.1
title: Images CCIS

description: CCIS solar tracker that monitors sky areas in a 150 ° image cone to get reflected surface coverage.
DOI: 10.17632/6pbyy7jc4x.1
title: Data set for Hurricane Dorian 2019

description: Tweets in English related to 2019 Hurricane Dorian culled from Twitter. Gephi files were made with opem source Gephi 0.9.2.
DOI: 10.21227/33a2-ky13
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live[1]. The last column, viz. sentiment score, is not the score estimated by the model. The LSTM model is still in the pre-alpha phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. The sentiment scores produced by our model will be made public after the project is completely open-sourced.####status/latest addition: Dec 29, 2019####Tweets containing the term "facebook": 0.77 million-plusTweets containing the term "android": 0.88 million-plusTweets containing the term "oneplus": 15 thousand-plusTweets containing the term "messi": 225 thousand-plusTweets containing the term "housefull 4": 346 thousand-plusTweets containing the term "the joker": 1 million-plusTweets containing the term "iphone 11": 2 million-plus##################References:[1]https://sentiment.live/ [2]https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/86ma-6280
title: Cocoa beans spectral image with three fermentation levels

description: The target scene consists of a black cardwith six cocoa beans of three different fermentation levels (High, correct, and low fermentation),two beans for each class, whosefalse-color composite is shown in the provided Figure (a),ground-truth map is shownin Fig. (b), and Fig. (c) presents its representative spectralsignatures. The spectral imagewas acquired by the AVT Stingray F-080Bcamera by acquiring one band each time from350 - 950 nm. The acquired image hasa spatial resolution of 1096x712 pixels and 300 spectral bands of 2 nm width.
DOI: 10.21227/arjs-t069
title: Arc Flash IE and Iarc Calculators

description: The excel spreadsheet files provide access to a functional Arc Flash Calculator that includes various equipment configurations (VCB, VCBB, HCB, VOA and HOA), Incident Energy and voltage ranges.As equipment’s configuration and input fields are presented, the user will be able to key entry specific data using the proper input fields and/or pull down menus provided. All variables and constants are defined. When all variables inputs have been entered, and proper equipment configuration has been selected, the Calculator will solve the equations and the answer is returned.The Calculator function within the spreadsheets provide quick access to data analysis that could increase the productivity for the user.THIS CALCULATOR IS NOT A PART OF IEEE 1584-2018 AND WAS NOT DEVELOPED BY THE IEEE P1584 WORKING GROUP.THIS SOFTWARE IS PROVIDED "AS IS" AND "WITH ALL FAULTS." IEEE AND ITS CONTRIBUTORS EXPRESSLY DISCLAIM ALL WARRANTIES (EXPRESS, IMPLIED AND STATUTORY) RELATED TO THE SOFTWARE, INCLUDING, BUT NOT LIMITED TO, THE WARRANTIES OF: MERCHANTABILITY; FITNESS FOR A PARTICULAR PURPOSE; NON-INFRINGEMENT; AND QUALITY, ACCURACY, EFFECTIVENESS, CURRENCY, OR COMPLETENESS OF THE SOFTWARE. IN ADDITION, IEEE AND ITS CONTRIBUTORS DISCLAIM ANY AND ALL CONDITIONS RELATING TO: RESULTS; WORKMANLIKE EFFORT; AND LACK OF VIRUSES.IN NO EVENT SHALL IEEE OR ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO: PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE AND REGARDLESS OF WHETHER SUCH DAMAGE WAS FORESEEABLE.YOU ARE SOLELY RESPONSIBLE FOR COMPLYING WITH ALL APPLICABLE LAWS AND REGULATIONS, INCLUDING, BUT NOT LIMITED TO, ANY THAT GOVERN YOUR USE, EXPORTATION OR IMPORTATION OF THIS SOFTWARE. IEEE AND ITS CONTRIBUTORS DISCLAIM ALL LIABILITY ARISING FROM YOUR USE OF THE SOFTWARE IN VIOLATION OF ANY APPLICABLE LAWS OR REGULATIONS.
DOI: 10.21227/nkkk-fz80
title: Python 3.6 (Spyder) program file_Detection of corrupt data in a PV plant database

description: This folder contains two csv files and one .py file. One csv file contains NIST canopy PV plant data imported from https://pvdata.nist.gov/. This csv file has 1041 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST canopy PV plant data. The program generates many PDF and JPG image plots to represent the findings of four methods. A PV plant user can use this Python program by replacing the NIST canopy PV plant data with their plant data to detect corrupt days.
DOI: 10.21227/skm5-ks23
title: PRECIS HAR

description: PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects. There are 800 videos in total, having both RGB and depth recordings.The dataset does not contain other people than the video subject that appear as environmental noise. Besides, the background does not change. However, its difficulties are:Small camera occlusions: from time to time, we added small occlusions to the RGB stream (we move the mouse from a part of the screen to the other), that simulate the presence of external, uncontrolled environment factors; for example, in a real scenario, a fly could pass in front of the camera.No stage directions: while filming the dataset, we did not instruct participants on how to perform each action. We simply told them the name of the action. Thus, they all interpreted them slightly differently, closer to the natural way in which a person would behave.Variable video length: because we did not impose constraints on how the participants had to perform each action, dataset videos have different lengths.Different lighting conditions: the source of light is variable, sometimes having scenes with pale light and other times with sun shining directly on scene parts. These lighting variations are however only visible in the RGB stream. The depth stream is not affected by environmental changes, as far as the light is concerned. We thus want to emphasize the utility of also examining the depth stream, while classifying videos from RGB-D cameras. When citing this dataset, please cite both the dataset and the article that introduces it: Fusion Mechanisms for Human Activity Recognition using Automated Machine Learning. Instructions will be added here once the article is published.
DOI: 10.21227/y5ab-4211
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: 10.21227/3162-3877
title: Road Surface Damages

description: Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection. Such dataset could be used in a great variety of applications; herein, it is intended to serve as the acquisition component of a physical asset management tool which can aid governments agencies for planning purposes, or by infrastructure maintenance companies. described in https://github.com/sekilab/RoadDamageDetector/ and available here https://mycityreport.s3-ap-northeast-1.amazonaws.com/02_RoadDamageDataset/RoadDamageDataset.tar.gz), by incoporating more examples of potholes and longitudinal and alligator cracks
DOI: 10.21227/5rrt-cg42
title: Research on Fading Characteristics of Ultrahigh Frequency Signals in Karst Landform

description: This dataset is data of research on fading characteristics of ultrashigh frequency signals in Karst landform
DOI: 10.21227/6hn7-0j65
title: Paper Industry

description: The dataset corresponds to the variables that affect the process of passing the sheet between rodsizer and the spooner section for a paper machine.
DOI: 10.21227/agsm-ag76
title: Myanmar Sign Language

description: The databases include arrays of alphabets and numbers, ["Ka Gyi" "Ka Kway" "Ga Nge" "Ga Gyi" "Nga" "Sa Lone" "Sa Lane" "0" '0' "Nya" "Ta Talin Jade" "Hta Won Bell" "Dain Yin Gouk" "Dain Yin Hmote" "Na Gyi" "Ta Won Bu" "Hta Sin Htoo" "Da Dway" "Da Out Chike" "Na Nge" "Pa Sout" "Pha Oo Htote" "Ba Htet Chike" "Ba Gone" "Ma" "Ya Pa Lat" "Ya Gout" "La" "Wa" "Tha" "Ha" "La Gyi" "Ah" '0' '1' '2' '3' '4' '5' '6' '7' '8' '9' "10"]. The symbols are recorded as gestures of palm by the MIIT research team and recorded audio file also for each number and alphabet. The symbolic representations of the characters were verified with the help of a school in Mandalay running for deaf and dumb people. Deaf charity Mandalay Myanmar helps in collecting database and other gestures were recorded. All 33 alphabets (from ‘Ka’ to ‘Ah’) and 11 numbers (‘0’ to ‘10’) are created and recorded as images. We also created sound or audio files for all these data with the help of Google Translate Text to Speak (TTS) assistant. Then, some preprocessing was applied to resize and reformat the images.
DOI: 10.21227/t0bk-rp56
title: THz Wireless Channel Measurements in between 240GHz and 300GHz

description: The experimental measurement setup is performed in an anechoic chamber to secure the LOS transmission at the Millimeter Wave and Terahertz Technologies Research Laboratories (MILTAL), Scientific and Technological Research Council of Turkey (TUBİTAK), Kocaeli. The dimensions of anechoic chamber are 7m × 4m × 3m. The dataset includes channel impulse response in between 240GHz and 300GHz for the transmitter-receiver distance of 20cm, 30cm, 40cm, 60cm, and 80cm.
DOI: 10.21227/xhqv-fk90
title: In-smoking respiration and hand gesture IMU signals

description: These CSV files contain the wearable sensor data (RIP and IMU )collected from forty subjects during multiple cigarette smoking sessions.Unix_time;Air_volume;Acc_x;Acc_y;Acc_z;Gyro_z;Gyro_y;Gyro_z;Puff_label1508422775.107716;0.152453;-0.299239;0.643658;0.551803;-19.422554;-5.030965;-0.513648;01508422775.117717;0.158024;-0.295076;0.644872;0.550053;-12.148512;-7.795601;1.466739;01508422775.127717;0.163883;-0.290974;0.647284;0.549203;-4.083356;-10.720964;3.481177;01508422775.137717;0.169834;-0.286920;0.650502;0.550134;4.671266;-13.769435;5.529415;01508422775.147718;0.175980;-0.282592;0.654563;0.553597;13.979452;-16.823568;7.575012;01508422775.157718;0.182117;-0.277179;0.659758;0.559042;23.628141;-19.757325;9.542117;01508422775.167718;0.188229;-0.270221;0.665873;0.564969;33.305498;-22.496849;11.374955;01508422775.177719;0.194513;-0.261979;0.673034;0.570075;42.701115;-24.985147;13.056668;01508422775.187719;0.200926;-0.253093;0.682405;0.573717;51.593177;-27.171810;14.570514;01508422775.197719;0.207171;-0.244214;0.695092;0.575952;59.874517;-29.030667;15.881838;01508422775.207719;0.213248;-0.235723;0.711265;0.577308;67.565673;-30.553937;16.963801;01508422775.217720;0.219274;-0.227696;0.730104;0.578480;74.787456;-31.742250;17.817791;01508422775.227720;0.225176;-0.220078;0.750075;0.579927;81.676203;-32.599463;18.456894;01508422775.237720;0.231179;-0.212882;0.769619;0.581492;88.295862;-33.132336;18.887571;01508422775.247721;0.237298;-0.206192;0.787704;0.582356;94.607785;-33.351479;19.108364;01508422775.257721;0.243359;-0.200163;0.803707;0.581440;100.475061;-33.270067;19.119319;01508422775.267721;0.249413;-0.195036;0.817074;0.577794;105.658952;-32.897144;18.930358;01508422775.277722;0.255340;-0.191107;0.827287;0.570926;109.820421;-32.231325;18.558514;01508422775.287722;0.261106;-0.188594;0.834244;0.561007;112.570786;-31.253126;18.023488;01508422775.297722;0.266806;-0.187517;0.838732;0.548850;113.562244;-29.928963;17.343627;01508422775.307723;0.272397;-0.187681;0.842282;0.535599;112.613273;-28.241053;16.536391;01508422775.317723;0.277908;-0.188643;0.846486;0.522365;109.817024;-26.209185;15.616212;0
DOI: 10.21227/3tsz-1g03
title: Simulation data for a Monte Carlo Wave-Particle Event-Based Interaction Model

description: The data made available are the simulations of a time-resolved Monte Carlo model for use in quantitative as well as qualitative analysis of different types of particle atmospheres.
DOI: 10.14469/hpc/6607
title: LiF 7Σ, CCSD(T)/Def2-TZVPP

description: Gaussian Calculation
DOI: 10.14469/hpc/6611
title: NN 7Σu excited state, CCSD(T)/Def2-TZVPP, stretch 21 cm-1.

description: Gaussian Calculation
DOI: 10.5519/qd.xvz1zoha
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1118754 records)
DOI: 10.3886/e116507v1-21501
title: Replication data for: Household Finance after a Natural Disaster: The Case of Hurricane Katrina

description: Little is known about how affected residents are able to cope with the financial shock of a natural disaster. This paper investigates the impact of flooding on household finance. Spikes in credit card borrowing and overall delinquency rates for the most flooded residents are modest in size and short-lived. Greater flooding results in larger reductions in total debt. Lower debt levels are driven by homeowners using flood insurance to repay their mortgages rather than to rebuild. Mortgage reductions are larger in areas where reconstruction costs exceeded pre-Katrina home values and where mortgages were likely to be originated by nonlocal lenders.
DOI: 10.5519/qd.c8ah0smu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4563 records)
DOI: 10.26302/sshade/experiment_bs_20130120_004
title: Mid-IR optical constants of amorpous H2O Ia at 15K and crystalline H2O Ih at 60K

description: Mid-IR optical constants spectrum of amorpous H2O Ia at 15K and crystalline H2O Ih at 60K
DOI: 10.5281/zenodo.3596954
title: A hcSE-SAMPI4 spectrum of sarcolipin in an unflipped bicelle

description: Example processing and PISA-SPARKY analysis of a hcSE-SAMPI4 spectrum of sarcolipin reconstituted into unlipped DMPC/POPC(4:1)/DHPC (q=4) bicelles. Bicelles contain 5% mole ratio of copper-chelated lipids to accelerate data data acquisition.
DOI: 10.5281/zenodo.3596955
title: A hcSE-SAMPI4 spectrum of sarcolipin in an unflipped bicelle

description: Example processing and PISA-SPARKY analysis of a hcSE-SAMPI4 spectrum of sarcolipin reconstituted into unlipped DMPC/POPC(4:1)/DHPC (q=4) bicelles. Bicelles contain 5% mole ratio of copper-chelated lipids to accelerate data data acquisition.
DOI: 10.3886/e116507v1-21503
title: Replication data for: Household Finance after a Natural Disaster: The Case of Hurricane Katrina

description: Little is known about how affected residents are able to cope with the financial shock of a natural disaster. This paper investigates the impact of flooding on household finance. Spikes in credit card borrowing and overall delinquency rates for the most flooded residents are modest in size and short-lived. Greater flooding results in larger reductions in total debt. Lower debt levels are driven by homeowners using flood insurance to repay their mortgages rather than to rebuild. Mortgage reductions are larger in areas where reconstruction costs exceeded pre-Katrina home values and where mortgages were likely to be originated by nonlocal lenders.
DOI: 10.6073/pasta/460003ebdbaf016d3f0b3292b56b956b
title: Cold air drainage transect studies at the Andrews Experimental Forest, 2002 to Present

description: Temperature data are being collected to investigate the effects of elevation and local topographic position on the patterns of cold air drainage and pooling in the HJ Andrews Forest, and how they vary with synoptic weather patterns. The main focus of the study is cold air drainage in the Lookout Creek area, but others sites have been added to sample temperature signatures at other locations of the forest with topographic positions that need to be better understood. Here, quality controlled 15-minute raw data and aggregated daily minimum and maximum temperatures are available from several stations along 4 separate transects or clusters with some additional single sensors also included. Several of the quality flags indicate that the data failed the test and should not be used in analysis.
DOI: 10.14272/xbjzutiebxawap-uhfffaoysa-n.1
title: 6,7,17,18-tetrafluoro-3,10-dithiatricyclo[10.2.2.25,8]octadeca-1(15),5(18),6,8(17),12(16),13-hexaene
title: InChI=1S/C16H12F4S2/c17-13-11-7-21-5-9-1-2-10(4-3-9)6-22-8-12(15(13)19)16(20)14(11)18/h1-4H,5-8H2

description: This dataset contains experimental data from a sample (6,7,17,18-tetrafluoro-3,10-dithiatricyclo[10.2.2.25,8]octadeca-1(15),5(18),6,8(17),12(16),13-hexaene, InChI=1S/C16H12F4S2/c17-13-11-7-21-5-9-1-2-10(4-3-9)6-22-8-12(15(13)19)16(20)14(11)18/h1-4H,5-8H2). The datasets from the following experiments are available: infrared absorption spectroscopy (IR) fast-atom bombardment mass spectrometry (FABMS) 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) 19F nuclear magnetic resonance spectroscopy (19F NMR)
DOI: 10.14272/xbjzutiebxawap-uhfffaoysa-n/chmo0000563
title: fast-atom bombardment mass spectrometry (FABMS) (6,7,17,18-tetrafluoro-3,10-dithiatricyclo[10.2.2.25,8]octadeca-1(15),5(18),6,8(17),12(16),13-hexaene)
title: InChI=1S/C16H12F4S2/c17-13-11-7-21-5-9-1-2-10(4-3-9)6-22-8-12(15(13)19)16(20)14(11)18/h1-4H,5-8H2

description: dataset for fast-atom bombardment mass spectrometry (FABMS) MS (3-NBA, FAB, %) m/z = 343 (9) [M–H]+.HRMS (FAB, [M–H]+, C16H11F432S2) calc.: 343.0238; found: 343.0239.
DOI: 10.14272/xbjzutiebxawap-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (6,7,17,18-tetrafluoro-3,10-dithiatricyclo[10.2.2.25,8]octadeca-1(15),5(18),6,8(17),12(16),13-hexaene)
title: InChI=1S/C16H12F4S2/c17-13-11-7-21-5-9-1-2-10(4-3-9)6-22-8-12(15(13)19)16(20)14(11)18/h1-4H,5-8H2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (126 MHz, CDCl3, ppm) δ = 145.1–144.7 (m, 2C, Cq., CAr), 143.2–142.7 (m, 2C, Cq., CAr), 135.2 (2C, Cq., CAr), 128.9 (+, 4C, CH, CAr), 115.3-115.0 (m, 2C, CAr), 37.6 (–, 2C, CH2), 24.3 (–, t, J = 2.1 Hz, 2C, CH2).
DOI: 10.14272/xbjzutiebxawap-uhfffaoysa-n/chmo0000597
title: 19F nuclear magnetic resonance spectroscopy (19F NMR) (6,7,17,18-tetrafluoro-3,10-dithiatricyclo[10.2.2.25,8]octadeca-1(15),5(18),6,8(17),12(16),13-hexaene)
title: InChI=1S/C16H12F4S2/c17-13-11-7-21-5-9-1-2-10(4-3-9)6-22-8-12(15(13)19)16(20)14(11)18/h1-4H,5-8H2

description: dataset for 19F nuclear magnetic resonance spectroscopy (19F NMR) 19F NMR (471 MHz, CDCl3, ppm) δ = –142.9.