cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/y8wr2rknys.2
title: Molecular Identification and Isolation of Antimicrobial, Phytochemical, Antioxidant Properties of Different Solvent Extracts from Wild Mushrooms

description: The main objective of the present research work was to identify the unknown wild mushrooms based on morphological characteristics and DNA based sequencing by using universal primers ITS 1 and ITS 4 which were collected from Andhra Pradesh and Telangana. To test the potential of various extracts of mushrooms by using three different solvents (Methanol, Hot Water and Hexane). To elucidate antimicrobial properties, qualitative and quantitative estimation of phytochemical properties and antioxidant activity was determined by DPPH free radical scavenging activity. Then, IC50 values were determined. The four isolates were identified on the basis of morphological and molecular characterization was identified as Trametes versicolor, Daedaleopsis confragosa, Schizophyllum communae and Tyromyces chioneus. Three different solvents had been shown good antimicrobial activity against all pathogenic microorganisms. The hot water extract of T. versicolor exhibited more amount of phenol content (11.13±0.081) and ascorbic acid content (9.62±0.086). Methanol extract of S. communae showed high amount of flavonoids content (12.58±0.138). Methanol extract of T. chioneus exhibited more amount of β-carotene (3.54±0.010) and hot water extract of D. confragosa exhibited more amount of lycopene content (2.29±0.012). The DPPH free radical scavenging activity and IC50 values was much more exhibited by methanol extracts then followed by hot water extracts and hexane extracts of wild mushrooms they all have exhibited stronger antioxidant activity between (40-50 µg/ml). This comprehensive analysis demonstrates that these mushrooms having a viable source of natural antioxidants that can utilized for functional foods, nutraceutical’s applications with beneficial biological activities and implementation of new drug discovery.
DOI: 10.17632/b5y2rm8dv4.1
title: Raw data supporting “Data set of the antioxidant properties of ascorbyl 2,6-dipalmitate stabilised nanoemulsions and their facilitated mucodiffusion due to the intestinal-protein corona”

description: Colloidal carriers have greatly contributed to overcome biopharmaceutical limitations of drugs with low oral bioavailability, a fact that is related to their physicochemical properties and composition that determine their biological behaviour. In this sense, we have developed α-tocopherol nanoemulsions stabilised by ascorbyl 2,6-dipalmitate (ADP), a hydrophobic derivative of the ascorbic acid, obtaining the formulations NEE1-ADP, NEE2-ADP and NEE3-ADP. In this repository we collect data supporting the document “Data set of the antioxidant properties of ascorbyl 2,6-dipalmitate stabilised nanoemulsions and their facilitated mucodiffusion due to the intestinal-protein corona”, submitted to Data in brief (November 2019). That data set includes results of the antioxidant properties of NEE-ADP formulations, their stability under saline conditions and the mucodiffusion of uncoated and intestinal-protein coated NEE-ADP nanosystems. The raw data of those experiments are included in this repository record. i) Raw data related to ABTS and DPPH assays (please see folder “raw data ABTS and DPPH assays”). Concretely, we have collected the absorbance values measured at 734 nm (ABTS assay) or at 515 nm (DPPH assay). This was the basis of results further detailed in the mentioned dataset (figures 2 and 3). ii) Raw data related to the ccc determination, showing results of the hydrodynamic mean size measurements carried out at increasing concentrations of CaCl2. These data, which can be found in the folder “raw data ccc determination”, were further analysed to obtain figure 4 included in the mentioned data set. iii) Raw data from particle tracking experiments (figure 5 in the related data set). In these studies, raw data consists on time-lapse videos. Samples analysis required 20 videos but we include a selection of 2 videos per sample due to the high size of the files. Please see folder “raw data Particle Tracking experiments” for further details. These videos show the interaction of NEE1-ADP and NEE3-ADP with intestinal porcine mucus, used as model of the human intestinal mucus (sub-folders “Videos NEE1-ADP (nanoemulsion)” and “Videos NEE3-ADP (nanoemulsion)”. Furthermore, we evaluated the effect of the intestinal protein corona in this process (sub-folders “Videos NEE1-ADP (protein-coated)” and “Videos NEE3-ADP (protein-coated)”).
DOI: 10.21227/ymxp-c939
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: 10.26300/6p6t-gh83
title: feronia_Athaliana_x_WT_Alyrata_Rep_05
DOI: 10.26300/amff-aq59
title: feronia_Athaliana_x_WT_Alyrata_Rep_06
DOI: 10.21227/jrm6-qd60
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: 10.5066/p90c629b
title: Post-wildfire debris flow and rainfall data, Whitewater-Baldy complex fire, southwestern New Mexico

description: During the spring and early summer of 2012, approximately 1,200 square kilometers of Gila National Forest in southwestern New Mexico, including the upper portions of the Whitewater Creek watershed, were burned by the Whitewater-Baldy Complex Fire. The following September 12-17, 2013, a near-record, one-week long storm event produced widespread, historic rainfall amounts throughout the southwest U.S. in four distinct pulses. Data published here include GIS shapefiles of documented erosional features associated with the September 2013 storms and associated rainfall data. Data files are numbered 1-9. Shapefiles provided in this data release include: 1) polygon of the study area; 2) point locations of 688 debris-flow initiations; 3) polyline shapefiles of 310 debris-flow channels within the Whitewater Creek watershed; 4) polygon shapefiles of areas experiencing erosion by rilling; 5) polyline shapefiles of field-surveyed features including 3 debris flows and 2 rill-area transects; and 6) point shapefile of debris-flow cross-sectional survey locations. Tabulated data provided in this data release include: 7) debris-flow channel cross-sectional survey data; 8) topographic transect data for areas experiencing erosion by rilling; and 9) rainfall data for the period beginning 00:00 2013-09-12 through 23:51 MST of 2013-09-17 for USGS rain gages 09442980 - Mineral Creek Near Glenwood, NM and 09443800 - Whitewater Creek at Catwalk NRT, Near Glenwood, NM.
DOI: 10.5281/zenodo.3538959
title: Simulated function placements in predefined mixed cloud-edge clusters

description: Experiments in a simulation environment to evaluate the placement quality of the Skippy Scheduler, an optimized container scheduler for serverless edge computing in Kubernetes.
DOI: 10.21227/ae5k-1688
title: Processed airborne hyperspectral images and mosaics of a Paranapanema River region in Capivara reservoir, Brazil

description: This database is a image set of a strongest glint-affected region of inland water Capivara reservoir, Brazil. We carried out a flight survey in September 2016 on the confluence region of the Tibagi and Paranapanema Rivers. We use the hyperspectral camera manufactured by Rikola, model FPI2014, wich collect 25 spectral bands at following intervals and full widths at half maximum (FWHM), both expressed in nanometers (nm): 505.37, (9.51), 515.31 (14.05), 528.55 (14.82), 539.87 (14.03), 546.99 (14.31), 554.99 (13.26), 560.22 (12.11), 570.44 (14.31), 579.58 (13.26), 592.57 (16.57), 605.73 (14.98), 620.22 (16.26), 625.92 (15.47), 655.06 (12.55), 665.72 (15.59), 670.03 (15.74), 681.33 (15.89), 695.04 (14.96), 700.46 (15.44), 707.96 (15.32), 715.19 (15.37), 725.37 (14.72), 737.29 (14.98), 749.87 (15.08) and 780.1 (14.72). The external orientation parameters (EOP) are acquired by dual frequency GPS and adjusted with tie points computed by bundle adjustment. We perform individual georeferencing on each individual image. This dataset was processed using nine approaches to mosaicking individual georeferenced images. Three of then are new proposed methods developed by the authors. Details of processing and methodology are described on the oficial paper (currently in review process of journal). The authors thank the Graduate Program in Cartographic Sciences (PPGCC) of the School of Science and Technology (UNESP), campus Presidente Prudente, for allowing the development of this research; the National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for financial assistance dedicated to the project. The authors extend special thanks to the São Paulo Research Foundation (FAPESP) for financial support for the hyperspectral camera (2013/50426-4).
DOI: 10.21227/qmzv-t791
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/t8z1-3170
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: 10.21227/5stv-e602
title: Bitcoin Scanner Data (2018)

description: Bitcoin is a decentralized digital currency that has gained significant attention and growth in recent years. Unlike traditional currencies, Bitcoin does not rely on a centralized authority to control the supply, distribution, and verification of the validity of transactions. Instead, Bitcoin relies on a peer-to- peer network of volunteers to distribute pending transactions and confirmed blocks, verify transactions, and to collectively implement a replicated ledger that everyone agrees on. This peer-to-peer (P2P) network is at the heart of Bitcoin and many other blockchain technologies. In this article, we present a comparative measurement study of nodes in the Bitcoin network. We measure and analyze how many so-called ‘volunteers’ are in Bitcoin P2P network by scanning the live Bitcoin network for 37 days in 2018, and compare them with data reported by prior work in 2013∼2016. Our work is motivated by the fact that Bitcoin has experienced explosive growth in terms of number of users, transactions, value, and interest over the recent couple years. Our investigation includes the IP addresses of Bitcoin nodes, size of the network, power-law in the geographic distribution, protocol and client versions, and network latencies, and show how today’s network is different from early days. In addition, based on the observations made from the measurement study, we propose a simple distance-based peer selection rule for improved connectivity and faster data propagation. Evaluation results show that our proposed light-weight and backward-compatible peer selection rule has potential to reduce data dissemination latency.
DOI: 10.21227/b5q4-cq29
title: Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems

description: Indoor positioning systems based on radio frequency systems such as UWB inherently present multipath related phenomena. This causes ranging systems such as UWB}to lose accuracy by detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will make important errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques for a previous classification and mitigation of the propagation effects. For this purpose, real cross scenarios are considered, where the data extracted from UWB low-cost devices for the training of the algorithms come from different environments than those considered for the real application and its analysis.
DOI: 10.21227/dk7w-9r75
title: DNN Classifier of Wide-angle Retinal Images in Computer-Aided Screening for ROP

description: Background: Retinopathy of prematurity (ROP) is one of the main causes of childhood blindness. However, insufficient ophthalmologists are qualified for ROP screening.
DOI: 10.21227/m6k5-8p74
title: Audio Steganalysis Dataset

description: The steganography and steganalysis of audio, especially compressed audio, have drawn increasing attention in recent years, and various algorithms are proposed. However, there is no standard public dataset for us to verify the efficiency of each proposed algorithm. Therefore, to promote the study field, we construct a dataset including 33038 stereo WAV audio clips with a sampling rate of 44.1 kHz and duration of 10s. And, all audio files are from the Internet through data crawling, which is for a better simulation of a real detection environment. The dataset is used for MP3 steganalysis at this stage. We provide corresponding MP3 encoder, LAME, and steganographic encoder, HCM, EECS and so on, which is developed based on LAME. What's more, some useful python scripts are supplied for samples make in batch. The dataset is still expanding, and we will include AAC, AMR and other audio formats in the future.Keywords: Audio, MP3, Steganalysis, Steganography
DOI: 10.21227/vw2s-g389
title: Dynamic Scenes Dataset

description: The "Dynamic Scenes" Dataset is provided for testing visual loop closure detection algorithms in highly dynamic scenes. It has a strong background in some crucial applications such as autonomous driving systems.The dataset was captured in the central commercial district of a city of ten million people. The “Dynamic Scenes” dataset contains two sequences as shown in the figure above. The first one, which was acquired by traveling around a lake as the blue trajectory, named “Road Ring.” The other sequence, named “CBD,” was acquired by traveling around a business district, and is depicted by the red and green trajectories.
DOI: 10.5281/zenodo.3531578
title: Training material for the SIGU course "Data analysis and interpretation for clinical genomics"

description: In years 2018-2019, we organized on behalf of the Italian Society of Human Genetics (SIGU) an itinerant Galaxy-based “hands-on-computer” training activity entitled “Data analysis and interpretation for clinical genomics”. This one-day course was offered to participants including clinical doctors, biologists, laboratory technicians and bioinformaticians. Topics covered by the course were NGS data quality check, detection of variants, copy number alterations and runs of homozygosity, annotation and filtering and clinical interpretation of sequencing results. To meet the constant need for training on basic NGS analysis and interpretation of sequencing data in the clinical setting, we designed an on-line Galaxy-based training resource dedicated to this topic, articulated in presentations and practical assignments by which students will learn how to approach NGS data processing at the level of FASTQ, BAM and VCF files and clinically-oriented examination of variants emerging from sequencing experiments such as whole exomes. This repository contains datasets required for the online training "Data analysis and interpretation for clinical genomics" available at https://sigu-training.github.io/clinical_genomics/. Tools used in the training are available at the European Galaxy instance running at https://usegalaxy.eu, which also includes a copy of this repository in the <em>Shared Data Libraries. </em>All the files are based on <strong>hg19</strong> reference genome.
DOI: 10.17632/6z4z63tfgs.1
title: Spectra for 208Pb(p,p') taken at MPI Kernphysik (Heidelberg, Germany) in 1968-1969

description: Spectra for 208Pb(p,p') taken with semiconductor detectors at scattering angles 80° - 170° covering isobaric analog resonances in 209Bi (g7/2, g9/2, d3/2, d5/2, j15/2) with E = 14.99, 16.30, 16.45, 16.60, 17.40, 17.47, 17.56 MeV
DOI: 10.21227/4br9-w081
title: VideoSet

description: A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed in [1]. Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California in [2, 3]. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-second sequences in four resolutions (i.e., 1920x1080, 1280x720, 960x540, and 640x 360). For each of the 880 video clips, we encode it using the H.264 codec with QP=1, ..., 51 and measure the first three JND points with 30+ subjects. The dataset is called the ``VideoSet", which is an acronym for ``Video Subject Evaluation Test (SET)". This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public here.[1] Joe Yuchieh Lin, et al., Experimental design and analysis of JND test on coded image/video. Proc. SPIE, Applications of Digital Image Processing XXXVIII, September 22, 2015.[2] L. Jin, et al., Statistical study on perceived JPEG image quality via MCL- JCI dataset construction and analysis, in: IST/SPIE Electronic Imaging, International Society for Optics and Photonics, 2016.[3] H. Wang, et al., MCL-JCV: A JND-based H.264/AVC video quality assessment dataset, in: 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 1509–1513.Update:As requested by some users, we prepared a copy of videoSet hosted on BaiduYun. Please try both servers, i.e. AWS in this page and BaiduYun and find the one that works better for you. Date: 02/14/2017.A typo fixed. The second header in 960x540_1st.csv, changed from anchor to jnd. Date: 03/01/2017Citation was updated in README_V2. The dataset paper was accepted by JVCIR and PDF attached. Date: 05/09/2017.