Find research datasets worth reusing

opencc-by-4.0Oct 2022View details →

Whole Torso Computational Models

Whole torso computational models generated from high-resolution CT data. All modes in format of: - nodes (.pts) - tetrahedral elements (.elem) - fibres (cardiac) (.lon) being [0 0 0] for extra-cardiac structures for direct use with simulation environments such as Open CARP https://opencarp.org/

dryad36/100

Computationally modelled structure of type IV pilus PilA of Aggregatibacter actinomycetemcomitans

Some bacterial type IV pili structures have been determined with Cryo-EM, X-ray diffraction or similar method. The type IVa pilus PilA of Aggregatibacter actinomycetemcomitans, an oral pathogen, shares sequence identity with other bacterial type IVa pili. To understand the structure of AaPilA at the molecular level, we performed computational modeling studies. To model the filament, we utilized the GalaxyGemini web server using different monomer conformations from molecular dynamics simulations as a seed; then, we extended the structure to 14-mer by employing the cryo-EM map file of type IV pilus from enterohemorrhagic E. coli. The structures were predicted for a full-length monomer PilA_D7S, N-terminally truncated monomer PilA_D7S_Δ1-27 and 14-homo-oligomer PilA_D7S_14mer. PilAD7S_Δ1-27 and PilAD7S share the same secondary structural elements: an N-terminal α-helix, four antiparallel β-strands, a hypervariable segment and a flexible C-terminus. The homo-oligomeric PilAD7S from our computational model resembles the interface seen in the cryo-EM structure of N. meningitidis type IV pili.

opencc-zeroNov 2022View details →

Halo power spectrum computed in real and redshift space

Halo power spectrum computed from the halo catalogs in- terpolated on a 4003 mesh, in real (P(k)) and redshift space, the lat- ter represented through the monopole P0(k), the quadrupole P2(k) and the hexadecapole P4(k). The main panels show the mean from 80 SLICS realizations (green dashed line) and the mean from the same number of BAM mocks (solid gray lines). The bottom panels show the ratio between the mean spectra from the BAM mocks to the mean from the SLICS. The gray area in the ratios show the 1σ region computed from the means and their respective errors.

openother-openNov 2022View details →

Monitoring the Internet Computer (Artifact)

This artifact accompanies the paper Monitoring the Internet Computer, which will be presented at the 25th International Symposium on Formal Methods (FM 2023). It provides the policy formulas described in Section 3.2 of the paper, the raw log files that were used in the evaluation, and all tools necessary to reproduce the experimental results, specifically those reported in Table 2 and Figure 5 in the paper. See README.md for additional information and instructions. Erratum (August 14, 2023): Unlike stated in Table 1 of the paper, the block-validation-latency policy included in this artifact does not contain a future operator. The operator was present in an earlier version of the policy. It was removed because it was not required given the structure of the log data. The future operator can be added by inserting <pre>EVENTUALLY [0,0]</pre> at the end of line 50 of the file policy-monitoring/mfotl-policies/block_validation_latency/formula.mfotl. We did not observe a meaningful change in the results when we repeated the experiments using both the included and the modified policy.

Raw data: Jiang et al. Plos Computational Biology

Raw data files associated with the publication Fine-grained, Nonlinear Registration of Live Cell Movies Reveals Spatiotemporal Organization of Diffuse Molecular Processes Xuexia Jiang, Tadamoto Isogai, Joseph Chi, Gaudenz Danuser* Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA *Correspondence: Gaudenz.Danuser@UTSouthwestern.edu The data is organized figure by figure. Time lapse image sequences are contained in folders of .tif series.

Brain surfaces computed with FreeSurfer 6 for all ABIDE I subjects

# ABIDE I FreeSurfer 6 'surfaces' data This archive contains the following ABIDE I FreeSurfer 6 meshes / surfaces: white, orig, sphere Note that the pial surface is part of the ABIDE I pial lgi dataset:    https://zenodo.org/record/7132610    DOI: 10.5281/zenodo.7132610   ## Credits This data is derived from the MRI scans of the ABIDE I dataset: * ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/ Quoting from that website:     "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first      ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17      international sites, sharing previously collected resting state functional      magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets      made available for data sharing with the broader scientific community.      This effort yielded 1112 dataset, including 539 from individuals with      ASD and 573 from typical controls (ages 7-64 years, median 14.7 years      across groups). This aggregate was released in August 2012. Its      establishment demonstrated the feasibility of aggregating resting      state fMRI and structural MRI data across sites; the rate of these      data use and resulting publications (see Manuscripts) have shown its      utility for capturing whole brain and regional properties of the brain      connectome in Autism Spectrum Disorder (ASD). In accordance with      HIPAA guidelines and 1000 Functional Connectomes Project / INDI      protocols, all datasets have been anonymized, with no protected      health information included." Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).           The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667. ## How this data was produced The following steps were used to create the data: * We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects). * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.     - We did not run any quality metrics on the scans or exclude any subjects. ## Contained files * In order to reduce the size of this dataset, for each subject, we only included the following files:     - <subject>/surf/lh.white      : the white surface for the left hemisphere     - <subject>/surf/rh.white      : the white surface for the right hemisphere     - <subject>/surf/lh.orig       : the orig surface for the left hemisphere     - <subject>/surf/rh.orig       : the orig surface for the right hemisphere     - <subject>/surf/lh.sphere     : the sphere surface for the left hemisphere     - <subject>/surf/rh.sphere     : the sphere surface for the right hemisphere All files are in binary FreeSurfer surf format and represent triangular meshes. All surfaces are full resolution. ## What is NOT contained * The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website. * The pial surface is not included, get it from https://zenodo.org/record/7132610 instead. ## Author and License Note: For the authors of the original ABIDE I dataset, see the Credits section above. This lgi data was created by:     Dr. Tim Schäfer     Postdoc Computational Neuroimaging     Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy     University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany     http://rcmd.org/ts The data is published under the following license: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code. See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

Native space mesh descriptors computed with FreeSurfer 6 for all ABIDE I subjects

# ABIDE I FreeSurfer 6 'native space descriptors' data This archive contains the following ABIDE I FreeSurfer 6 native space mesh descriptors: thickness, area, volume, sulc, curv, jacobian_white. ## Credits This data is derived from the MRI scans of the ABIDE I dataset: * ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/ Quoting from that website:     "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first      ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17      international sites, sharing previously collected resting state functional      magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets      made available for data sharing with the broader scientific community.      This effort yielded 1112 dataset, including 539 from individuals with      ASD and 573 from typical controls (ages 7-64 years, median 14.7 years      across groups). This aggregate was released in August 2012. Its      establishment demonstrated the feasibility of aggregating resting      state fMRI and structural MRI data across sites; the rate of these      data use and resulting publications (see Manuscripts) have shown its      utility for capturing whole brain and regional properties of the brain      connectome in Autism Spectrum Disorder (ASD). In accordance with      HIPAA guidelines and 1000 Functional Connectomes Project / INDI      protocols, all datasets have been anonymized, with no protected      health information included." Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).           The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667. ## How this data was produced The following steps were used to create the data: * We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects). * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.     - We did not run any quality metrics on the scans or exclude any subjects. ## Contained files * In order to reduce the size of this dataset, for each subject, we only included the following files:     - <subject>/surf/lh.thickness      : the per-vertex cortical thickness for the left hemisphere     - <subject>/surf/rh.thickness      : the per-vertex cortical thickness for the right hemisphere     - <subject>/surf/lh.area           : the per-vertex area of the white surface for the left hemisphere     - <subject>/surf/rh.area           : the per-vertex area of the white surface for the right hemisphere     - <subject>/surf/lh.volume         : the per-vertex cortical volume for the left hemisphere     - <subject>/surf/rh.volume         : the per-vertex cortical volume for the right hemisphere     - <subject>/surf/lh.sulc           : the per-vertex sulcal depth for the left hemisphere     - <subject>/surf/rh.sulc           : the per-vertex sulcal depth for the right hemisphere     - <subject>/surf/lh.curv           : the per-vertex mean curvature of the white surface for the left hemisphere     - <subject>/surf/rh.curv           : the per-vertex mean curvature of the white surface for the right hemisphere     - <subject>/surf/lh.jacobian_white : the per-vertex jacobian for the left hemisphere     - <subject>/surf/rh.jacobian_white : the per-vertex jacobian for the right hemisphere All files are in binary FreeSurfer curv format. ## What is NOT contained * The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website. ## Author and License Note: For the authors of the original ABIDE I dataset, see the Credits section above. This lgi data was created by:     Dr. Tim Schäfer     Postdoc Computational Neuroimaging     Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy     University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany     http://rcmd.org/ts The data is published under the following license: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code. See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

Brain labels computed with FreeSurfer 6 for all ABIDE I subjects

# ABIDE I FreeSurfer 6 'label' data This archive contains the following ABIDE I FreeSurfer 6 labels: cortex, aparc, aparc.a2009s. ## Credits This data is derived from the MRI scans of the ABIDE I dataset: * ABIDE I dataset: https://fcon_1000.projects.nitrc.org/indi/abide/ Quoting from that website:     "The Autism Brain Imaging Data Exchange I (ABIDE I) represents the first      ABIDE initiative. Started as a grass roots effort, ABIDE I involved 17      international sites, sharing previously collected resting state functional      magnetic resonance imaging (R-fMRI), anatomical and phenotypic datasets      made available for data sharing with the broader scientific community.      This effort yielded 1112 dataset, including 539 from individuals with      ASD and 573 from typical controls (ages 7-64 years, median 14.7 years      across groups). This aggregate was released in August 2012. Its      establishment demonstrated the feasibility of aggregating resting      state fMRI and structural MRI data across sites; the rate of these      data use and resulting publications (see Manuscripts) have shown its      utility for capturing whole brain and regional properties of the brain      connectome in Autism Spectrum Disorder (ASD). In accordance with      HIPAA guidelines and 1000 Functional Connectomes Project / INDI      protocols, all datasets have been anonymized, with no protected      health information included." Citation: Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014).           The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667. ## How this data was produced The following steps were used to create the data: * We downloaded all available MRI scans for the ABIDE I subjects (1035 subjects). * We pre-processed all subjects in FreeSurfer version 6 (https://freesurfer.net) by running the full recon-all pipeline for each subject.     - We did not run any quality metrics on the scans or exclude any subjects. ## Contained files * In order to reduce the size of this dataset, for each subject, we only included the following files:     - <subject>/label/lh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for left hemisphere.     - <subject>/label/rh.cortex.label : label identifying which mesh vertices belong to cortex versus medial wall, for right hemisphere.     - <subject>/label/lh.aparc.annot : Desikan atlas surface parcellation for left hemisphere.     - <subject>/label/rh.aparc.annot : Desikan atlas surface parcellation for right hemisphere.     - <subject>/label/lh.aparc.a2009s.annot : Destrieux atlas surface parcellation for left hemisphere.     - <subject>/label/rh.aparc.a2900s.annot : Destrieux atlas surface parcellation for right hemisphere. All files are in ASCII FreeSurfer label format. For the annot files, see FreeSurferColorLut.txt that comes with FreeSurfer for meaning of integers specifying a region. ## What is NOT contained * The ABIDE demographics information (metadata on the subjects, like age, ...) is not included, you can get it from the ABIDE website. ## Author and License Note: For the authors of the original ABIDE I dataset, see the Credits section above. This label data was created by:     Dr. Tim Schäfer     Postdoc Computational Neuroimaging     Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy     University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany     http://rcmd.org/ts The data is published under the following license: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) See https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode.txt or the file LICENSE for the full legal code. See https://creativecommons.org/licenses/by-nc-sa/3.0/ for an easy explanation of what this license means for you.

opencc-zeroDec 2022View details →

dryad36/100

Folding-unfolding asymmetry and a RetroFold computational algorithm

We treat protein folding as the molecular self-assembly, while unfolding is viewed as disassembly. Self-assembly and disassembly (fracture) are two opposite non-equilibrium dynamic processes; however, they cannot be converted to each other by a simple time variable reversal. Fracture is typically a much faster process than self-assembly. Self-assembly is often an exponentially decaying process, since energy relaxes due to dissipation, while fracture may be a constant rate process as the driving force is opposed by damping. Typically, protein folding takes two orders of magnitude longer time than unfolding, and it consumes a lot of computational resources to model folding. Based on energy dissipation rates, we suggest a mathematical transformation of variables, which makes it possible to view self-assembly as time-reversed disassembly, thus folding can be studied as reversed unfolding. We investigate the molecular dynamics modeling of folding and unfolding of the short Trp-cage protein. Folding time constitutes about 800 ns while unfolding (denaturation) takes only about 5.0 ns, and therefore, fewer computational resources are needed for its simulation. This "RetroFold" approach can be used for the design of a novel computation algorithm, which, while approximate, is less time-consuming than traditional folding algorithms.

opencc-by-4.0Aug 2022View details →

Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)

Data regarding Experimental and computational approach to biomechanical human head modelling: advanced Head models for safety Enhancement And medical Development (aHEAD)

Interactive computational and experimental approaches improve the sensitivity of periplasmic binding protein-based nicotine biosensors for measurements in biofluids

Here are the raw simulation trajectories (water removed).

Data used in: Born rule as a test of the accuracy of a public quantum computer

A data and scripts used during the preparation of Born rule as a test of the accuracy of a public quantum computer.

opencc-by-4.0Dec 2022View details →

Figures from the paper "The diversity of canonical and ubiquitous progress in computer vision: A dynamic topic modeling approach"(v2))

Figures from the paper  "The diversity of canonical and ubiquitous progress in computer vision: A dynamic topic modeling approach". The second version: Corrections to Figure 1. (fig1-> fig_v2).

Computational Modeling Of Human Multisensory Spatial Representation By A Neural Architecture

Dataset including both performance of human observers and the neural architecture, related to the manuscript: Computational Modeling Of Human Multisensory Spatial Representation By A Neural Architecture

Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"

Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"

98 selected publications analysed in-depth in the context of a systematic literature review on Computational Thinking in compulsory education

This is the list of the 98 publications from between 2016 and 2021 analysed in-depth through a review matrix in the context of the "<a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a>" research study. The <a href="https://computhink2study.eu/">study</a> was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU). Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., Earp, J., Horvath, M.A., Jasutė, E., Malagoli, C., Masiulionytė-Dagienė, V., & Stupurienė, G. (2022). Reviewing Computational Thinking in Compulsory Education. Publications Office of the European Union. <a href="https://doi.org/10.2760/126955">https://doi.org/10.2760/126955</a>

In materia implementation strategies of physical reservoir computing with memristive nanonetworks - Dataset

This is the dataset of "In materia implementation strategies of physical reservoir computing with memristive nanonetworks"

opencc-zeroFeb 2023View details →

dryad36/100

Data from: Computational model of the full-length TSH receptor

The receptor for thyroid stimulating hormone (TSHR), a GPCR, is of particular interest as the primary antigen in autoimmune hyperthyroidism (Graves' disease) caused by stimulating TSHR antibodies. To date, only one domain of the extracellular region of the TSHR has been crystallized. We have run a 1000ns Molecular Dynamic simulation on a model of the entire TSHR generated by merging the extracellular region of the receptor, obtained using artificial intelligence, with our recent homology model of the transmembrane domain, embedded it in a lipid membrane solvated it with water and counterions. The simulations showed that the structure of the transmembrane and leucine-rich domains were remarkably constant while the linking region (LR), known more commonly as the "hinge region", showed significant flexibility, forming several transient secondary structural elements. Furthermore, the relative orientation of the leucine-rich domain with the rest of the receptor was also seen to be variable. These data suggest that this linker region is an intrinsically disordered protein (IDP). Furthermore, preliminary data simulating the full TSHR model complexed with its ligand (TSH) showed that (a) there is a strong affinity between the linker region and TSH ligand and (b) the association of the linker region and the TSH ligand reduces the structural fluctuations in the linker region. This full-length model illustrates the importance of the linker region in responding to ligand binding and lays the foundation for studies of pathologic TSHR autoantibodies complexed with the TSHR to give further insight into their interaction with the flexible linker region. The dataset represents the coordinates of a model of the Thyroid Stimulating Hormone Recpetor (TSHR) built from the AI-based alphafold2 model of the TSHR ectodomain and the MD geverated model of the transmembrane domain (TMD) of TSHR.

Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data

This repository contains the code and data for reproducibility of the paper 'Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms'.  The following files are included: - TA.zip and DEM.zip: The code used for the TA and DEM algorithms respectively. - experiment_runner: Python file which was used to run the black-box optimization algorithms on the discrepancy problems from IOHexperimenter (requires package 'ioh', version 0.3.6 or higher). This generates data in IOH-format, which is included in 'raw_data.zip' - process_stardicr.R: R script which uses IOHanalyzer to extract the performance from the raw data into csv files for visualization. The resulting csvs are included in 'csv_with_pos' for the final results including the corresponding coordinates and 'csv_perf.zip', which contains the convergence information. - Found_Values: The discrepancy values found by TA and DEM, separated by sampler. - A csv file of the relative performance of each of the optimizers compared to the values found by TA is included in 'final_precision_table.csv' - Plot_StarDiscr: the python notebook used to generate all figures, except figure 3 which was created using the IOHanalyzer GUI (iohanalyzer.liacs.nl). The full dataset is available on the website under the source 'star_discrepancy' - Figures: some additional figures which were not included in the paper because of space constraints + higher quality versions of some of the landscape plots.

Computational Museology

This presentation addresses the potential for computational practices within archival and museological domains by charting a positional shift occurring in digital archives; from working with an object orientation and containment – to computing with a dimension orientation, segmentation, analytics and visualization. Computational museology allows us to conceive new trajectories that link all forms of cultural materiality: objects, knowledge systems, representation and participation. These topics will be explored through research at the Laboratory for Experimental Museology. It is a transdisciplinary initiative at the intersection of cultural heritage, imaging technologies, immersive visualisation, visual analytics and digital aesthetics. eM+ engages in research from scientific, artistic and humanistic perspectives and promotes a post-cinematic multisensory engagement using experimental platforms. The research explores the ways in which mechanistic descriptions of database logic can be replaced and computation can become ‘experiential, spatial and materialized; embedded and embodied’, a landscapes for the senses. The title for this talk ‘computational museology’ is a framework that unites machine intelligence with data curation and ontology with visualization and immersion.