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  • Rural Wards of Dodoma Urban District shapefile was extracted from the 2012 Wards shapefile obtained from the National Bureau of Statistics (NBS) of Tanzania downloaded at https://www.nbs.go.tz/index.php/en/census-surveys/gis/386-2012-phc-shapefiles-level-three. The shapefile of the randomly sampled nine Rural Wards studied in this study was extracted from the Rural Wards of Dodoma Urban District shapefile. The household size of each of the nine Rural Wards was extracted from the 2012 Population and Housing Census report of the NBS [24]. Waterpoint data were derived from the Directorate of Rural Water Supply (DRWS) of Tanzania through the Government Basic Statistics Portal. The catchment areas were generated in this study using water point data as input and buffer tool in QGIS 3.10.5 software. Households shapefile was generated by extracting households within catchment areas from Google Earth through digitization.
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  • The data contains a shapefile with IDs in "Regionalization shapefile.zip" and a respective KML layer (for Google Earth) in "regionalization_layer_v1.kmz". It is meant to be used to aggregate, store and share regionalized results from LCA and LCIA, as the layer covers the main features for impact assessment and policy level. It contains 50’626 units (48’612 terrestrial and 2'014 marine regions). The ID description and correspondance to the original layer are provided in "Description shapefile layer.xlsx", incl. some additional information from the original layers. The information related to countries is in yellow, ecoregions in green, urban in grey and watersheds in light blue. Coastal marine ecoregions are in marked dark blue and fisheries purple. Through the information in this layer, existing regionalized LCIA results such as recommended methods for land and water use can be directly linked. However, they can also be linked just based on ecoregions, watersheds, countries or urban areas using the original shapefiles used to compile this dataset through respective IDs. The following 6 layers have been used: - Urban areas: Natural Earth, Urban Areas, version 4.0.0, 11877 Urban areas Schneider, A., M. A. Friedl and D. Potere (2009) A new map of global urban extent from MODIS data. Environmental Research Letters, volume 4, article 044003. - Country boundaries (subunits) Natural Earth, Admin 0 – Details, version 4.1.0, 197 countries https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/ - Ecoregions Terrestrial Ecoregions of the World, WWF (2012), 867 terrestrial ecoregions Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938. - Watersheds Aware method: Input data (WaterGAP), 11049 watersheds; http://www.wulca-waterlca.org/aware.html Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M., and Döll, P.: Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration, Hydrol. Earth Syst. Sci., 18, 3511-3538, doi:10.5194/hess-18-3511-2014, 2014 - Marine ecosystems: Marine Ecoregions of the World, WWF (2007) Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas (2007) Spalding M Fox H Allen G Davidson N Ferdaña Z et. al. BioScience 2007 vol: 57 (7) pp: 573-583 - Fishing areas: FAO Statistical Areas for Fishery Purposes - FAO Statistical areas (Marine ) - No coastline (for use with custom coastline resolutions) - GIS data (WFS - SHP). FAO (2019). FAO Statistical Areas for Fishery Purposes. In: FAO Fisheries and Aquaculture Department [online]. Rome.
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  • It is a data for paper named "A progressive and combined building simplification approach with local structure classification and backtracking strategy". Three datasets are provided. One is the original data, which is collected from an open data product named OS Open Map –Local and provided by Ordnance Survey. Another two datasets are buildings which are simplified based on the original dataset into scales of 1: 25, 000 and 1: 50, 000. The simplification approach are proposed in the paper "A progressive and combined building simplification approach with local structure classification and backtracking strategy" .
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  • The current study aims to investigate the relationships between personal relative deprivation and employees’ attitudes towards job and organization and the underlying psychological mechanisms of the associations. Drawing on the self-determination theory, we propose the personal relative deprivation leads to the three work-related basic needs unsatisfied, which in turn lower the job satisfaction and affective commitment. We collected data from 390 participants recruited from a professional research participation system. The results indicated that higher level of personal relative deprivation significantly reduce individual’s job satisfaction and affective commitment. The indirect effects of personal relative deprivation on the job satisfaction or affective commitment through the satisfaction of the autonomy needs or the satisfaction of the relatedness needs are significant, while the mediating effects of the satisfaction of competence needs are nonsignificant.
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  • Code and data files associated with Current Biology publication. Please contact lead author if you are interested in using data for any reason.
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  • High accuracy classification of COVID-19 coughs using Mel-frequency cepstral coefficients and a Convolutional Neural Network with a use case for smart home devices. Diagnosing COVID-19 early in domestic settings is possible through smart home devices that can classify audio input of coughs, and determine whether they are COVID-19. Research is currently sparse in this area and data is difficult to obtain. How- ever, a few small data collection projects have en- abled audio classification research into the application of different machine learning classification algorithms, including Logistic Regression (LR), Support Vector Machines (SVM), and Convolution Neural Networks (CNN). We show here that a CNN using audio converted to Mel-frequency cepstral coefficient spectrogram images as input can achieve high accuracy results; with classification of validation data scoring an accuracy of 97.5% cor- rect classification of covid and not covid labelled audio. The work here provides a proof of concept that high accuracy can be achieved with a small dataset, which can have a significant impact in this area. The results are highly encouraging and provide further opportunities for research by the academic community on this important topic. Preprint: https://www.researchgate.net/publication/343376336_High_accuracy_classification_of_COVID-19_coughs_using_Mel-frequency_cepstral_coefficients_and_a_Convolutional_Neural_Network_with_a_use_case_for_smart_home_devices
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  • This dataset is published for Bengali continuous speech recognition. The dataset has three files "Script Files" contain the Bengali text for continuous speech and "Speech Files" contain the recorded audio speech. "Speakers_information" CSV file has stored the speaker's information.
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  • The recordings in this database were collected for the purpose of evaluating the ability of a playback attack detector to safeguard a remote-access speaker-verified and passphrase-protected system from playback attacks. This database includes multiple utterances of the same phrase by the same person in addition to a variety of distorted versions of many of the utterances. Multiple distortions of an utterance were obtained, in part, by simultaneously recording the utterance at both ends of a telecommunication channel – using a digital voice recorder to obtain the user-end (i.e., in-person) recording and a telephony board to obtain the system-end recording. While the former suffers little distortion, the latter suffers the “non-stationary” distortion imposed by the channel. Additional distortions of the same utterance were captured at the system-end of the channel when the in-person recording was replayed at the user-end; these additional recordings simulate playback attacks and suffer the distortion of both the playback device and the channel. The database may be used: to evaluate the vulnerability of a speaker verification system (SVS) to playback attacks; to evaluate the performance of a copy-detection or distortion-detection based playback attack detector (PAD); to evaluate the overall security of a speaker verification system in tandem with a playback attack countermeasure; or to investigate the distortion imposed by various telecommunication channels and/or playback speakers.
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  • This database was created through generous funding from The Voice Foundation's Advancing Scientific Voice Research Grant and contains voice samples which have been rated by experienced voice professionals (at least 3 different raters with a minimum of 2 years’ clinical experience) in order to provide educators with standardized materials to better train pre-service clinical voice professionals. It contains 296 audio files consisting of the sustained /a/ and /i/ vowels and the sentences from the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V; Kempster, 2007). All recordings were made in a quiet clinical environment using a head-mounted condenser microphone at a 6-centimeter distance from the corner of the mouth and the Computerized Speech Lab (CSL) using 16-bit encryption and a sampling rate of 48k. Audio recordings have been edited as best as possible to remove all clinician instructions. However, please listen to and look at each file carefully just in case there was simultaneous clinician-client talk. Listeners rated approximately 50 files each and each file was rated twice for reliability measurement (for a total of approximately 100 ratings per rater). Raters used a computer to listen to the samples and rate voice quality via a web-based system that included custom-made electronic scales for the CAPE-V (Kempster, 2007) and the GRBAS (Hirano, 1981) using Qualtrics survey software. Listeners rated each file on a 100-point visual analogue scale (VAS) to mimic the paper-based CAPE-V protocol. Please note that severity markers (mild, moderate, severe) were not included on the 100-point VAS to avoid influencing the concurrent rating using the GRBAS scale. Raters were urged to rate the samples over several days to avoid fatigue. Further description of methods is located in the folders below. Questions about the database can be directed to Patrick R. Walden, Ph.D., CCC-SLP at waldenp@stjohns.edu. References: Kempster G. CAPE-V: Development and Future Direction. Perspect Voice Voice Dis. 2007;17(2):11-13. doi:10.1044/vvd17.2.11 Hirano M. Clinical Examination of Voice. Springer-Verlag; 1981.
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