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54 results for “computational humanities”
Subjective human thresholds over computer generated images
<p>Realistic image computation mimics the natural process of acquiring pictures by simulating the physical interactions of light between all the objects, lights and cameras lying within a modelled 3D scene. This process is known as global illumination and was formalised by Kajiya with the following rendering Equation:<br> <span class="math-tex">\(\begin{equation} \label{eq:rendering_equation} L_o(x, \omega_o) = {L_e(x, \omega_o)} + \int_{\Omega}^{} {L_i(x, \omega_i)} \cdot f_r(x, \omega_i \rightarrow \omega_o) \cdot \cos \theta_i d\omega_i \end{equation}\)</span></p> <p>where:</p> <ul> <li> <span class="math-tex">\(L_o(x, \omega_o)\)</span> is the luminance traveling from point <span class="math-tex">\(x\)</span> in direction <span class="math-tex">\(\omega_o\)</span>;</li> <li><span class="math-tex">\(L_e(x, \omega_o)\)</span> is point <span class="math-tex">\(x\)</span> emitted luminance (it is null if point x does not lie on a ligth source surface);</li> <li>the integral represents the set of luminances <span class="math-tex">\(L_i\)</span>incident in <span class="math-tex">\(x \)</span> from the hemisphere of the directions <span class="math-tex">\(\Omega\)</span> and reflected in the direction <span class="math-tex">\(\omega_o\)</span>. The reflected luminances are weighted by the materials reflecting properties (bidirectionnal reflectance function <span class="math-tex">\(f_r(x, \omega_i \rightarrow \omega_o)\)</span>) and the cosinus of the incident angle.</li> </ul> <p>This equation cannot be analytically solved and Monte Carlo approaches are generally used to estimate the value of the pixels of the final image.</p> <p>This proposed dataset is composed of 80 points of view of photo realistics images with different level of samples (following the Monte Carlo approach) for each. Each image is 800 x 800 pixels in size. The most noisy image is of 20 samples and the reference one (the most converged image obtained) is of 10000 samples. The <a href="https://www.pbrt.org/index.html">pbrt</a> rendering engine (version 3) was used to generate these images.</p> <p>By exploiting these levels of samples obtained and therefore of noise perceptible in the images, average subjective human thresholds were collected. For this purpose, the images were divided into 16 areas of 200 x 200 pixels in size for each point of view.</p> <p>The proposed image database is composed of the following files:</p> <ul> <li><strong>human-thresholds.csv</strong> : the set of human subjective thresholds obtained on 40 points of view. A line is composed of the name of the point of view followed by all the thresholds obtained for each of the 16 zones;</li> <li><strong>SIN3D_dataset.tar.gz</strong> : is an archive containing all the images from 20 to 10000 samples in steps of 20 samples for each point of view (i.e. 500 images per point of view). Each folder in the archive corresponds to a point of view.</li> </ul> <p><em>This image database has been exploited in order to propose an objective model for noise detection in photo-realistic computer-generated images (article referenced to this image database).</em></p> <p><strong>Note:</strong> Some of the proposed scenes come from:</p> <ul> <li><a href="https://pbrt.org/scenes-v3">https://pbrt.org/scenes-v3</a></li> <li><a href="https://benedikt-bitterli.me/resources/">https://benedikt-bitterli.me/resources/</a></li> </ul> <p><strong>Funding:</strong> This research was funded by ANR support: project ANR-17-CE38-0009.</p> <p> </p>
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Genomic DNA transposition induced by human PGBD5 (Accompanying Scripts for Computational Analyses)
<p>We include the bash scripts, python code and command-line parameters used to prepare, map, and analyze NGS sequencing data generated from a modified version of flanking sequence exponential anchored (FLEA) PCR to identify genomic insertion locations of a transposable element reporter construct. In summary, we map these reads to a hybrid genome consisting in the human reference hg19 and the reporter plasmid, identify reads that span the insertion breakpoint and thus recover the genomic insertion loci. </p> <p>Please find more details in the supplied README. </p> <p> </p> <p>This collection of scripts corresponds to the data analysis in the following publication:</p> <p>Genomic DNA transposition induced by human PGBD5</p> <p>Anton Henssen, Elizabeth Henaff, Eileen Jiang, Amy Eisenberg, Julianne R. Carson, Camila Villasante, Mondira Ray, Eric Still, Melissa Burns, Jorge Gandara, Cedric Feschotte, Christopher E. Mason, Alex Kentsis</p> <p> </p> <p> </p>
The AFFECT-HRI data set: physiological data for affective computing in human-robot interaction with anthropomorphic service robots
<p>We provide a comprehensive data set <strong>AFFECT-HRI </strong>containing physiological data labeled with human affect (i.e., mood and emotion) gathered during an empirical study consisting of a complex human-robot interaction (HRI). A realistic retail scenario served as an experimental environment. In prior research, we showed the necessity to combine the expertise of the research fields of psychology, computer science, and law in the design of a responsible human-centered HRI. Therefore, we implemented five conditions (neutral, transparency, liability, moral, and immoral) covering the perspectives from these three research fields and used two different anthropomorphic service robots. Our study followed a multi-method approach, resulting in a data set containing and combining objective physiological sensor data with subjective human-affect assessments. Additionally, the data set includes insights from 146 participants regarding affect, demographics, and socio-technical questionnaire ratings, as well as robot gestures and robot speech. Our study can be split into three scenes: a consultation regarding products, a request for sensitive personal information while opening a customer account, and a successful or failing handover when buying a mold remover. Thus, this data set offers for the first time the possibility to prove established or develop new emotion recognition methods and technological capabilities for HRI. Further, our data set provides the possibility to combine affective computing with research about robot behavior (gestures, speech, and handover), liability (questionnaire), transparency (questionnaire), and psychological aspects, allowing an encompassing, human-centered view of HRI.</p> <p>The detailed data descriptor has been published in Nature Scientific Data. For more details on the data set, please check the paper below.</p> <p><strong>Please cite the following paper if the dataset is used in a publication:</strong><br>Heinisch, J.S., Kirchhoff, J., Busch, P. <em>et al.</em> Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set. <em>Sci Data</em> <strong>11</strong>, 333 (2024). https://doi.org/10.1038/s41597-024-03128-z</p> <p><strong>Acknowledgements</strong><br>This research was conducted as part of RoboTrust, a project of the Centre Responsible Digitality, supported by the Hessian Minister for Digital Strategy and Innovation. The authors would like to thank all participants for their participation in the study. We particularly want to thank Ruth Stock-Homburg for her support and for making Elenoide available. Further, we want to thank Mona Kegel, Vignesh Prasad, and all the research assistants who supported the study. We also thank the leap in time lab for serving as study location. A special thanks goes to Amer Altizini, who supported us by helping to prepare the data for publication. We want to thank Niklas Jungermann for his valuable comments on the statistical evaluation.</p>
Serum albumin domain structures in human blood serum by mass spectrometry and computational biology
<p>Contact prediction data generated by EPC-map used in the paper "Serum Albumin Domain Structures in Human Blood Serum by Mass Spectrometry and Computational Biology" by Rappsilber et al.</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 2. Processes and Interfaces of the SAH Human Brain Model
<p>The proposed SAH Human Brain Model starts assigning the main attributes to the “heavy pieces” (king, queen, rooks, bishops, knights) and assigning to pawns the interfaces as an advanced guard. The interface represents senses and processed human actions (equilibrium, movements, and speech) and it results from the brain activity (see Figure 2). </p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 4. Name and role of the Chess Pieces
<p>In assigning the brain function to the computational processing units the strategy of the chess game will be pursued: 1 king – consciousness, mind, resolving undefined situations, undetermined risk analysis, feedback: 1 queen – implementation strategy, thinking, learning; 2 rooks – initial knowledge memory and learning memory; 2 bishops – good or updated, time or emergency decision; 2 knights – rules, open schemes, fixed processes, templates; 8 pawns – interfaces with own senses and actions. Double chess pieces will be assigned in the model with initial knowledge (‘ marked) that can be updated as a learning experience to a second set (“ marked).</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 3. Assigning ` the "SAH" Human Brain Model the role of the Chess Pieces
<p>The components are not topically subordinated to each other but in a strong interoperability and used for outputs reflected as result of thinking, actions to receiving information from the sensor of the interfaces, movement or speaking. </p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 5. Block Diagram of the SAH Human Brain Model
<p>Three vertical areas are defined in the field of activities: Processes units, Computational Intelligence Block, and Smart Interfaces Block (Details are presented in Figure 5). The Processes units fully communicate with the Computational Intelligence Block, Central Processing Unit and Smart Interfaces. Some specific links and functions are not specified here. Smart interfaces defined for “sight, sound, taste, touch and hearing senses” are bidirectional and completed by input-output interfaces that ensure the communication for output actions like “speech, sound, movements” and other commands resulting in the thinking process. An important issue is the ‘equilibrium’ that must be treated in either “decision or movement” framework.</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 1. Being Brain and Chess Game Strategy - similarities
<p>Finally, the following similar reactions between a chess player and a human being must be mentioned and considered. The power of reason for every being, human brain, or chess game player lies in similarities and has three main directions (see Figure 1)</p>
Data from: Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces
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Computationally-informed point of departure evaluation for proarrhythmic cardiotoxicity assessment using 3D engineered cardiac microtissues from human iPSC-derived cardiomyocytes
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Computational Studies of Human Class V Alcohol Dehydrogenase - The Odd Sibling
<p>A number of class I-VI ADH protein sequences were obtained from the UniProt, NCBI protein database, and Ensembl databases in October 2015. The sequences were aligned using the L-INS-i approach of MAFFT 7.266.</p> <p>The tree was generated using Phyml release 20151210. In the published figure, the tree was visualised as a radial phylogram in Dendroscope 3.</p>
Computer Generated-Human Morph Stimuli
<p>This stimulus set contains images of human faces (from the Radboud Faces Database; Langner et al., 2010) morphed with computer-generated versions of the same faces, made with FaceGen Modeller (Singular Inversions, Toronto, Canada). Morph continua are provided at 10% intervals (11 total images per morph) and 2% intervals (50 total images per morph). 16 total morphs are provided in the stimulus set, comprising 4 female faces with neutral expressions (labelled FN), 4 female faces with happy expressions (FH), 4 male faces with neutral expressions (MN) and 4 male faces with happy expressions (MH). All faces are Caucasian, and are photographed as a frontal view, with external features covered by an oval frame.</p> <p>For more information about this stimulus set, please contact n.bowling@gold.ac.uk</p> <p>Radboud Faces Database:</p> <p>Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D. H. J., Hawk, S. T., & van Knippenberg, A. (2010). Presentation and validation of the Radboud Faces Database. <em>Cognition & Emotion, 24</em>(8), 1377-1388.</p>
A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2
<p>Data produced and analyzed in the manuscript "A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2" by Pasquadibisceglie et al.</p> <p><br> If you include these data in your manuscript, please cite: Pasquadibisceglie A, Leccese A and Polticelli F (2022) A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2. <em>Front. Chem.</em> 10:1004815. doi: 10.3389/fchem.2022.1004815</p>
Dataset of "Tracking Urban Human Activity from Mobile Phone Calling Patterns" PLOS Computational Biology paper
<p>This are the dataset file for "Tracking Urban Human Activity from Mobile Phone Calling Patterns", to be published in PLOS Computational Biology.</p> <p>The files contain probability distributions of finding a first, last, or any call as a function of time, derived from anonymized call detail records for a 12 months period in the year 2007 from a mobile phone service provider in a European country. The first data file contains the data obtained fom 30 different cities. the second for the six most populated cities, splitting the data into different age and gender groups.</p> <p>Details in README files.</p>
The OpenEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing
<p>The OpenEar Dataset provides a library consisting of eight three-dimensional models of the human temporal bone to enable surgical training including color data. Each dataset is based on a combination of multimodal imaging including Cone Beam Computed Tomography (CBCT) and micro-slicing. 3D reconstruction of micro-slicing images and subsequent registration to CBCT images allowed for relatively efficient multimodal segmentation of inner ear compartments, middle ear bones, tympanic membrane, relevant nerve structures, blood vessels and the temporal bone. Raw data from the experiment as well as voxel data and triangulated models from the segmentation are provided in full for use in surgical simulators or any other application which relies on high quality models of the human temporal bone.</p>
Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)
<p>Data regarding <strong>Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)</strong></p>
Computational Modeling Of Human Multisensory Spatial Representation By A Neural Architecture
<p>Dataset including both performance of human observers and the neural architecture, related to the manuscript:</p> <p>Computational Modeling Of Human Multisensory Spatial Representation By A Neural Architecture</p>
Computationally defined and in vitro validated putative genomic safe harbour loci for transgene expression in human cells
<p>Selection of the target site is an inherent question for any project aiming for directed transgene integration. Genomic safe harbour (GSH) loci have been proposed as safe sites in the human genome for transgene integration. Although several sites have been characterised for transgene integration in the literature, most of these do not meet criteria set out for a GSH, and the limited set that do have not been characterised extensively. Here, we conducted a computational analysis using publicly available data to identify 25 unique putative GSH loci that reside in active chromosomal compartments. We validated stable transgene expression and minimal disruption of the native transcriptome in three GSH sites <em>in vitro</em> using human embryonic stem cells (hESCs) and their differentiated progeny. Furthermore, for easily targeted transgene expression, we have engineered constitutive landing pad expression constructs into the three validated GSH in hESCs.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.