cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5075110
title: Perceived fairness and perceived transparency of AI systems according to system's characteristics, personality traits and demographic characteristics

description: We collected data of 3197 users' fairness perception regarding various configurations of a AI-based system in the recruitment domain, as well as, the demographic and personally characteristics of the participants. The dataset includes the following columns: <strong>:System characteristics</strong> :Certification Uncertificated system (U) Certificated system (C) :Input data High quality input data (H) Low quality input data (L) :Output Positive outcome (P) Borderline outcome (B) Negative outcome (N) :Explanation style Control- no explanation (CON) Case-based (CAS) Certification-based (CER) Demographic-based (DEM) Input influence-based (INP) Sensitivity-based (SEN) <strong>:Demographic characteristics</strong> :Gender Female Male :Age 18-34 35-50 50+ :Residence Unites states of America India Other :Education level High school degree or less Bachelor's degree Master's or doctoral degree :Employment status Not employed Employed :Income level Above average Average Below average <strong>:Personality characteristics</strong> (TIPI questionnaire) Extraverted, enthusiastic 1-7 (1= disagree strongly up to 7= agree strongly) Critical, quarrelsome 1-7 (1= disagree strongly up to 7= agree strongly) Dependable, self-disciplined 1-7 (1= disagree strongly up to 7= agree strongly) Anxious, easily upset 1-7 (1= disagree strongly up to 7= agree strongly) Open to new experiences, complex 1-7 (1= disagree strongly up to 7= agree strongly) Reserved, quiet 1-7 (1= disagree strongly up to 7= agree strongly) Sympathetic, warm 1-7 (1= disagree strongly up to 7= agree strongly) Disorganized, careless 1-7 (1= disagree strongly up to 7= agree strongly) Calm, emotionally stable 1-7 (1= disagree strongly up to 7= agree strongly) Conventional, uncreative 1-7 (1= disagree strongly up to 7= agree strongly) <strong>:Participants responses</strong> :Fairness evaluation The participants were requested to report their level of perceived fairness (their view about the fairness of the system - at what level they consider the system as a fair system) on a 6-point Likert scale, from "Extremely fair" (represented as 3) to "Extremely unfair" (represented as -3). The option of "neither fair or unfair" (represented as 0) was excluded from the scale. :Transparency evaluation the participants were requested to report their level of perceived transparency (their understanding why the system produced the specific output - at what level they understand why this output was given) on a 6-point Likert scale, from " Thoroughly understand" (represented as 3) to " Thoroughly don't understand" (represented as -3). The option of "neither understand or don't understand" (represented as 0) was excluded from the scale. :Output Expectation The participants were requested to report their expectation for the specific output based on the input they received according to the system's scale, 5-point Likert scale from "Strongly recommended" (represented as 2) to "Strongly not recommended" (represented as -2).
DOI: 10.5281/zenodo.5052018
title: Generation of transcriptional novelty by transposable element insertions in Arabidopsis, Genome Sequencing and eccDNA Data

description: <strong>Raw Illumina sequencing data from the Manuscript entitled "Generation of transcriptional novelty by transposable element insertions in Arabidopsis"</strong> <strong>A. Illumina genome sequencing reads of Arabidopsis control and hcLines that contain novel transposable element insertions.</strong> To identify the genomic position of the new <em>ONSEN</em> insertions, the extracted DNA of the 11 selected lines (nine lines with new insertions and two control lines) was sent to BGI, Hong-Kong for Illumina paired-end 150 bp sequencing, aiming for a minimum of 20X sequencing coverage. Quality control of the raw reads was done using FastQC (Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc) and trimming/clipping was done using Trimmomatic with parameters ILLUMINACLIP: TruSeq3:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 and MINLEN:36. Quality of the reads was deemed excellent and no further actions were taken. Samples identifications: genome_hcLineX with "_1" indicating the forward and "_2" the reverse reads. <strong>B. Illumina eccDNA sequencing of Arabidopsis control and hcLines following stress treatments</strong> Extrachromosomal circular DNA was prepared and sequenced as follows: twenty plants from each petri dish were pooled separately and DNA was extracted using the CTAB method (dx.doi.org/10.17504/protocols.io.quidwue). Following the mobilome-seq method described in (Lanciano et al., 2017), for all samples, we digested linear DNA from 2 µg of total DNA for 17 hours at 37<sup>o</sup>C using 10 U of PlasmidSafe (<em>LubioScience cat# E3101K</em>), followed by enzyme denaturation (30 mins at 70<sup>o</sup>C). Digested DNA was precipitated with isopropanol supplemented with 1 µg of GlycoBlue coprecipitant (<em>Fisher Scientific cat# 10391565</em>). Circular DNA was then amplified through rolling circle amplification (RCA) with the Illustra TempliPhi kit (<em>GE Healthcare cat# 25-6400-10</em>), following the manufacturer recommendation and leaving the reaction for 16h at 30<sup>o</sup>C. DNA was once again precipitated with isopropanol and sent for Illumina paired end 150 bp sequencing at BGI, Hong Kong. Samples identification: eccDNA_A.thaliana_ctrl: control reads eccDNA_A.thaliana_HS: heat stressed plants reads eccDNA_A.thaliana_AZ_HS: reads of alpha-amanitin, zebularine and heat-stressed plants "R1" indicates forward and "R2" reverse reads.
DOI: 10.5281/zenodo.5075109
title: Perceived fairness and perceived transparency of AI systems according to system's characteristics, personality traits and demographic characteristics

description: We collected data of 3197 users' fairness perception regarding various configurations of a AI-based system in the recruitment domain, as well as, the demographic and personally characteristics of the participants. The dataset includes the following columns: <strong>:System characteristics</strong> :Certification Uncertificated system (U) Certificated system (C) :Input data High quality input data (H) Low quality input data (L) :Output Positive outcome (P) Borderline outcome (B) Negative outcome (N) :Explanation style Control- no explanation (CON) Case-based (CAS) Certification-based (CER) Demographic-based (DEM) Input influence-based (INP) Sensitivity-based (SEN) <strong>:Demographic characteristics</strong> :Gender Female Male :Age 18-34 35-50 50+ :Residence Unites states of America India Other :Education level High school degree or less Bachelor's degree Master's or doctoral degree :Employment status Not employed Employed :Income level Above average Average Below average <strong>:Personality characteristics</strong> (TIPI questionnaire) Extraverted, enthusiastic 1-7 (1= disagree strongly up to 7= agree strongly) Critical, quarrelsome 1-7 (1= disagree strongly up to 7= agree strongly) Dependable, self-disciplined 1-7 (1= disagree strongly up to 7= agree strongly) Anxious, easily upset 1-7 (1= disagree strongly up to 7= agree strongly) Open to new experiences, complex 1-7 (1= disagree strongly up to 7= agree strongly) Reserved, quiet 1-7 (1= disagree strongly up to 7= agree strongly) Sympathetic, warm 1-7 (1= disagree strongly up to 7= agree strongly) Disorganized, careless 1-7 (1= disagree strongly up to 7= agree strongly) Calm, emotionally stable 1-7 (1= disagree strongly up to 7= agree strongly) Conventional, uncreative 1-7 (1= disagree strongly up to 7= agree strongly) <strong>:Participants responses</strong> :Fairness evaluation The participants were requested to report their level of perceived fairness (their view about the fairness of the system - at what level they consider the system as a fair system) on a 6-point Likert scale, from "Extremely fair" (represented as 3) to "Extremely unfair" (represented as -3). The option of "neither fair or unfair" (represented as 0) was excluded from the scale. :Transparency evaluation the participants were requested to report their level of perceived transparency (their understanding why the system produced the specific output - at what level they understand why this output was given) on a 6-point Likert scale, from " Thoroughly understand" (represented as 3) to " Thoroughly don't understand" (represented as -3). The option of "neither understand or don't understand" (represented as 0) was excluded from the scale. :Output Expectation The participants were requested to report their expectation for the specific output based on the input they received according to the system's scale, 5-point Likert scale from "Strongly recommended" (represented as 2) to "Strongly not recommended" (represented as -2).
DOI: 10.6084/m9.figshare.14915253
title: test

description: test
DOI: 10.6084/m9.figshare.14915253.v1
title: test

description: test
DOI: 10.5519/qd.lcv331kw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1719 records)
DOI: 10.5281/zenodo.5055790
title: Tugas EAS

description: Tugas
DOI: 10.5281/zenodo.5055789
title: Tugas EAS

description: Tugas
DOI: 10.6084/m9.figshare.14723493
title: Assessing joint commitment as a process in great apes

description: data and r code for figures and models
DOI: 10.6084/m9.figshare.14723493.v1
title: Assessing joint commitment as a process in great apes

description: data and r code for figures and models
DOI: 10.6084/m9.figshare.14915268
title: ENQI and PCI layers for Extend NTL Collections

description: This dataset includes several ENQI and PCI layers. They provide pixel-scale quality assessment flags for the most widely used ENTL products (LiNTL and ChenNTL).<br>
DOI: 10.6084/m9.figshare.14915268.v1
title: ENQI and PCI layers for Extend NTL Collections

description: This dataset includes several ENQI and PCI layers. They provide pixel-scale quality assessment flags for the most widely used ENTL products (LiNTL and ChenNTL).<br>
DOI: 10.21227/zex2-rs59
title: A Topological Approach to Gait Generation for Biped Robots: Supplemental Material

description: The content in this zip file contains a video showing animations of all gaits presented in the figures of the paper as well as the source code for computing walking gaits from equilibria using numerical continuation methods. Further information can be found in the README.txt file. The content can also be downloaded for free at https://github.com/nr-codes/BipedalGaitGeneration.
DOI: 10.15161/oar.it/73896
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.25345/c59g04
title: MassIVE MSV000087759 - Proteome and secretome analysis of pancreatic cancer cell lines vs. normal epithelial ductal pancreatic cells
title: We have run shotgun and PRM proteomic analysis of cellular proteome and secretome from pancreatic cancer cell lines (PANC-1, PaCa-44, MIA PaCa-2 and BxPC-3) vs. normal epithelial ductal pancreatic cells (HPDE) in LC-MS/MS. Stable isotopic labelling was performed and digestion was done with Trypsin/Lys-C mix endoproteinase.
DOI: 10.5281/zenodo.5075194
title: OCTA imsges dataset with pixel-level mask annotaion for FAZ segmentation

description: This dataset is the affiliated data for the reaserch "A Deep Learning-based Quality Assessment and Segmentation System and A Large Benchmark Datatset for Optical Coherence Tomographic Angiography Image" Detail: This dataset is the pixel-level mask annotaion for FAZ segmentation. 1,101 3 × 3 mm<sup>2</sup> sOCTA images chosen from gradable and best OCTA images randomly in subset sOCTA-3x3-10k, and 1,143 3 × 3 mm<sup>2</sup>dOCTA images were annotated by an experienced ophthalmologist.
DOI: 10.6084/m9.figshare.14169389.v1
title: Supplementary Material for: Neurofeedback Training versus Treatment-as-Usual for Alcohol Dependence: Results of an Early-Phase Randomized Controlled Trial and Neuroimaging Correlates

description: <b><i>Introduction:</i></b> Alcohol dependence is one of the most common substance use disorders, and novel treatment options are urgently needed. Neurofeedback training (NFT) based on real-time functional magnetic resonance imaging (rtf­MRI) has emerged as an attractive candidate for add-on treatments in psychiatry, but its use in alcohol dependence has not been formally investigated in a clinical trial. We investigated the use of rtfMRI-based NFT to prevent relapse in alcohol dependence. <b><i>Methods:</i></b> Fifty-two alcohol-dependent patients from the UK who had completed a detoxification program were randomly assigned to a treatment group (receiving rtfMRI NFT in addition to standard care) or the control group (receiving standard care only). At baseline, alcohol consumption was assessed as the primary outcome measure and a variety of psychological, behavioral, and neural parameters as secondary outcome measures to determine feasibility and secondary training effects. Participants in the treatment group underwent 6 NFT sessions over 4 months and were trained to downregulate their brain activation in the salience network in the presence of alcohol stimuli and to upregulate frontal activation in response to pictures related to positive goals. Four, 8, and 12 months after baseline assessment, both groups were followed up with a battery of clinical and psychometric tests. <b><i>Results:</i></b> Primary outcome measures showed very low relapse rates for both groups. Analysis of neural secondary outcome measures indicated that the majority of patients modulated the salience system in the desired directions, by decreasing activity in response to alcohol stimuli and increasing activation in response to positive goals. The intervention had a good safety and acceptability profile. <b><i>Conclusion:</i></b> We demonstrated that rtfMRI-neurofeedback targeting hyperactivity of the salience network in response to alcohol cues is feasible in currently abstinent patients with alcohol dependence.
DOI: 10.5258/soton/d1881
title: Dataset for Thesis: An investigation into topological defects in polarities condensates

description: Experimental data and numerical simulation results used to create the remaining figures in the University of Southampton Doctoral Thesis - An investigation into topological defects in polarities condensates.
DOI: 10.6084/m9.figshare.14915403.v1
title: tc_covs_lwf_12elec_15sub.npy

description: Covariance matrices of EEG signals from 15 subjects presented with visual stimuli of 3D-Tunnels and Checkerboards. This dataset is the basis of an article named "Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans".<br>
DOI: 10.6084/m9.figshare.14915403
title: tc_covs_lwf_12elec_15sub.npy

description: Covariance matrices of EEG signals from 15 subjects presented with visual stimuli of 3D-Tunnels and Checkerboards. This dataset is the basis of an article named "Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans".<br>
DOI: 10.5519/qd.xo28mylz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (789 records)
DOI: 10.5281/zenodo.5070682
title: Terretrial LiDAR data collected from Russell Square, London, UK

description: Terrestrial LiDAR data collected by the team at University College London.This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices<b>UCL project name</b>: 2017-02-08.001.riproject<b>Plot ID</b>: RSQ<b>State or region</b>: London<b>Date project started</b>: 2/8/2017<b>Instrument</b>: UCL RIEGL VZ-400<b>Scan pattern</b>: Spiral (11 pos)<b>Angular resolution</b>: 0.04<b>Images captured</b>: Yes<b>Number of scans</b>: 22.0<b>Google Maps URL</b>: https://www.google.co.uk/maps/place/Russell+Square+Gardens/@51.5216334,-0.1261532,20.51z/data=!4m13!1m7!3m6!1s0x48761b2e1672c317:0x2eb39a3b3d33d9f5!2sMalet+St,+London!3b1!8m2!3d51.5214099!4d-0.1302396!3m4!1s0x48761b313fbfd0a1:0x494a624c4d12c02f!8m2!3d51.5216396!4d-0.1259804<b>Publications</b>: https://doi.org/10.1186/s13021-018-0098-0For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017Please acknowldege the producers of this data set if using this data for publication.
DOI: 10.5281/zenodo.5070681
title: Terretrial LiDAR data collected from Russell Square, London, UK

description: Terrestrial LiDAR data collected by the team at University College London.This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices<b>UCL project name</b>: 2017-02-08.001.riproject<b>Plot ID</b>: RSQ<b>State or region</b>: London<b>Date project started</b>: 2/8/2017<b>Instrument</b>: UCL RIEGL VZ-400<b>Scan pattern</b>: Spiral (11 pos)<b>Angular resolution</b>: 0.04<b>Images captured</b>: Yes<b>Number of scans</b>: 22.0<b>Google Maps URL</b>: https://www.google.co.uk/maps/place/Russell+Square+Gardens/@51.5216334,-0.1261532,20.51z/data=!4m13!1m7!3m6!1s0x48761b2e1672c317:0x2eb39a3b3d33d9f5!2sMalet+St,+London!3b1!8m2!3d51.5214099!4d-0.1302396!3m4!1s0x48761b313fbfd0a1:0x494a624c4d12c02f!8m2!3d51.5216396!4d-0.1259804<b>Publications</b>: https://doi.org/10.1186/s13021-018-0098-0For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017Please acknowldege the producers of this data set if using this data for publication.
DOI: 10.5281/zenodo.5070675
title: Terretrial LiDAR data collected from Malet St, London, UK

description: Terrestrial LiDAR data collected by the team at University College London. This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices <strong>UCL project name</strong>: 2017-02-14.002.riproject <strong>Plot ID</strong>: MST <strong>State or region</strong>: London <strong>Date project started</strong>: 2/14/2017 <strong>Instrument</strong>: UCL RIEGL VZ-400 <strong>Scan pattern</strong>: transect along street <strong>Angular resolution</strong>: 0.04 <strong>Images captured</strong>: No <strong>Number of scans</strong>: 24.0 For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017 Please acknowldege the producers of this data set if using this data for publication.
DOI: 10.5281/zenodo.5070674
title: Terretrial LiDAR data collected from Malet St, London, UK

description: Terrestrial LiDAR data collected by the team at University College London. This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices <strong>UCL project name</strong>: 2017-02-14.002.riproject <strong>Plot ID</strong>: MST <strong>State or region</strong>: London <strong>Date project started</strong>: 2/14/2017 <strong>Instrument</strong>: UCL RIEGL VZ-400 <strong>Scan pattern</strong>: transect along street <strong>Angular resolution</strong>: 0.04 <strong>Images captured</strong>: No <strong>Number of scans</strong>: 24.0 For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017 Please acknowldege the producers of this data set if using this data for publication.
DOI: 10.5517/ccdc.csd.cc25q5sw
title: CCDC 2017318: Experimental Crystal Structure Determination

description: Related Article: Katerina A. Vinogradova, Nikita A. Shekhovtsov, Alexey S. Berezin, Taisiya S. Sukhikh, Maxim I. Rogovoy, Alexander V. Artem'ev, Mark B. Bushuev|2021|Dalton Trans.|50|9317|doi:10.1039/D1DT00826A
DOI: 10.5517/ccdc.csd.cc25q5tx
title: CCDC 2017319: Experimental Crystal Structure Determination

description: Related Article: Katerina A. Vinogradova, Nikita A. Shekhovtsov, Alexey S. Berezin, Taisiya S. Sukhikh, Maxim I. Rogovoy, Alexander V. Artem'ev, Mark B. Bushuev|2021|Dalton Trans.|50|9317|doi:10.1039/D1DT00826A
DOI: 10.5517/ccdc.csd.cc25q5vy
title: CCDC 2017320: Experimental Crystal Structure Determination

description: Related Article: Katerina A. Vinogradova, Nikita A. Shekhovtsov, Alexey S. Berezin, Taisiya S. Sukhikh, Maxim I. Rogovoy, Alexander V. Artem'ev, Mark B. Bushuev|2021|Dalton Trans.|50|9317|doi:10.1039/D1DT00826A
DOI: 10.5517/ccdc.csd.cc24ysws
title: CCDC 1994846: Experimental Crystal Structure Determination

description: Related Article: Javier Pitarch-Jarque, Hermas R. Jiménez, Elina Kalenius, Salvador Blasco, Alberto Lopera, M. Paz Clares, Kari Rissanen, Enrique García-España|2021|Dalton Trans.|50|9010|doi:10.1039/D1DT01302E
DOI: 10.5517/ccdc.csd.cc24ysvr
title: CCDC 1994845: Experimental Crystal Structure Determination

description: Related Article: Javier Pitarch-Jarque, Hermas R. Jiménez, Elina Kalenius, Salvador Blasco, Alberto Lopera, M. Paz Clares, Kari Rissanen, Enrique García-España|2021|Dalton Trans.|50|9010|doi:10.1039/D1DT01302E
DOI: 10.5517/ccdc.csd.cc24ysxt
title: CCDC 1994847: Experimental Crystal Structure Determination

description: Related Article: Javier Pitarch-Jarque, Hermas R. Jiménez, Elina Kalenius, Salvador Blasco, Alberto Lopera, M. Paz Clares, Kari Rissanen, Enrique García-España|2021|Dalton Trans.|50|9010|doi:10.1039/D1DT01302E
DOI: 10.5517/ccdc.csd.cc26zv68
title: CCDC 2055430: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc2750hy
title: CCDC 2060399: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc2754n7
title: CCDC 2060528: Experimental Crystal Structure Determination

description: Related Article: Mohammed Enamullah, Mohammad Anwar Hossain, Mohammad Khairul Islam, Dennis Woschko, Christoph Janiak|2021|Dalton Trans.|50|9236|doi:10.1039/D1DT01671G
DOI: 10.5517/ccdc.csd.cc2754p8
title: CCDC 2060529: Experimental Crystal Structure Determination

description: Related Article: Mohammed Enamullah, Mohammad Anwar Hossain, Mohammad Khairul Islam, Dennis Woschko, Christoph Janiak|2021|Dalton Trans.|50|9236|doi:10.1039/D1DT01671G
DOI: 10.5517/ccdc.csd.cc275z9q
title: CCDC 2061323: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc275zbr
title: CCDC 2061324: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc275zcs
title: CCDC 2061325: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc275zdt
title: CCDC 2061326: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc275zfv
title: CCDC 2061327: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc275zgw
title: CCDC 2061328: Experimental Crystal Structure Determination

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