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11,710 results for “interaction”
RICCH: An Interactive Analysis Tool for Risk and Impacts of Climate Change on Hydropower Database
<p>The "RICCH: An Interactive Analysis Tool for Risk and Impacts of Climate Change on Hydropower" Database, referred to as the RICCH Database, includes a "Hydropower Plants Database" and the incorporation of future hydropower usable capacity for 542 hydropower plants in the Global South. The "Hydropower Plants Database" includes the main design and location characteristics of 542 hydropower plants across 52 countries. Additionally, we incorporate the results of future usable capacity simulations using a multi-model ensemble of 21 Global Climate Models (GCMs) and two representative concentration pathways (RCPs). We use a multi-model ensemble of 21 GCMs from NASA's NEX GDDP dataset RCP 4.5 and RCP 8.5. We aggregate the mean monthly usable capacity (MW) for each model and scenario, for the early century (2010-2039), mid-century (2040-2069), and the end-of-the-century (2070-2099). We include these results for every power plant in the RICCH database. </p> <p><em>Example: Itaipu (power plant in Brazil) has a record for its simulated mean monthly usable capacity for January (1) in the early century (2010-2039), under GCM ACCESS1_0 and RCP 4.5.</em></p>
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
HRI30: An Action Recognition Dataset for Industrial Human-Robot Interaction
<p>A thorough analysis of the existing human action recognition datasets demonstrates that only a few HRI datasets are available that target real-world applications, all of which are adapted to home settings. Therefore, given the shortage of datasets in industrial tasks, we aim to provide the community with a dataset created in a laboratory setting that includes actions commonly performed within manufacturing and service industries. In addition, the proposed dataset meets the requirements of deep learning algorithms for the development of intelligent learning models for action recognition and imitation in HRI applications.</p>
Machine-Learning of Aerosol-Cloud-Climate Interactions Reveals an increased Cloud Fraction
<p>Data presented in the manuscript "Machine-Learning of Aerosol-Cloud-Climate Interactions Reveals an increased Cloud Fraction" by Chen et al. (2022).</p>
2019 search and interaction log from the data catalogue: Research Data Australia
<p>In order to provide a better support to user's data discovery activity, we analysed a data search log in order to understand how data seekers interact with a data search system when they search for data. The data search log is from the research data discovery portal: <a href="https://researchdata.edu.au">Research Data Australia (RDA)</a>. RDA is the data discovery service of the Australian Research Data Commons (ARDC). ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program.</p> <p>Please read the research paper "<a href="https://doi.org/10.1108/JD-12-2021-0245">Large-scale Analysis of Query Logs to Profile Users for Dataset Search</a>" for detailed description and analysis of the datasets, and the software "<a href="https://zenodo.org/record/6321621#.Yh79Tt9xUmA">Python code for processing and clustering a data search log</a>" for the data process and analysis.</p> <p>The search log consists of the entire user-front activity log data for the duration of January to December 2019. During this period, the catalogue contained about 150,000 metadata records of datasets.</p> <p>The dataset (2019_search_log_sessioned.txt) was generated from raw log data with following steps:</p> <ul> <li>Remove entries that were likely from machines instead of human users. Those recorded machine activities may result from downstream aggregators who harvested metadata from RDA by directly sending queries to the catalogue URL instead of using the API endpoint.</li> <li>Identify search sessions from a user - a search session includes all activities a user conducts with a search system in order to satisfy a (information/data) search needs. We followed the following steps to identify search sessions. First, we identified a user by IP address, where a unique IP address was considered a single user. We recognise the limitation of this approach, as several users may share the same IP address, however the IP address is the only information available for identifying a user. <br> Past research in log analysis usually apply the following two methods to identify a session: 30 minutes from the same IP address, and/or more than 30 minutes of inactivity between the current activity event and its immediate preceding event. We examined both methods carefully for our log data and concluded that both ended with large unwanted sessions from machine activities. Therefore, we take a brutal approach, by taking only a session from an IP address with a maximum 30 minutes duration.</li> <li>We also removed sessions whose 40% of activities resulted in ’page not found’ or whose activities were all about accessing grants. Within a session, we removed "duplicated" activities that were exactly as their precedent activity with less than one second time span (this could have been a result of reloading a page).</li> </ul> <p>The dataset (id_to_title_subject.csv) lists title and subject headings per record id.</p> <p> </p>
Coral calcification mechanisms in a warming ocean and the interactive effects of temperature and light
<p>Ross et al 2022 Supplementary data for coral (<em>Acropora nasuta</em>) temperature and light experiments. </p>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
Pathogen-sugar interactions revealed by universal saturation transfer analysis
<p>Supporting data for the "Pathogen-sugar interactions revealed by universal saturation transfer analysis" manuscript.</p>
Dataset supporting the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures. ACS Nano 14, 6285 (2020)"
<p>Dataset corresponding to theoretical calculations in the paper "Molecular Approach for Engineering Interfacial Interactions in Magnetic/Topological Insulator Heterostructures" ACS Nano 14, 6285 (2020), DOI: <a href="https://doi.org/10.1021/acsnano.0c02498">10.1021/acsnano.0c02498</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain the following files:</p> <ul> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).<br> </li> </ul>
Interactive Visualizations for: "Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties"
<p>This deposit includes 13 HTML 3D interactive visualizations of filaments and galaxies investigated in the accepted article, "<em>Virgo Filaments II: Catalog and First Results on the Effect of Filaments on galaxy properties</em>" by Castignani et al. (accepted, 20-Oct-2021).</p> <p>The specific files correspond to the filaments listed in Table 2 of the accepted manuscript:</p> <table align="left"> <caption>Tabulated HTML files and filaments</caption> <thead> <tr> <th scope="col">HTML File</th> <th scope="col">Filament (Table 2)</th> </tr> </thead> <tbody> <tr> <td> <p>SG_cube_Virgo_Serpens_Filament.html</p> </td> <td> <p>Serpens F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Coma_Berenices_Filament.html</p> </td> <td> <p>Coma Berenices F.</p> </td> </tr> <tr> <td> <p>SG_cube_VirgoIII_Filament.html</p> </td> <td> <p>VirgoIII F.</p> </td> </tr> <tr> <td> <p>SG_cube_Ursa_Major_Cloud.html</p> </td> <td> <p>Ursa Major Cloud</p> </td> </tr> <tr> <td> <p>SG_cube_NGC5353_4_Filament.html</p> </td> <td> <p>NGC5353/4 F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_Filament.html</p> </td> <td> <p>Leo Minor F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_B_Filament.html</p> </td> <td> <p>LeoII B F.</p> </td> </tr> <tr> <td> <p>SG_cube_Canes_Venatici_Filament.html</p> </td> <td> <p>Canes Venatici F</p> </td> </tr> <tr> <td> <p>SG_cube_W-M_Sheet.html</p> </td> <td> <p>W-M Sheet</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Draco_Filament.html</p> </td> <td> <p>Draco F.</p> </td> </tr> <tr> <td> <p>SG_cube_Virgo_Bootes_Filament.html</p> </td> <td> <p>Bootes F.</p> </td> </tr> <tr> <td> <p>SG_cube_Leo_Minor_B_Filament.html</p> </td> <td> <p>Leo Minor B F.</p> </td> </tr> <tr> <td> <p>SG_cube_LeoII_A_Filament.html</p> </td> <td> <p>LeoII A F.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>For each visualization galaxies within 2 Mpc are color-coded by the 3D local density, and galaxies with separations greater than 2 Mpc are shown with the grey points. The filament spine is shown with the black curve.</p> <p>The files were created with plotly.js v1.58.4.</p> <p> </p>
Paramecium Polycomb Repressive Complex 2 physically interacts with the small RNA binding PIWI protein to repress transposable elements
<p>Polycomb Repressive Complex 2 (PRC2) maintains transcriptionally silent genes in a repressed state via deposition of histone H3 K27 trimethyl (me3) marks. PRC2 has also been implicated in silencing transposable elements (TEs), yet how PRC2 is targeted to TEs remains unclear. To address this question, we identified proteins that physically interact with the <em>Paramecium</em> Enhancer-of-zeste Ezl1 enzyme, which catalyzes H3K9me3 and H3K27me3 deposition at TEs. We show that the <em>Paramecium</em> PRC2 core complex comprises four subunits, each required <em>in vivo</em> for catalytic activity. We also identify PRC2 cofactors, including the RNA interference (RNAi) effector Ptiwi09, which are necessary to target H3K9me3 and H3K27me3 to TEs. We find that the physical interaction between PRC2 and the RNAi pathway is mediated by a RING finger protein and that small RNA recruitment of PRC2 to TEs is analogous to the small RNA recruitment of H3K9 methylation SU(VAR)3-9 enzymes.</p>
Data for "Transient Density-Induced Dipolar Interactions in a Thin Vapor Cell"
<p>Data for "Transient Density-Induced Dipolar Interactions in a Thin Vapor Cell" as .npz files. The "datasets.csv" file lists, where each dataset is used in the journal publication. Additionally, there are two Python scripts (.py) which show how to load and plot a dataset. The dataset can be selected by changing the file name string in the script. Note, that for loading and plotting the simulation dataset the "plot_simulation.py" script has to be used.</p>
(dataset) Interactions of irradiation defects with nitrogen in α-Fe: an integrated experimental and theoretical study
<p>This dataset contains data , simulations and plot scripts in support of the manuscript "<em>Interactions of irradiation defects with nitrogen in <span class="math-tex">\(\alpha\)</span>-Fe: an integrated experimental and theoretical study</em>"</p>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Learning to embed lifetime social behavior from interaction dynamics - Data
<p><strong>Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics"</strong></p> <p>The following files are included:</p> <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> <p>alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual</p> </li> <li> <p>rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the <span class="math-tex">\(R^2\)</span> value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window</p> </li> <li> <p>indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa</p> </li> <li> <p>time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected</p> </li> </ul> <p>See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.</p>
Data from: The interaction of ice and law in Arctic marine accessibility
<p>Sea ice levies an impost on maritime navigability in the Arctic. But ice cover diminution due to anthropogenic climate change is generating expectations for improved accessibility in coming decades. Projections of sea ice cover retreating preferentially from the eastern Arctic suggest key provisions of international law of the sea will require revision. Specifically, protections against marine pollution in ice covered seas enshrined in Article 234 of the United Nations Convention on the Law of the Sea have been used in recent decades to extend jurisdictional competence over the Northern Sea Route only loosely associated with environmental outcomes. Projections show that plausible open water routes through international waters may be accessible by mid-century under all but the most aggressive of emissions control scenarios. While inter- and intra-annual variability places the economic viability of these routes in question for some time, the inevitability of a seasonally ice-free Arctic will be attended by a reduction of regulatory friction and a recalibration of associated legal frameworks.</p>
Magnetic interactions between radical pairs in chiral graphene nanoribbons
<p>OPEN DATA related to the research publication:</p> <p>T. Wang, S. Sanz, J. Castro-Esteban, J. Lawrence, A. Berdonces-Layunta, M. S. G. Mohammed, M. Vilas-Varela, M. Corso, D. Peña, T. Frederiksen, and D. G. de Oteyza<br> <em>Magnetic interactions between radical pairs in chiral graphene nanoribbons</em><br> Nano Lett. <strong>22</strong>, 164-171 (2022) [arXiv:2108.13473]</p> <p>Abstract: Open-shell graphene nanoribbons have become promising candidates for future applications, including quantum technologies. Here, we characterize magnetic states hosted by chiral graphene nanoribbons (chGNRs). The substitution of a hydrogen atom at the chGNR edge by a ketone effectively adds one p<sub>z</sub> electron to the π-electron network, producing an unpaired π-radical. A similar scenario occurs for regular ketone-functionalized chGNRs in which one ketone is missing. Two such radical states can interact via exchange coupling, and we study those interactions as a function of their relative position, which includes a remarkable dependence on the chirality, as well as on the nature of the surrounding ribbon, that is, with or without ketone functionalization. Besides, we determine the parameters whereby this type of system with oxygen heteroatoms can be adequately described within the widely used mean-field Hubbard model. Altogether, we provide insight to both theoretically model and devise GNR-based nanostructures with tunable magnetic properties.</p>
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
TRPC3 interaction with cholesterol as explored through MD (raw data)
<p>Transient receptor potential canonical 3 (TRPC3) channel belongs to the superfamily of transient receptor potential (TRP) channels which mediate Ca<sup>2+</sup> influx into the cell. These channels constitute essential elements of cellular signalling. TRPC3 is primarily gated by lipids, and its surface expression has been shown to be dependent on cholesterol, yet a comprehensive exploration of its interaction with this lipid has thus far not emerged. Here, through 80 µs of coarse-grained molecular dynamics simulations, we show that cholesterol interacts with multiple elements of the transmembrane machinery of TRPC3. Through our approach, we identify an annular binding site for cholesterol on the pre-S1 helix, and a non-annular site at the interface between the voltage-sensor like domain and pore domains. Here cholesterol interacts with exposed polar residues, and possibly acts to stabilise the domain interface.</p> <p> </p> <p><br> p { margin-bottom: 0.08in; color: #000000; line-height: 0.24in; text-align: justify; orphans: 2; widows: 2; background: transparent }p.western { font-family: "Palatino Linotype", serif; font-size: 12pt }p.cjk { font-family: "Palatino Linotype", serif; font-size: 12pt; so-language: de-DE }p.ctl { font-family: "Palatino Linotype", serif }a:visited { color: #954f72; text-decoration: underline }a:link { color: #0000ff; text-decoration: underline }</p> <p> </p>
Human-mouse syntenic Long Range Interactions in Neural Stem Cells
<p>This repository contains the list of human DNA regions corresponding to long-range interactions in RNA polII-mediated long-range interactions in mouse. They are named human-mouse syntenic Long Range Interactions (hmsLRI) and were obtained via synteny from mouse neonatal forebrain stem cells. They were also annotated for their overlap with DNA sequence variants (SNPs; CNVs) associated with human neurodevelopmental disease (NDD). The lists of genes identified via inferred that are potentially involved in NDD, eye-development, and traits (schizophrenia, bipolar disorder, intelligence).</p> <p>This is a resource for exploring the potential of the non-coding regions of DNA, the largest part of the genome (~98%), in NDD. Indeed, despite the numerous NDD-causative genes identified, only 42% of patients with severe developmental disorders carry pathogenic <em>de novo</em> mutations within coding sequences. More than a half of patient could be diagnosed and treated with further research and technologies, one option is studying the non-coding genome and how alterations affect genes. </p> <p>Tracks for visualization onto UCSC Genome Browser and WashU, are also provided.<br> See README for more details. </p> <p>The peer-reviewed publication for this dataset has now been published in International Journal of Molecular Science, available OA at: <a href="https://www.mdpi.com/1422-0067/23/14/7964">https://www.mdpi.com/1422-0067/23/14/7964</a>. Please cite this when using the dataset.</p>
ScienceDex guides
Understand access before you commit
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.