dataesr/scientific-paragraphs-categorization huggingface dataset:
We did not take into account cross-excitation and possible fluorescence emitted in the range less than 10 nm from the excitation wavelength. The image acquisition and analysis were performed in software LAS AF 3. 1. 2 (Leica Microsystems GmbH). Data were averaged from 200 × 200 µm area, approximately 30 µm deep in the calibration solution with a 20×/0. 75 immersion objective lens.
Fluorescence Lifetime Measurements Lifetime measurements of C-SNARF-4 calibration samples and C-SNARF-4 stained cheese samples were performed with Olympus FV1200 confocal laser scanning microscope based on Olympus IX83 inverted fluorescence microscope (Olympus Europe GmbH) equipped with two single photon counting hybrid detectors, two channel time correlator and 485 nm pulsed picosecond laser (PicoQuant GmbH). The laser repetition rate was set to 40 MHz and the laser power was adjusted to keep the mean detected photon rate below 2% of the laser repetition rate. Two spectral ranges were measured simultaneously (channel 1 corresponding to 570-620 nm, channel 2 corresponding to 650-700 nm). Typically, calibration data were averaged from 100 × 100 µm area, approximately 30 µm deep in the calibration solution with a 60×/1.
2 water immersion objective lens. Typically, 120 s acquisition was necessary to accumulate 10 5 detection events. When measuring cheese samples the focus was set to the cheese surface, i. e. , the plane of the strongest fluorescence. The measured fluorescence decay curves were fitted to a two-or three-component exponential model (a sum of two or three exponential decays) according to I(t)= N n = 1 a n e -t/ τ m = N n = 1 a n τ n N n = 1 a n (2) using the Beker & Hickl SPCImage software. Here τ m is the mean lifetime, other symbols are defined in Equation (1).
Microscopic pH Of Cheese Samples Measured With C-SNARF-4 Based on calibration data we tried to utilize the pH dependence of C-SNARF-4 fluorescence in the red spectral channel for cheese pH measurements on the microscopic scale. However, both the fluorescence intensity and the fluorescence lifetime distribution were essentially homogeneous across the sample. We have also observed a strong affinity of the dye toward the cheese surface. We measured a total of 4 cheeses (designated vat 1 to vat 4), each multiple times. Surprisingly, a new decay component with relatively long lifetime (τ 3 ≥ 3. 5 ns) appeared (Table  3 ). Moreover, the τ 2 component, which is assumed to correspond to the pH dependent component of the calibration data, has significantly shorter lifetime than the value deduced from calibration (expected value is about 2.
Estimation of Diffusion Coefficients Images obtained after a time series of acquisition were analyzed using ImageJ software. A line measuring 512 pixels long and 10 pixels wide was drawn through the axial diameter of the colony in order to quantify the profile of RITC fluorescence intensity (also named gray-level value) along the direction of the diffusion front and at each acquisition time. The resulting fluorescence intensity profiles vs. time were then exported into a single spreadsheet. According to  Crank (1975) , in the case of a plane one-dimensional diffusion, induced by an instantaneous source and after a short time t of diffusion, the concentration gradient of the investigated solute is given by the following relationship: C(x, t) = M 8 √ πDt exp - x 2 4Dt ( 2 ) where C is the concentration expressed as the amount of diffusing solute per unit area of surface, x is the perpendicular axis to the surface of the model cheese in µm (x = 0 corresponded to the surface), t is the time, D is the diffusion coefficient and M is the surface concentration of the diffusing solute, which corresponds to the total amount of the investigated fluorescently labeled solute in the gel related to the gel surface unit.
ACKNOWLEDGMENTS This work was supported by project  PFV/10/002  ( Center of Excellence OPTEC-Optimization in Engineering ) of the  KU Leuven Research Council FWO-1518913N (KaN Multi-Objective Dyn. Opt. )  project  G. 0930. 13   of the Fund for Scientific Research-Flanders , and the  Belgian Program on Interuniversity Poles of Attraction , initiated by the  Belgian Federal Science Policy Office (IAP Phase   VII-19 DYSCO ).
Magnetic properties and X-ray spectroscopy. The susceptibilitytemperature product, wT, of 1-3 (Supplementary Fig. 5 ) is practically temperature independent (Curie's law) confirming the lack of significant magnetic interactions between Ir IV spins and thus the absence of any magnetic order at least down to 1. 8 K. The complete set of magnetization data (M versus m 0 HT -1 ; Supplementary Figs  6 7 8 ) could be fitted to the Brillouin function for an effective spin-1/2 (with gE2), which corroborates the Curie susceptibility and demonstrates the presence of an energetically isolated J eff ¼ 1/2 ground state. Orbital (M orbital ) and spin (M spin ) contributions to the total magnetic moments (M total ) have been determined experimentally using XMCD, which is defined as the difference between two X-ray absorption spectra (XAS) recorded with either opposite helicity or magnetization direction.
The product was filtered off and washed three times with water and dried in a dynamic vacuum. Yield: 453 mg (91%). Anal. calcd. (found) for C 48 H 40 Cl 6 IrP 2 : C: 53. 20% (53. 24%), H: 3. 72% (3. 67%). Crystallography. Powder X-ray diffraction patterns of Na 2 [IrF 6 ] were obtained on samples mounted under nitrogen in an air-tight aluminium alloy cell and collected on a PANanalytical Bragg-Brentano y-2y geometry diffractometer (Cu Ka radiation) equipped with a secondary monochromator over an angular 2y range of 5-80°. Rietveld refinement of the data was performed in the TOPAS (version 4) program  40. Atomic positions were allowed to refine freely without restraints, while thermal parameters for all atoms were fixed. A suitable fit (R wp ¼ 13. 46%) was obtained using a structural model similar to that of Na 2 [GeF 6 ] (ref.
41), with an expanded unit cell (space group P321, a ¼ b ¼ 9. 32858(24) Å, c ¼ 5. 13417(19) Å, a ¼ b ¼ 90°, g ¼ 120°; Supplementary Fig. 32 ). Single-crystal X-ray diffraction studies on 1-3 (Supplementary Table  1 ) were performed at 122(1) K on a Bruker D8 VENTURE diffractometer equipped with Mo Ka high-brilliance ImS radiation (l ¼ 0. 71073 Å), a multilayer X-ray mirror, a PHOTON 100 CMOS detector, and an Oxford Cryosystems low-temperature device. The instrument was controlled with the APEX2 software package. The structures were solved in Olex2 using the olex2. solve structure solution program (Charge Flipping) and refined using the olex2. refine program  42. All non-hydrogen atoms were refined anisotropically and hydrogen atoms were placed at calculated positions and refined as riding atoms with isotropic displacement parameters.
Magnetometry. The magnetic measurements were carried out between 1. 8 and 280 K with applied d. c. fields ranging from -9 to þ 9 T, with the use of a MPMS-XL Quantum Design SQUID magnetometer and a PPMS-9 Quantum Design susceptometer. Measurements were performed on polycrystalline samples sealed in polyethylene bags (typically 3 Â 0. 5 Â 0. 02 cm; 15-30 mg) and immobilized in mineral oil. a. c. susceptibility measurements were performed with an oscillating field of 1-6 Oe with a frequency from 1 to 10,000 Hz. Before the experiments, the field-dependent magnetization was measured at 100 K to confirm the absence of any bulk ferromagnetic impurities. The magnetic data were corrected for the diamagnetic contributions from the sample, sample holder and mineral oil. Micro-SQUID magnetic measurements on single crystals were carried out in the field range from -1.
The octahedral component of the ligand field, D O ¼ 27,000 cm -1 , would agree with the AOM parameter e s ¼ 9,000 cm -1 in the cubic model. In the more realistic model, a e s /e p ratio of 5, and a distance dependence of e s pd Ir-F -5 and e p pd Ir-F -7 (where d Ir-F is the Ir-F bond length) was assumed (yielding e s z ¼ 11,904 cm -1 , e s xy ¼ 12,457 cm -1 , e p z ¼ 2,356 cm -1 and e p xy ¼ 2,507 cm -1 ). All ab initio calculations were performed with the ORCA 3. 0 programme package 46 using ZORA  47, 48  for relativistic corrections and the def2-TZVP basis set for the [IrX 6 ] 2 À moieties and the def2-SVP basis set for the 1-vinylimidazole ligands in calculations when requested in the model. State-averaged CASSCF calculations were performed on experimental geometries using Kohn-Sham starting orbitals obtained with the TPSSh functional.
Active spaces of CAS(5,3) (Ir t 2g orbitals-3 doublet CSFs) and CAS(5,5) (Ir dorbitals-12 doublet configuration state functions (CSFs), 6 quartet CSFs, 1 sextet CSF) were used and the SO interaction taken into account through a mean field approximation (Supplementary Table  2 )  49. EPR spectroscopy. The spectra were acquired at T ¼ 5 K on a Bruker Elexsys E500 spectrometer equipped with a Bruker ER 4116 DM dual mode cavity, an EIP 538B frequency counter and an ER035M NMR Gauss-meter, and with a microwave radiation frequency of 9. 634 GHz. The spectra were simulated using home-written software considering an effective electronic J eff of 1/2 and taking into account the natural isotopic abundance of 191 Ir (37. 3%) and 193 Ir (62. 3%), which have both a nuclear I Ir ¼ 3/2 spin, as well as the  19  F nuclear spin (I F ¼ 1/2, 100%).
Acknowledgements We thank  E. Lebraud  and  P. Voisin  for technical and experimental assistance on powder X-ray diffraction and on XAS/XMCD experiments, respectively. The m þ SR and X-ray spectroscopy experiments were performed at the  Paul Scherrer Institute (Swiss Muon Source (SmS), Villigen, Switzerland  and at the  European Synchrotron Radiation Facility  ( ESRF, Grenoble, France ), respectively. Y. L. and W. W. thank the  EU  for financial support within the  FP7 FET-Proactive  project  MoQuaS No610449  and the  Agence Nationale de la Recherche (ANR)  project  MolQuSpin ,  ANR-13-BS10. K. S. P. and R. C. thank the  Danish Research Council for Independent Research for a DFF-Sapere Aude Research Talent grant  ( 4090-00201 ), the  University of Bordeaux , the  Region Aquitaine , the  ANR , the  CNRS  and the GdR MCM-2: Magnetisme et Commutation Moleculaires.
Future plans for an OMIP Phase II The present document focuses on Phase I of OMIP, which is a straightforward extension of the CORE-II project making use of the  Large and Yeager (2009)  atmospheric state. There is ongoing research coordinated through the CLIVAR Ocean Model Development Panel (OMDP) to develop a Phase II experiment for OMIP. 2  The OMIP Phase II experiment will use an atmospheric state and runoff dataset based on the JRA-55 reanalysis  (Kobayashi et al. , 2015). JRA-55 has a finer spatial resolution, starts at year 1958, and remains updated to recent months. More details will be forthcoming when available.
, 2016). Along with the data itself, the provenance of the data will be recorded, and DOI's will be assigned to collections of output so that they can be appropriately cited. This information will be made readily available so that published research results can be verified and credit can be given to the modelling groups providing the data. The WIP is coordinating and encouraging the development of the infrastructure needed to archive and deliver this information. In order to run the experiments, datasets for natural and anthropogenic forcings are required. These forcing datasets are described in separate invited contributions to this Special Issue. The forcing datasets will be made available through the ESGF with version control and DOIs assigned. Acknowledgements. This document benefitted from the direct and indirect input of numerous ocean and climate scientists who contributed to the various CORE-II analysis papers (see Section 2.
Data were collected in the range 10°≤ 2θ ≤ 80°. For a precise determination of the unit-cell parameter a 0 , separate measurements were performed with addition of a silicon standard with a well-known lattice constant (a 0 = 5. 43088 Å) to each sample. XRPD patterns were recorded on fresh samples immediately after synthesis as well as on samples that were stored in air for 1 and 3 weeks, respectively. The unit-cell parameter a 0 was determined by Rietveld refinement using the program Topas V2. 1 (Bruker). 2. 4. NPD. Neutron powder diffraction measurements were performed at the Institut Laue-Langevin (ILL) in Grenoble, France. Powder diffraction data were collected at room temperature in constant wavelength mode with λ = 1. 594 Å at the D2B diffractometer. Experiments were performed with a step width of 0.
04°in the range 5. 8°≤ 2θ ≤ 159. 7°. Data treatment and refinement were carried out with the FULLPROF suite of programs. 38 In all data evaluations, the La, (Zr + Bi), and O atom positions were refined using anisotropic atomic displacement parameters; the atomic displacement factor for the Li1 positions was refined isotropically while that of Li2 was set to 3. 5 to avoid large correlations with the occupation factor. Site occupancies on Li sites were then allowed to refine unconstrained and (Zr + Bi) was fixed to full occupation of this site; however, the relative amounts of Bi and Zr were allowed to refine freely. The results for Bi and Zr occupancies agree well with those of XRPD refinements. 2. 5. Raman Spectroscopy. Raman spectra of the sample pellets were collected at room temperature with a Thermo Scientific DXR Raman microscope using a 10 mW depolarized internal laser light source with an excitation wavelength of 780 nm.
The Li 2 CO 3 contents were quantified for all LLZBO samples, yielding about 2% Li 2 CO 3. Small contents of LiOH were found as well. As determined by XRPD, the sample with x int = 0. 10 still shows both tetragonal I4 1 /acd and cubic Ia3̅ d garnet modifications. In contrast to the evaluation of XRPD data, a model with two different cubic (space group Ia3̅ d) LLZBO phases was used for the evaluation of NPD data of samples with x int ≥ 0. 30, as the data quality and resolution of data obtained from NPD are better than those obtained from XRPD. This model consists of an LLZBO phase that is unaffected or only slightly affected by protonation and a second LLZBO phase which is strongly affected by protonation on the 96h site, therefore showing a larger unit-cell parameter. For this second phase only H + was assumed to occupy 96h; fractional atomic coordinates are close to those reported recently for protonated LLZO with space group Ia3̅ d.
48 In the unprotonated material, the Li content decreases with increasing Bi content, for both the Li1 and Li2 sites to similar extents; however, data overlap is large so that no additional detailed structural parameters can be extracted. Unit-cell parameters of unprotonated and protonated LLZBO phases of different samples obtained by Rietveld refinements of NPD data are shown in Table  S1  and Figure  S3  in the Supporting Information; the corresponding structural data of selected samples are given in Table  S2  in the Supporting Information. Unit-cell parameter values for unprotonated LLZBO samples are similar to values of as-synthesized values obtained from XPRD; while the unit-cell parameters of protonated LLZBO phases are similar to XRPD-derived values of samples that have been stored in air for 1 week.
DISCUSSION The main goal of this study is to observe the influence of a partial substitution of Zr 4+ by Bi 5+ in LLZO and the stabilization of the cubic phase. Pure-phase cubic samples were obtained for Bi contents x int ≥ 0. 20, which is consistent with the results of previous studies. 26 It has to be noted that pure-phase cubic garnet samples are obtained by comparatively low Bi contents, as much higher dopant concentrations are needed if other substituting cations, such as Ta 5+ and Nb 5+ , are used as substituents for Zr 4+. 50, 55 Rietveld refinement of XRPD data confirms a systematic increase of the unit-cell parameter with increasing Bi 5+ content; additionally, the increase of the unit-cell parameter is partially caused by a protonation of the garnet phase. The increase of the unit-cell parameter due to protonation and the observation of Li 2 CO 3 from NPD as well as from Raman spectroscopy indicate that LLZBO is unstable in air.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. inorgchem. 6b01825. Results of Rietveld refinement of NPD measurements of samples with x int = 0. 20 and x int = 0. 90, respectively, unitcell parameters of LLZBO obtained from Rietveld refinement of NPD measurements, unit-cell parameters of LLZBO obtained by Rietveld refinement of NPD measurements, and structural models of samples with x int = 0. 20 and x int = 0. 80 derived from Rietveld refinement of NPD data (PDF).
25 • spatial resolution. Note that land-cover data and forest/non-forest data, as well as land-use transitions, will be provided in the new data set in order to help minimize misinterpretation of the land-use data set that occurred in CMIP5, where, for example, the strong afforestation in RCP4. 5 was not captured in Community Earth System Model (CESM) simulations because of differing assumptions embedded within the CESM land-use translator (a software package that translates the LUH data into CESM land-cover data sets) and the LUH data set  (Di Vittorio et al. , 2014). Several harmonized future land-use trajectories will be processed for the period 2016-2100 in support of the ScenarioMIP Shared Socioeconomic Pathway scenarios (see Sect. 2. 3. 2). Cropland is disaggregated into five crop functional types based on input data from  FAO and Monfreda et al.
(2008). Crop rotations are also included. Grazing lands are disaggregated into managed pastures and rangelands based on input data from the updated HYDE3. 2 data set (updated from  HYDE3. 1, Klein Goldewijk et al. , 2011) , which also provides inputs for gridded cropland, urban, and irrigated areas. The modeling process includes new underlying maps of potential biomass density and biomass recovery rate, which are used to disaggregate both primary and secondary natural vegetation into forested and non-forested land. It also includes a new representation of shifting cultivation rates and   2015 ), gridded irrigated areas (based on HYDE3. 2), and gridded areas flooded for rice (also based on HYDE3. 2), as well as the disaggregation of wood harvest into fuel wood and industrial roundwood (i. e.
ucar. edu/lumip) and will be described in a separate publication in this CMIP6 Special Issue. Guidance on use of the data will be provided in the LUH2 data set paper and through the LUMIP website. Second, an efficient model experiment design, including both idealized and scenario-based cases, is defined that will enable isolation and quantification of land-use effects on climate and the carbon cycle (see Sect. 2). The LUMIP experimental protocol enables integrated analysis of coupled and land-only (forced with observed meteorology) models that will support understanding and assessment of the forced response and climate feedbacks associated with land use and the relationship of these responses with land and atmosphere model biases. Third, a set of metrics and diagnostic protocols will be developed to quantify model performance, and related sensitivities, with respect to land use (see Sect.
In most land models, physical, ecological, and biogeochemical land state and surface flux variables are calculated separately for several different land surface type or land management "tiles" (e. g. , natural and secondary vegetation, crops, pasture, urban, lake, glacier). Frequently, including in the CMIP5 archive, tilespecific quantities are averaged and only grid-cell mean values are reported. Consequently, a large amount of valuable information is lost with respect to how each land-use type responds to and interacts with climate change and direct anthropogenic modifications of the land surface. LUMIP has developed a protocol and associated data request for CMIP6 for selected key variables on separate land-use tiles within each grid cell (primary and secondary land, crops, pastureland, urban; see Sect.
If the fire model utilizes population density or other anthropogenic forcings to determine fire ignition and/or suppression rates, then this forcing should also be held constant. We recognize that the diversity of model approaches means that the definition and requirements for constant land management may differ across models. Groups will need to make their own decisions with respect to the treatment of land management in constant land-use scenarios, for example with respect to specification of harvesting on croplands, grazing on pastureland, application of fertilizers, level of irrigation, and wood harvest. Wood harvest, in particular, may require model-specific treatment since turning off wood harvest in the ScenarioMIP 2100-2300 extension runs is likely to result in unrealistic carbon stock trends, while maintaining wood harvest at year 2100 levels for an additional 200 years could unrealistically decimate the forests where the LUH2 data sets indicate wood harvest is happening in 2100.
These limitations pose a challenge when comparing the model outputs (like latent heat flux, for example) to observed estimates because biases may actually be a function of biases in the meteorological forcing data set rather than deficiencies in the model. While the land-only LUMIP simulations will only be driven with a single atmospheric forcing data set (the reference data set used in the land-hist experiment of LS3MIP), the sensitivity of land model output to uncertainty in atmospheric forcing will be assessed in more depth within LS3MIP, which can inform the assessment of the land-only LUMIP simulations.
Data will be harmonized to ensure consistency between the end of the historical period and the beginning of the projection period for each of the scenarios. Note that not all IAMs predict all the LUH2 land management quantities (e. g. , wood harvest is missing for SSP5-8. 5). The missing land management variables will be generated during the harmonization process in a manner that is consistent with the underlying scenario. LUMIP also proposes to develop a set of analysis metrics that succinctly quantify a model response to land use across a range of spatial scales and temporal scales that can then be used to quantitatively compare model response across different models, regions, and land-management scenarios. For a given variable, say surface air temperature, diagnostic calculations will be completed for a pair of simulations (offline or coupled) with and without land-use change.
Subgrid data reporting To address challenges of analyzing effects of LULCC on the physical and biogeochemical state of land and its interactions with the atmosphere (e. g. , analyses proposed in Sects. 3. 2-3. 5), LUMIP is including a Tier 1 data request of sub-grid information for four sub-grid categories (i. e. , tiles) to permit more detailed analysis of land-use-induced surface heterogeneity. The rationale for this request is that relevant and interesting sub-grid-scale data that represent the heterogeneity of the land surface are available from current land models but are not being used since sub-grid-scale quantities are typically averaged to grid cell means prior to delivery to the CMIP database. Several recent studies have demonstrated that valuable insight can be gained through analysis of subgrid information.