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  • Comparison between non-modified (neat) and pre-treated SMP-10 and PSZ-20. Low-temperature thermal treatment allows the modification of the corresponding viscoelastic states, so that a transiently-plastic filament can be formed from an initially highly inviscid polymer.
    Data Types:
    • Dataset
    • Audio
  • This dataset consists of 348 non-zero onset Vietnamese speeches (with their transcripts and the labelled start and end times of each speech) extracted from approximately 30-hour of FPT Open Speech Data (released publicly in 2018 by FPT Corporation). The extraction process was done automatically by a Python program written by the contributor. The speeches are in *.mp3 format and *.wav format (Mono, 48 kHz, 32-bit float) while the transcript file is in *.txt format with utf-8 encoding scheme. The dataset is useful for any onset detection research and development since the start and end times of each speech are already labelled. Copyright 2018 FPT Corporation Permission is hereby granted, free of charge, non-exclusive, worldwide, irrevocable, to any person obtaining a copy of this data or software and associated documentation files (the “Data or Software”), to deal in the Data or Software without restriction, including without limitation the rights to use, copy, modify, remix, transform, merge, build upon, publish, distribute and redistribute, sublicense, and/or sell copies of the Data or Software, for any purpose, even commercially, and to permit persons to whom the Data or Software is furnished to do so, subject to the following conditions: The above copyright notice, and this permission notice, and indication of any modification to the Data or Software, shall be included in all copies or substantial portions of the Data or Software. THE DATA OR SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR SOFTWARE OR THE USE OR OTHER DEALINGS IN THE DATA OR SOFTWARE. Patent and trademark rights are not licensed under this FPT Public License.
    Data Types:
    • Dataset
    • Text
    • Audio
  • This folder contains BatClassify results files used to create Temporal Pass Plots in the accompanying article Fig. 2 (Richmond_Myotis_BatClassify_Results.csv), Fig. 3a (Richmond_Ppyg_Site_A_BatClassify_Results.csv) and Fig. 3b (Richmond_Ppyg_Site_B_BatClassify_Results.csv). All other files are used in the TPP vignette, which is provided in the supplementary material.
    Data Types:
    • Tabular Data
    • Dataset
    • Audio
  • This data includes all the input data for the test instances used in the experiments. The input data consists of three sets of benchmarks including OR-Lib set (40 graphs), TSP-Lib set (20 graphs) and University of Florida Sparse Matrix Collection (3 graphs) .
    Data Types:
    • Dataset
    • Text
    • Audio
  • CAD and material properties of the monumental atrium of the Engineering Faculty Building in Bologna
    Data Types:
    • Other
    • Tabular Data
    • Dataset
    • Audio
  • This dataset is the complete directory of all Trygve's web pages. The web page HTML code is found from its URL. For example, the HTML for http://folk.uio.no/trygver/themes/Personal/pp-index.html is in the file at themes/Personal/pp-index.html The University of Oslo is terminating its Web service after 25 years of operation. My gigabyte of web pages have been collected over the years and will no longer be accessible over the Net. The pages are stored in this dataset and it may be possible to transfer them to another service if required. It should in any case be possible to read the dataset with an HTML reader.
    Data Types:
    • Other
    • Slides
    • Software/Code
    • Image
    • Video
    • Dataset
    • Document
    • Text
    • Audio
    • File Set
  • Temporal activity patterns can potentially reveal useful information about behaviour, phenological changes and emergence times for bat species; however, detailed assessments of temporal activity are infrequently performed or published for bats. Passive electronic devices, such as autonomous recording units and camera traps, are increasingly being used as a means of monitoring various species, communities and habitats. Data recorded by these devices inherently contain file metadata detailing the dates and times when data capture took place. We have utilised this metadata to create the Temporal Pass Plot (TPP), which provides intuitive, yet highly detailed, visualisations of temporal bat activity over prolonged periods of time. Furthermore, TPPs are produced using a common scale based upon activity within predetermined time-blocks, enabling direct comparisons between different sites and species to be performed. TPPs reveal inter- and intra-specific differences, and seasonal changes, in temporal activity. As a relatively untapped area of research, further study is required to evaluate associations between activity patterns and different behaviours (e.g. roosting, commuting and swarming). However, if this can be achieved, the scope of assessments that could be performed with passive monitoring technologies could be significantly expanded, enabling more detailed evaluations of habitat use to be performed with minimal disturbance to the target species. Although the TPP was principally designed for the purpose of studying bat activity, it can easily be adapted for other species that can are monitored using autonomous recording devices. Article data: This folder contains the data files used to create all three Temporal Pass Plots shown in the main article. Vignette data: This folder contains all files described in the TPP vignette, which is provided in the supplementary materials of the main article.
    Data Types:
    • Tabular Data
    • Dataset
    • Audio
  • Origin graph files of the manuscript LiFePO4_S cathode proof. Figure 1 Cyclic Voltammetry of LiFePO4-S composite with LiPF6 electrolyte. Figure 2 Cyclic Voltammetry of LiFePO4-S composite with LiTFSI electrolyte. Figure 3 dQ/dV curves calculated from charge/discharge cycling data of the LiFePO4-S composite cathode with LiTFSI electrolyte: Lithiation in cycle 3 (a) and cycle 5 (b), and delithiation in cycle 3 (c) and cycle 5 (d). Figure 5 XRD pattern of hydrothermal carbon - LiFePO4 composite prepared in acetic acid. The pattern of LiFePO4 without carbon coating is shown as a reference. * Peak corresponding to graphitic carbon. Figure 6 Raman spectrum of hydrothermal carbon-LiFePO4 composite after thermal treatment at 650 °C. The Raman spectrum of LiFePO4 reagent without carbon coating is shown as a reference. Figure 7 dQ/dV curves calculated from charge/discharge cycling data of composite cathodes of carbon-coated LiFePO4 (prepared in acetic acid) and sulfur infiltrated in porous carbon: Lithiation in cycle 3 (a) and cycle 5 (b), and delithiation in cycle 3 (c) and cycle 5 (d).
    Data Types:
    • Dataset
    • Audio
  • In computer security, network botnets still represent a major cyber threat. Concealing techniques such as the dynamic addressing and the Domain Name Generation Algorithms (DGAs) require an improved and more effective detection process. To this extent, this data descriptor presents a collection of over 30 million manually-labelled algorithmically generated domain names decorated with a feature set ready-to-use for Machine Learning analysis. This proposed data set enables researchers to move forward the data collection, organization and pre-processing phases, eventually enabling them to focus on the analysis and the production of Machine-Learning powered solutions for network intrusion detection. To be as exhaustive as possible, 50 among the most important malware variants have been selected. Each family is available both as list of domains and as collection of features. To be more precise, the former is generated by executing the malware DGAs in a controlled environment with fixed parameters, while the latter is generated by extracting a combination of statistical and Natural Language Processing (NLP) metrics.
    Data Types:
    • Other
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
    • Text
    • Audio
    • File Set
  • Griffin Walker's demo, released in 2004. It's not mastered, please be kind.
    Data Types:
    • Dataset
    • Audio
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