cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5258/soton/d1110
title: Egocentric visual distance estimation in vista space in real and virtual environments

description: Dataset supporting Experiment 1 of Doctoral thesis: "Auditory fitness for duty: Acoustic stealth awareness".DESCRIPTION OF THE DATA Participants had to estimate the distance between themselves and a human target at distances of 25, 50, 75, 100 and 125 m, on Southampton Common (real environment) and in a virtual model of Southampton Common (virtual environment, viewed via Oculus Rift virtual reality headset). Each participant (n = 23) viewed the target at three different azimuths: -10, 0 and 10 degrees relative to facing forward (in the dataset this is referred to as L/C/R). They estimated the egocentric distance at each target location 3 times. The procedure was identical in both real and virtual environments. Participants gave their estimates verbally. This dataset contains the verbal estimates for each environment, at each distance, at each azimuth, for each repeat. Each row represents a different participant. Each column represents a different condition. The dataset should be opened using Microsoft Excel. This experiment was approved by the University Ethics board (ERGO ID: 26625).
DOI: 10.5281/zenodo.3522381
title: List of the names of all ex-members of the New Zealand Expeditionary Force, suffering permanent disability from 20% to 100%

description: Compiled from returns supplied by the Commissioner of Pensions, in respect to: a) Permanent War Pensions, and from returns supplied by the Director-General of Medical Services, regarding: b) Discharged and undischarged hospital patients, assessed as suffering permanent disability, but not yet in receipt of a pension at date (28-4-20) A further list of the names of ex-members of the New Zealand Expeditionary Force suffering permanent disability from 20% to 100% acts as supplement to the first publication. The list is arranged in Military District under the three headings mentioned, and is subdivided into the principal Patriotic Societies and War Relief Associations operating in each district. NOTE: No permanent pensions awarded soldiers' dependants; and no temporary pensions of any description, are included herein. Registers were digitised and transcribed by Auckland Museum 2013. Dataset has been matched to Online Cenotaph Auckland Museum Collections Online Records
DOI: 10.21227/13k5-qm35
title: Isolating Change Propagation in Software Architecture Metrics

description: This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
DOI: 10.5258/soton/d1109
title: Aural detectability judgements in normal-hearing civilians

description: This dataset supports the thesis entitled: “Auditory fitness for duty: Acoustic stealth awareness” AWARDED BY: University of Southampton DATE OF AWARD: 2019DESCRIPTION OF THE DATA This dataset contains the aural detectability judgements of normal-hearing civilians which underpin experiment 2 of the thesis “Auditory fitness for duty: Acoustic stealth awareness”. The participant was stood in an anechoic chamber wearing a virtual reality headset, and was viewing a virtual environment resembling Southampton Common. The participant viewed a human target located 25, 50 or 100 m away in the distance, then listened to a sound presented at their feet or 1 m behind them (at head height), and had to respond “Yes” or “No” (via an xbox controller) whether they judged that sound to be aurally detectable by the target in the distance (Yes = the sound was audible to the target; No = the sound was not audible to the target). Three sounds were used (whispered digits, white noise and pine cone crunching sounds). There were 9 conditions in total. The method of constants was used in each condition: - Sounds were presented at six different levels. - Each level had 10 repeats, in order to calculate the proportion of “Yes” responds at each level so the psychometric function could be obtained. - Levels for each condition were selected based on a practice period of 108 trials prior to the main data collection period. - All trials for all conditions were presented in a random order. - The subject’s view of the target was impeded between each trial.This dataset contains a MATLAB .mat file containing the structure ‘PFs’. Within this structure there are two nested structures for each participant (n = 28). Each structure contains data for each session (2 sessions in total). Each session structure (for each participant) contains: - condition identifier (note, each row represents a different condition) - the sound levels presented in that condition (expressed as: 1. sensation level (dB SL) at the target, predicted by a sound propagation and loudness model; 2. The RMS level (dB SPL) at the subject; 3. The peak level (dB SPL) at the subject) - the number of repeats - the number of ‘yes’ responses at each level - the psychometric function parameters and fit, as fitted by the Palamedes toolbox. Note, this dataset can only be opened using MATLAB. This experiment was approved by the University of Southampton ethics board (ERGO ID: 23783).Date of data collection: October 2018 - December 2018Legal: Copyright: CC BY This doctoral project was funded by the Royal Centre for Defence Medicine.
DOI: 10.5258/soton/d1111
title: Threshold measurements for comparing aural detectability judgments

description: This dataset supports the thesis entitled: “Auditory fitness for duty: Acoustic stealth awareness” AWARDED BY: University of Southampton DATE OF AWARD: 2019DESCRIPTION OF THE DATA This dataset contains the threshold levels for whispered digits, white noise and pine cones measured in Experiment 3 in the thesis “Auditory fitness for duty: Acoustic stealth awareness”. The threshold represent the sound level (in dB SPL) at the target location in the situation simulated in Experiment 2 (see relevant dataset and ReadMe file for Experiment 2). The thresholds are expressed as levels (dB SPL) at the target for each sound for each distance (25, 50 and 100 m). Thresholds were measured using an automated threshold-seeking procedure based on that used for automated pure tone audiometry. Sound were presented via a loudspeaker directly in front of the seated participant in an anechoic chamber. This experiment was approved by the University of Southampton ethics board (ERGO ID: 23783.A1)Date of data collection: December 2018Legal: Copyright: CC BY This doctoral project was funded by the Royal Centre for Defence Medicine.
DOI: 10.7910/dvn/5ktv2q/ouoici
title: (Variable Description) PCRSPF analysis_HQ_V1 October Revised 2017.tab

description: :unav
DOI: 10.21227/6rzm-h578
title: Isolating Change Propagation in Software Architecture Metrics

description: This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
DOI: 10.21227/8yx6-n221
title: Isolating Change Propagation in Software Architecture Metrics

description: This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
DOI: 10.21227/jvzk-ct93
title: Isolating Change Propagation in Software Architecture Metrics

description: This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
DOI: 10.21227/wkkp-bj59
title: Isolating Change Propagation in Software Architecture Metrics

description: This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
DOI: 10.5281/zenodo.3522308
title: Data of publication Coherent optical and spin spectroscopy of nanoscale Pr3+ : Y2O3

description: Data corresponding to the figures of the publication " Coherent optical and spin spectroscopy of nanoscale Pr3+: Y2O3" by D. Serrano et al. (file:///C:/Users/diana.serrano/Zotero/storage/86IZ7H73/PhysRevB.100.html). A text file describes data in each compressed folder, please refer to the publication for more details.
DOI: 10.5281/zenodo.3522309
title: Data of publication Coherent optical and spin spectroscopy of nanoscale Pr3+ : Y2O3

description: Data corresponding to the figures of the publication " Coherent optical and spin spectroscopy of nanoscale Pr3+: Y2O3" by D. Serrano et al. (file:///C:/Users/diana.serrano/Zotero/storage/86IZ7H73/PhysRevB.100.html). A text file describes data in each compressed folder, please refer to the publication for more details.
DOI: 10.5519/qd.ze5628k5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (12 records)
DOI: 10.7910/dvn/q7a4me/2wppb3
title: Dose-full.fasta

description: Fig3 fasta
DOI: 10.7910/dvn/q7a4me/af1evi
title: Cranfrac-full - Copy.fasta

description: :unav
DOI: 10.7910/dvn/q7a4me/dbsqti
title: Fig2.fasta

description: :unav
DOI: 10.7910/dvn/q7a4me/engxrf
title: Fig1Bfasta.fasta

description: :unav
DOI: 10.7910/dvn/q7a4me/hntq3w
title: Cranfrac-mapping2 - Copy.txt

description: :unav
DOI: 10.7910/dvn/q7a4me/jmchpi
title: Fig2.qual

description: :unav
DOI: 10.7910/dvn/q7a4me/rewkzr
title: Dose-mapping2.txt

description: Fig3 mapping
DOI: 10.7910/dvn/q7a4me/yw9bss
title: Ocean Spray manuscript data to deposit.qual

description: Fig3 qual
DOI: 10.21227/d33h-dw78
title: Isolating Change Propagation in Software Architecture Metrics

description: This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
DOI: 10.21227/ntg2-sp61
title: Isolating Change Propagation in Software Architecture Metrics

description: This dataset contains the files provided in support of the paper entitled 'Isolating Change Propagation in Software Architectures' authored by Andrew Leigh, Michel Wermelinger and Andrea Zisman of the Open University.If you have any questions please contact: andrew.leigh@open.ac.uk the files have been shared to allow other researchers to reproduce the work. The data set includes a README file that explains the file purpose and contents in detail.
DOI: 10.7910/dvn/q7a4me/62xydh
title: 1Aqual.zip

description: :unav
DOI: 10.7910/dvn/q7a4me/qyoeie
title: Fig2-mapping.txt

description: :unav
DOI: 10.7910/dvn/q7a4me/ujnf3m
title: Fig1Bfasta.txt

description: :unav
DOI: 10.7910/dvn/q7a4me/yira9q
title: Fig1Bqual.zip

description: :unav
DOI: 10.18112/openneuro.ds002282.v1.0.0
title: TODO: name of the dataset
DOI: 10.18112/openneuro.ds002283.v1.0.0
title: TODO: name of the dataset
DOI: 10.5281/zenodo.3522386
title: weecology/PortalData 1.139.0

description: Official Repo of the Portal Project Data
DOI: 10.6084/m9.figshare.10073150.v1
title: 1.xlsx

description: The data set stored at figshare.
DOI: 10.7910/dvn/852sxy
title: Stereotyping Indigeneity: the Case of Native American

description: Raising awareness of indigeneity comes forward with strengthening of cultural globalization. The lack of relevant knowledge of indigenous cultures results in persistent stereotyping. Regarded the representation of an ethnic group, a stereotype is based on implicit assumptions and behavioral implications that are reflected in evaluative characteristics. Stereotypes are spread by mainstream culture through popular media and cinema. This work attempts to profile the stereotype of Indigenous American created by mainstream American culture on the results of qualitative analysis of media discourse. Treated as uncivilized, Native Americans are portrayed as unbending and cruel warriors or as wise and helpful friends of White. America’s attitude to Indians is changing from banishing to extolling. The deconstruction of stereotypes in humanitarian paradigm is the matter of acknowledgment of cultural uniqueness and establishment of social equality.
DOI: 10.7910/dvn/852sxy/3zkzbb
title: Стаття Любимової.pdf

description: :unav
DOI: 10.5519/qd.z46z0kj1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (350346 records)
DOI: 10.21227/5n9r-nn89
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score estimated by the model deployed at sentiment.live. The LSTM model is still in the beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores produced by our model will be made public after the project's documentation part is finished.####current status###Tweets database containing term "iPhone 11": 2 million-plusTweets database containing term "the joker": 1 million-plus##################New databases will be added to this dataset collection every week.The deep learning model is currently live at https://sentiment.live.
DOI: 10.21227/93b9-qd91
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score estimated by the model deployed at sentiment.live. The LSTM model is still in the beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores produced by our model will be made public after the project's documentation part is finished.####current status###Tweets database containing the term "iphone 11": 2 million-plusTweets database containing the term "the joker": 1 million-plusTweets database containing the term "housefull 4": 0.35 million-plus##################New databases will be added to this dataset collection every week.The deep learning model is currently live at https://sentiment.live.
DOI: 10.5281/zenodo.3522198
title: Raw and sinogram data of simulated GATE PET data

description: This dataset contains simulated positron emission tomography (PET) data saved in sinogram format as well as in a raw data format for use in MATLAB/Octave. The simulated phantom contains three rods/cylinders of varying size and activity inside a cylindrical water phantom. The simulation was performed by using the GATE software (http://www.opengatecollaboration.org/). Prompt, true, scatter, random and delayed coincidences are included. These datafiles have been created with the OMEGA software (https://github.com/villekf/OMEGA) and the primary purpose is to serve as a test data for the software. The sinogram data can be used anywhere, but the raw data should be used with OMEGA. The organization of the raw data can be found from the OMEGA GitHub wiki. For the sinogram, the first dimension is the number of angular positions (views), second number of angles (tangential positions) and third the number of sinograms. Machine information can be found from OMEGA documentation and the GATE macros on the GitHub page. An "ideal image" is also included, that contains the counts in their original decay locations.
DOI: 10.5281/zenodo.3522199
title: Raw and sinogram data of simulated GATE PET data

description: This dataset contains simulated positron emission tomography (PET) data saved in sinogram format as well as in a raw data format for use in MATLAB/Octave. The simulated phantom contains three rods/cylinders of varying size and activity inside a cylindrical water phantom. The simulation was performed by using the GATE software (http://www.opengatecollaboration.org/). Prompt, true, scatter, random and delayed coincidences are included. These datafiles have been created with the OMEGA software (https://github.com/villekf/OMEGA) and the primary purpose is to serve as a test data for the software. The sinogram data can be used anywhere, but the raw data should be used with OMEGA. The organization of the raw data can be found from the OMEGA GitHub wiki. For the sinogram, the first dimension is the number of angular positions (views), second number of angles (tangential positions) and third the number of sinograms. Machine information can be found from OMEGA documentation and the GATE macros on the GitHub page. An "ideal image" is also included, that contains the counts in their original decay locations.
DOI: 10.21227/g895-6r48
title: CVBL Iris Super Resolution Dataset

description: Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images. Considering the importance of this research area and its commercial applications, it is highly essential for researchers to make use of efficient color features in their algorithms which necessitates the production of color iris image databases. The present work introduces an iris image database for gender classification. The database consists of iris images taken from 704 subjects including 392 females and 312 males in university students. For each student, more than 6 images were taken from his/her both left and right eyes. After examining the images, 3 images from the left eye and 3 images from the right eye were selected among the most appropriate images and were included in the database. All 4320 images from this database were taken under the same condition and by the same color camera.
DOI: 10.21227/ttej-ak04
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score estimated by the model deployed at sentiment.live. The LSTM model is still in the beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores produced by our model will be made public after the project's documentation part is finished.####current status###Tweets database containing the term "iphone 11": 2 million-plusTweets database containing the term "the joker": 1 million-plusTweets database containing the term "housefull 4": 0.346 million-plus##################New databases will be added to this dataset collection every week.The deep learning model is currently live at https://sentiment.live.
DOI: 10.21227/rj8b-wd12
title: Twitter Sentiment Analysis Data

description: Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live. The last column, viz. sentiment score, is not the score estimated by the model deployed at sentiment.live. The LSTM model is still in the beta phase. Therefore, to make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob has been appended as the last column. The sentiment scores produced by our model will be made public after the project's documentation part is finished.####current status###Tweets containing the term "iphone 11": 2 million-plusTweets containing the term "the joker": 1 million-plusTweets containing the term "housefull 4": 0.346 million-plus##################New databases will be added to this dataset collection every week.The deep learning model is currently live at https://sentiment.live.
DOI: 10.5281/zenodo.3522560
title: Reasearch data for publication "On optimizing metaheuristics performance with different error normalization approaches"

description: These are the results of experiments performed for publication "On optimizing metaheuristics performance with different error normalization approaches" using authors own software -- EvANN -- which is available on github.
DOI: 10.5281/zenodo.3522561
title: Reasearch data for publication "On optimizing metaheuristics performance with different error normalization approaches"

description: These are the results of experiments performed for publication "On optimizing metaheuristics performance with different error normalization approaches" using authors own software -- EvANN -- which is available on github.