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11,710 results for “interactions”
Confocal Microscopy Visualizes Particle-Crack Interactions in Epoxy Composites with Optical Force Probe-Crosslinked Rubber Particles
<p>Data (*.csv and *.lif) corresponding to Figures 2-7 of the manuscript and Figures S1-S2 of the Supporting Information.</p>
Supplemental data for characterization of alpha and beta interactions using the HeXe setup [Eur. Phys. J. C 82, 361]
<p>Repository with supplemental data to:<br> <strong>Characterization of alpha and beta interactions in liquid xenon</strong>. Jörg, F., Cichon, D., Eurin, G. <em>et al. Eur. Phys. J. C</em> <strong>82, </strong>361 (2022) <a href="https://doi.org/10.1140/epjc/s10052-022-10259-3">10.1140/epjc/s10052-022-10259-3</a><br> A pre-print of the article is available <em>on arXiv: </em><a href="http://arxiv.org/abs/2109.13735">2109.13735</a></p> <p><strong>Note: </strong>When re-using the data, please make sure to cite the article (and not only the dataset)</p> <p><br> The files contain the measured data points (as well as their statistical and systematic uncertainties) as shown in the publication.<br> All datasets are stored in the .csv format.</p> <ul> <li><strong>20210924_yields_hexe_kr83m.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>83m</sup>Kr source. The data is shown in Figure 16 (dots) of the publication. Furthermore the file contains the LY ratio between the two Isomeric transitions of the <sup>83m</sup>Kr source, shown in Figure 17 of the article.</li> <li><strong>20210924_yields_hexe_rn222.csv</strong><br> This file contains the normalized light and charge yields as a function of the applied field from the measurement with the <sup>222</sup>Rn source. The data is shown in Figure 18 (blue-ish points) of the publication</li> <li><strong>20210924_drift_velocity_hexe_rn222.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>222</sup>Rn source. Drift velocity is given in units of mm/µs and the datapoints are shown in Figure 20 (black dots) of the publication </li> <li><strong>20210924_drift_velocity_hexe_kr83m.csv</strong><br> This file contains the measured electron drift velocity in liquid xenon at a temperature of 174.4 K in dependence of the field. The data was acquired using the <sup>83m</sup>Kr source. Drift velocity is given in units of mm/µs and are not displayed in the publications due to visibility reasons.</li> </ul> <p><strong>Minimum working example to plot the drift velocity using the <sup>83m</sup>Kr data:</strong></p> <pre><code class="language-python"> 1 import numpy as np 2 import matplotlib.pyplot as plt 3 4 # load the data set 5 data = np.loadtxt("20220427_drift_velocity_hexe_kr83m.csv", delimiter=",") 6 7 # Plot the systematic uncertainty on the drift field 8 plt.errorbar(data[:,0], data[:,2], xerr=data[:,1], fmt="o", capsize=2, ecolor="darkgray", 9 alpha=0.7, elinewidth=3, color="black") 10 11 # Plot the actual data points 12 plt.errorbar(data[:,0], data[:,2], yerr=data[:,3], fmt="o", color="black") 13 14 # Label the axis and define the range 15 plt.ylabel("Drift Velocity [mm/µs]") 16 plt.xlabel("Drift Field [kV/cm]") 17 plt.xscale("log") 18 plt.xlim(0.006, 2) 19 plt.ylim(0, 2.4) 20 plt.show() </code></pre> <p> </p>
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
Simulated Photon-Matter interaction of 3fs 4.96 keV European XFEL pulses with 2NIP
<p>Simulated photon-matter interaction trajectories for x-ray pulses from SASE1 beamline at European XFEL with a protein (pdb entry 2NIP).</p> <p>Input: https://dx.doi.org/10.5281/zenodo.884873 (WPG coherent wavefront propagation)</p> <p>Simulation Code: The PMI simulation was run with the code XMDYN, x-ray cross sections and transitions rates were calculated with XATOM. Both codes are developed at Center for Free Electron Laser Science, Theory Division, DESY, Hamburg, Germany.</p>
How does Mg2+(aq.) interact with ATP(aq.)? Observations through the lens of liquid-jet photoelectron spectroscopy - data
<p>Dataset pertaining to the article "How does Mg2+(aq) interact with ATP(aq)? Biomolecular Structure through the Lens of Liquid-Jet Photoemission Spectroscopy", published in Journal of the American Chemical Society (<a href="https://doi.org/10.1021/jacs.4c03174" target="_blank" rel="noopener">doi: 10.1021/jacs.4c03174</a>). Here, we arrive at new information on the interaction of ATP with Mg under physiological conditions by interpreting photoelectron spectra and intermolecular Coulombic decay from a liquid microjet.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data'). For ATP spectra, the ADP overview spectrum, and ADP/Mg2+ Mg 2s spectra, a binding energy correction shifting the liquid 1b1 feature to 11.33 eV is applied.<br>2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.</p> <p><br>The following files are provided:</p> <p>Photoemission data pertaining to adenosine phosphate PES measurements:<br>atp-mg.h5 - ATP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>adp-mg.h5 - ADP photoemission spectra in the presence of Mg2+ cations in varying concentration<br>amp-mg.h5 - AMP photoemission spectra in the presence of Mg2+ cations (a single concentration)<br>atp-adp-amp.h5 - ATP, ADP, AMP photoemission without Mg admixture<br>mg-only.h5 - Mg 2s core level spectra without ATP<br>tham-only.h5 - VB band measured with only THAM (tris(hydroxymethyl)aminomethane), used as buffer for pH stabilization<br>atp-icd.h5 - ATP photoemission spectra in the presence of Mg2+ cations, kinetic energy range of ICD features (publication is based on the last three entries).<br><br></p> <p>Numeric representations of the traces shown in the article's figures:<br>Figure_3-data.txt<br>Figure_5a-Mg2p.txt<br>Figure_5a-Mg2s.txt<br>Figure_5a-Mgonly.txt<br>Figure_5a-P2p.txt<br>Figure_5a-P2s.txt<br>Figure_5b.txt<br>Figure_5c.txt<br>Figure_6-ADP.txt<br>Figure_6-AMP.txt<br>Figure_6-ATP.txt<br>Figure_8a-data.txt<br>Figure_S2-Tris.txt<br>Figure_S2-Tris_with_Mg2+.txt<br>Figure_S4-data.txt.</p> <p>Version history<br>3: updated to reflect changes in Figure numbering between ArXiv-post and version published in JACS, additional Figure 5-data added<br>2: NeXus-data added<br>1: initial upload</p> <p>Contact person for questions regarding this data set: Uwe Hergenhahn, uhe@fhi.mpg.de . If you use these data for your scientific work we are curious to learn about it.</p>
Anthropomorphic Mechanisms for User Acceptance in Human-Robot Interaction - PRISMA pass data
<p>This is the data produced in the course of selecting relevant literature for the <em>"User Acceptance in Human-Robot Interaction"</em> literature review article.</p> <p><strong>Contents:</strong></p> <ul> <li>Initial pass records: <em>prisma0_wos.xlsx + prisma0_scopus.xlsx</em></li> <li>Initial pass eligibility assessment:<em><strong> </strong>prisma0_eval.xlsx</em></li> <li>Second pass records, filtering and coarse assessment:<em><strong> </strong>prisma1.xlsx</em></li> <li>Third pass records, filtering and coarse assessment:<em><strong> </strong>prisma2.xlsx</em></li> <li>Fine eligibility assessment of 2nd and 3rd pass: <em>prisma_avalanche_1_and_2_report_update_04_26.pdf</em></li> </ul> <p> </p>
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
<p>Files generated from the study described in <a href="https://doi.org/10.1101/2024.02.08.579534">Fernandes et. al (2024)</a> .</p> <p>The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment.</p> <p>The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios.</p> <p>The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study.</p> <p>Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (<a href="https://doi.org/10.1186/s13104-023-06421-z">Lima et. al 2024</a>).</p>
Drug Interaction Study Data from the Drug Approval Package for Epidiolex (Cannabidiol)
<p>Data from the drug interaction studies reported in the drug approval package for Epidiolex (cannabidil), U.S. Food and Drug Administration: https://www.accessdata.fda.gov/drugsatfda_docs/nda/2018/210365Orig1s000TOC.cfm.</p> <p>The data was manually extracted by a trained pharmacist from the PDF documents uploaded to drugs@fda for Epidolex drug approval package. The data extraction was reviewed for quality by an expert in pharamacology and natural product-drug interactions.</p>
Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip: Experimental and simulation data sets
<p>This data set collates the experimental and simulation data for the research paper "Coherent vortex dynamics in a strongly-interacting superfluid on a silicon chip".</p>
Molecular Interactions of Photosystem I and ZIF-8 in Bio-Nanohybrid Materials
<p>Supporting data for the article "Molecular Interactions of Photosystem I and ZIF-8 in Bio-Nanohybrid Materials", published in Physical Chemistry Chemical Physics (DOI: <span> <a href="https://doi.org/10.1039/D4CP03021D">10.1039/D4CP03021D)</a></span></p>
Graphic Illustration of Kendra Phelp's Talk: A harmonized taxonomic resource is critical for accurately interpreting host-pathogen interactions
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Kendra Phelps at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Simultaneous lidar and radar measurements for aeroso-cloud interaction studies
<p>Optical and microphysical aerosol and cloud properties derived from lidar and radar for aeroso-cloud interaction studies.</p>
Atmosphere-cryosphere interactions during the last phase of the LGM (21 ka BP) in the European Alps
<p>This dataset refers to: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmosphere-cryosphere interactions at 21 ka BP in the European Alps, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-43, in review, 2022. </p> <p> </p> <p>We used the regional climate model RegCM4 to investigate the physical processes sustaining the glacier extent during the Last Glacial Maximum (LGM) and pre-industrial time (PI) over the European Alps. After a bias-correction of precipitation and temperature data, we reconstructed the environmental equilibrium line altitude (envELA) of the Alpine glaciers, which resulted consistent with geological records. </p> <p>#----------------------------------------------------------</p> <p> </p> <p>LGM in the file names referes to 21 ka BP</p> <p>PI refers to pre-industrial</p> <p>#----------------------------------------------------------</p> <p> </p> <p><strong>This dataset contains:</strong></p> <p><strong>NetCDF files ------------------------------------------------------------------------------------</strong></p> <p> </p> <ul> <li><strong>Monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>RegCM4 monthly mean near-surface air temperature (TAS)</li> <li>RegCM4 monthly mean precipitation (PR)</li> <li>model topography (topo)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = RCM forced with MPI-ESM-P</li> <li>remapped = no</li> <li>resolution = 12 km</li> <li>files = <ul> <li>LGM_PR_TAS_monmean.nc</li> <li>PI_PR_TAS_monmean.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Bias-corrected monthly mean TAS and PR</strong> <ul> <li>variables = <ul> <li>model topography (topo)</li> <li>Bias-corrected RegCM4 monthly mean precipitation (PR)</li> <li>Bias-corrected RegCM4 monthly mean near-surface air temperature (TAS)</li> </ul> </li> <li>units = TAS [°C], PR [mm/day], topo [m a.s.l.]</li> <li>model = RegCM4 (ICTP)</li> <li>method = bias-correction based on HISTALP (TAS) and LAPrec (PR) of RegCM4 data</li> <li>remapped = onto HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files= <ul> <li>LGM_PR_TAS_monmean_BC.nc</li> <li>PI_PR_TAS_monmean_BC.nc</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>ELA</strong> <ul> <li>variables = <ul> <li>ELA </li> <li>average RegCM-HISTALP-LAPrec topography</li> </ul> </li> <li>units = m a.s.l.</li> <li>data = calculated from bias-corrected RegCM4 data</li> <li>method = Zebre et al. (2020)</li> <li>remapped = on HISTALP grid</li> <li>resolution = 5 arcmin</li> <li>files = <ul> <li>LGM_ELA.nc</li> <li>PI_ELA.nc</li> </ul> </li> </ul> </li> </ul> <p><br> <strong>csv files ------------------------------------------------------------------------------------</strong></p> <p><strong>* dates refer to model dates, not real ones!!!</strong><br> tj_700_hpa_pr_lgm : Tagliamento glacier daily wind and precipitation at the 21 ka BP<br> tj_700_hpa_pr_pi : Tagliamento glacier daily wind and precipitation at the PI<br> db_700_hpa_pr_lgm : Dora Baltea glacier daily wind and precipitation at 21 ka BP<br> db_700_hpa_pr_pi : Dora Baltea glacier daily wind and precipitation at the PI<br> r_700_hpa_pr_lgm : Rhine glacier daily wind and precipitation at 21 ka BP<br> r_700_hpa_pr_pi : Rhine glacier daily wind and precipitation at the PI<br> ist_700_hpa_pr_lgm : Inn-Salzach-Traun glacier daily wind and precipitation at 21 ka BP<br> ist_700_hpa_pr_pi : Inn-Salzach-Traun glacier daily wind and precipitation at the PI</p> <p> </p>
Dataset from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters"
<p>This repository contains several data from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters".</p> <p> </p> <p><strong>BBH_mergers_cat_*.dat</strong> contains the data for the BBH merger population produced by the three-body simulations. These data can be used to reproduce figures 4,5, and 8 of the paper. The file is organized in columns as:</p> <ul> <li>ID of the simulation.</li> <li>outcome of the simulation (12, merger triggered by a flyby event, 13 and 23 merger triggered after an exchange event in which the secondary (primary) BH is replaced by the intruder, 123 second generation BBH merger.</li> <li>mass of the primary BH in solar masses</li> <li>mass of the secondary BH in solar masses</li> <li>Chirp mass of the system in solar masses</li> <li>coalescence time since the beginning of the simulation in year (note that all the simulation with tcoal<1e5 yr have merged during the direct N-body simulation, while all the mergers that take place after this value are evolved with the equations by Peters 1964)</li> <li>eccentricity of the binary at 10 Hz in the detector frame</li> <li>tilt angle in radiant, defined as the angle between the orbital plane of the initial binary at the beginning of the simulation and the orbital plane of the final binary at the end of the simulation.</li> </ul> <p>The files named <strong>data_*.txt</strong> contains the masses, the position and the velocities at each timestep for the three simulations showed in fig.1 in the paper. The data are referred to the center-of-mass of the three-body system. The file is organized as follows:</p> <ul> <li>The first line of the file reports the masses in solar masses of the three BHs.</li> <li>Column 0 reports the time in yr</li> <li>Colum 1-3 report the x,y,z position for the m1 BH in parsec</li> <li>Colum 4-6 report the x,y,z position for the m2 BH in parsec</li> <li>Colum 7-9 report the x,y,z position for the m3 BH in parsec</li> <li>Colum 10-12 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 13-15 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 16-18 report the x,y,z components of the velocities of the m1 BH in km/s</li> </ul> <p>Finally, <strong>outcomes_*.dat</strong> contains two columns:</p> <ul> <li>Column 0 reports the ID of the simulation</li> <li>Column 1 reports the outcome of the simulation as: 12 flyby (or merger after a flyby), 13 and 23 exchange (or merger after an exchange) in which the secondary (primary) BH is replaced by the intruder, 0 in the system is ionized in three single BHs, 3 if the system is still interacting at 1Myr, i.e. when we stop our simulation.</li> </ul> <p>This file might be useful to train a machine-lerning classificator, and can be used to reproduce Fig. 2 of the paper.</p> <p> </p> <p><strong>Contacts:</strong></p> <p>Marco Dall'Amico</p> <p>marco.dallamico@phd.studenti.unipd.it</p> <p>marco.dallamico@pd.infn.it</p>
Bee Interaction Data from Global Biotic Interactions
<p>New versions of this dataset are found at: <a href="https://doi.org/10.5281/zenodo.16689326">https://doi.org/10.5281/zenodo.16689326</a></p> <p> </p> <p>This repository includes the following:</p> <ol> <li><strong>interactions-GloBI-September-14-2021.tsv.gz</strong>: a full version of the Global Biotic Interactions downloaded on September 14, 2021. No data transformations have occurred on this dataset after the download</li> <li><strong>globi_bee_data.sh</strong>: Shell script for extracting bee records using bee family names from the full version of Global Biotic Interactions</li> <li><strong>all_bee_data_unique.txt</strong>: a file that includes only bee interactions, based on extracting bee names from interactions-GloBI-September-14-2021.tsv.gz</li> </ol> <p>Global Biotic Interactions (GloBI - https://globalbioticinteractions.org) aims to simplify access to existing records of species interactions, such as predator-prey, plant-pollinator, and virus-host interactions. To achieve this, GloBI follows a process where existing, versioned datasets on species interactions are transformed into various aggregate formats, including tsv, csv, neo4j, rdf/nquad, and darwin core-ish archives, with applied name maps included for explicit taxonomic linking.</p> <p>GloBI owes its success to researchers, collections, projects, and institutions that openly share their datasets. Whenever you use this data, please credit the original data contributors, including citing the specific datasets used in derivative work. Each species interaction record in GloBI is linked to a reference and dataset citation. If you have any suggestions on how to make it easier to cite original datasets, you are welcome to join a discussion on https://globalbioticinteractions.org or related projects.</p> <p><strong>Introduction to Global Bee Interaction Data</strong></p> <p>The dataset available here includes all bee interactions recorded in the <a href="https://www.globalbioticinteractions.org/">Global Biotic Interactions</a> (GloBI; Poelen et al. 2014) index as of September 21, 2021. These interactions are gathered quarterly by the <a href="http://big-bee.net/">Big Bee Project </a>(Seltmann et al. 2021) from various sources, including natural history collections, community science observations (such as iNaturalist), and scientific literature. The dataset covers a wide range of bee interactions, including flower visitation, parasitic interactions (such as mite and viral interactions), and lecty, among others. The dataset is filtered for unique records based on interaction description and source citation to ensure accuracy and consistency. For other versions of the bee interaction dataset, please refer to <a href="https://zenodo.org/record/7315159">Seltmann, 2022</a>.</p> <p><strong>Data Description</strong><br>Please see the <a href="https://www.globalbioticinteractions.org/process">integration process page</a> to better understand how Global Biotic Interactions combines datasets from various sources. The complete interaction dataset for all species can be accessed via <a href="https://www.globalbioticinteractions.org/data">https://www.globalbioticinteractions.org/data</a> and the <a href="https://doi.org/10.5281/zenodo.3950589">GloBI Community Zenodo publication</a>.</p> <p><strong>Dataset column names</strong> definitions <a href="https://api.globalbioticinteractions.org/interactionFields">https://api.globalbioticinteractions.org/interactionFields</a> or <a href="https://api.globalbioticinteractions.org/interactionFields">https://api.globalbioticinteractions.org/interactionFields</a></p> <p><strong>References</strong></p> <p>Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a></p> <p>Katja C. Seltmann. (2022). Global Bee Interaction Data (v2.02) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7315159">https://doi.org/10.5281/zenodo.7315159</a></p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p>
Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.
<p><strong>ABSTRACT </strong></p> <p>It contains the data of drug targets (gene names), uniprot identifiers, secondary linked data sources (e.g., PharmGKB), market drug name, chembl identifier, and pubchem compound identifier obtained from DGIdb.</p> <p><strong>Instructions: </strong></p> <p>Data were cleaned and duplicates were removed. Data were all categorical features.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "DRG_DEPOT" summer 2023 team project. It is used for constructing R2G dataset, which will map drugs to their drug targets (gene -> protein = drug target)</p> <p><strong>Acknowledgements</strong></p> <p>Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, Griffith M, Griffith OL, Wagner AH. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021 Jan 8;49(D1):D1144-D1151. doi: 10.1093/nar/gkaa1084. PMID: 33237278; PMCID: PMC7778926.</p> <p>https://www.dgidb.org/</p> <p><strong>U-BRITE last update date:</strong> 06/09/2023</p>
Short-Range Electronic Interactions between Vanadium and Molybdenum in Bimetallic SAPO‑5 Catalysts Revealed by Hyperfine Spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, Computer Simulation and Analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DTA</strong>, and <strong>m</strong>.</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong> and <strong>DTA</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension<strong> m</strong></li> </ul> </li> <li>Are the data <strong>generated</strong> (e.g. by a machine) or <strong>collected</strong> (e.g. by means of a survey)? <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with ER5106QT cavity and ER035 M NMR gaussmeter produced by Bruker.</li> </ul> </li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230612_01_CW </strong>folder includes CW-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; simulations in m format.</li> <li>Files in <strong>PARACAT_WP4_20230612_02_Pulse</strong> folder includes Pulsed-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; files in m format were used to process the data.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>HYSCORE </strong>– HYperfine Sublevel CORrelation spectroscopy</li> <li>definitions of variables: <strong>Magnetic field, Temperature</strong></li> <li>units of measurement: <strong>Gauss (G), K</strong></li> </ul> </li> </ul>
Big Bee indexed biotic interactions and review summary
<p><strong>Extending Anthophila research through image and trait digitization (Big-Bee) indexed biotic interactions and review summary.</strong></p> <p>Declining populations of bees impact plant-pollinator interactions in both natural and agricultural systems. While bees and other insects pollinate most wild plants and are critical to sustaining a large proportion of global food production, they are decreasing in both numbers and diversity. Our understanding of the factors driving these declines is limited because we lack sufficient data on the distribution of bee species, and on the behavioral and anatomical traits that may make them either vulnerable or resilient to human-induced environmental changes, such as habitat loss and climate change. Fortunately, wild bees have been collected by researchers and deposited in natural history collections for over 100 years, retaining a wealth of associated attributes that can be extracted from specimen images. This project will digitally capture data and images from these historic specimens, develop tools to measure bee traits from these images and generate a comprehensive bee trait and image dataset to measure changes through time. This will increase our understanding of specific traits that put bee species at risk of decline - a critical need for both sustaining our agricultural economy and the conservation of our natural resources. In addition, the large image datasets created by this project can be used for new artificial intelligence identification tools that will help improve our future pollinator observation and monitoring efforts.</p> <p>The Big-Bee project began in 2021 and is funded by the National Science Foundation to mobilize data about worldwide bee species to data aggregators (e.g., iDigBio, GBIF). The Big-Bee Thematic Collection Network (Big-Bee) will create over one million high-resolution 2D and 3D images of bee specimens, representing over 5,000 worldwide bee species, including all of the major pollinating species of the United States. The Big-Bee network includes 13 institutions and partnerships with US government agencies. Novel mechanisms for sharing image datasets will be developed and datasets of bee traits will be available through an open data portal, the Bee Library, for research and education. The Big-Bee project will engage the general public in research through community science via crowdsourcing trait measurements and data transcription from images. In addition, training and professional development for natural history collection staff, researchers, and university students in data science will be provided through the creation and implementation of workshops focusing on bee traits and species identification. All data resulting from this award will be shared with and publicly available through the national digitized biocollections resource, iDigBio.org.</p> <p>This is the first archive of Big-Bee data indexed by Global Biotic Interactions (GloBI). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open-source software. This version of the Big Bee dataset includes interactions that are not just bees. Also in this version, the datasets included in this publication are specifically those institutions in the Big Bee project network and do not represent all bee interaction data found at Global Biotic Interactions.</p> <p><strong>Bee Library Information - Statistics about Big Bee data providers</strong></p> <p>The specimens indexed by GloBI are also found in the <a href="https://library.big-bee.net/portal/">Bee Library</a>. To date, the number of specimens and images in the library are listed below. The Bee Library taxonomic backbone is not yet complete, so information regarding the number of species is not yet available. Further summary statistics are available in the Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf file.</p> <p><strong>From Bee Library (partner indexed records)</strong><br> 1,234,107 occurrence records<br> 993,692 (81%) georeferenced<br> 351,592 (28%) occurrences imaged<br> 986,323 (80%) identified to species<br> 9 families<br> 526 genera<br> 10,700 species<br> 11,386 total taxa (including subsp. and var.)</p> <p><strong>Statistics Per Collection</strong></p> <table> <tbody> <tr> <td>Collection</td> <td>Occurrences</td> <td>Georeferenced</td> <td>Imaged</td> <td>Interactions Indexed in GloBI (all)</td> <td>Interactions Indexed in GloBI (bees)</td> </tr> <tr> <td>ASU Hasbrouck Insect Collection - Bee<br> Records</td> <td>13223</td> <td>13221</td> <td>2352</td> <td>21300</td> <td>3834</td> </tr> <tr> <td>Bee Biology and Systematics Laboratory,<br> USDA-ARS Pollinating Insect-Biology,<br> Management, Systematics Research</td> <td>561820</td> <td>547461</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <td>California Academy of Sciences</td> <td>884</td> <td>300</td> <td>3</td> <td>16984</td> <td>117</td> </tr> <tr> <td>California Academy of Sciences - Type<br> Collection</td> <td>1838</td> <td>59</td> <td>83</td> <td>0</td> <td>0</td> </tr> <tr> <td>Essig Museum of Entomology, University<br> of California Berkeley</td> <td>58551</td> <td>55028</td> <td>0</td> <td> </td> <td>0</td> </tr> <tr> <td>Florida State Collection of Arthropods</td> <td>17134</td> <td>12349</td> <td>7816</td> <td>559</td> <td> </td> </tr> <tr> <td>Museum of Comparative Zoology, Harvard<br> University</td> <td>22020</td> <td>21099</td> <td>11595</td> <td>6777</td> <td>1535</td> </tr> <tr> <td>Natural History Museum of Los Angeles<br> County</td> <td>24685</td> <td>7421</td> <td>3480</td> <td>0</td> <td>0</td> </tr> <tr> <td>San Diego Natural History Museum<br> Entomology Department</td> <td>4065</td> <td>1690</td> <td>1982</td> <td>8688</td> <td>90</td> </tr> <tr> <td>University of California Santa Barbara<br> Invertebrate Zoology Collection</td> <td>8674</td> <td>8410</td> <td>2751</td> <td>1940</td> <td>660</td> </tr> <tr> <td>University of Colorado Museum of Natural<br> History, Entomology Collection</td> <td>18043</td> <td>18043</td> <td>0</td> <td>9589</td> <td>4723</td> </tr> <tr> <td>University of Kansas Natural History<br> Museum Entomology Division</td> <td>464927</td> <td>275200</td> <td>304415</td> <td>119963</td> <td>112677</td> </tr> <tr> <td>University of Michigan Museum of Zoology<br> Division of Insects</td> <td>17764</td> <td>15305</td> <td>15269</td> <td>53755</td> <td>4134</td> </tr> <tr> <td>University of New Hampshire, Donald S.<br> Chandler Entomological Collection</td> <td>17685</td> <td>17393</td> <td>0</td> <td>3137</td> <td>3137</td> </tr> <tr> <td>USGS Native Bee Inventory and Monitoring<br> Lab</td> <td>101</td> <td>101</td> <td>0</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <p><strong>GloBI Data Review Report - Datasets in Review from Global Biotic Interactions</strong></p> <p>Datasets under review:<br> - UUniversity of Michigan Museum of Zoology, Division of Insects accessed via https://github.com/globalbioticinteractions/ummz-ummzi/archive/d9282e51f29f3157af2e5869a09ea8a111ddea34.zip on 2023-07-24T22:06:08.671Z<br> - Arizona State University Hasbrouck Insect Collection accessed via https://github.com/globalbioticinteractions/asu-asuhic/archive/4ed77cb9ca8e526269d4678692e2844c950022f8.zip on 2023-07-24T22:07:09.630Z<br> - California Academy of Sciences Entomology and Entomology Type Collection accessed via https://github.com/globalbioticinteractions/cas-ent/archive/47d385b73a63aa379cd5e6d3615005ba78b0ffc1.zip on 2023-07-24T22:08:13.753Z<br> - University of California Berkeley, Essig Museum of Entomology accessed via https://github.com/globalbioticinteractions/emec/archive/93b17a3db566baa001ce9190e6fbdb60fa99dda4.zip on 2023-07-24T22:08:24.495Z<br> - Florida State Collection of Arthropods accessed via https://github.com/globalbioticinteractions/fsca/archive/2cdcf9475b7e0ef2a728a96535608bc0ce2ac5ca.zip on 2023-07-24T22:08:49.972Z<br> - University of Kansas Natural History Museum accessed via https://github.com/globalbioticinteractions/ku-semc/archive/a9c7cb81050eef68b4428667206a219da458f517.zip on 2023-07-24T22:09:17.016Z<br> - Natural History Museum of Los Angeles County accessed via https://github.com/globalbioticinteractions/lacm-lacmec/archive/dafbf532c53fbadba126c81186c26d52677aa781.zip on 2023-07-24T22:11:11.442Z<br> - Harvard University M, Morris P J (2021). Museum of Comparative Zoology, Harvard University. Museum of Comparative Zoology, Harvard University. accessed via https://github.com/globalbioticinteractions/mcz/archive/b33635a9fc75fd7931ad968cbc11180e6467bfd7.zip on 2023-07-24T22:21:32.961Z<br> - San Diego Natural History Museum accessed via https://github.com/globalbioticinteractions/sdnhm-sdmc/archive/7238d8b804f543250eb487b43144e1125fb3688a.zip on 2023-07-24T22:26:25.503Z<br> - University of Colorado Museum of Natural History Entomology Collection accessed via https://github.com/globalbioticinteractions/ucm-ucmc/archive/60530dcc82d33c9675a4026ad60dc40bea8f2a91.zip on 2023-07-24T22:26:50.178Z<br> - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/66a4e39589d1dfa299d07985546c4be522ff60d8.zip on 2023-07-24T22:27:13.801Z<br> - University of New Hampshire Donald S. Chandler Entomological Collection accessed via https://github.com/globalbioticinteractions/unhc-unhc/archive/d7668a6bb4545dc4da0645ecc383169ba547b0f5.zip on 2023-07-24T22:27:28.670Z</p> <p>Generated on:<br> 2023-07-24</p> <p>by:<br> GloBI's Elton 0.12.6 <br> (see https://github.com/globalbioticinteractions/elton).</p> <p>Note that all files ending with .tsv are files formatted <br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv: <br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar: <br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p>indexed_interactions_bees.tsv:<br> All indexed bee interactions <br> </p> <p>datasets.zip:<br> All datasets reviewed for this publication</p> <p> Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf:<br> Summary statistics from the Bee Library and GloBI about data partners</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/Big-Bee-Network/issues-observations-and-questions/discussions or contact the authors by email.</p> <p><strong>Funding:</strong><br> The creation of this archive was made possible by the National Science Foundation award Collaborative Research: Digitization TCN: Extending Anthophila research through image and trait digitization (Big-Bee). Award numbers: <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102006">DBI:2102006</a>, DBI:2101929, DBI:2101908, DBI:2101876, DBI:2101875, DBI:2101851, DBI:2101345, DBI:2101913, DBI:2101891 and DBI:2101850.</p> <p>References:<br> Poelen JH, Simons JD and Mungall CH. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a>.</p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p> <p>Jorrit Poelen, Tobias Kuhn, & Katrin Leinweber. (2022). globalbioticinteractions/elton: 0.12.5 (0.12.5). Zenodo. https://doi.org/10.5281/zenodo.7267926</p>
Adsorption free energies and potentials of mean-force for interactions between amino acids, lipid fragments, and nanoparticles
<p>This dataset contains tabulated potentials of mean force (PMFs) and associated adsorption (binding) free energies for interactions of amino acids side chain analogues and lipid fragments (LF) with a range of materials: titanium dioxide, iron oxide, amorphous silica, quartz, and a range of carbon-based materials including amorphous carbon, graphene and carbon nanotubes both in a pristine form and functionalized by certain chemical groups. All data were computed from atomistic molecular dynamics simulations as a part of the SmartNanoTox project 2016-2020. Version 2 of the dataset includes additional materials: zink oxide, zink sulfate in pristine and PMMA-coated forms computed within NanoSolveIt project (2019-2023). The data are intended to be used in coarse-grained models describing interactions of nanomaterials with nanoparticles, for the prediction of the binding affinity of proteins and lipids to nanoparticles, and as biological "fingerprints" of nanomaterials characterizing behavior of the nanomaterials in biological environments. </p>
The interactive effects of predator stress, predation, and the herbicide Roundup®
These data are from a mesocosm experiment examined how lethal predators and cues from caged predators potentially interacted with four concentrations of the herbicide Roundup® in its effects on three species of tadpoles (gray tree frogs, green frogs, bulfrogs).
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