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An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures. Related Article: Jeff K. Kerkovius, Andrea Stegner, Aneta Turlik, Pik Hoi Lam, Kendall N. Houk, Sarah E. Reisman|2022|J.Am.Chem.Soc.|144|15938|doi:10.1021/jacs.2c06584

Related Article: Billy Deng, Xiang Wang, Suning Wang|2019|Chem.-Eur.J.|25|14694|doi:10.1002/chem.201903534

Additional file 2: Table S1. Raw data used for all statistical analyses.

- CERN-LHC. This article presents measurements of the $t$-channel single top-quark $(t)$ and top-antiquark $(\bar t)$ total production cross sections $\sigma(tq)$ and $\sigma(\bar tq)$, their ratio $R_{t}=\sigma(tq)/\sigma(\bar tq)$, and a measurement of the inclusive production cross section $\sigma(tq+\bar tq)$ in proton-proton collisions at $\sqrt{s}~=~7~\mathrm{TeV}$ at the LHC. Differential cross sections for the $tq$ and $\bar tq$ processes are measured as a function of the transverse momentum and the absolute value of the rapidity of $t$ and $\bar t$, respectively. The analyzed data set was recorded with the ATLAS detector and corresponds to an integrated luminosity of $4.59~\mathrm{fb}^{-1}$. Selected events contain one charged lepton, large missing transverse momentum, and two or three jets. The cross sections are measured by performing a binned maximum-likelihood fit to the output distributions of neural networks. The resulting measurements are $\sigma(tq)=46 \pm 1(\mathrm{stat})\pm 6(\mathrm{syst})~\mathrm{pb}$, $\sigma(\bar tq)=23 \pm 1(\mathrm{stat})\pm 3(\mathrm{syst})~\mathrm{pb}$, $R_{t}=2.04 \pm 0.13(\mathrm{stat})\pm 0.12(\mathrm{syst})$, and $\sigma(tq+\bar tq)=68 \pm 2(\mathrm{stat})\pm 8(\mathrm{syst})~\mathrm{pb}$, consistent with the Standard Model expectation. The uncertainty on the measured cross sections is dominated by systematic uncertainties, while the uncertainty on $R_{t}$ is mainly statistical. Using the ratio of $\sigma(tq+\bar tq)$ to its theoretical prediction, and assuming that the top-quark-related CKM matrix elements obey the relation $|V_{tb}|\gg |V_{ts}|$, $|V_{td}|$, we determine $|V_{tb}|=1.02 \pm 0.07$. Detailed list of the contribution of each source of uncertainty to the total relative uncertainty on the measured $\dfrac{\mathrm{d}\sigma}{\mathrm{d}|y(\bar t)|}$ distribution given in percent for each bin. The list includes only those uncertainties that contribute with more than $1\%$. The following uncertainties contribute to the total uncertainty with less than $1\%$ to each bin content$:$ JES detector, JES statistical, JES physics modeling, JES mixed detector and modeling, JES close-by jets, JES pileup, JES flavor composition, JES flavor response, $b-$JES, jet-vertex fraction, $b/\bar{b}$ acceptance, mistag efficiency, $E_{\mathrm{T}}^{\mathrm{miss}}$ modeling, $W+$ jets shape variation, $t \bar{t}$ generator, $t \bar{t}$ ISR/FSR, and unfolding. In cases when the uncertainty is report to be "$<1\%$" in the table of the paper the uncertainty is approximated by a value of $0.5\%$.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. No data abstract available.

$\rho_{4}$ Standard method, for Pb+Pb 5.02 TeV, $|\eta|$<2.5, 0.5< $p_{T}$ <2.0 GeV vs $N^{rec}_{ch}$ based Centrality. No data abstract available.

Related Article: Edouard Duchamp, Benoit Deschênes Simard, Stephen Hanessian|2019|Org.Lett.|21|6593|doi:10.1021/acs.orglett.9b01842

Related Article: Paige M. E. Hawkins, Dennis Y. Liu, Roger G. Linington, Richard J. Payne|2021|Org.Biomol.Chem.|19|6291|doi:10.1039/D1OB01062J

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures. Related Article: Qiuming Liang and Datong Song|2017|Inorg.Chem.|56|11956|doi:10.1021/acs.inorgchem.7b01918

This dataset contains methane and nitrous oxide dissolved gas concentration, dissolved methane carbon isotope, and ancillary hydrographic data from research cruises in the North American Arctic Ocean between 2015-2018. Ocean samples for methane and nitrous oxide analysis were collected from Niskin bottles mounted on a CTD rosette. Water was collected into glass serum bottles and allowed to overflow three times before preserving with mercuric chloride and sealing with with butyl rubber stoppers and aluminum crimp seals. Gas concentrations were determined using a purge and trap system coupled to a gas chromatograph/mass spectrometer, following the method of Capelle et al. (2015). Equilibrium dry atmospheric concentrations were 328.25, 329.14, 330.11, and 330.96 ppb for N2O and 1919.64, 1933.67, 1934.92, and 1933.50 ppb for CH4 in 2015, 2016, 2017, and 2018, respectively. Equilibrium dissolved concentrations were calculated from the measured temperature and salinity following Wiesenburg and Guinasso (1979) for CH4 and Weiss and Price (1980) for N2O. Equilibrium concentrations were calculated based on sample temperature and salinity and the atmospheric N2O or CH4 concentrations measured at Barrow, Alaska by the NOAA Earth System Research Laboratory Global Monitoring Division (Dlugokencky et al., 2020a,b), with corrections to local sea level pressure and 100% humidity. Oxygen concentration was determined using an oxygen sensor mounted on the Niskin rosette, calibrated with discrete samples analyzed by Winkler titration. The mixed layer depth was defined based on a potential density difference criterion of 0.125 kg/m³ relative to the density at 5 m depth, using CTD profiles binned to 1 m. The mixed layer depth was set to 5 m as a minimum. The instantaneous gas transfer velocities and fluxes are based on the instantaneous wind speed at the time of sampling. The 30-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using up to the prior 30 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). The 60-day weighted gas transfer velocities and fluxes are integrated over the residence time of the gas in the mixed layer, using the prior 60 days of observations, following the method of Teeter et al. (2018) as described in the main manuscript of Manning et al. (2022). Atmospheric sea level pressure was obtained from the NCEP/NCAR reanalysis product, which is provided by the NOAA-ESRL Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded). Fractional ice cover was obtained from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (https://osi-saf.eumetsat.int). Sea ice concentration product AMSR-2 (identifier OSI-408) was used in 2017–2018 and SSMIS (identifier OSI-401-b) was used in 2015–2016.