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opencc-by-4.0Oct 2023View details →

An observed population of intermediate-mass helium stars that have been stripped in binaries - theoretical, computational and observational data

This Zenodo repository contains the observational and computational data presented in the manuscript "An observed population of intermediate-mass helium stars stripped in binaries" by Drout, Götberg, Ludwig, Groh, de Mink, O'Grady and Smith.We organize the data as follows:<ul><li>The stacked spectra presented in Figures S16-S21 are located in stacked_spectra.tar.gz, which contains a text file for each star. The text files have three columns that correspond to wavelength in Angstrom, normalized counts, and errors, respectively.  </li><li>The spectral model grid computed based on binary evolutionary model output and presented in detail in the Supplementary information section S1.2.1, is labeled with names starting S121. The file S121_evol_based_006_absolute_magnitudes.txt contains the absolute AB magnitudes for the models in UV and optical filters. The .tar.gz S121_evol_based_006_spectra.tar.gz contains files with the full spectral energy distribution and normalized spectra of each model. The .tar.gz S121_evol_based_006_complete_models.tar.gz contains the full CMFGEN models.  </li><li>For the stellar atmosphere model grid presented in Supplementary material section S1.2.2, we refer to the Zenodo repository 10.5281/zenodo.7976200, which is made available in association with the second paper in our series. We note that we used a subset of that grid in the article associated with this Zenodo repository. We refer to section S1.2.2 for more details.  </li><li>The spectral models demonstrating the mass loss rate variations in Supplementary information section S1.2.3 are presented here with names starting with S123. There is one file containing the absolute magnitudes (S123_mdot_variation_absolute_magnitudes.txt), the S123_mdot_variation_spectra.tar.gz contains the spectral energy distributions and normalized spectra for each of the models, and the S123_mdot_variation_complete_models.tar.gz contains the full CMFGEN models.  </li><li>The spectral model grid computed based on main-sequence evolutionary model output and presented in detail in the Supplementary information section S1.3.1, is labeled with names starting S131. The file S131_MS_evol_based_006_absolute_magnitudes.tar.gz contains three files with the absolute AB magnitudes for the models in the UV and optical filters, each file corresponding to either 20%, 60%, or 90% through the main-sequence evolution and labeled f20, f60, and f90, respectively. S131_MS_evol_based_006_spectra.tar.gz contains three folders labeled f20, f60 and f90, which each contains the SEDs (in Flambda and ABmag) and normalized spectra for the corresponding models. The files S131_MS_evol_based_006_complete_models_fX0.tar.gz contain the complete CMFGEN models.  </li><li>The custom index files we use for astrometry.net in section S3.1.1 are located in the zip file called S311_astrometry_index_files.zip. This information was used to recalculate the astrometry on the Swift UVOT images of the Magellanic Clouds.  </li><li>To make Figure 2B, we calculated the equivalent widths for a set of models assuming a signal-to-noise ratio of 35. This procedure is described in Section S3.7.2. In Figure2B_Model_EWs.zip, we provide three files that each contain these modeled equivalent widths for (1) stripped star models, (2) OB star models, and (3) composite models.   </li><li>To make Figure S7 (see also Sections S1.2.3 and S2.2), which is similar to Figure 2B, but presents the effects of varying the wind mass loss of stripped stars, we used a similar set of modeled equivalent widths as when we produced Figure 2B. These modeled equivalent widths are provided in FigureS7_Model_EWs.zip.   </li><li>To make Figure 3, we calculated equivalent widths for the model grid described in Section S1.2.2 and the TLUSTY OB star grids (see Section S1.3.2) assuming a signal-to-noise ratio of 100. These model equivalent widths are provided in the file called Figure3_Model_EWs.zip. </li></ul>

opencc-zeroOct 2023View details →

A computational neuroscience framework for quantifying warning signals

Animal warning signals show remarkable diversity, yet subjectively appear to share certain visual features that make defended prey stand out and look different from more cryptic palatable species. For example, many (but far from all) warning signals involve high contrast elements, such as stripes and spots, and often involve the colours yellow and red. How exactly do aposematic species differ from non-aposematic ones in the eyes (and brains) of their predators? Here we develop a novel computational modelling approach, to quantify prey warning signals and establish what visual features they share. First, we develop a model visual system, made of artificial neurons with realistic receptive fields, to provide a quantitative estimate of the neural activity in the first stages of the visual system of a predator in response to a pattern. The system can be tailored to specific species. Second, we build a novel model that defines a 'neural signature', comprising quantitative metrics that measure the strength of stimulation of the population of neurons in response to patterns. This framework allows us to test how individual patterns stimulate the model predator visual system. For the predator-prey system of birds foraging on lepidopteran prey, we compared the strength of stimulation of a modelled avian visual system in response to a novel database of hyperspectral images of aposematic and undefended butterflies and moths. Warning signals generate significantly stronger activity in the model visual system, setting them apart from the patterns of undefended species. The activity was also very different from that seen in response to natural scenes. Therefore, to their predators, lepidopteran warning patterns are distinct from their non-defended counterparts, and stand out against a range of natural backgrounds. For the first time, we present an objective and quantitative definition of warning signals based on how the pattern generates population activity in a neural model of the brain of the receiver. This opens new perspectives for understanding and testing how warning signals have evolved, and, more generally, how sensory systems constrain signal design.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

We uploaded the raw data related to extracted features of the manuscript "Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. 2023 Nov;128(11):1310-1332. doi: 10.1007/s11547-023-01710-w. Epub 2023 Sep 11. PMID: 37697033."

Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP

The dataset uploaded herein is associated with the paper published under the title "Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications" with the Royal Society of Chemistry - Physical Chemistry Chemical Physics Journal. This dataset includes the .vasp files for all modeled structures, an example dftb_in.hsd file, which is the instructional file for geometry optimization with DFTB+, an Excel spreadsheet with atom number densities and total energy values for all modeled geometries, and, finally, a Python script that was used to calculate the Enthalpy of Formation and Cohesive Energies for all structures. This data has been made available to the scientific community in the interest of open-source and accessible data. The authors request that you please cite the associated paper and Zenodo dataset if used.AbstractTwo-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that the effect of varying cross-linking concentration on the framework stability is negligible and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane separations.

Different adaptation error types in affective computing have different effects on user experience: a Wizard-of-Oz study

The record consists of one Excel file that contains individual participant data for the study "Different adaptation error types in affective computing have different effects on user experience: a Wizard-of-Oz study". The study included 97 participants who were randomly divided into five groups corresponding to five adaptation behaviors (SingleSmall, SingleModerate, ImmediateLow, ImmediateHigh, IrreversibleHigh). Each participant took part in three 11-minute intervals. Difficulty changed every 60 seconds in each 11-minute interval, and there are thus 11 difficulty values per interval. At the end of each interval, participants self-reported their experience using the NASA Task Load Index (6 items) and Intrinsic Motivation Inventory (8 items). After the third interval, participants were asked to rate how much they liked the 3 intervals on a visual analog scale that was converted to 1-100 numerical scores.

opencc-by-4.0Oct 2023View details →

Cutting the Frame: An In-Depth Look at the Hitchcock Computer Vision Dataset

The Hitchcock Computer Vision Dataset is a comprehensive collection of annotated frames from fifteen Alfred Hitchcock films, created for the purpose of advancing research in film studies and digital humanities. The dataset leverages the power of Google's Vision API to provide detailed annotations such as object detection, facial recognition, web-entity analysis, explicit content filtering, and more. The dataset consists of a CSV file containing about 105,000 frames, uniformly extracted from the films using a time-based approach. Each frame is accompanied by metadata, including the film name, timestamp, and release year, allowing researchers to explore the evolution of Hitchcock's cinematic techniques and recurring themes.

Ergo: A Gesture-Based Computer Interaction Device

This dataset accompanies the Master of Science (Computer Science) thesis by B.R. Kane titled "Ergo: A Gesture-Based Computer Interaction Device". It contains the raw sensor recordings in CSV format (`train/`), the pre-processed training-validation and testing datasets `trn_20_10.npz` and `tst_20_10.npz`, the dataset of the literature in `bibtex` format as well as in `csv` format.The code to train machine learning models on the raw sensor data is available on GitHub: https://github.com/beyarkay/masters-code/The code to analyse the gesture recognition is also available on GitHub (along with the source code of the thesis): https://github.com/beyarkay/masters-thesis/

opencc-by-4.0May 2023View details →

Linking provenance and its metadata for an AI-based computation using CPM and RO-Crate

This dataset is a prototype implementation of a mechanism for linking provenance information and its metadata, also called provenance of provenance or meta-provenance. This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> that bundles artifacts of an AI-based computational pipeline. The resulting RO-Crate contains (directly or by a reference) artifacts of the pipeline execution, such as input dataset, intermediate and final results, configuration files, pipeline implementation, log files, or provenance files. The RO-Crate is based on the <a href="https://w3id.org/cpm/ro-crate/0.2">CPM RO-Crate profile</a>, which integrates the <a href="https://doi.org/10.1038/s41597-022-01537-6">Common Provenance Model</a> (CPM) and <a href="https://w3id.org/ro/wfrun/process/0.2">Process Run Crate profile</a>. The description of the AI pipeline and an explanation of how the CPM RO-Crate profile is applied to bundle the pipeline execution artifacts is provided in our <a href="https://doi.org/10.5281/zenodo.7676924">previous work</a>. As this dataset aims to demonstrate the mechanism for linking provenance and meta-provenance, the input dataset used for the AI model training and testing is reduced only to a few images, as the size of the input dataset does not affect the mechanism. The images used in the input are from the <a href="http://gigadb.org/dataset/100439">Camelyon16 dataset</a>.

Dataset for "Computational prediction of structure, function and interaction of Myzus persicae (green peach aphid) salivary effector proteins "

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Simulations from "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors"

<h1>IL6R/IL8R Antibody Binding Model Code</h1> Christina M.P. Ray, Huilin Yang, Jamie B. Spangler, Feilim Mac Gabhann This dataset contains all simulation output files generated for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". The model is comprised of a coupled set of ordinary differential equations (ODEs) where each individual ODE describes one molecule (antibody or receptor) or molecular complex (antibody + receptor). The terms in the ODEs represent each binding interaction (binding and unbinding processes) in the system. The code for the binding model and for the analysis and visualization results is available on GitHub at <a href="https://github.com/christyray/bispecific-binding-model">christyray/bispecific-binding-model</a>. <h2>Specific Simulations</h2> The <code>.csv</code> and  <code>.rds</code> files in the correspond to the results from the simulations performed for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". These files can be read into R using the <code>import_data()</code> function included in the <a href="https://github.com/christyray/bispecific-binding-model">GitHub repository</a>. The <code>id</code> files contain simulation IDs to link the molecule concentrations (<code>yin</code>) and parameter values (<code>params</code>) with the simulation results (<code>out</code>). When applicable, the <code>norm</code> files contain normalized simulation output, and the <code>occupied</code> files contain receptor fractional occupancy values calculated from the simulation output. <ul> <li><code>optimization</code>: Optimization of binding rate constants (association and dissociation) to experimental in vitro flow cytometry data; results displayed in Figure 2</li> <li><code>binding-curve</code>: Model simulations using the best-fit parameter set for comparison to the experimental data used to fit the model parameters; results displayed in Figure 3</li> <li><code>compare-opt</code>: Model simulations using each of the optimized parameter sets; results displayed in the Supplemental Information</li> <li><code>time</code>: Simulations of antibody binding dynamics over time; results displayed in Figure 4</li> <li><code>concentration</code>: Simulations with varying antibody concentrations and receptor expression levels; results displayed in Figure 5</li> <li><code>monovalent</code>: Simulations restricted to monovalent antibody binding only; results displayed in Figure 6</li> <li><code>compare-ab</code> and <code>compare-recep</code>: Simulations of both the bispecific antibody BS1 and the combination of monospecific antibodies tocilizumab and 10H2 for comparsion; results displayed in Figure 7</li> <li><code>local</code> and <code>global</code>: Local and global univariate sensitivity analyses; results displayed in Figure 8</li> </ul> <h2>References</h2> <blockquote> H. Yang, M. N. Karl, W. Wang, B. Starich, H. Tan, A. Kiemen, A. B. Pucsek, Y.-H. Kuo, G. C. Russo, T. Pan, E. M. Jaffee, E. J. Fertig, D. Wirtz, and J. B. Spangler. Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis. Molecular Therapy, 30(11):3430–3449, Nov. 2022. doi:<a href="https://doi.org/10.1016/j.ymthe.2022.07.008">10.1016/j.ymthe.2022.07.008</a> </blockquote>

opencc-by-sa-4.0Dec 2022View details →

Coupling the COST reference plasma jet to a microfluidic device: a computational study - Figure Data

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opencc-zeroJan 2024View details →

Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition

This dataset can be used to reproduce the figures in the corresponding manuscript, in preparation for publication in the Journal of Chemical Physics. As described in the manuscript, the (mostly statistical) quantities presented here were computed from ensembles of simulated disordered solids (harmonic sphere packings, diluted spring networks, and packing derived networks). The individual simulations involve the minimization of the system's energy which is computed from positional degrees of freedom (of particles in a packing or nodes in a network). Macroscopic quantities (such as shear modulus or contact number/excess connectivity) are then computed for each sample. The displacement response feilds are computed by solving a linear matrix equation involving the Hessian of 2D packings of harmonic spheres. See the manuscript for more details.

opencc-zeroJan 2024View details →

Data from: In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways, and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo beta-lactamase activity in E. coli TEM-1 variants. These variants retain family-like functionality while being more active than their WT predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. In this dataset, we include input datasets, simulation trajectories as well as experimental data to support the publication: "In vivo functional phenotypes from a computationa epistatic model of evolution".

opencc-zeroJan 2024View details →

Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles

Without comprehensive examination of available literature on health disparities and minority health (HDMH), the field is left vulnerable to disproportionately focus on specific populations or conditions, curtailing our ability to fully advance health equity. Using scalable open-source methods, we conducted a computational scoping review of more than 200,000 articles to investigate major populations, conditions, and themes in the literature as well as notable gaps. We also compared trends in studied conditions to their relative prevalence in the general population using insurance claims (42 million Americans). HDMH publications represent 1% of articles in MEDLINE. Most studies are observational in nature, though randomized trial reporting has increased five-fold in the last twenty years. Half of all HDMH articles concentrate on only three disease groups (cancer, mental health, endocrine/metabolic disorders), while hearing, vision, and skin-related conditions are among the least well-represented despite substantial prevalence. To support further investigation, we also present HDMH Monitor, an interactive dashboard and repository generated from the HDMH bibliome.

opencc-by-4.0Feb 2024View details →

Data for: Caspase-Based Fusion Protein Technology: Substrate Cleavability Described by Computational Modeling and Simulation

This dataset contains all files necessary to set up the simulations conducted in this work. It further contains the scripts that were used to do the stitching and combining of the CASPON-tag and the N-termini of the POIs. The manuscript was just submitted and accepted: <a href="https://doi.org/10.1021/acs.jcim.4c00316">10.1021/acs.jcim.4c00316</a>

opencc-by-4.0Feb 2024View details →

The AFFECT-HRI data set: physiological data for affective computing in human-robot interaction with anthropomorphic service robots

We provide a comprehensive data set AFFECT-HRI 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. The detailed data descriptor has been published in Nature Scientific Data. For more details on the data set, please check the paper below. Please cite the following paper if the dataset is used in a publication: Heinisch, J.S., Kirchhoff, J., Busch, P. et al. Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set. Sci Data 11, 333 (2024). https://doi.org/10.1038/s41597-024-03128-z Acknowledgements 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.

opencc-by-4.0Mar 2024View details →

Hyperscanning brain-computer interface based on synchronous and asynchronous interindividual SSVEP signals

<div> Here we provide hyperscanning EEG (electroencephalogram) data recorded during BCI (brain-computer interface) control. The BCI was intended for the decoding of brain synchrony during visual stimulation, specifically the stimuli flickered at two different fequencies. Each of seven pairs of participants performed more than 100 trials, which included 5s visual stimulation of synchronous or asynchronous flicker. See the PDF file for detailed description. </div>

opencc-by-4.0Apr 2024View details →

Computed data for "Structural and Electronic Impacts of the Axial Substitution at the Phosphorus Center of C(sp3)-Bridged P-Heterotriangulenes"

Computed structures and TD-DFT raw data of the article "Structural and Electronic Impacts of the Axial Substitution at the Phosphorus Center of C(sp3)-Bridged P-Heterotriangulenes" published in Eur. J. Org.Chem. <a href="https://doi.org/10.1002/ejoc.202400368">https://doi.org/10.1002/ejoc.202400368</a>

FIGURE 5 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 5. Inner layer of Concavicaris woodfordi (Cooper, 1932). A–C, anterior part of the specimen. A, tomogram (transversal slice). B, tomogram (transversal slice; colour-marked). C, anterior view (3D rendering). D–F, posterior part of the specimen. D, tomogram (transversal slice). E, tomogram (transversal slice; colour-marked). F, cross-section (3D rendering). G, cross-section of cephalothorax of a reptantian decapod (after Glaessner, 1969). H, I, cross-section of the carapace structure of a myodocopan (Euphilomedes japonica (Müller, 1890); after Yamada, 2019). H, attached region. I, duplicated region. Arrow indicates the rotation of the posterior part of the inner layer. Abbreviations: am, adductor muscles; app, appendage; cb, chitinous body; ef, epimeral fold; g, gills; hi, hinge; il, inner layer of the shield; ila, anterior part of the inner layer; ilp, posterior part of the inner layer; mua, attractor muscles; ol, outer layer of the shield; pl, pleural part; pta, posterior trunk appendages; so, shield outline; st, sternal part; te, tergal part. Scales: 5 mm.

FIGURE 2 in New look at Concavicaris woodfordi (Euarthropoda: Pancrustacea?) using micro-computed tomography

FIGURE 2. General view of Concavicaris woodfordi (Cooper, 1932). A, B, Right and left lateral views. C, line drawing (lateral view). D, location of virtual slices presented in the figures (dorsal view). Abbreviations: vld, ventro-lateral depression. Arrows indicate the anterior side of the specimen. Yellow doted lines indicate longitudinal sections. Green dotted lines indicate transversal sections. Scales: 10 mm. Photos: T. A. Hegna.