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11,710 results for “interaction”
Data for: Mobile impurities interacting with a few one-dimensional lattice bosons
<p>Dataset for <em>Mobile impurities interacting with a few one-dimensional lattice bosons</em> (<a href="https://iopscience.iop.org/article/10.1088/1361-6455/acb51b">10.1088/1361-6455/acb51b</a>). It corresponds to exact diagonalization results for energies and bipolaron sizes.</p> <p>The files correspond to the following figures in the preprint:</p> <p>Fig. 1a: Ep_UBB2.dat <br> Fig. 1b: Ep_UBB4.dat <br> Fig. 1c: Ep_UBB6.dat <br> Fig. 1d: Ep_UBB8.dat <br> Fig. 2: Ep_UBI50.dat<br> Fig. 5a: Ebp_UBB2.dat <br> Fig. 5b: Ebp_UBB4.dat <br> Fig. 5c: Ebp_UBB6.dat <br> Fig. 5d: Ebp_UBB8.dat <br> Fig. 6: Ebp_UBI50.dat <br> Fig. 7a: rbp_UBB2.dat <br> Fig. 7b: rbp_UBB4.dat <br> Fig. 7c: rbp_UBB6.dat <br> Fig. 7d: rbp_UBB8.dat <br> Fig. 8: rbp_UBI50.dat</p>
Pollinator-flower interactions in gardens during the COVID-19 pandemic lockdown of 2020
<p>During the main COVID-19 pandemic lockdown period of 2020 an impromptu set of pollination ecologists came together via social media and personal contacts to carry out standardised surveys of the flower visits and plants in their gardens. The surveys involved 67 rural, suburban and urban gardens, of various sizes, ranging from 61.18<sup>o</sup> North in Norway to 37.96<sup>o</sup> South in Australia and resulted in a data set of 25,174 rows long and comprising almost 47,000 visits to flowers, as well as records of plants that were not visited by pollinators. In this first publication from the project we present a brief description of the data and make it freely available for any researchers to use in the future, the only restriction being that they cite this paper in the first instance. As well as producing a data set that we hope will be widely used in the future, the project helped enormously with the health and mental wellbeing of the participants, a by-product of ecological field work that cannot be over-estimated.</p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate FGOB10Ptm-1. </p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate P-A14. </p>
Protein Protein Interaction Prdiction Datasets of H.Pylori and S.cerevisiae Species
<p><br> Protein protein interaction prediction datasets related to 2 different species. These datasets have been comprehensively used in published literature to assess the performance of protein-protein interaction predictors.<br> </p>
Influence of Large-scale Land-sea Atmosphere Interaction on Ozone Pollution in Coastal Cities in the Northern Bohai Sea
<p><strong>O3_obs </strong>includes ozone observations for Qinhuangdao (QHD), Jinzhou (JZ), Yingkou (YK), Dalian (DL) from 29 August to 5 September 2017, and the information of four sites including station code, longitude and latitude. <strong>O3_sim</strong> includes ozone simulation in the four sites extracted according to location of them. <strong>Met_obs</strong> and <strong>Met_sim</strong> include the observations of 2 m temperature (℃), 2 m relative humidity (RH2) and 10 m wind speed for the 4 stations from 29 August to 5 September 2017, and the information of four stations including station code and their location. <strong>Slp_wind_9km.nc</strong> is mean sea-level pressure and wind in Phase Ⅰ and Phase Ⅱ. <strong>O3_wind_9km.nc</strong> is mean simulated surface ozone mixing ratios and wind at 10 m in 19:00-09:00 LT and 10:00-18:00 LT during Phase Ⅰ and Phase Ⅱ. <strong>Process_contribution </strong>includes mean surface O<sub>3</sub> mixing ratios and O<sub>3</sub> contribution at the bottom level in Phase Ⅰ, Phase Ⅱ, and at different heights (AGL) in Phase Ⅱ in four sites, respectively. <strong>O3_source_site</strong> includes time series of O<sub>3 </sub>source in QHD, JZ, YK, and DL. <strong>Mean_source_base_27km.nc </strong>is the mean O­<sub>3</sub> contribution in Phase Ⅰ and Phase Ⅱ from five primary exogenous source regions. <strong>Mean_source_control_27km.nc</strong> is the O<sub>3</sub> contribution in Phase Ⅱ from the BTH and NEC emissions in Phase I, in which BTH and NEC’s emissions in Phase Ⅱ are set zero. <strong>Trjectory_conc_pa</strong> includes three trajectories analyzed in this work and vertical O<sub>3</sub> and NO<sub>X</sub> mixing ratios, and the chemical generations and consumptions of O<sub>3</sub> within the air masses along the trajectories.</p>
Input features of E. coli proteome for predicting and modeling protein-protein interactions with AF2Complex
<p>Input features to be used with AF2Complex for predicting protein-protein interactions among ~4400 E. coli proteins. A pickled feature file was generated by the feature data pipeline of AF2Complex for each E. coli protein. To reduce storage size, we limited up to 10,000 MSA sequences and up to 10 structural templates from the Protein Data Bank. The cutoff date for sequence libraries and the Protein Data Bank releases used for feature generation is no later than 11-30-2021.</p> <ul> <li>ecoli_af2c_fea.txt -- A list of all E coli protein with pre-generated input features</li> <li>af2c_fea_ecoli_220331_msa10ktem10.tar -- Input features named after the UniProt ID of each proteins. Note that after untar the tarball, you may use the gzipped feature pickle files directly with AF2Complex w/o gunzip.</li> </ul>
Datasets for "Flavour-selective localization in interacting lattice fermions"
<p>This submission includes the datasets shown in the figures of journal article</p> <p>"Flavour-selective localization in interacting lattice fermions" by D. Tusi et al.<br> DOI: 10.1038/s41567-022-01726-5</p> <p>The naming of the files corresponds to the figure numbering in the original article.</p>
Dataset of "Chronic TCR-MHC (self)-interactions limit the functional potential of TCR affinityincreased CD8 T lymphocytes"
<p><strong>Background</strong>: Affinity-optimized T cell receptor (TCR)-engineered lymphocytes targeting tumor antigens can mediate potent antitumor responses in cancer patients, but also bear substantial risks for off-target toxicities. Most preclinical studies have focused on T cell responses to antigen-specific stimulation. In contrast, little is known on the regulation of T cell responsiveness through continuous TCR triggering and consequent tonic signaling. Here, we addressed the question whether increasing the TCR affinity can lead to chronic interactions occurring directly between TCRs and MHC-(self) molecules, which may modulate the overall functional potency of tumor-redirected CD8 T cells. For this purpose, we developed two complementary human CD8 T cell models (i.e. HLA-A2 knock-in and knock-out) engineered with incremental-affinity TCRs to the HLA-A2/NY-ESO-1 tumor antigen.<br> <strong>Methods</strong>: The impact of HLA-A2 recognition, depending on TCR affinity, was assessed at the levels of the TCR/CD3 complex, regulatory receptors, and signaling, under steady-state conditions and in kinetic studies. The quality of<br> CD8 T cell responses was further evaluated by gene expression and multiplex cytokine profiling, as well as real-time quantitative cell killing, combined with co-culture assays.<br> <strong>Results</strong>: We found that HLA-A2 per se (in absence of cognate peptide) can trigger chronic activation followed by a tolerance-like state of tumor-redirected CD8 T cells with increased-affinity TCRs. HLA-A2pos but not HLA-A2neg T cells displayed an activation phenotype, associated with enhanced upregulation of c-CBL and multiple inhibitory receptors. T cell activation preceded TCR/CD3 downmodulation, impaired TCR signaling and functional<br> hyporesponsiveness. This stepwise activation-to-hyporesponsive state was dependent on TCR affinity and already detectable at the upper end of the physiological affinity range (KD ≤ 1 μM). Similar findings were made when<br> affinity-increased HLA-A2neg CD8 T cells were chronically exposed to HLA-A2pos-expressing target cells.<br> <strong>Conclusions</strong>: Our observations indicate that sustained interactions between affinity-increased TCR and self-MHC can directly adjust the functional potential of T cells, even in the absence of antigen-specific stimulation. The<br> observed tolerance-like state depends on TCR affinity and has therefore potential implications for the design of affinity-improved TCRs for adoptive T cell therapy, as several engineered TCRs currently used in clinical trials share<br> similar affinity properties.</p>
Idealized wave data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from idealized solutions reported by Romero 2019</p> <p>Data are in Netcdf format and include metadata.</p> <p>Each file corresponds to a duration-limited solution with constant wind speed as indicated in the filename</p> <p>(e.g. 10mps means 10 m/s winds)</p> <p> </p>
Level 2 Winter data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020.</p> <p>These data are Level 2 of the nested configuration with a horizontal resolution of 270 m. Also included in this repository are the Level 2 and Level 3 grid files.</p> <p>Data are in Netcdf format including the metadata.</p> <p>The two simulation periods are December 2006 and Spring 2007.</p> <p>List of files:</p> <p>L2_Dec2006.nc -- December control solution only forced by winds</p> <p>L2_cew_Dec2006.nc -- December solution including forcing by both winds and currents.</p> <p>L2_grid.nc -- Level 2 grid</p> <p>L3_grid.nc -- Level 3 grid</p> <p> </p> <p> </p> <p> </p> <p> </p>
A complete map of specificity encoding for a partially fuzzy protein interaction
<p>All data required to run analyses for "A complete map of specificity encoding for a partially fuzzy protein interaction". Please see <a href="https://github.com/lehner-lab/fuzzy_specificity">https://github.com/lehner-lab/fuzzy_specificity</a> for instructions. </p>
Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps
<p><span>Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.</span></p>
Dataset for: Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies
<p>Dataset containing electronic, mechical, firmware, and software design files associated with the article: </p> <p><br>"Biohybrid superorganisms - on the design of a robotic system for thermal interactions with honeybee colonies"<br>By R. Barmak, D. N. Hofstadler, M. Stefanec, L. Piotet, R. Cherfan, T. Schmickl, F. Mondada, and R. Mills. EPFL, Switzerland and Univeristy of Graz, Austria.<br>IEEE Access, 2024, Vol 12, pp 50849-50871.</p> <p>doi: 10.1109/ACCESS.2024.3385658</p> <p><a href="https://doi.org/10.1109/ACCESS.2024.3385658">https://doi.org/10.1109/ACCESS.2024.3385658</a></p> <p> </p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1_hw_pcb_schematics_rev2.pdf</td> <td>Electrical schematic of the robotic frame</td> </tr> <tr> <td>2_hw_pcb_stackup_rev2.pdf </td> <td>Technical specifications for the robotic frame PCB manufacturing</td> </tr> <tr> <td>3_hw_pcb_bom_rev2.pdf </td> <td>Electronics Bill of Materials (BoM)</td> </tr> <tr> <td>4_hw_pcb_gerber_rev2.zip </td> <td>Robotic frame PCB manufacturing files (gerbers)</td> </tr> <tr> <td>5_hw_pcb_altium_rev2.zip</td> <td>Altium Designer project files</td> </tr> <tr> <td>6_hw_mechanical_rev2.zip</td> <td>DXF and STEP files of the mechanical structure of the robotic frame</td> </tr> <tr> <td>7_sw_firmware_rev2.zip</td> <td>Firmware source code and compiled binaries for STM32 microcontroller</td> </tr> <tr> <td>8_sw_handlers-1.0.1.zip</td> <td>Software for high-level interface to robot from a host device </td> </tr> </tbody> </table>
Datasets for "Advancing Drug-Target Interactions Prediction: Leveraging a Large-Scale Dataset with a Rapid and Robust Chemogenomic Algorithm"
<p>All datasets required to reproduce the results of publication "Drug-Target Interactions Prediction at Scale: the Komet Algorithm with the LCIdb Dataset"</p>
Data for a publication "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells"
<p>A dataset containing data for the published article "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells".</p> <p> </p> <p>For more details, please read the <strong>README - Description of data and analysis informations.txt</strong> file.</p> <p><strong>Dataset versions:</strong></p> <p><strong>V1:</strong> The first dataset containing a majority of the data.</p> <p><strong>V2:</strong> Dataset contains all the data mentioned in the article in the appropriate file formats for long-term preservation and accessibility.</p>
From social categorization to implicit citizenship theories: Advancing the socio-cognitive foundations of state–citizen interactions
<p>Data for the following article: Vogel, R., Vogel, D., Liegat, M. C., & Hensel, D. (2024). From social categorization to implicit citizenship theories: Advancing the socio‐cognitive foundations of state–citizen interactions. Public Administration Review, Article puar.13844. Advance online publication. https://doi.org/10.1111/puar.13844</p>
Assemblies, synapse clustering and network topology interact with plasticity to explain structure-function relationships of the cortical connectome
<p>Dataset linked to the article with the same title</p> <p>The model itself is very similar to its non-plastic counterpart under the following DOI: <a href="../record/7930275">10.5281/zenodo.7930275</a>, i.e. a 1.5 mm diameter cortical tissue comprising 211,712 neurons and their connectivity in the front limb and jaw subregions and the dysgranular zone of the Paxinos & Watson rat brain atlas. It's formatted in the open <a href="https://github.com/AllenInstitute/sonata">SONATA</a> standard and contains neuron locations and their properties (such as morphological types, cortical layer, etc.), their detailed morphologies, and synaptic connectivity (with all their anatomical and physiological parameters). The main difference from the non-plastic version is the addition of plasticity related parameters to <em>O1/S1nonbarrel_neurons__S1nonbarrel_neurons__chemical/edges.h5. </em>Extrinsic synaptic connections from the thalamus are included in this release, but for inputs from neurons in the remainder of non-barrel somatosensory cortex please see the non-plastic version of the circuit.</p> <p><strong>Analyzing the model</strong></p> <p>The model can be analyzed in terms of its anatomy, physiology and connectivity using the packages <a href="https://neurom.readthedocs.io/en/stable/">NeuroM</a>, <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a> and <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>. (see first Jupyter notebook)</p> <p><strong>Simulating the model</strong></p> <p>To simulate the model we'd recommend using out using our open-source simulator <a href="https://github.com/BlueBrain/neurodamus">Neurodamus</a>. The reference version is the branch <em>nbS1-2023</em>, which is archived under the following DOI: <a href="http://doi.org/10.5281/zenodo.8075202">10.5281/zenodo.8075202</a>. Instructions on how to use the simulator are provided on the GitHub page linked above. Briefly, you'll first have to <a href="https://github.com/BlueBrain/neurodamus#install-neurodamus">install Neurodamus</a>. Next, build a <em>"special"</em> executable that include compiled versions of ion channel and synapse models. To do that, follow <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files">these instructions</a>, where <em>mod-files-from-released-circuit </em>is replaced by the location of <em>O1/mods</em> on your system. Finally, <a href="https://github.com/BlueBrain/neurodamus#examples">run a simulation</a>. The specific simulation conditions and stimuli are specified in simulation configuration files. An exemplary simulation configuration is included in this release (<em>simulation_config.zip</em>).</p> <p><strong>Analyzing simulation results</strong></p> <p>Simulation results can be analyzed with <a href="https://bluebrainsnap.readthedocs.io/en/stable/">BlueBrain SNAP</a>, <a href="https://github.com/BlueBrain/ConnectomeUtilities">ConnectomeUtilities</a>, and <a href="https://github.com/BlueBrain/assemblyfire">assemblyfire</a>. Notebooks 2-5 go though these analysis and recreate some of the panels from our article. In most cases the notebooks can be run with the shared HDF5 files and don't require running any simulations.</p> <p><strong>Version 2</strong></p> <p>Bug fix in simulation_config.json and therefore new version of results (and corresponding notebooks). The underlying circuit model (O1.xz) did not change from v1.</p> <p>--</p> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
<p>Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.</p>
Temperature moisture interactions soil respiration experiment
<p>These files are from a soil incubation experiment looking at combined effects of temperature and moisture on soil C fluxes. They are prepared for model run and model-data comparison. Description of the data, e.g units, is not in the files themselves.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/mtdata_model_input.csv">mtdata_model_input.csv</a></p> <p>Contains model input for simulating the experimental setup.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/mtdata_co2.csv">mtdata_co2.csv</a></p> <p>Contains the measured data with averages and standard deviation of three replicates samples for treatment.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/site_Closeaux.csv">site_Closeaux.csv </a></p> <p>Containts soil properties required as model input.</p> <p> </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.