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Lawrence, David. "HOSS!" EPJ Web of Conferences 251 (2021): 04005. http://dx.doi.org/10.1051/epjconf/202125104005.
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The Hall-D Online Skim System (HOSS) was developed to simultaneously solve two issues for the high intensity GlueX experiment. One was to parallelize the writing of raw data files to disk in order to improve bandwidth. The other was to distribute the raw data across multiple compute nodes in order to produce calibration skims of the data online. The highly configurable system employs RDMA, RAM disks, and zeroMQ driven by Python to simultaneously store and process the full high intensity GlueX data stream.
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Ivanova-Brashinskaya, Anna. "The Axe-Files." Maska 31, no. 179 (September 1, 2016): 80–91. http://dx.doi.org/10.1386/maska.31.179-180.80_1.
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The St. Petersburg-based AXE was founded in 1989 and describes itself as “Russian Engineering Theatre”. Their productions are not a ready-made stage performances but rather an interactive building process between actor and pace. Instead of the polished result of their work, AXE shares their raw and unfinished process with the audience. Due to the absence of an adequate vocabulary which would allow the author to describe and analyze their work, we present a lexicon, consisting of professional terms and images, thus enabling a discourse on this 27-year-old theatre phenomenon.
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Silva, Vítor, Daniel de Oliveira, Patrick Valduriez, and Marta Mattoso. "Analyzing related raw data files through dataflows." Concurrency and Computation: Practice and Experience 28, no. 8 (August 4, 2015): 2528–45. http://dx.doi.org/10.1002/cpe.3616.
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Zhang, Ning, Yutao Tian, Lei Zhang, Zhuo Yang, Tao Zhang, and Jishou Ruan. "Desktop Software for Patch-Clamp Raw Binary Data Conversion and Preprocessing." Journal of Electrical and Computer Engineering 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/251215.
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Since raw data recorded by patch-clamp systems are always stored in binary format, electrophysiologists may experience difficulties with patch clamp data preprocessing especially when they want to analyze by custom-designed algorithms. In this study, we present desktop software, called PCDReader, which could be an effective and convenient solution for patch clamp data preprocessing for daily laboratory use. We designed a novel class module, called clsPulseData, to directly read the raw data along with the parameters recorded from HEKA instruments without any other program support. By a graphical user interface, raw binary data files can be converted into several kinds of ASCII text files for further analysis, with several preprocessing options. And the parameters can also be viewed, modified and exported into ASCII files by a user-friendly Explorer style window. The real-time data loading technique and optimized memory management programming makes PCDReader a fast and efficient tool. The compiled software along with the source code of the clsPulseData class module is freely available to academic and nonprofit users.
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Karanović, Lj, and D. Poleti. "A FORTRAN Program for Conversion of PC-APD Data Files into ASCII Files." Powder Diffraction 7, no. 3 (September 1992): 179. http://dx.doi.org/10.1017/s0885715600018595.
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Recently, Dahan and co-workers (Dahan, 1991) suggested processing the XRD data by spreadsheet computer programs. Treated in this manner the XRD data became very flexible and made comparison with other data sets, as well as graphical presentation, much easier. In this note a simple FORTRAN 77 program for conversion of PC-APD data files into ASCII files suitable for import into spreadsheets is reported.In our laboratory XRD data are collected on a Philips 1710 diffractometer operated by the PC-APD version 2.0 (PC-APD Software, 1989). Each experiment usually generates its files containing collected raw intensity data (.RD file), background data (.BK file) and file with peak positions and their intensities (.DI file). The XRD data can be further processed: after smoothing, data are stored in files with extension .SM (.SM file) and, after Kα2 stripping, into files with extension .A2 (.A2 file). All files are stored in the binary format.
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Guedes, Thaylon, Vítor Silva, José Camata, Marcos V. N. Bedo, Marta Mattoso, and Daniel De Oliveira. "Towards an Empirical Evaluation of Scientific Data Indexing and Querying." Journal of Information and Data Management 9, no. 1 (June 20, 2018): 84. http://dx.doi.org/10.5753/jidm.2018.1638.
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Computational simulations usually produce large amounts of data on a regular time-step basis. Heterogeneous simulation outputs are stored in different file formats and on distinct storage devices. Therefore, the main challenges for accessing simulation data are related to time-to-query, which is the effort spent for setting all data into a common framework, the issuing of a high-level query statement, and obtaining the result set. The simulation data loading into DataBase Management Systems (DBMS) are either unpractical, as they demand a prohibitive time for data preparation, or unfeasible, as data files are still needed in their original form (scientific applications still need to read and write contents to those files). In this article, we discuss the complementary approaches of adaptive querying and raw data file indexing for accessing simulation results stored in multiple sources (e.g., raw data files) without data loading. In particular, we review (i) NoDB PostgresRAW routines for adaptive query processing, and (ii) FastBit methods for raw data file indexing and querying. We examine the behavior of both strategies regarding a real case study of computational fluid dynamics simulation in the domain of sediment deposition. In this experimental evaluation, we measured the elapsed time for index construction and query processing regarding six distinct query categories over 62 time steps, which sums up to different 372 queries on 44,160 files (12.2 GB) produced by the computational simulation. Results show that FastBit is faster than PostgresRAW for query execution in all but low-selectivity query scenarios. In a complementary manner, results also show PostgresRAW outperforms FastBit whenever users are interested in reducing time-to-query rather than the query execution time itself.
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Hanuš, Oto, Libor Janů, Jens Schuster, Josef Kučera, Marcela Vyletělová, and Václava Genčurová. "Exploratory analysis of dynamics of frequency distribution of raw cow milk quality indicators in the Czech Republic." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 1 (2011): 83–100. http://dx.doi.org/10.11118/actaun201159010083.
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A consistent link of the raw milk quality (RMQ) to the farmer price is essential. The aim was to analyse the properties of milk quality indicators (MQIs) and propose a new synthetic relative MQI (SQSM) from among various individual MQIs. SQSM could serve for consistent inclusion each quality change into the price. The paper was focused on exploratory analysis (normality (N) testing of files of MQIs). On the basis of the results, the MQIs were divided into two groups without and with necessity of original data transformation (TRN). Log and Box–Cox TRNs were tested in terms of possibilities of the files approach to the normal data frequency distribution (FD). The compositional MQIs deviated less and health and hygienic MQIs more from normal FD in original data (P < 0.05). The TRNs approached the data files to N very markedly in health and hygienic MQIs. The synthesis of various values of MQIs into SQSM was proposed. SQSM values were derived from model file of real data about MQIs and validated for use at farmer milk price modifications by the normality FD test. 33.3% of month SQSM files were normal (P > 0.05), the other were very close to the N with negligible deviations. The useability of the SQSM system for the balancing of raw milk purchase price premiums and penalties was tentatively confirmed.
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Hamilton, Michael, Paul Wessel, Brian Taylor, and Joaquim Luis. "Producing marine geophysical archive files from raw underway data." Computers & Geosciences 133 (December 2019): 104321. http://dx.doi.org/10.1016/j.cageo.2019.104321.
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Doebelin, Nicola, and Reinhard Kleeberg. "Profex: a graphical user interface for the Rietveld refinement programBGMN." Journal of Applied Crystallography 48, no. 5 (August 29, 2015): 1573–80. http://dx.doi.org/10.1107/s1600576715014685.
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Profexis a graphical user interface for the Rietveld refinement programBGMN. Its interface focuses on preservingBGMN's powerful and flexible scripting features by giving direct access toBGMNinput files. Very efficient workflows for single or batch refinements are achieved by managing refinement control files and structure files, by providing dialogues and shortcuts for many operations, by performing operations in the background, and by providing import filters for CIF and XML crystal structure files. Refinement results can be easily exported for further processing. State-of-the-art graphical export of diffraction patterns to pixel and vector graphics formats allows the creation of publication-quality graphs with minimum effort.Profexreads and converts a variety of proprietary raw data formats and is thus largely instrument independent.ProfexandBGMNare available under an open-source license for Windows, Linux and OS X operating systems.
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Porebski, Benjamin T., Bosco K. Ho, and Ashley M. Buckle. "Interactive visualization tools for the structural biologist." Journal of Applied Crystallography 46, no. 5 (August 3, 2013): 1518–20. http://dx.doi.org/10.1107/s0021889813017858.
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In structural biology, management of a large number of Protein Data Bank (PDB) files and raw X-ray diffraction images often presents a major organizational problem. Existing software packages that manipulate these file types were not designed for these kinds of file-management tasks. This is typically encountered when browsing through a folder of hundreds of X-ray images, with the aim of rapidly inspecting the diffraction quality of a data set. To solve this problem, a useful functionality of the Macintosh operating system (OSX) has been exploited that allows custom visualization plugins to be attached to certain file types. Software plugins have been developed for diffraction images and PDB files, which in many scenarios can save considerable time and effort. The direct visualization of diffraction images and PDB structures in the file browser can be used to identify key files of interest simply by scrolling through a list of files.
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Penmatsa, Ravi Kiran Varma, Akhila Kalidindi, and S. Kumar Reddy Mallidi. "Feature Reduction and Optimization of Malware Detection System Using Ant Colony Optimization and Rough Sets." International Journal of Information Security and Privacy 14, no. 3 (July 2020): 95–114. http://dx.doi.org/10.4018/ijisp.2020070106.
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Malware is a malicious program that can cause a security breach of a system. Malware detection and classification is one of the burning topics of research in information security. Executable files are the major source of input for static malware detection. Machine learning techniques are very efficient in behavioral-based malware detection and need a dataset of malware with different features. In windows, malware can be detected by analyzing the portable executable (PE) files. This work contributes to identifying the minimum feature set for malware detection employing a rough set dependent feature significance combined with Ant Colony Optimization (ACO) as the heuristic-search technique. A malware dataset named claMP with both integrated features and raw features was considered as the benchmark dataset for this work. The analytical results prove that 97.15% and 92.8% data size optimization has been achieved with a minimum loss of accuracy for claMP integrated and raw datasets, respectively.
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Musa, Aliyu, Matthias Dehmer, Olli Yli-Harja, and Frank Emmert-Streib. "Exploiting Genomic Relations in Big Data Repositories by Graph-Based Search Methods." Machine Learning and Knowledge Extraction 1, no. 1 (November 22, 2018): 205–10. http://dx.doi.org/10.3390/make1010012.
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We are living at a time that allows the generation of mass data in almost any field of science. For instance, in pharmacogenomics, there exist a number of big data repositories, e.g., the Library of Integrated Network-based Cellular Signatures (LINCS) that provide millions of measurements on the genomics level. However, to translate these data into meaningful information, the data need to be analyzable. The first step for such an analysis is the deliberate selection of subsets of raw data for studying dedicated research questions. Unfortunately, this is a non-trivial problem when millions of individual data files are available with an intricate connection structure induced by experimental dependencies. In this paper, we argue for the need to introduce such search capabilities for big genomics data repositories with a specific discussion about LINCS. Specifically, we suggest the introduction of smart interfaces allowing the exploitation of the connections among individual raw data files, giving raise to a network structure, by graph-based searches.
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Artimo, Panu, Séverine Duvaud, Mikhail Pachkov, Vassilios Ioannidis, Erik van Nimwegen, and Heinz Stockinger. "The ISMARA client." F1000Research 5 (December 15, 2016): 2851. http://dx.doi.org/10.12688/f1000research.9794.1.
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ISMARA (ismara.unibas.ch) automatically infers the key regulators and regulatory interactions from high-throughput gene expression or chromatin state data. However, given the large sizes of current next generation sequencing (NGS) datasets, data uploading times are a major bottleneck. Additionally, for proprietary data, users may be uncomfortable with uploading entire raw datasets to an external server. Both these problems could be alleviated by providing a means by which users could pre-process their raw data locally, transferring only a small summary file to the ISMARA server. We developed a stand-alone client application that pre-processes large input files (RNA-seq or ChIP-seq data) on the user's computer for performing ISMARA analysis in a completely automated manner, including uploading of small processed summary files to the ISMARA server. This reduces file sizes by up to a factor of 1000, and upload times from many hours to mere seconds. The client application is available from ismara.unibas.ch/ISMARA/client.
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Wilkins, Oscar G., Charlotte Capitanchik, Nicholas M. Luscombe, and Jernej Ule. "Ultraplex: A rapid, flexible, all-in-one fastq demultiplexer." Wellcome Open Research 6 (June 7, 2021): 141. http://dx.doi.org/10.12688/wellcomeopenres.16791.1.
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Background: The first step of virtually all next generation sequencing analysis involves the splitting of the raw sequencing data into separate files using sample-specific barcodes, a process known as “demultiplexing”. However, we found that existing software for this purpose was either too inflexible or too computationally intensive for fast, streamlined processing of raw, single end fastq files containing combinatorial barcodes. Results: Here, we introduce a fast and uniquely flexible demultiplexer, named Ultraplex, which splits a raw FASTQ file containing barcodes either at a single end or at both 5’ and 3’ ends of reads, trims the sequencing adaptors and low-quality bases, and moves unique molecular identifiers (UMIs) into the read header, allowing subsequent removal of PCR duplicates. Ultraplex is able to perform such single or combinatorial demultiplexing on both single- and paired-end sequencing data, and can process an entire Illumina HiSeq lane, consisting of nearly 500 million reads, in less than 20 minutes. Conclusions: Ultraplex greatly reduces computational burden and pipeline complexity for the demultiplexing of complex sequencing libraries, such as those produced by various CLIP and ribosome profiling protocols, and is also very user friendly, enabling streamlined, robust data processing. Ultraplex is available on PyPi and Conda and via Github.
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Kiweler, Marina, Mario Looso, and Johannes Graumann. "MARMoSET – Extracting Publication-ready Mass Spectrometry Metadata from RAW Files." Molecular & Cellular Proteomics 18, no. 8 (May 16, 2019): 1700–1702. http://dx.doi.org/10.1074/mcp.tir119.001505.
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Semeraro, Roberto, and Alberto Magi. "PyPore: a python toolbox for nanopore sequencing data handling." Bioinformatics 35, no. 21 (April 16, 2019): 4445–47. http://dx.doi.org/10.1093/bioinformatics/btz269.
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Abstract Motivation The recent technological improvement of Oxford Nanopore sequencing pushed the throughput of these devices to 10–20 Gb allowing the generation of millions of reads. For these reasons, the availability of fast software packages for evaluating experimental quality by generating highly informative and interactive summary plots is of fundamental importance. Results We developed PyPore, a three module python toolbox designed to handle raw FAST5 files from quality checking to alignment to a reference genome and to explore their features through the generation of browsable HTML files. The first module provides an interface to explore and evaluate the information contained in FAST5 and summarize them into informative quality measures. The second module converts raw data in FASTQ format, while the third module allows to easily use three state-of-the-art aligners and collects mapping statistics. Availability and implementation PyPore is an open-source software and is written in Python2.7, source code is freely available, for all OS platforms, in Github at https://github.com/rsemeraro/PyPore Supplementary information Supplementary data are available at Bioinformatics online.
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Nielsen, S. S., K. Noergaard Toft, D. Snakenborg, M. G. Jeppesen, J. K. Jacobsen, B. Vestergaard, J. P. Kutter, and L. Arleth. "BioXTAS RAW, a software program for high-throughput automated small-angle X-ray scattering data reduction and preliminary analysis." Journal of Applied Crystallography 42, no. 5 (August 8, 2009): 959–64. http://dx.doi.org/10.1107/s0021889809023863.
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A fully open source software program for automated two-dimensional and one-dimensional data reduction and preliminary analysis of isotropic small-angle X-ray scattering (SAXS) data is presented. The program is freely distributed, following the open-source philosophy, and does not rely on any commercial software packages.BioXTAS RAWis a fully automated program that,viaan online feature, reads raw two-dimensional SAXS detector output files and processes and plots data as the data files are created during measurement sessions. The software handles all steps in the data reduction. This includes mask creation, radial averaging, error bar calculation, artifact removal, normalization andqcalibration. Further data reduction such as background subtraction and absolute intensity scaling is fast and easyviathe graphical user interface.BioXTAS RAWalso provides preliminary analysis of one-dimensional data in terms of the indirect Fourier transform using the objective Bayesian approach to obtain the pair-distance distribution function, PDDF, and is thereby a free and open-source alternative to existing PDDF estimation software. Apart from the TIFF input format, the program also accepts ASCII-format input files and is currently compatible with one-dimensional data files from SAXS beamlines at a number of synchrotron facilities.BioXTAS RAWis written in Python with C++ extensions.
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Kia, A., and S. M. Samimi Ardestani. "Qualitative evaluation of archived files of pasychiatric ward of Imam Hossein Hospital during years of 2007 to 2008." European Psychiatry 26, S2 (March 2011): 545. http://dx.doi.org/10.1016/s0924-9338(11)72252-0.
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IntroductionMedical records include information about the status and the hospital course of a patient and the instructions given to the patient. The accurate, accessible and up to date information can be used for further evaluation of the patient and also probable research projects.ObjectivesQualitative study of the archived patients’ files.AimsEvaluating the quality of the medical records at the psychiatric ward.MethodThe study was an observational descriptive study in which 400 archived files at the psychiatric ward of Imam Hossein Hospital during the years of 2007 and 2008 were qualitatively studied. The data was gathered by a data sheet and was analyzed using SPSS v. 15.0.ResultsAmong patient's demographic information, place of birth was not recorded in 82% of the files followed by birth date that was missing in 38% of the files. Case history, psychiatric formulations and progressive notes of patients were not appropriately recorded in 2.5% of the patients’ files. Finally, the summary sheets did not clarify the main complaint in 11.7% of the files and 7.5% of them lacked information to indicate dates of admission and discharge. Only 21.25% of the summaries defined initial and final diagnosis.ConclusionWith regards to the results and since there were incomplete data; the information could not be properly used as the raw material for researches. It seems that more supervision is needed to completely fill the files.
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Goebel, Joel A., Jason M. Hanson, and Douglas G. Fishel. "Interlaboratory Variability of Rotational Chair Test Results." Otolaryngology–Head and Neck Surgery 110, no. 4 (April 1994): 400–405. http://dx.doi.org/10.1177/019459989411000409.
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Test-retest reliability of rotational chair testing for a single facility has previously been examined by others. The actual data analysis methods, however, have received far less attention. The variety of both hardware and software currently used theoretically may affect the results for a given subject tested at different facilities. The purposes of this study were, first, to quantify the amount of variability in the analysis of Identical raw data files at multiple rotational chair testing facilities by using automated analysis; second, to evaluate the effect of operator intervention on the anaiysis; and third, to identify possible sources of variability. Raw data were collected from 10 normal subjects at 0.05 Hz and 0.5 Hz (50 degrees per second peak velocity). Diskettes containing raw electro-oculogram data files were then distributed to eight participating laboratories for analysis by two methods: (1) using automated analysis algorithms and (2) using the same algorithms but allowing operator intervention into the analysis. Response parameters calculated were gain and phase (re: velocity). The SD of gain values per subject for automated analysis ranged from 0.01 to 0.32 gain units and of phase values from 0.4 to 13.7 degrees. For analysis with operator Intervention, the SD of gain values ranged from 0.02 to 0.10 gain units and of phase values from 0.4 to 4.4 degrees. The difference between automated analysis and analysis with operator intervention was significant for gain calculations ( p < 0.02) but not for phase calculations ( p > 0.05). This study demonstrates significant variability in automated analysis of rotational chair raw data for gain and phase. Operator intervention into the analysis significantly reduces variability for gain but not for phase.
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Gaida, Antje, and Steffen Neumann. "MetHouse: Raw and Preprocessed Mass Spectrometry Data." Journal of Integrative Bioinformatics 4, no. 1 (March 1, 2007): 107–14. http://dx.doi.org/10.1515/jib-2007-56.
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Abstract We are developing a vendor-independent archive and on top of that a data warehouse for mass spectrometry metabolomics data. The archive schema resembles the communitydeveloped object model, the Java implementation of the model classes, and an editor (for both mzData XML files and the database) have been generated using the Eclipse Modeling Framework. Persistence is handled by the JDO2 -compliant framework JPOX. The main content of the Data Warehouse are the results of the signal processing and peak-picking tasks, carried out using the XCMS package from Bioconductor, putative identification and mass decomposition are added to the warehouse afterwards. We present the system architecture, current content, performance observations and describe the analysis tools on top of the warehouse. Availability: http://msbi.ipb-halle.de/
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Alvarez-Ayllon, Alejandro, Manuel Palomo-Duarte, and Juan-Manuel Dodero. "Interactive Data Exploration of Distributed Raw Files: A Systematic Mapping Study." IEEE Access 7 (2019): 10691–717. http://dx.doi.org/10.1109/access.2018.2882244.
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Jeong, Young-Seob, Jiyoung Woo, SangMin Lee, and Ah Reum Kang. "Malware Detection of Hangul Word Processor Files Using Spatial Pyramid Average Pooling." Sensors 20, no. 18 (September 15, 2020): 5265. http://dx.doi.org/10.3390/s20185265.
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Malware detection of non-executables has recently been drawing much attention because ordinary users are vulnerable to such malware. Hangul Word Processor (HWP) is software for editing non-executable text files and is widely used in South Korea. New malware for HWP files continues to appear because of the circumstances between South Korea and North Korea. There have been various studies to solve this problem, but most of them are limited because they require a large amount of effort to define features based on expert knowledge. In this study, we designed a convolutional neural network to detect malware within HWP files. Our proposed model takes a raw byte stream as input and predicts whether it contains malicious actions or not. To incorporate highly variable lengths of HWP byte streams, we propose a new padding method and a spatial pyramid average pooling layer. We experimentally demonstrate that our model is not only effective, but also efficient.
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Pullan, S. E. "Recommended standard for seismic (/radar) data files in the personal computer environment." GEOPHYSICS 55, no. 9 (September 1990): 1260–71. http://dx.doi.org/10.1190/1.1442942.
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This paper is the result of the work of a subcommittee of SEG’s Engineering and Groundwater Geophysics Committee. It recommends a data file format for raw or processed shallow seismic or digital radar data in the small computer environment. It is recommended that this format be known as the SEG-2 format.
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Bukin, Yu S., L. S. Buzoleva, Y. S. Golozubova, and Yu P. Galachyants. "New Procedure of Raw Illumina MiSeq Data Filtering For the Amplicon Metagenomic Libraries." Mathematical Biology and Bioinformatics 13, no. 1 (May 15, 2018): 159–68. http://dx.doi.org/10.17537/2018.13.159.
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In this paper we present an algorithm to filter amplicon paired-end NGS raw data which is used to capture genetic and taxonomic diversity of communities of unicellular microorganisms. The suggested approach allows one to overcome the issue of massive data loss during filtration of raw sequences and increases the static representativeness of analyzed amplicons. Furthermore, an unequal elimination of sequences belonging to different taxonomic groups was shown to occur if one applies standard trimming methods based on filtration of quality of raw reads, for instance, using sliding window approach. This bias may result in a skew of taxon counts and depletion of taxonomic diversity of analyzed communities. The suggested method does not introduce the errors of this kind. The implementation of the algorithm on R programming language as well as a number of example files for analysis is available at https://github.com/barnsys/metagenomic_analysis.
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Narang, Yashraj S., Balakumar Sundaralingam, Karl Van Wyk, Arsalan Mousavian, and Dieter Fox. "Interpreting and predicting tactile signals for the SynTouch BioTac." International Journal of Robotics Research 40, no. 12-14 (November 26, 2021): 1467–87. http://dx.doi.org/10.1177/02783649211047634.
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In the human hand, high-density contact information provided by afferent neurons is essential for many human grasping and manipulation capabilities. In contrast, robotic tactile sensors, including the state-of-the-art SynTouch BioTac, are typically used to provide low-density contact information, such as contact location, center of pressure, and net force. Although useful, these data do not convey or leverage the rich information content that some tactile sensors naturally measure. This research extends robotic tactile sensing beyond reduced-order models through (1) the automated creation of a precise experimental tactile dataset for the BioTac over a diverse range of physical interactions, (2) a 3D finite-element (FE) model of the BioTac, which complements the experimental dataset with high-density, distributed contact data, (3) neural-network-based mappings from raw BioTac signals to not only low-dimensional experimental data, but also high-density FE deformation fields, and (4) mappings from the FE deformation fields to the raw signals themselves. The high-density data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible. Datasets, CAD files for the experimental testbed, FE model files, and videos are available at https://sites.google.com/nvidia.com/tactiledata .
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Igual, Jorge. "Photographic Noise Performance Measures Based on RAW Files Analysis of Consumer Cameras." Electronics 8, no. 11 (November 4, 2019): 1284. http://dx.doi.org/10.3390/electronics8111284.
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Photography is being benefited from the huge improvement in CMOS image sensors. New cameras extend the dynamic range allowing photographers to take photos with a higher quality than they could imagine one decade ago. However, the existence of different technologies make more complicated the photographic analysis of how to determine the optimal camera exposure settings. In this paper, we analyze how the different noise models are translated to different signal to noise S N R curve patterns and which factors are relevant. In particular, we discuss profoundly the relationships between exposure settings (shutter speed, aperture and ISO). Since a fair comparison between cameras can be tricky because of different pixel size, sensor format or ISO scale definition, we explain how the pixel analysis of a camera can be translated to a more helpful universal photographic noise measure based on human perception and common photography rules. We analyze the RAW files of different camera models and show how the noise performance analysis ( S N R and dynamic range) interact with photographer’s requirements.
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Li, LiLi. "Measuring an Academic Library’s Performance and Service: A Case Study in Georgia Southern University Library." International Journal of Librarianship 2, no. 1 (July 25, 2017): 53. http://dx.doi.org/10.23974/ijol.2017.vol2.1.26.
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This paper shares the Georgia Southern University Library’s experience of LibQUAL in user-centered and service-oriented academic learning environment. On the basis of reviewing the library literature on LibQUAL, this study presents the process of data analysis and data visualization for academic library assessments. Using the 2016 LibQUAL Survey at the Georgia Southern University Library as a sample, this study illustrates basic methods of analyzing and interpreting the LibQUAL Survey Raw Data and User Response Raw Data saved in Excel files. Also mentioned in this study are other common statistical tools and a set of general procedures, including data analysis, data validation, data migration, data mining, and data visualization, for academic library assessments to identify patron needs and satisfactory factors.
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Arif Rachmat, Nuqson Masykur Huda, and Sri Anita. "Predictive Analytic Klasifikasi Penentuan Tarif Sewa Bus." Jurnal Sistem Cerdas 2, no. 2 (September 1, 2019): 135–49. http://dx.doi.org/10.37396/jsc.v2i2.29.
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Currently, the bus rental business has become the choice of consumers in traveling, because of the decision of flexibility and better availability. The government regulates that consumer and business owner agreements determine bus rental rates without routes. In this study intends to do clustering from the history of raw data that already exists before. Data is obtained from companies in the form of spreadsheet files originating from non-information systems. The raw data is combined and normalized, to eliminate the noise data and the data is not abnormal. The clustering results using the K-Means algorithm and Louvain clustering produce several tariff groups that can be used as a reference for determining fare. In this paper also concludes about unbalanced data, which can cause data clustering errors.
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Hernández Villanueva, Michel, and Ikuo Ueda. "The Belle II Raw Data Management System." EPJ Web of Conferences 245 (2020): 04005. http://dx.doi.org/10.1051/epjconf/202024504005.
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The Belle II experiment, a major upgrade of the previous e+e− asymmetric collider experiment Belle, is expected to produce tens of petabytes of data per year due to the luminosity increase from the upgraded SuperKEKB accelerator. The distributed computing system of the Belle II experiment plays a key role, storing and distributing data in a reliable way to be easily accessed and analyzed by more than 1000 collaborators. In particular, the Belle II Raw Data Management system has been developed with an aim to upload output files onto grid storage, register them into the file and metadata catalogs, and make two replicas of the full raw data set using the Belle II Distributed Data Management system. It has been implemented as an extension of DIRAC (Distributed Infrastructure with Remote Agent Control) and consists of a database, services, client and monitoring tools, and several agents that treat the data automatically. The first year of data taken with the Belle II full detector has been managed by the Belle II Raw Data Management system successfully. The design, current status, and performance are presented. Prospects for improvements towards the full luminosity data taking are also reviewed.
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Bright, D. S., and K. G. Milans. "Lispix: A Public Domain Scientific Image Analysis Program for the PC and Macintosh*." Microscopy and Microanalysis 6, S2 (August 2000): 1022–23. http://dx.doi.org/10.1017/s1431927600037600.
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MacLispix, a public domain image processing system for the Macintosh, has been applied to a variety of image processing problems such as analysis of diffraction spots, uniform display of x-ray maps, determination of fractal dimension of particle outlines, and analysis of data cubes. Due to interest from the PC community, we have ported the software to Windows, renamed it Lispix', and distributed it for both platforms, along with example images, source and documentation.Lispix reads TIFF files and raw files (no image header), both with pixel types of signed and unsigned 8, 16 and 32 bit integers, 4 and 8 byte IEEE standard floating point numbers, and 3x8 bit RGB color. Lispix has a variety of standard image processing operations, such as thresholding, edge finding (gradient), filtering, scaling, Linear Hough Transform, false coloring, RGB color overlays and particle measurement.
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Rousseau, Hervé, Belinda Chan Kwok Cheong, Cristian Contescu, Xavier Espinal Curull, Jan Iven, Hugo Gonzalez Labrador, Massimo Lamanna, et al. "Providing large-scale disk storage at CERN." EPJ Web of Conferences 214 (2019): 04033. http://dx.doi.org/10.1051/epjconf/201921404033.
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The CERN IT Storage group operates multiple distributed storage systems and is responsible for the support of the infrastructure to accommodate all CERN storage requirements, from the physics data generated by LHC and non-LHC experiments to the personnel users' files. EOS is now the key component of the CERN Storage strategy. It allows to operate at high incoming throughput for experiment data-taking while running concurrent complex production work-loads. This high-performance distributed storage provides now more than 250PB of raw disks and it is the key component behind the success of CERNBox, the CERN cloud synchronisation service which allows syncing and sharing files on all major mobile and desktop platforms to provide offline availability to any data stored in the EOS infrastructure. CERNBox recorded an exponential growth in the last couple of year in terms of files and data stored thanks to its increasing popularity inside CERN users community and thanks to its integration with a multitude of other CERN services (Batch, SWAN, Microsoft Office). In parallel CASTOR is being simplified and transitioning from an HSM into an archival system, focusing mainly in the long-term data recording of the primary data from the detectors, preparing the road to the next-generation tape archival system, CTA. The storage services at CERN cover as well the needs of the rest of our community: Ceph as data back-end for the CERN OpenStack infrastructure, NFS services and S3 functionality; AFS for legacy home directory filesystem services and its ongoing phase-out and CVMFS for software distribution. In this paper we will summarise our experience in supporting all our distributed storage system and the ongoing work in evolving our infrastructure, testing very-dense storage building block (nodes with more than 1PB of raw space) for the challenges waiting ahead.
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Bhat, Anirudh, Aryan Likhite, Swaraj Chavan, and Leena Ragha. "File Fragment Classification using Content Based Analysis." ITM Web of Conferences 40 (2021): 03025. http://dx.doi.org/10.1051/itmconf/20214003025.
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One of the major components in Digital Forensics is the extraction of files from a criminal’s hard drives. To achieve this, several techniques are used. One of these techniques is using file carvers. File carvers are used when the system metadata or the file table is damaged but the contents of the hard drive are still intact. File carvers work on the raw fragments in the hard disk and reconstruct files by classifying the fragments and then reassembling them to form the complete file. Hence the classification of file fragments has been an important problem in the field of digital forensics. The work on this problem has mainly relied on finding the specific byte sequences in the file header and footer. However, classification based on header and footer is not reliable as they may be modified or missing. In this project, the goal is to present a machine learningbased approach for content-based analysis to recognize the file types of file fragments. It does so by training a Feed-Forward Neural Network with a 2-byte sequence histogram feature vector which is calculated for each file. These files are obtained from a publicly available file corpus named Govdocs1. The results show that content-based analysis is more reliable than relying on the header and footer data of files.
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Liu, Xiao Yun, and Jian Shen. "Design and Implementation of English Electronic Dictionary System for Android." Applied Mechanics and Materials 631-632 (September 2014): 1003–6. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.1003.
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In this paper, based on the Android operating system, an electronic dictionary system on mobile devices is designed. Local word searching is achieved by SQLite database storing and managing data, local word library being built on the res\raw directory in the Android project, and then the Input Stream data being written to corresponding files in other directories; By using the interface of Unabridged Dictionary, calling utf8 encoding scheme to implement the web-based word search.
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Urgese, Gianvito, Emanuele Parisi, Orazio Scicolone, Santa Di Cataldo, and Elisa Ficarra. "BioSeqZip: a collapser of NGS redundant reads for the optimization of sequence analysis." Bioinformatics 36, no. 9 (January 30, 2020): 2705–11. http://dx.doi.org/10.1093/bioinformatics/btaa051.
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Abstract Motivation High-throughput next-generation sequencing can generate huge sequence files, whose analysis requires alignment algorithms that are typically very demanding in terms of memory and computational resources. This is a significant issue, especially for machines with limited hardware capabilities. As the redundancy of the sequences typically increases with coverage, collapsing such files into compact sets of non-redundant reads has the 2-fold advantage of reducing file size and speeding-up the alignment, avoiding to map the same sequence multiple times. Method BioSeqZip generates compact and sorted lists of alignment-ready non-redundant sequences, keeping track of their occurrences in the raw files as well as of their quality score information. By exploiting a memory-constrained external sorting algorithm, it can be executed on either single- or multi-sample datasets even on computers with medium computational capabilities. On request, it can even re-expand the compacted files to their original state. Results Our extensive experiments on RNA-Seq data show that BioSeqZip considerably brings down the computational costs of a standard sequence analysis pipeline, with particular benefits for the alignment procedures that typically have the highest requirements in terms of memory and execution time. In our tests, BioSeqZip was able to compact 2.7 billion of reads into 963 million of unique tags reducing the size of sequence files up to 70% and speeding-up the alignment by 50% at least. Availability and implementation BioSeqZip is available at https://github.com/bioinformatics-polito/BioSeqZip. Supplementary information Supplementary data are available at Bioinformatics online.
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Kim, Tong Min, Seo-Joon Lee, Hwa Young Lee, Dong-Jin Chang, Chang Ii Yoon, In-Young Choi, and Kun-Ho Yoon. "CIMI: Classify and Itemize Medical Image System for PFT Big Data Based on Deep Learning." Applied Sciences 10, no. 23 (November 30, 2020): 8575. http://dx.doi.org/10.3390/app10238575.
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The value of pulmonary function test (PFT) data is increasing due to the advent of the Coronavirus Infectious Disease 19 (COVID-19) and increased respiratory disease. However, these PFT data cannot be directly used in clinical studies, because PFT results are stored in raw image files. In this study, the classification and itemization medical image (CIMI) system generates valuable data from raw PFT images by automatically classifying various PFT results, extracting texts, and storing them in the PFT database and Excel files. The deep-learning-based optical character recognition (OCR) technology was mainly used in CIMI to classify and itemize PFT images in St. Mary’s Hospital. CIMI classified seven types and itemized 913,059 texts from 14,720 PFT image sheets, which cannot be done by humans. The number, type, and location of texts that can be extracted by PFT type are all different, but CIMI solves this issue by classifying the PFT image sheets by type, allowing researchers to analyze the data. To demonstrate the superiority of CIMI, the validation results of CIMI were compared to the results of the other four algorithms. A total of 70 randomly selected sheets (ten sheets from each type) and 33,550 texts were used for the validation. The accuracy of CIMI was 95%, which was the highest accuracy among the other four algorithms.
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Pavlova, Anna, Vera Belova, Robert Afasizhev, Irina Bulusheva, Denis Rebrikov, and Dmitriy Korostin. "Runcer-Necromancer: a method to rescue data from an interrupted run on MGISEQ-2000." F1000Research 10 (January 14, 2021): 22. http://dx.doi.org/10.12688/f1000research.27763.1.
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During the sequencing process, problems can occur with any device, including the MGISEQ-2000 (DNBSEQ-G400) platform. We encountered a power outage that resulted in a temporary shutdown of a sequencer in the middle of the run. Since barcode reading in MGISEQ-2000 takes place at the end of the run, it was impossible to use non-demultiplexed raw data. We decided to completely use up the same cartridge with reagents and flow cell loaded with DNB and started a new run in a shortened custom mode. We figured out how the MGISEQ-2000 converts preliminary data in .cal format into .fastq files and wrote a script named “Runcer-Necromacer” for merging .fastq files based on the analysis of their headers (available online: https://github.com/genomecenter/runcer-necromancer). Read merging proved to be possible because the MGISEQ-2000 flow cell has a patterned structure and each DNB has invariable coordinates on it, regardless of its position on the flow cell stage. We demonstrated the correctness of data merging by comparing sample analysis results with previously obtained .fastq files for them. Thus, we confirmed that it is possible to restart the device and save both parts of the interrupted run.
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Pavlova, Anna, Vera Belova, Robert Afasizhev, Irina Bulusheva, Denis Rebrikov, and Dmitriy Korostin. "Runcer-Necromancer: a method to rescue data from an interrupted run on MGISEQ-2000." F1000Research 10 (February 14, 2022): 22. http://dx.doi.org/10.12688/f1000research.27763.2.
Full textAbstract:
During the sequencing process, problems can occur with any device, including the MGISEQ-2000 (DNBSEQ-G400) platform. We encountered a power outage that resulted in a temporary shutdown of a sequencer in the middle of the run. Since barcode reading in MGISEQ-2000 takes place at the end of the run, it was impossible to use non-demultiplexed raw data. We decided to completely use up the same cartridge with reagents and flow cell loaded with DNB and started a new run in a shortened custom mode. We figured out how the MGISEQ-2000 converts preliminary data in .cal format into .fastq files and wrote a script named “Runcer-Necromacer” for merging .fastq files based on the analysis of their headers (available online: https://github.com/genomecenter/runcer-necromancer). Read merging proved to be possible because the MGISEQ-2000 flow cell has a patterned structure and each DNB has invariable coordinates on it, regardless of its position on the flow cell stage. We demonstrated the correctness of data merging by comparing sample analysis results with previously obtained .fastq files for them. Thus, we confirmed that it is possible to restart the device and save both parts of the interrupted run.
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Ozadam, Hakan, Michael Geng, and Can Cenik. "RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution." Bioinformatics 36, no. 9 (January 13, 2020): 2929–31. http://dx.doi.org/10.1093/bioinformatics/btaa028.
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Abstract Summary Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named ‘ribo’. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling. Availability and implementation For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling. Supplementary information Supplementary data are available at Bioinformatics online.
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Pathak, Sudipta, and Sanguthevar Rajasekaran. "RETRACTED: LFQC: a lossless compression algorithm for FASTQ files." Bioinformatics 35, no. 9 (October 24, 2014): e1-e7. http://dx.doi.org/10.1093/bioinformatics/btu701.
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Abstract Motivation Next-generation sequencing (NGS) technologies have revolutionized genomic research by reducing the cost of whole-genome sequencing. One of the biggest challenges posed by modern sequencing technology is economic storage of NGS data. Storing raw data is infeasible because of its enormous size and high redundancy. In this article, we address the problem of storage and transmission of large Fastq files using innovative compression techniques. Results We introduce a new lossless non-reference-based fastq compression algorithm named lossless FastQ compressor. We have compared our algorithm with other state of the art big data compression algorithms namely gzip, bzip2, fastqz, fqzcomp, G-SQZ, SCALCE, Quip, DSRC, DSRC-LZ etc. This comparison reveals that our algorithm achieves better compression ratios. The improvement obtained is up to 225%. For example, on one of the datasets (SRR065390_1), the average improvement (over all the algorithms compared) is 74.62%. Availability and implementation The implementations are freely available for non-commercial purposes. They can be downloaded from http://engr.uconn.edu/∼rajasek/FastqPrograms.zip.
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Phogat, Apurva, Gerhard Kronschnabl, Christian Plötz, Walter Schwarz, and Torben Schüler. "Short Baseline Observations at Geodetic Observatory Wettzell." Data 3, no. 4 (December 10, 2018): 64. http://dx.doi.org/10.3390/data3040064.
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The Geodetic Observatory Wettzell (GOW), jointly operated by the Federal Agency for Cartography and Geodesy (BKG), Germany and the Technical University of Munich, Germany is equipped with three radio telescopes for Very Long Baseline Interferometry (VLBI). Correlation capability is primarily designed for relative positioning of the three Wettzell radio telescopes i.e., to derive the local ties between the three telescopes from VLBI raw data in addition to the conventional terrestrial surveys. A computing cluster forming the GO Wettzell Local Correlator (GOWL) was installed in 2017 as well as the Distributed FX (DiFX) software correlation package and the Haystack Observatory Postprocessing System (HOPS) for fringe fitting and postprocessing of the output. Data pre-processing includes ambiguity resolution (if necessary) as well as the generation of the geodetic database and NGS card files with υ Solve. The final analysis is either carried out with local processing software (LEVIKA short baseline analysis) or with the Vienna VLBI and Satellite (VieVS) software. We will present an overview of the scheduling, correlation and analysis capabilities at GOW and results obtained so. The dataset includes auxiliary files (schedule and log files) which contain information about the participating antenna, observed sources, clock offset between formatter and GPS time, cable delay, meteorological parameters (temperature, barometric pressure, and relative humidity) and ASCII files created after fringe fitting and final analysis. The published dataset can be used by the researchers and scientists to further explore short baseline interferometry.
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Takano, Atsuko, Yasuhiko Horiuchi, Yu Fujimoto, Kouta Aoki, Hiromune Mitsuhashi, and Akira Takahashi. "Simple but long-lasting: A specimen imaging method applicable for small- and medium-sized herbaria." PhytoKeys 118 (February 18, 2019): 1–14. http://dx.doi.org/10.3897/phytokeys.118.29434.
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Major international herbaria, natural history museums and universities have recently begun to digitise their collections to facilitate studies and improve access to collections. In Japan, more than 10 million herbarium specimens are housed in various universities/museums; however, only 1% of these have been digitised. In this paper, we describe a new method for imaging herbarium specimens that is applicable to local/small herbaria. It is safe, fast, simple and inexpensive, but also satisfies usage guidelines for minimum image quality and can produce digital files suitable for long-term storage and future post production. During an eight-month trial at the Museum of Nature and Human Activities, Hyogo, with three part-time workers using a custom-made copy stand and a mirrorless interchangeable lens camera with a large LED light bank system, we were able to image 73,180 herbarium specimens (571 per day on average), obtaining two RAW and two JPEG files for each specimen.
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Bravo, Afonso M., Athanasios Typas, and Jan-Willem Veening. "2FAST2Q: a general-purpose sequence search and counting program for FASTQ files." PeerJ 10 (October 25, 2022): e14041. http://dx.doi.org/10.7717/peerj.14041.
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Background The increasingly widespread use of next generation sequencing protocols has brought the need for the development of user-friendly raw data processing tools. Here, we explore 2FAST2Q, a versatile and intuitive standalone program capable of extracting and counting feature occurrences in FASTQ files. Despite 2FAST2Q being previously described as part of a CRISPRi-seq analysis pipeline, in here we further elaborate on the program’s functionality, and its broader applicability and functions. Methods 2FAST2Q is built in Python, with published standalone executables in Windows MS, MacOS, and Linux. It has a familiar user interface, and uses an advanced custom sequence searching algorithm. Results Using published CRISPRi datasets in which Escherichia coli and Mycobacterium tuberculosis gene essentiality, as well as host-cell sensitivity towards SARS-CoV2 infectivity were tested, we demonstrate that 2FAST2Q efficiently recapitulates published output in read counts per provided feature. We further show that 2FAST2Q can be used in any experimental setup that requires feature extraction from raw reads, being able to quickly handle Hamming distance based mismatch alignments, nucleotide wise Phred score filtering, custom read trimming, and sequence searching within a single program. Moreover, we exemplify how different FASTQ read filtering parameters impact downstream analysis, and suggest a default usage protocol. 2FAST2Q is easier to use and faster than currently available tools, efficiently processing not only CRISPRi-seq / random-barcode sequencing datasets on any up-to-date laptop, but also handling the advanced extraction of de novo features from FASTQ files. We expect that 2FAST2Q will not only be useful for people working in microbiology but also for other fields in which amplicon sequencing data is generated. 2FAST2Q is available as an executable file for all current operating systems without installation and as a Python3 module on the PyPI repository (available at https://veeninglab.com/2fast2q).
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AGRAWAL, R. P., R. AGRAWAL, M. N. RANGA, and M. RIZWAN. "Insulin like protein from camel milk and similarity with human insulin." Indian Journal of Animal Sciences 92, no. 1 (February 1, 2022): 101–4. http://dx.doi.org/10.56093/ijans.v92i1.120933.
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From experimental studies, clinical studies and epidemiological studies it is proved beyond doubt that camel milk has potential role in prevention and treatment of diabetes. The main purpose of the study to isolate camel milk protein and compare its similarity with human insulin so that camel milk can be used as an adjunct therapy for diabetes. Raw camel milk (30 ml) was used for isolation of protein and peptides. The complete process included trypsin digestion, peptide fractionation and LC-MS technique. Digested and fractionized peptide sample was processed further for liquid chromatography and mass spectra/ tandem mass spectra were recorded in positive-ion and high sensitivity mode. MS/MS spectra were automatically calibrated during dynamic LC-MS. Raw data files were converted to Mascot Generic Format (MGF) and these MGF files were searched against UniPort, NCBI and common MS contaminant database. In our study 13 proteins and 22 peptide sequences were found similar to insulin/ insulin like growth factor and isoform. In our study some very large peptide sequence were identified which were seen similar to NUAK family SNF1-like kinase and this peptide sequence gives evidence of role of camel milk in cancer treatment. Observing so many similar peptides in camel milk sample with human insulin, isoform of insulin, receptors and others give strong evidence that camel milk have proteins/ peptides of such proteins similar to human insulin and give support to finding that camel milk contains insulin like molecule that mimics insulin interaction with its receptors.
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Fennig, Karsten, Marc Schröder, Axel Andersson, and Rainer Hollmann. "A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures." Earth System Science Data 12, no. 1 (March 20, 2020): 647–81. http://dx.doi.org/10.5194/essd-12-647-2020.
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Abstract. The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF) comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available. Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeΔT), quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).
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Crichton, Susan, and Elizabeth Childs. "Clipping and Coding Audio Files: A Research Method to Enable Participant Voice." International Journal of Qualitative Methods 4, no. 3 (September 2005): 40–49. http://dx.doi.org/10.1177/160940690500400303.
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Qualitative researchers have long used ethnographic methods to make sense of complex human activities and experiences. Their blessing is that through them, researchers can collect a wealth of raw data. Their challenge is that they require the researcher to find patterns and organize the various themes and concepts that emerge during the analysis stage into a coherent narrative that a reader can follow. In this article, the authors introduce a technology-enhanced data collection and analysis method based on clipped audio files. They suggest not only that the use of appropriate software and hardware can help in this process but, in fact, that their use can honor the participants' voices, retaining the original three-dimensional recording well past the data collection stage.
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SANTO, PAULO ROGÉRIO Q. DO ESPIRITO, MIGUEL SABINO NETO, FABIANE FURTADO, THALES WALTENIOR TRIGO JUNIOR, ANDREA DO AMARAL Q. DO ESPIRITO SANTO, FÁBIO LUIS NONATO, RAFAEL ALVES TUMEH, and LYDIA MASAKO FERREIRA. "Getting to know the types of JPEG and RAW photo files used in research." Revista Brasileira de Cirurgia Plástica (RBCP) – Brazilian Journal of Plastic Sugery 33, no. 1 (2018): 89–95. http://dx.doi.org/10.5935/2177-1235.2018rbcp0013.
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Belcastro, Vincenzo, Stephane Cano, Diego Marescotti, Stefano Acali, Carine Poussin, Ignacio Gonzalez-Suarez, Florian Martin, et al. "GladiaTOX: GLobal Assessment of Dose-IndicAtor in TOXicology." Bioinformatics 35, no. 20 (March 14, 2019): 4190–92. http://dx.doi.org/10.1093/bioinformatics/btz187.
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Abstract Summary GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the ‘tcpl’ core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate PDF reports for quality control and data processing. Availability and implementation GladiaTOX R package (bioconductor). Also available via: git clone https://github.com/philipmorrisintl/GladiaTOX.git. Supplementary information Supplementary data are available at Bioinformatics online.
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Sams, B., C. Litchfield, L. Sanchez, and N. Dokoozlian. "Two methods for processing yield maps from multiple sensors in large vineyards in California." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 530–33. http://dx.doi.org/10.1017/s2040470017000516.
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Yield mapping techniques have only recently started to be implemented by the Californian wine grape industry, but the advancement has necessitated new processing methods for large vineyards. The process for mapping large blocks harvested with multiple machines has only recently occurred and implies that their yield monitors have to be calibrated and corrected to the same scale. Here we discuss two methods for processing yield maps at the commercial level. Method 1 depends on many calibrations with delivered fruit weight to a winery. Method 2 normalizes raw files automatically can reduce total processing time by up to 90%.
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du Plessis, Anton, Johan Els, Stephan le Roux, Muofhe Tshibalanganda, and Toni Pretorius. "Data for 3D printing enlarged museum specimens for the visually impaired." Gigabyte 2020 (July 1, 2020): 1–7. http://dx.doi.org/10.46471/gigabyte.3.
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Museums are embracing new technologies and one of these is the use of 3D printing. 3D printing allows for creating physical replicas of items which may, due to great value or significance, not be handled by the public, or which are too small or fragile to be handled or even seen with the naked eye. One such application of new technologies has been welcomed by the National Museum in Bloemfontein, Free State, South Africa. Here, blown-up (enlarged) Museum specimens were 3D printed for various interactive exhibits that are aimed at increasing the accessibility of their permanent displays for visually impaired visitors who rely greatly on touch as a source of observation. A selection of scorpions, pseudoscorpions, mites and archetypal bird skulls were scanned, processed and 3D printed to produce enlarged, highly functional nylon models. This data paper provides the raw micro Computed Tomography (micro-CT) scan data and print ready STL files processed from this data. The STL files may be used in their current format and details of the printing are provided.
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Alzahrani, Abdullah I. A., Manel Ayadi, Mashael M. Asiri, Amal Al-Rasheed, and Amel Ksibi. "Detecting the Presence of Malware and Identifying the Type of Cyber Attack Using Deep Learning and VGG-16 Techniques." Electronics 11, no. 22 (November 9, 2022): 3665. http://dx.doi.org/10.3390/electronics11223665.
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Malware is malicious software (harmful program files) that targets and damage computers, devices, networks, and servers. Many types of malware exist, including worms, viruses, trojan horses, etc. With the increase in technology and devices every day, malware is significantly propagating more and more on a daily basis. The rapid growth in the number of devices and computers and the rise in technology is directly proportional to the number of malicious attacks—most of these attacks target organizations, customers, companies, etc. The main goal of these attacks is to steal critical data and passwords, blackmail, etc. The propagation of this malware may be performed through emails, infected files, connected peripherals such as flash drives and external disks, and malicious websites. Many types of research in artificial intelligence and machine learning fields have recently been released for malware detection. In this research work, we will focus on detecting malware using deep learning. We worked on a dataset that consisted of 8970 malware and 1000 non-malware (benign) executable files. The malware files were divided into five types in the dataset: Locker, Mediyes, Winwebsec, Zeroaccess, and Zbot. Those executable files were pre-processed and converted from raw data into images of size 224*224*3. This paper proposes a multi-stage architecture consisting of two modified VGG-19 models. The first model objective is to identify whether the input file is malicious or not, while the second model objective is to identify the type of malware if the file is detected as malware by the first model. The two models were trained on 80% of the data and tested on the remaining 20%. The first stage of the VGG-19 model achieved 99% accuracy on the testing set. The second stage using the VGG-19 model was responsible for detecting the type of malware (five different types in our dataset) and achieved an accuracy of 98.2% on the testing set.